From e8b1c8e1eadc3d29967393ea8ce0788d390befac Mon Sep 17 00:00:00 2001 From: Kalash Jindal <37014842+erickeagle@users.noreply.github.com> Date: Thu, 1 Oct 2020 15:13:38 +0530 Subject: [PATCH 1/2] Add files via upload --- Machine Learning/Breast_cancer_data.csv | 570 ++++++++++++ ... On Breast Cancer Prediction Dataset.ipynb | 871 ++++++++++++++++++ 2 files changed, 1441 insertions(+) create mode 100644 Machine Learning/Breast_cancer_data.csv create mode 100644 Machine Learning/Hyparameter Tuning- Grid search vs Bayesian optimization On Breast Cancer Prediction Dataset.ipynb diff --git a/Machine Learning/Breast_cancer_data.csv b/Machine Learning/Breast_cancer_data.csv new file mode 100644 index 00000000..8671a46c --- /dev/null +++ b/Machine Learning/Breast_cancer_data.csv @@ -0,0 +1,570 @@ +mean_radius,mean_texture,mean_perimeter,mean_area,mean_smoothness,diagnosis +17.99,10.38,122.8,1001.0,0.1184,0 +20.57,17.77,132.9,1326.0,0.08474,0 +19.69,21.25,130.0,1203.0,0.1096,0 +11.42,20.38,77.58,386.1,0.1425,0 +20.29,14.34,135.1,1297.0,0.1003,0 +12.45,15.7,82.57,477.1,0.1278,0 +18.25,19.98,119.6,1040.0,0.09463,0 +13.71,20.83,90.2,577.9,0.1189,0 +13.0,21.82,87.5,519.8,0.1273,0 +12.46,24.04,83.97,475.9,0.1186,0 +16.02,23.24,102.7,797.8,0.08206,0 +15.78,17.89,103.6,781.0,0.0971,0 +19.17,24.8,132.4,1123.0,0.0974,0 +15.85,23.95,103.7,782.7,0.08401,0 +13.73,22.61,93.6,578.3,0.1131,0 +14.54,27.54,96.73,658.8,0.1139,0 +14.68,20.13,94.74,684.5,0.09867,0 +16.13,20.68,108.1,798.8,0.117,0 +19.81,22.15,130.0,1260.0,0.09831,0 +13.54,14.36,87.46,566.3,0.09779,1 +13.08,15.71,85.63,520.0,0.1075,1 +9.504,12.44,60.34,273.9,0.1024,1 +15.34,14.26,102.5,704.4,0.1073,0 +21.16,23.04,137.2,1404.0,0.09428,0 +16.65,21.38,110.0,904.6,0.1121,0 +17.14,16.4,116.0,912.7,0.1186,0 +14.58,21.53,97.41,644.8,0.1054,0 +18.61,20.25,122.1,1094.0,0.0944,0 +15.3,25.27,102.4,732.4,0.1082,0 +17.57,15.05,115.0,955.1,0.09847,0 +18.63,25.11,124.8,1088.0,0.1064,0 +11.84,18.7,77.93,440.6,0.1109,0 +17.02,23.98,112.8,899.3,0.1197,0 +19.27,26.47,127.9,1162.0,0.09401,0 +16.13,17.88,107.0,807.2,0.104,0 +16.74,21.59,110.1,869.5,0.0961,0 +14.25,21.72,93.63,633.0,0.09823,0 +13.03,18.42,82.61,523.8,0.08983,1 +14.99,25.2,95.54,698.8,0.09387,0 +13.48,20.82,88.4,559.2,0.1016,0 +13.44,21.58,86.18,563.0,0.08162,0 +10.95,21.35,71.9,371.1,0.1227,0 +19.07,24.81,128.3,1104.0,0.09081,0 +13.28,20.28,87.32,545.2,0.1041,0 +13.17,21.81,85.42,531.5,0.09714,0 +18.65,17.6,123.7,1076.0,0.1099,0 +8.196,16.84,51.71,201.9,0.086,1 +13.17,18.66,85.98,534.6,0.1158,0 +12.05,14.63,78.04,449.3,0.1031,1 +13.49,22.3,86.91,561.0,0.08752,1 +11.76,21.6,74.72,427.9,0.08637,1 +13.64,16.34,87.21,571.8,0.07685,1 +11.94,18.24,75.71,437.6,0.08261,1 +18.22,18.7,120.3,1033.0,0.1148,0 +15.1,22.02,97.26,712.8,0.09056,0 +11.52,18.75,73.34,409.0,0.09524,1 +19.21,18.57,125.5,1152.0,0.1053,0 +14.71,21.59,95.55,656.9,0.1137,0 +13.05,19.31,82.61,527.2,0.0806,1 +8.618,11.79,54.34,224.5,0.09752,1 +10.17,14.88,64.55,311.9,0.1134,1 +8.598,20.98,54.66,221.8,0.1243,1 +14.25,22.15,96.42,645.7,0.1049,0 +9.173,13.86,59.2,260.9,0.07721,1 +12.68,23.84,82.69,499.0,0.1122,0 +14.78,23.94,97.4,668.3,0.1172,0 +9.465,21.01,60.11,269.4,0.1044,1 +11.31,19.04,71.8,394.1,0.08139,1 +9.029,17.33,58.79,250.5,0.1066,1 +12.78,16.49,81.37,502.5,0.09831,1 +18.94,21.31,123.6,1130.0,0.09009,0 +8.888,14.64,58.79,244.0,0.09783,1 +17.2,24.52,114.2,929.4,0.1071,0 +13.8,15.79,90.43,584.1,0.1007,0 +12.31,16.52,79.19,470.9,0.09172,1 +16.07,19.65,104.1,817.7,0.09168,0 +13.53,10.94,87.91,559.2,0.1291,1 +18.05,16.15,120.2,1006.0,0.1065,0 +20.18,23.97,143.7,1245.0,0.1286,0 +12.86,18.0,83.19,506.3,0.09934,1 +11.45,20.97,73.81,401.5,0.1102,1 +13.34,15.86,86.49,520.0,0.1078,1 +25.22,24.91,171.5,1878.0,0.1063,0 +19.1,26.29,129.1,1132.0,0.1215,0 +12.0,15.65,76.95,443.3,0.09723,1 +18.46,18.52,121.1,1075.0,0.09874,0 +14.48,21.46,94.25,648.2,0.09444,0 +19.02,24.59,122.0,1076.0,0.09029,0 +12.36,21.8,79.78,466.1,0.08772,1 +14.64,15.24,95.77,651.9,0.1132,1 +14.62,24.02,94.57,662.7,0.08974,1 +15.37,22.76,100.2,728.2,0.092,0 +13.27,14.76,84.74,551.7,0.07355,1 +13.45,18.3,86.6,555.1,0.1022,1 +15.06,19.83,100.3,705.6,0.1039,0 +20.26,23.03,132.4,1264.0,0.09078,0 +12.18,17.84,77.79,451.1,0.1045,1 +9.787,19.94,62.11,294.5,0.1024,1 +11.6,12.84,74.34,412.6,0.08983,1 +14.42,19.77,94.48,642.5,0.09752,0 +13.61,24.98,88.05,582.7,0.09488,0 +6.981,13.43,43.79,143.5,0.117,1 +12.18,20.52,77.22,458.7,0.08013,1 +9.876,19.4,63.95,298.3,0.1005,1 +10.49,19.29,67.41,336.1,0.09989,1 +13.11,15.56,87.21,530.2,0.1398,0 +11.64,18.33,75.17,412.5,0.1142,1 +12.36,18.54,79.01,466.7,0.08477,1 +22.27,19.67,152.8,1509.0,0.1326,0 +11.34,21.26,72.48,396.5,0.08759,1 +9.777,16.99,62.5,290.2,0.1037,1 +12.63,20.76,82.15,480.4,0.09933,1 +14.26,19.65,97.83,629.9,0.07837,1 +10.51,20.19,68.64,334.2,0.1122,1 +8.726,15.83,55.84,230.9,0.115,1 +11.93,21.53,76.53,438.6,0.09768,1 +8.95,15.76,58.74,245.2,0.09462,1 +14.87,16.67,98.64,682.5,0.1162,0 +15.78,22.91,105.7,782.6,0.1155,0 +17.95,20.01,114.2,982.0,0.08402,0 +11.41,10.82,73.34,403.3,0.09373,1 +18.66,17.12,121.4,1077.0,0.1054,0 +24.25,20.2,166.2,1761.0,0.1447,0 +14.5,10.89,94.28,640.7,0.1101,1 +13.37,16.39,86.1,553.5,0.07115,1 +13.85,17.21,88.44,588.7,0.08785,1 +13.61,24.69,87.76,572.6,0.09258,0 +19.0,18.91,123.4,1138.0,0.08217,0 +15.1,16.39,99.58,674.5,0.115,1 +19.79,25.12,130.4,1192.0,0.1015,0 +12.19,13.29,79.08,455.8,0.1066,1 +15.46,19.48,101.7,748.9,0.1092,0 +16.16,21.54,106.2,809.8,0.1008,0 +15.71,13.93,102.0,761.7,0.09462,1 +18.45,21.91,120.2,1075.0,0.0943,0 +12.77,22.47,81.72,506.3,0.09055,0 +11.71,16.67,74.72,423.6,0.1051,1 +11.43,15.39,73.06,399.8,0.09639,1 +14.95,17.57,96.85,678.1,0.1167,0 +11.28,13.39,73.0,384.8,0.1164,1 +9.738,11.97,61.24,288.5,0.0925,1 +16.11,18.05,105.1,813.0,0.09721,0 +11.43,17.31,73.66,398.0,0.1092,1 +12.9,15.92,83.74,512.2,0.08677,1 +10.75,14.97,68.26,355.3,0.07793,1 +11.9,14.65,78.11,432.8,0.1152,1 +11.8,16.58,78.99,432.0,0.1091,0 +14.95,18.77,97.84,689.5,0.08138,1 +14.44,15.18,93.97,640.1,0.0997,1 +13.74,17.91,88.12,585.0,0.07944,1 +13.0,20.78,83.51,519.4,0.1135,1 +8.219,20.7,53.27,203.9,0.09405,1 +9.731,15.34,63.78,300.2,0.1072,1 +11.15,13.08,70.87,381.9,0.09754,1 +13.15,15.34,85.31,538.9,0.09384,1 +12.25,17.94,78.27,460.3,0.08654,1 +17.68,20.74,117.4,963.7,0.1115,0 +16.84,19.46,108.4,880.2,0.07445,1 +12.06,12.74,76.84,448.6,0.09311,1 +10.9,12.96,68.69,366.8,0.07515,1 +11.75,20.18,76.1,419.8,0.1089,1 +19.19,15.94,126.3,1157.0,0.08694,0 +19.59,18.15,130.7,1214.0,0.112,0 +12.34,22.22,79.85,464.5,0.1012,1 +23.27,22.04,152.1,1686.0,0.08439,0 +14.97,19.76,95.5,690.2,0.08421,1 +10.8,9.71,68.77,357.6,0.09594,1 +16.78,18.8,109.3,886.3,0.08865,0 +17.47,24.68,116.1,984.6,0.1049,0 +14.97,16.95,96.22,685.9,0.09855,1 +12.32,12.39,78.85,464.1,0.1028,1 +13.43,19.63,85.84,565.4,0.09048,0 +15.46,11.89,102.5,736.9,0.1257,0 +11.08,14.71,70.21,372.7,0.1006,1 +10.66,15.15,67.49,349.6,0.08792,1 +8.671,14.45,54.42,227.2,0.09138,1 +9.904,18.06,64.6,302.4,0.09699,1 +16.46,20.11,109.3,832.9,0.09831,0 +13.01,22.22,82.01,526.4,0.06251,1 +12.81,13.06,81.29,508.8,0.08739,1 +27.22,21.87,182.1,2250.0,0.1094,0 +21.09,26.57,142.7,1311.0,0.1141,0 +15.7,20.31,101.2,766.6,0.09597,0 +11.41,14.92,73.53,402.0,0.09059,1 +15.28,22.41,98.92,710.6,0.09057,0 +10.08,15.11,63.76,317.5,0.09267,1 +18.31,18.58,118.6,1041.0,0.08588,0 +11.71,17.19,74.68,420.3,0.09774,1 +11.81,17.39,75.27,428.9,0.1007,1 +12.3,15.9,78.83,463.7,0.0808,1 +14.22,23.12,94.37,609.9,0.1075,0 +12.77,21.41,82.02,507.4,0.08749,1 +9.72,18.22,60.73,288.1,0.0695,1 +12.34,26.86,81.15,477.4,0.1034,0 +14.86,23.21,100.4,671.4,0.1044,0 +12.91,16.33,82.53,516.4,0.07941,1 +13.77,22.29,90.63,588.9,0.12,0 +18.08,21.84,117.4,1024.0,0.07371,0 +19.18,22.49,127.5,1148.0,0.08523,0 +14.45,20.22,94.49,642.7,0.09872,0 +12.23,19.56,78.54,461.0,0.09586,1 +17.54,19.32,115.1,951.6,0.08968,0 +23.29,26.67,158.9,1685.0,0.1141,0 +13.81,23.75,91.56,597.8,0.1323,0 +12.47,18.6,81.09,481.9,0.09965,1 +15.12,16.68,98.78,716.6,0.08876,0 +9.876,17.27,62.92,295.4,0.1089,1 +17.01,20.26,109.7,904.3,0.08772,0 +13.11,22.54,87.02,529.4,0.1002,1 +15.27,12.91,98.17,725.5,0.08182,1 +20.58,22.14,134.7,1290.0,0.0909,0 +11.84,18.94,75.51,428.0,0.08871,1 +28.11,18.47,188.5,2499.0,0.1142,0 +17.42,25.56,114.5,948.0,0.1006,0 +14.19,23.81,92.87,610.7,0.09463,0 +13.86,16.93,90.96,578.9,0.1026,0 +11.89,18.35,77.32,432.2,0.09363,1 +10.2,17.48,65.05,321.2,0.08054,1 +19.8,21.56,129.7,1230.0,0.09383,0 +19.53,32.47,128.0,1223.0,0.0842,0 +13.65,13.16,87.88,568.9,0.09646,1 +13.56,13.9,88.59,561.3,0.1051,1 +10.18,17.53,65.12,313.1,0.1061,1 +15.75,20.25,102.6,761.3,0.1025,0 +13.27,17.02,84.55,546.4,0.08445,1 +14.34,13.47,92.51,641.2,0.09906,1 +10.44,15.46,66.62,329.6,0.1053,1 +15.0,15.51,97.45,684.5,0.08371,1 +12.62,23.97,81.35,496.4,0.07903,1 +12.83,22.33,85.26,503.2,0.1088,0 +17.05,19.08,113.4,895.0,0.1141,0 +11.32,27.08,71.76,395.7,0.06883,1 +11.22,33.81,70.79,386.8,0.0778,1 +20.51,27.81,134.4,1319.0,0.09159,0 +9.567,15.91,60.21,279.6,0.08464,1 +14.03,21.25,89.79,603.4,0.0907,1 +23.21,26.97,153.5,1670.0,0.09509,0 +20.48,21.46,132.5,1306.0,0.08355,0 +14.22,27.85,92.55,623.9,0.08223,1 +17.46,39.28,113.4,920.6,0.09812,0 +13.64,15.6,87.38,575.3,0.09423,1 +12.42,15.04,78.61,476.5,0.07926,1 +11.3,18.19,73.93,389.4,0.09592,1 +13.75,23.77,88.54,590.0,0.08043,1 +19.4,23.5,129.1,1155.0,0.1027,0 +10.48,19.86,66.72,337.7,0.107,1 +13.2,17.43,84.13,541.6,0.07215,1 +12.89,14.11,84.95,512.2,0.0876,1 +10.65,25.22,68.01,347.0,0.09657,1 +11.52,14.93,73.87,406.3,0.1013,1 +20.94,23.56,138.9,1364.0,0.1007,0 +11.5,18.45,73.28,407.4,0.09345,1 +19.73,19.82,130.7,1206.0,0.1062,0 +17.3,17.08,113.0,928.2,0.1008,0 +19.45,19.33,126.5,1169.0,0.1035,0 +13.96,17.05,91.43,602.4,0.1096,0 +19.55,28.77,133.6,1207.0,0.0926,0 +15.32,17.27,103.2,713.3,0.1335,0 +15.66,23.2,110.2,773.5,0.1109,0 +15.53,33.56,103.7,744.9,0.1063,0 +20.31,27.06,132.9,1288.0,0.1,0 +17.35,23.06,111.0,933.1,0.08662,0 +17.29,22.13,114.4,947.8,0.08999,0 +15.61,19.38,100.0,758.6,0.0784,0 +17.19,22.07,111.6,928.3,0.09726,0 +20.73,31.12,135.7,1419.0,0.09469,0 +10.6,18.95,69.28,346.4,0.09688,1 +13.59,21.84,87.16,561.0,0.07956,1 +12.87,16.21,82.38,512.2,0.09425,1 +10.71,20.39,69.5,344.9,0.1082,1 +14.29,16.82,90.3,632.6,0.06429,1 +11.29,13.04,72.23,388.0,0.09834,1 +21.75,20.99,147.3,1491.0,0.09401,0 +9.742,15.67,61.5,289.9,0.09037,1 +17.93,24.48,115.2,998.9,0.08855,0 +11.89,17.36,76.2,435.6,0.1225,1 +11.33,14.16,71.79,396.6,0.09379,1 +18.81,19.98,120.9,1102.0,0.08923,0 +13.59,17.84,86.24,572.3,0.07948,1 +13.85,15.18,88.99,587.4,0.09516,1 +19.16,26.6,126.2,1138.0,0.102,0 +11.74,14.02,74.24,427.3,0.07813,1 +19.4,18.18,127.2,1145.0,0.1037,0 +16.24,18.77,108.8,805.1,0.1066,0 +12.89,15.7,84.08,516.6,0.07818,1 +12.58,18.4,79.83,489.0,0.08393,1 +11.94,20.76,77.87,441.0,0.08605,1 +12.89,13.12,81.89,515.9,0.06955,1 +11.26,19.96,73.72,394.1,0.0802,1 +11.37,18.89,72.17,396.0,0.08713,1 +14.41,19.73,96.03,651.0,0.08757,1 +14.96,19.1,97.03,687.3,0.08992,1 +12.95,16.02,83.14,513.7,0.1005,1 +11.85,17.46,75.54,432.7,0.08372,1 +12.72,13.78,81.78,492.1,0.09667,1 +13.77,13.27,88.06,582.7,0.09198,1 +10.91,12.35,69.14,363.7,0.08518,1 +11.76,18.14,75.0,431.1,0.09968,0 +14.26,18.17,91.22,633.1,0.06576,1 +10.51,23.09,66.85,334.2,0.1015,1 +19.53,18.9,129.5,1217.0,0.115,0 +12.46,19.89,80.43,471.3,0.08451,1 +20.09,23.86,134.7,1247.0,0.108,0 +10.49,18.61,66.86,334.3,0.1068,1 +11.46,18.16,73.59,403.1,0.08853,1 +11.6,24.49,74.23,417.2,0.07474,1 +13.2,15.82,84.07,537.3,0.08511,1 +9.0,14.4,56.36,246.3,0.07005,1 +13.5,12.71,85.69,566.2,0.07376,1 +13.05,13.84,82.71,530.6,0.08352,1 +11.7,19.11,74.33,418.7,0.08814,1 +14.61,15.69,92.68,664.9,0.07618,1 +12.76,13.37,82.29,504.1,0.08794,1 +11.54,10.72,73.73,409.1,0.08597,1 +8.597,18.6,54.09,221.2,0.1074,1 +12.49,16.85,79.19,481.6,0.08511,1 +12.18,14.08,77.25,461.4,0.07734,1 +18.22,18.87,118.7,1027.0,0.09746,0 +9.042,18.9,60.07,244.5,0.09968,1 +12.43,17.0,78.6,477.3,0.07557,1 +10.25,16.18,66.52,324.2,0.1061,1 +20.16,19.66,131.1,1274.0,0.0802,0 +12.86,13.32,82.82,504.8,0.1134,1 +20.34,21.51,135.9,1264.0,0.117,0 +12.2,15.21,78.01,457.9,0.08673,1 +12.67,17.3,81.25,489.9,0.1028,1 +14.11,12.88,90.03,616.5,0.09309,1 +12.03,17.93,76.09,446.0,0.07683,1 +16.27,20.71,106.9,813.7,0.1169,0 +16.26,21.88,107.5,826.8,0.1165,0 +16.03,15.51,105.8,793.2,0.09491,0 +12.98,19.35,84.52,514.0,0.09579,1 +11.22,19.86,71.94,387.3,0.1054,1 +11.25,14.78,71.38,390.0,0.08306,1 +12.3,19.02,77.88,464.4,0.08313,1 +17.06,21.0,111.8,918.6,0.1119,0 +12.99,14.23,84.08,514.3,0.09462,1 +18.77,21.43,122.9,1092.0,0.09116,0 +10.05,17.53,64.41,310.8,0.1007,1 +23.51,24.27,155.1,1747.0,0.1069,0 +14.42,16.54,94.15,641.2,0.09751,1 +9.606,16.84,61.64,280.5,0.08481,1 +11.06,14.96,71.49,373.9,0.1033,1 +19.68,21.68,129.9,1194.0,0.09797,0 +11.71,15.45,75.03,420.3,0.115,1 +10.26,14.71,66.2,321.6,0.09882,1 +12.06,18.9,76.66,445.3,0.08386,1 +14.76,14.74,94.87,668.7,0.08875,1 +11.47,16.03,73.02,402.7,0.09076,1 +11.95,14.96,77.23,426.7,0.1158,1 +11.66,17.07,73.7,421.0,0.07561,1 +15.75,19.22,107.1,758.6,0.1243,0 +25.73,17.46,174.2,2010.0,0.1149,0 +15.08,25.74,98.0,716.6,0.1024,0 +11.14,14.07,71.24,384.6,0.07274,1 +12.56,19.07,81.92,485.8,0.0876,1 +13.05,18.59,85.09,512.0,0.1082,1 +13.87,16.21,88.52,593.7,0.08743,1 +8.878,15.49,56.74,241.0,0.08293,1 +9.436,18.32,59.82,278.6,0.1009,1 +12.54,18.07,79.42,491.9,0.07436,1 +13.3,21.57,85.24,546.1,0.08582,1 +12.76,18.84,81.87,496.6,0.09676,1 +16.5,18.29,106.6,838.1,0.09686,1 +13.4,16.95,85.48,552.4,0.07937,1 +20.44,21.78,133.8,1293.0,0.0915,0 +20.2,26.83,133.7,1234.0,0.09905,0 +12.21,18.02,78.31,458.4,0.09231,1 +21.71,17.25,140.9,1546.0,0.09384,0 +22.01,21.9,147.2,1482.0,0.1063,0 +16.35,23.29,109.0,840.4,0.09742,0 +15.19,13.21,97.65,711.8,0.07963,1 +21.37,15.1,141.3,1386.0,0.1001,0 +20.64,17.35,134.8,1335.0,0.09446,0 +13.69,16.07,87.84,579.1,0.08302,1 +16.17,16.07,106.3,788.5,0.0988,1 +10.57,20.22,70.15,338.3,0.09073,1 +13.46,28.21,85.89,562.1,0.07517,1 +13.66,15.15,88.27,580.6,0.08268,1 +11.08,18.83,73.3,361.6,0.1216,0 +11.27,12.96,73.16,386.3,0.1237,1 +11.04,14.93,70.67,372.7,0.07987,1 +12.05,22.72,78.75,447.8,0.06935,1 +12.39,17.48,80.64,462.9,0.1042,1 +13.28,13.72,85.79,541.8,0.08363,1 +14.6,23.29,93.97,664.7,0.08682,0 +12.21,14.09,78.78,462.0,0.08108,1 +13.88,16.16,88.37,596.6,0.07026,1 +11.27,15.5,73.38,392.0,0.08365,1 +19.55,23.21,128.9,1174.0,0.101,0 +10.26,12.22,65.75,321.6,0.09996,1 +8.734,16.84,55.27,234.3,0.1039,1 +15.49,19.97,102.4,744.7,0.116,0 +21.61,22.28,144.4,1407.0,0.1167,0 +12.1,17.72,78.07,446.2,0.1029,1 +14.06,17.18,89.75,609.1,0.08045,1 +13.51,18.89,88.1,558.1,0.1059,1 +12.8,17.46,83.05,508.3,0.08044,1 +11.06,14.83,70.31,378.2,0.07741,1 +11.8,17.26,75.26,431.9,0.09087,1 +17.91,21.02,124.4,994.0,0.123,0 +11.93,10.91,76.14,442.7,0.08872,1 +12.96,18.29,84.18,525.2,0.07351,1 +12.94,16.17,83.18,507.6,0.09879,1 +12.34,14.95,78.29,469.1,0.08682,1 +10.94,18.59,70.39,370.0,0.1004,1 +16.14,14.86,104.3,800.0,0.09495,1 +12.85,21.37,82.63,514.5,0.07551,1 +17.99,20.66,117.8,991.7,0.1036,0 +12.27,17.92,78.41,466.1,0.08685,1 +11.36,17.57,72.49,399.8,0.08858,1 +11.04,16.83,70.92,373.2,0.1077,1 +9.397,21.68,59.75,268.8,0.07969,1 +14.99,22.11,97.53,693.7,0.08515,1 +15.13,29.81,96.71,719.5,0.0832,0 +11.89,21.17,76.39,433.8,0.09773,1 +9.405,21.7,59.6,271.2,0.1044,1 +15.5,21.08,102.9,803.1,0.112,0 +12.7,12.17,80.88,495.0,0.08785,1 +11.16,21.41,70.95,380.3,0.1018,1 +11.57,19.04,74.2,409.7,0.08546,1 +14.69,13.98,98.22,656.1,0.1031,1 +11.61,16.02,75.46,408.2,0.1088,1 +13.66,19.13,89.46,575.3,0.09057,1 +9.742,19.12,61.93,289.7,0.1075,1 +10.03,21.28,63.19,307.3,0.08117,1 +10.48,14.98,67.49,333.6,0.09816,1 +10.8,21.98,68.79,359.9,0.08801,1 +11.13,16.62,70.47,381.1,0.08151,1 +12.72,17.67,80.98,501.3,0.07896,1 +14.9,22.53,102.1,685.0,0.09947,0 +12.4,17.68,81.47,467.8,0.1054,1 +20.18,19.54,133.8,1250.0,0.1133,0 +18.82,21.97,123.7,1110.0,0.1018,0 +14.86,16.94,94.89,673.7,0.08924,1 +13.98,19.62,91.12,599.5,0.106,0 +12.87,19.54,82.67,509.2,0.09136,1 +14.04,15.98,89.78,611.2,0.08458,1 +13.85,19.6,88.68,592.6,0.08684,1 +14.02,15.66,89.59,606.5,0.07966,1 +10.97,17.2,71.73,371.5,0.08915,1 +17.27,25.42,112.4,928.8,0.08331,0 +13.78,15.79,88.37,585.9,0.08817,1 +10.57,18.32,66.82,340.9,0.08142,1 +18.03,16.85,117.5,990.0,0.08947,0 +11.99,24.89,77.61,441.3,0.103,1 +17.75,28.03,117.3,981.6,0.09997,0 +14.8,17.66,95.88,674.8,0.09179,1 +14.53,19.34,94.25,659.7,0.08388,1 +21.1,20.52,138.1,1384.0,0.09684,0 +11.87,21.54,76.83,432.0,0.06613,1 +19.59,25.0,127.7,1191.0,0.1032,0 +12.0,28.23,76.77,442.5,0.08437,1 +14.53,13.98,93.86,644.2,0.1099,1 +12.62,17.15,80.62,492.9,0.08583,1 +13.38,30.72,86.34,557.2,0.09245,1 +11.63,29.29,74.87,415.1,0.09357,1 +13.21,25.25,84.1,537.9,0.08791,1 +13.0,25.13,82.61,520.2,0.08369,1 +9.755,28.2,61.68,290.9,0.07984,1 +17.08,27.15,111.2,930.9,0.09898,0 +27.42,26.27,186.9,2501.0,0.1084,0 +14.4,26.99,92.25,646.1,0.06995,1 +11.6,18.36,73.88,412.7,0.08508,1 +13.17,18.22,84.28,537.3,0.07466,1 +13.24,20.13,86.87,542.9,0.08284,1 +13.14,20.74,85.98,536.9,0.08675,1 +9.668,18.1,61.06,286.3,0.08311,1 +17.6,23.33,119.0,980.5,0.09289,0 +11.62,18.18,76.38,408.8,0.1175,1 +9.667,18.49,61.49,289.1,0.08946,1 +12.04,28.14,76.85,449.9,0.08752,1 +14.92,14.93,96.45,686.9,0.08098,1 +12.27,29.97,77.42,465.4,0.07699,1 +10.88,15.62,70.41,358.9,0.1007,1 +12.83,15.73,82.89,506.9,0.0904,1 +14.2,20.53,92.41,618.4,0.08931,1 +13.9,16.62,88.97,599.4,0.06828,1 +11.49,14.59,73.99,404.9,0.1046,1 +16.25,19.51,109.8,815.8,0.1026,0 +12.16,18.03,78.29,455.3,0.09087,1 +13.9,19.24,88.73,602.9,0.07991,1 +13.47,14.06,87.32,546.3,0.1071,1 +13.7,17.64,87.76,571.1,0.0995,1 +15.73,11.28,102.8,747.2,0.1043,1 +12.45,16.41,82.85,476.7,0.09514,1 +14.64,16.85,94.21,666.0,0.08641,1 +19.44,18.82,128.1,1167.0,0.1089,0 +11.68,16.17,75.49,420.5,0.1128,1 +16.69,20.2,107.1,857.6,0.07497,0 +12.25,22.44,78.18,466.5,0.08192,1 +17.85,13.23,114.6,992.1,0.07838,1 +18.01,20.56,118.4,1007.0,0.1001,0 +12.46,12.83,78.83,477.3,0.07372,1 +13.16,20.54,84.06,538.7,0.07335,1 +14.87,20.21,96.12,680.9,0.09587,1 +12.65,18.17,82.69,485.6,0.1076,1 +12.47,17.31,80.45,480.1,0.08928,1 +18.49,17.52,121.3,1068.0,0.1012,0 +20.59,21.24,137.8,1320.0,0.1085,0 +15.04,16.74,98.73,689.4,0.09883,1 +13.82,24.49,92.33,595.9,0.1162,0 +12.54,16.32,81.25,476.3,0.1158,1 +23.09,19.83,152.1,1682.0,0.09342,0 +9.268,12.87,61.49,248.7,0.1634,1 +9.676,13.14,64.12,272.5,0.1255,1 +12.22,20.04,79.47,453.1,0.1096,1 +11.06,17.12,71.25,366.5,0.1194,1 +16.3,15.7,104.7,819.8,0.09427,1 +15.46,23.95,103.8,731.3,0.1183,0 +11.74,14.69,76.31,426.0,0.08099,1 +14.81,14.7,94.66,680.7,0.08472,1 +13.4,20.52,88.64,556.7,0.1106,0 +14.58,13.66,94.29,658.8,0.09832,1 +15.05,19.07,97.26,701.9,0.09215,0 +11.34,18.61,72.76,391.2,0.1049,1 +18.31,20.58,120.8,1052.0,0.1068,0 +19.89,20.26,130.5,1214.0,0.1037,0 +12.88,18.22,84.45,493.1,0.1218,1 +12.75,16.7,82.51,493.8,0.1125,1 +9.295,13.9,59.96,257.8,0.1371,1 +24.63,21.6,165.5,1841.0,0.103,0 +11.26,19.83,71.3,388.1,0.08511,1 +13.71,18.68,88.73,571.0,0.09916,1 +9.847,15.68,63.0,293.2,0.09492,1 +8.571,13.1,54.53,221.3,0.1036,1 +13.46,18.75,87.44,551.1,0.1075,1 +12.34,12.27,78.94,468.5,0.09003,1 +13.94,13.17,90.31,594.2,0.1248,1 +12.07,13.44,77.83,445.2,0.11,1 +11.75,17.56,75.89,422.9,0.1073,1 +11.67,20.02,75.21,416.2,0.1016,1 +13.68,16.33,87.76,575.5,0.09277,1 +20.47,20.67,134.7,1299.0,0.09156,0 +10.96,17.62,70.79,365.6,0.09687,1 +20.55,20.86,137.8,1308.0,0.1046,0 +14.27,22.55,93.77,629.8,0.1038,0 +11.69,24.44,76.37,406.4,0.1236,1 +7.729,25.49,47.98,178.8,0.08098,1 +7.691,25.44,48.34,170.4,0.08668,1 +11.54,14.44,74.65,402.9,0.09984,1 +14.47,24.99,95.81,656.4,0.08837,1 +14.74,25.42,94.7,668.6,0.08275,1 +13.21,28.06,84.88,538.4,0.08671,1 +13.87,20.7,89.77,584.8,0.09578,1 +13.62,23.23,87.19,573.2,0.09246,1 +10.32,16.35,65.31,324.9,0.09434,1 +10.26,16.58,65.85,320.8,0.08877,1 +9.683,19.34,61.05,285.7,0.08491,1 +10.82,24.21,68.89,361.6,0.08192,1 +10.86,21.48,68.51,360.5,0.07431,1 +11.13,22.44,71.49,378.4,0.09566,1 +12.77,29.43,81.35,507.9,0.08276,1 +9.333,21.94,59.01,264.0,0.0924,1 +12.88,28.92,82.5,514.3,0.08123,1 +10.29,27.61,65.67,321.4,0.0903,1 +10.16,19.59,64.73,311.7,0.1003,1 +9.423,27.88,59.26,271.3,0.08123,1 +14.59,22.68,96.39,657.1,0.08473,1 +11.51,23.93,74.52,403.5,0.09261,1 +14.05,27.15,91.38,600.4,0.09929,1 +11.2,29.37,70.67,386.0,0.07449,1 +15.22,30.62,103.4,716.9,0.1048,0 +20.92,25.09,143.0,1347.0,0.1099,0 +21.56,22.39,142.0,1479.0,0.111,0 +20.13,28.25,131.2,1261.0,0.0978,0 +16.6,28.08,108.3,858.1,0.08455,0 +20.6,29.33,140.1,1265.0,0.1178,0 +7.76,24.54,47.92,181.0,0.05263,1 diff --git a/Machine Learning/Hyparameter Tuning- Grid search vs Bayesian optimization On Breast Cancer Prediction Dataset.ipynb b/Machine Learning/Hyparameter Tuning- Grid search vs Bayesian optimization On Breast Cancer Prediction Dataset.ipynb new file mode 100644 index 00000000..c8880b4d --- /dev/null +++ b/Machine Learning/Hyparameter Tuning- Grid search vs Bayesian optimization On Breast Cancer Prediction Dataset.ipynb @@ -0,0 +1,871 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hyparameter Tuning- Grid search vs Bayesian optimization On Breast Cancer Prediction Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Grid Search is the process of scanning the data to configure optimal parameters for a given model. Depending on the type of model utilized, certain parameters are necessary. Grid-searching does NOT only apply to one model type. Grid-searching can be applied across machine learning to calculate the best parameters to use for any given model. It is important to note that Grid-searching can be extremely computationally expensive and may take your machine quite a long time to run. Grid-Search will build a model on each parameter combination possible. It iterates through every parameter combination and stores a model for each combination." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bayesian Optimization provides a technique based on Bayes Theorem to direct a search of a global optimization problem that is efficient and effective. It works by building a probabilistic model of the objective function, called the surrogate function, that is then searched efficiently with an acquisition function before candidate samples are chosen for evaluation on the real objective function.\n", + "Bayesian Optimization is often used in applied machine learning to tune the hyperparameters of a given well-performing model on a validation dataset.It is an approach that is most useful for objective functions that are complex, noisy, and/or expensive to evaluate.\n", + "\n", + "Bayes Theorem is an approach for calculating the conditional probability of an event:\n", + "
  • P(A|B) = P(B|A) * P(A) / P(B)
    • \n", + "We can simplify this calculation by removing the normalizing value of P(B) and describe the conditional probability as a proportional quantity. This is useful as we are not interested in calculating a specific conditional probability, but instead in optimizing a quantity.\n", + "
  • P(A|B) = P(B|A) * P(A)
    • \n", + "The conditional probability that we are calculating is referred to generally as the posterior probability, the reverse conditional probability is sometimes referred to as the likelihood, and the marginal probability is referred to as the prior probability, for example:\n", + "
  • posterior = likelihood * prior
    • \n", + "\n", + "This provides a framework that can be used to quantify the beliefs about an unknown objective function given samples from the domain and their evaluation via the objective function.\n", + "\n", + "We can devise specific samples (x1, x2, …, xn) and evaluate them using the objective function f(xi) that returns the cost or outcome for the sample xi. Samples and their outcome are collected sequentially and define our data D, e.g. D = {xi, f(xi), … xn, f(xn)} and is used to define the prior. The likelihood function is defined as the probability of observing the data given the function P(D | f). This likelihood function will change as more observations are collected.\n", + "
  • P(f|D) = P(D|f) * P(f)
    • \n", + "The posterior represents everything we know about the objective function. It is an approximation of the objective function and can be used to estimate the cost of different candidate samples that we may want to evaluate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Surrogate Function: Bayesian approximation of the objective function that can be sampled efficiently.\n", + "The surrogate function gives us an estimate of the objective function, which can be used to direct future sampling. Sampling involves careful use of the posterior in a function known as the “acquisition” function, e.g. for acquiring more samples. We want to use our belief about the objective function to sample the area of the search space that is most likely to pay off, therefore the acquisition will optimize the conditional probability of locations in the search to generate the next sample." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Acquisition Function: Technique by which the posterior is used to select the next sample from the search space." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### So, lets implement both hyperparameter tuning method for the dataset that is available on the kaggle, the Breast Canceer Prediction\n", + "Link to the kaggle dataset https://www.kaggle.com/merishnasuwal/breast-cancer-prediction-datas" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    mean_radiusmean_texturemean_perimetermean_areamean_smoothnessdiagnosis
    017.9910.38122.801001.00.118400
    120.5717.77132.901326.00.084740
    219.6921.25130.001203.00.109600
    311.4220.3877.58386.10.142500
    420.2914.34135.101297.00.100300
    .....................
    56421.5622.39142.001479.00.111000
    56520.1328.25131.201261.00.097800
    56616.6028.08108.30858.10.084550
    56720.6029.33140.101265.00.117800
    5687.7624.5447.92181.00.052631
    \n", + "

    569 rows × 6 columns

    \n", + "
    " + ], + "text/plain": [ + " mean_radius mean_texture mean_perimeter mean_area mean_smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + ".. ... ... ... ... ... \n", + "564 21.56 22.39 142.00 1479.0 0.11100 \n", + "565 20.13 28.25 131.20 1261.0 0.09780 \n", + "566 16.60 28.08 108.30 858.1 0.08455 \n", + "567 20.60 29.33 140.10 1265.0 0.11780 \n", + "568 7.76 24.54 47.92 181.0 0.05263 \n", + "\n", + " diagnosis \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "564 0 \n", + "565 0 \n", + "566 0 \n", + "567 0 \n", + "568 1 \n", + "\n", + "[569 rows x 6 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('Breast_cancer_data.csv')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dividing the value in X & Y to make prediction and spliting the dataset for training and testing" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop('diagnosis', axis=1)\n", + "Y = df['diagnosis']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing some sklearn metrices for calculating the accuracy, precision and recall score and form a function for to calculate all the metrices" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import recall_score" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_classification(y_test, y_pred):\n", + " \n", + " acc = accuracy_score(y_test, y_pred, normalize=True)\n", + " num_acc = accuracy_score(y_test, y_pred, normalize=False)\n", + "\n", + " prec = precision_score(y_test, y_pred)\n", + " recall = recall_score(y_test, y_pred)\n", + " \n", + " print(\"Test data count: \",len(y_test))\n", + " print(\"accuracy_count : \" , num_acc)\n", + " print(\"accuracy_score : \" , acc)\n", + " print(\"precision_score : \" , prec)\n", + " print(\"recall_score : \", recall)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing the Grid search from the sklearn model selection and forming a variable parameter contining the max depth for fiting the decision tree with the best parameter suggested by the grid search" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'max_depth': 7}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameters = {'max_depth': [1,2,3,4,5,6,7,8,9,10,11,12]}\n", + "\n", + "grid_search = GridSearchCV(DecisionTreeClassifier(), parameters, cv=3, return_train_score=True)\n", + "grid_search.fit(x_train, y_train)\n", + "\n", + "grid_search.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "decision_tree_model = DecisionTreeClassifier(max_depth = grid_search.best_params_['max_depth']).fit(x_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Summary of the calculation metrices achieve after predicting the values for x_test and then checking the accuracy by comparing the y_test and y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data count: 114\n", + "accuracy_count : 100\n", + "accuracy_score : 0.8771929824561403\n", + "precision_score : 0.9230769230769231\n", + "recall_score : 0.8695652173913043\n" + ] + } + ], + "source": [ + "y_pred = decision_tree_model.predict(x_test)\n", + "summarize_classification(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Two popular libraries for Bayesian Optimization include\n", + "\n", + "
  • Scikit-Optimize
    • \n", + "
  • HyperOpt
    • \n", + "In machine learning, these libraries are often used to tune the hyperparameters of algorithms.\n", + "Hyperparameter tuning is a good fit for Bayesian Optimization because the evaluation function is computationally expensive (e.g. training models for each set of hyperparameters) and noisy (e.g. noise in training data and stochastic learning algorithms)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I have used Scikit-Optimize library to optimize the hyperparameters for this classification problem. The Scikit-Optimize project is designed to provide access to Bayesian Optimization for applications that use SciPy and NumPy, or applications that use scikit-learn machine learning algorithms." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### pip install scikit-optimize is used to install it" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### importing the important libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# example of bayesian optimization with scikit-optimize\n", + "from numpy import mean\n", + "from sklearn.model_selection import cross_val_score\n", + "from skopt.space import Integer\n", + "from skopt.utils import use_named_args\n", + "from skopt import gp_minimize" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " There are many warning messages while using the gp_minimize,\n", + " such as: UserWarning: The objective has been evaluated at this point before.\n", + "\n", + "This is to be expected and is caused by the same hyperparameter configuration being evaluated more than once." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# define the model\n", + "model_tree = DecisionTreeClassifier()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Search Space \n", + "It is used to set the parameter is going to tuned or the dimensionality on which wwe are apllying the hyperarameter tuning.
    \n", + "Each search dimension can be defined either as\n", + "\n", + "
  • a (lower_bound, upper_bound) tuple (for Real or Integer dimensions),
    • \n", + "\n", + "
  • a (lower_bound, upper_bound, \"prior\") tuple (for Real dimensions),
    • \n", + "\n", + "
  • as a list of categories (for Categorical dimensions), or
    • \n", + "\n", + "
  • an instance of a Dimension object (Real, Integer or Categorical).
    • \n", + "\n", + "Also you can refer to : https://scikit-optimize.github.io/stable/modules/generated/skopt.space.space.check_dimension.html" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# define the search space of hyperparameters to search\n", + "search_space = [Integer(1, 12, name='max_depth')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### use_named_args & set_params\n", + "We can use the use_named_args() decorator from the scikit-optimize project on the function definition that allows the function to be called directly with a specific set of parameters from the search space.\n", + "\n", + "As such, our custom function will take the hyperparameter values as arguments, which can be provided to the model directly in order to configure it. We can define these arguments generically in python using the **params argument to the function, then pass them to the model via the set_params function." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# define the function used to evaluate a given configuration\n", + "@use_named_args(search_space)\n", + "def evaluate_model(**params):\n", + " # something\n", + " model_tree.set_params(**params)\n", + " # calculate 10-fold cross validation\n", + " result = cross_val_score(model_tree, x_train, y_train, cv=10, n_jobs=-1, scoring='accuracy')\n", + " # calculate the mean of the scores\n", + " estimate = mean(result)\n", + " return 1.0 - estimate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### gp_ minimize\n", + "Bayesian optimization using Gaussian Processes.
    \n", + "If every function evaluation is expensive, for instance when the parameters are the hyperparameters of a neural network and the function evaluation is the mean cross-validation score across ten folds, optimizing the hyperparameters by standard optimization routines would take for ever!
    \n", + "The idea is to approximate the function using a Gaussian process. In other words the function values are assumed to follow a multivariate gaussian. The covariance of the function values are given by a GP kernel between the parameters. Then a smart choice to choose the next parameter to evaluate can be made by the acquisition function over the Gaussian prior which is much quicker to evaluate.\n", + "https://scikit-optimize.github.io/stable/modules/generated/skopt.gp_minimize.html#skopt.gp_minimize" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# perform optimization\n", + "result = gp_minimize(evaluate_model, search_space)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Accuracy: 1\n", + "Best Parameters: max_depth=12\n" + ] + } + ], + "source": [ + "print('Best Accuracy: %.f' % (1.0 - result.fun))\n", + "print('Best Parameters: max_depth=%d' % (result.x[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "model_tree= DecisionTreeClassifier( max_depth = result.x[0]).fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data count: 114\n", + "accuracy_count : 102\n", + "accuracy_score : 0.8947368421052632\n", + "precision_score : 0.9354838709677419\n", + "recall_score : 0.8787878787878788\n" + ] + } + ], + "source": [ + "y_pred_tree = model_tree.predict(x_test)\n", + "summarize_classification(y_test, y_pred_tree)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from skopt.plots import plot_convergence\n", + "plot_convergence(result);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now using the KNeighborsClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Accuracy: 1\n", + "Best Parameters: n_neighbors=3, p=1\n" + ] + } + ], + "source": [ + "# define the model\n", + "model_kn =KNeighborsClassifier()\n", + "\n", + "\n", + "# define the search space of hyperparameters to search\n", + "search_space = [Integer(1, 12, name='n_neighbors'), Integer(1, 3, name='p')]\n", + "\n", + "# define the function used to evaluate a given configuration\n", + "@use_named_args(search_space)\n", + "def evaluate_model(**params):\n", + " # something\n", + " model_kn.set_params(**params)\n", + " # calculate 10-fold cross validation\n", + " result = cross_val_score(model_kn, x_train, y_train, cv=10, n_jobs=-1, scoring='accuracy')\n", + " # calculate the mean of the scores\n", + " estimate = mean(result)\n", + " return 1.0 - estimate\n", + "\n", + "# perform optimization\n", + "result = gp_minimize(evaluate_model, search_space)\n", + "# summarizing finding:\n", + "print('Best Accuracy: %.f' % (1.0 - result.fun))\n", + "print('Best Parameters: n_neighbors=%d, p=%d' % (result.x[0], result.x[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "model_kn = KNeighborsClassifier( n_neighbors = result.x[0],p=result.x[1]).fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data count: 114\n", + "accuracy_count : 98\n", + "accuracy_score : 0.8596491228070176\n", + "precision_score : 0.8571428571428571\n", + "recall_score : 0.9090909090909091\n" + ] + } + ], + "source": [ + "y_pred = model_kn.predict(x_test)\n", + "summarize_classification(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from skopt.plots import plot_convergence\n", + "plot_convergence(result);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import tree, model_selection" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'fit_time': array([0.00612926, 0.00793409, 0.00648522, 0.00653386, 0.00688672,\n", + " 0.00996494, 0.00953984, 0.01045251, 0.00822282, 0.0082016 ]),\n", + " 'score_time': array([0.0010345 , 0.00200129, 0.00301623, 0.0030067 , 0.00308418,\n", + " 0.004915 , 0.00572562, 0.00488901, 0.00896358, 0.00399947]),\n", + " 'test_score': array([0.91304348, 0.95652174, 0.82608696, 0.80434783, 0.91304348,\n", + " 0.95555556, 0.91111111, 0.86666667, 0.86666667, 0.97777778])}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifer = DecisionTreeClassifier()\n", + "classifer.fit(x_train,y_train)\n", + "results = model_selection.cross_validate(classifer, x_train, y_train, cv = 10)\n", + "\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Mean Score : 0.8990821256038647\n" + ] + } + ], + "source": [ + "print(\"Test Mean Score :\",results.get('test_score').mean())\n", + "print(\"Train Mean Score :\",results.get('train_score').mean())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5da7a1db7f6d4a429f64175eda14c2168599ca52 Mon Sep 17 00:00:00 2001 From: Kalash Jindal <37014842+erickeagle@users.noreply.github.com> Date: Thu, 1 Oct 2020 15:16:01 +0530 Subject: [PATCH 2/2] Add files via upload --- .../01-ExploringTheTitanicDataset.ipynb | 1788 +++++++++++++++++ ...ion_LogisticRegression_Titanic-Copy1.ipynb | 834 ++++++++ ...ification_LogisticRegression_Titanic.ipynb | 679 +++++++ ...MultipleClassificationModels_Titanic.ipynb | 1171 +++++++++++ ...4-HyperparameterTuningWithGridSearch.ipynb | 485 +++++ 5 files changed, 4957 insertions(+) create mode 100644 Machine Learning/01-ExploringTheTitanicDataset.ipynb create mode 100644 Machine Learning/02-BinaryClassification_LogisticRegression_Titanic-Copy1.ipynb create mode 100644 Machine Learning/02-BinaryClassification_LogisticRegression_Titanic.ipynb create mode 100644 Machine Learning/03-MultipleClassificationModels_Titanic.ipynb create mode 100644 Machine Learning/04-HyperparameterTuningWithGridSearch.ipynb diff --git a/Machine Learning/01-ExploringTheTitanicDataset.ipynb b/Machine Learning/01-ExploringTheTitanicDataset.ipynb new file mode 100644 index 00000000..0d3fff52 --- /dev/null +++ b/Machine Learning/01-ExploringTheTitanicDataset.ipynb @@ -0,0 +1,1788 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already up-to-date: scikit-learn in /anaconda3/lib/python3.7/site-packages (0.20.2)\r\n", + "Requirement already satisfied, skipping upgrade: numpy>=1.8.2 in /anaconda3/lib/python3.7/site-packages (from scikit-learn) (1.16.1)\r\n", + "Requirement already satisfied, skipping upgrade: scipy>=0.13.3 in /anaconda3/lib/python3.7/site-packages (from scikit-learn) (1.2.1)\r\n" + ] + } + ], + "source": [ + "!pip install -U scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.20.2\n" + ] + } + ], + "source": [ + "print(sklearn.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.16.1\n" + ] + } + ], + "source": [ + "print(np.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.23.4\n" + ] + } + ], + "source": [ + "print(pd.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Titanic dataset\n", + "\n", + "Source: https://www.kaggle.com/francksylla/titanic-machine-learning-from-disaster" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
    0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
    1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
    2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
    3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
    4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
    5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
    6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
    7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
    8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
    91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
    \n", + "
    " + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "5 6 0 3 \n", + "6 7 0 1 \n", + "7 8 0 3 \n", + "8 9 1 3 \n", + "9 10 1 2 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "5 Moran, Mr. James male NaN 0 \n", + "6 McCarthy, Mr. Timothy J male 54.0 0 \n", + "7 Palsson, Master. Gosta Leonard male 2.0 3 \n", + "8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 \n", + "9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + "5 0 330877 8.4583 NaN Q \n", + "6 0 17463 51.8625 E46 S \n", + "7 1 349909 21.0750 NaN S \n", + "8 2 347742 11.1333 NaN S \n", + "9 0 237736 30.0708 NaN C " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = pd.read_csv('titanic_train.csv')\n", + "\n", + "titanic_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(891, 12)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked
    003male22.0107.2500S
    111female38.01071.2833C
    213female26.0007.9250S
    311female35.01053.1000S
    403male35.0008.0500S
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked\n", + "0 0 3 male 22.0 1 0 7.2500 S\n", + "1 1 1 female 38.0 1 0 71.2833 C\n", + "2 1 3 female 26.0 0 0 7.9250 S\n", + "3 1 1 female 35.0 1 0 53.1000 S\n", + "4 0 3 male 35.0 0 0 8.0500 S" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], 'columns', inplace=True)\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Survived 179\n", + "Pclass 179\n", + "Sex 179\n", + "Age 2\n", + "SibSp 179\n", + "Parch 179\n", + "Fare 179\n", + "Embarked 177\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df[titanic_df.isnull().any(axis=1)].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "titanic_df = titanic_df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(712, 8)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Survived 0\n", + "Pclass 0\n", + "Sex 0\n", + "Age 0\n", + "SibSp 0\n", + "Parch 0\n", + "Fare 0\n", + "Embarked 0\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df[titanic_df.isnull().any(axis=1)].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassAgeSibSpParchFare
    count712.000000712.000000712.000000712.000000712.000000712.000000
    mean0.4044942.24016929.6420930.5140450.43258434.567251
    std0.4911390.83685414.4929330.9306920.85418152.938648
    min0.0000001.0000000.4200000.0000000.0000000.000000
    25%0.0000001.00000020.0000000.0000000.0000008.050000
    50%0.0000002.00000028.0000000.0000000.00000015.645850
    75%1.0000003.00000038.0000001.0000001.00000033.000000
    max1.0000003.00000080.0000005.0000006.000000512.329200
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare\n", + "count 712.000000 712.000000 712.000000 712.000000 712.000000 712.000000\n", + "mean 0.404494 2.240169 29.642093 0.514045 0.432584 34.567251\n", + "std 0.491139 0.836854 14.492933 0.930692 0.854181 52.938648\n", + "min 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000\n", + "25% 0.000000 1.000000 20.000000 0.000000 0.000000 8.050000\n", + "50% 0.000000 2.000000 28.000000 0.000000 0.000000 15.645850\n", + "75% 1.000000 3.000000 38.000000 1.000000 1.000000 33.000000\n", + "max 1.000000 3.000000 80.000000 5.000000 6.000000 512.329200" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked
    003male22.0107.2500S
    111female38.01071.2833C
    213female26.0007.9250S
    311female35.01053.1000S
    403male35.0008.0500S
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked\n", + "0 0 3 male 22.0 1 0 7.2500 S\n", + "1 1 1 female 38.0 1 0 71.2833 C\n", + "2 1 3 female 26.0 0 0 7.9250 S\n", + "3 1 1 female 35.0 1 0 53.1000 S\n", + "4 0 3 male 35.0 0 0 8.0500 S" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualizing relationships" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Survived')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAHjCAYAAAADuoh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3X+0ZnV9H/r3hzMgI0IJMtg4YEBDMEZUkrmCoSslGipCllBDKlxZN+2yurqaH+01pQsvLlONLGjosrGN6Q1JbZPU4iUmoTTSUGpM2uuNxCEYEJEEAWVAZfz9C+VHvveP85zhmTPP2fvMfOeZc87weq01a87+8d37s7/Pfr77Pc/sZ59qrQUAANg3h6x1AQAAsJEJ1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6bFrrAvbWscce20488cS1LgMAgIPcrbfe+oXW2pax9TZcoD7xxBOzffv2tS4DAICDXFV9ejXrueUDAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADpsmteGq+o9SX48ycOttRfOWF5J3pXk3CTfSvL3W2t/Pq96elx/24N523+9M1/+1mMzlz/ryMNyy+Vn75p+3a//aT78qS/tmj7zecfkvW94WZLk9Ctuzue//uiKbZ9/+Y359hNt1/ThC5VPXnHurumz3/nH+auHv7lr+uTjjsjNbzorSfKW6+/Itbc8kCday0JVLj79hLzjglN3O46rb7o7D33lkTz76M259JWn5ILTtu5aPtR+bNtj/Te03566hvp6rL+S5EW/8If52nee2DV91NMWcvvbzhldliQnXfaBPPlKJZXkvqvOW9XysbrHzpMTL/tAlrt/su2etmPHPdYnQ9seO7e/980fyONTHbapknuufLKuoW2PtV3+WiTJ1qM37zrnvvj1b+9W2/465rHlY+f+2Pk79FqP1T32nh5qP1Z37/k9ZGjfY8fc059j/TV2zEN197RdTW1DxvY9pGe/Y8fVu20ODmPn/npSrS2/zOynDVf9SJJvJPmtFQL1uUl+NouB+vQk72qtnT623W3btrXt27fv73JXdP1tD+bS9/9FHntiuJ+WBt7lg9OSM593TO55+Bu7DdbL2y4PHEuWgsfyi8GSk487Iqc/95n5Tx/5zB7LLjnjOXnHBafm+tsezJt/74488tiTF5vNhy7kytecmgtO25q3XH/Hiu2TDG57yNh+e+q6b+c3Vuzr977hZYP9dfObztrj4rvkqKctJMmKy25/2zkzA1ryZGgeWv7DzztmsO7lF/UlS+fJrIA2vc6+tr3/qvO6+mRo24cv1OC5vTwQL1kKxkPb3lQZbLvSazGm95jvv2q47l9+7UsGz/2x83foPHnk0ScG6x56X73jglMHz4O3X3DqYN1DY+Bqzu8hQ+PFW6+/Y/CYe/rz7B/4m4P9NXbMQ3X/zvbP7HPbsTFybHweq3tIz36T4ddy+6e/1LVtDg5j5/6BUlW3tta2ja03t1s+Wmv/M8me79QnnZ/FsN1aax9JcnRVffe86tlXV99092iYTrJrIJ41OC3NnzVYT7edFTim58+6GCzNv/aWB2YuW5p/9U1373ZSJskjjz2Rq2+6e7f1ZrUf2/aQsf321DXU18lwfyWzg9LS/KFlSVYMaG3Z37OWj9U9dp4M6Wmb9PXJkLFze1YgHpq/mnWW5u/rRwa9xzxm7NwfO3+HXuuxusfe00Ptx+qe5/k9tO+xY+7pz7H+Gjvmobp72k7XsFJtQ8b2PaRnv8nwcfVum4PD2Lm/3qzlPdRbk0y/O3ZM5u2hqt5YVduravvOnTsPSHFLHvrKIwd0f/vqiRX+p2Fp/krHsTR/qP3YtoeM7benLtjIxs79eZrne3qe1mrfveNQT93rdYycZ58Y90nWdqzZF2sZqGvGvJnvltbaNa21ba21bVu2bJlzWbt79tGbD+j+9tVCzerOJ+evdBxL84faj217yNh+e+qCjWzs3J+neb6n52mt9t07DvXUvV7HyHn2iXGfZG3Hmn2xloF6R5ITpqaPT/LQGtWyoktfeUoOXRh/Ez/ryMOSLN57NsuZzztm1zortT18hf0szT/5uCNmLj/5uCNy8eknzFy2NP/SV56SzYcu7LZs86ELufSVp+y23qz2Y9seMrbfnrqG+joZ7q/kyXtklzvqaQuDy5LZ/xqcnj+0fKzusfNkSE/bpK9Phoyd25tW6LCV5q9mnaX5+3oJ7j3mMWPn/tj5O/Raj9U99p4eaj9W9zzP76F9jx1zT3+O9dfYMQ/V3dN2uoaVahsytu8hPftNho+rd9scHMbO/fVmLQP1DUn+j1p0RpKvttY+u4b1zHTBaVtz9YUvznc9/dAV15n+Ms173/CyPQajpS943HL52XsM2tNtP3nFuXsEj+knIdz8prP2uCgsfaHmHRecmkvOeM6uf8EvVO32BY4LTtuaK19zarYevTmVxSccTN/YP9R+bNtj/Te03566hvp6rL+S5Pa3nbPHRXjpC0xDy5LFLx4uD2rTT/EYWj5W99h5Mv0EiWn3X3VeV9ukr0+Gtj12bt9z5Xl7BOPpJ3UMbXus7azXIslu59zy2vbHMY8tHzv3x87fodd6rO6x9/RQ+7G6e8/vIUP7Hjvmnv4c66+xYx6qu6dtMv5aDhnb95Ce/Y4dV++2OTiMnfvrzTyf8nFtkrOSHJvk80l+IcmhSdJa+78nj837lSTnZPGxef+gtTb6+I4D/ZQPAACemlb7lI+5PYe6tXbxyPKW5KfntX8AADgQ/KZEAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOcw3UVXVOVd1dVfdU1WUzlj+nqj5UVbdV1e1Vde486wEAgP1tboG6qhaSvDvJq5K8IMnFVfWCZau9Jcl1rbXTklyU5FfnVQ8AAMzDPD+hfmmSe1pr97bWHk3yviTnL1unJTlq8vPfSPLQHOsBAID9bp6BemuSB6amd0zmTfsXSS6pqh1Jbkzys7M2VFVvrKrtVbV9586d86gVAAD2yTwDdc2Y15ZNX5zkP7bWjk9ybpLfrqo9amqtXdNa29Za27Zly5Y5lAoAAPtmnoF6R5ITpqaPz563dLw+yXVJ0lr70ySHJzl2jjUBAMB+Nc9A/dEkJ1fVSVV1WBa/dHjDsnU+k+QVSVJV35/FQO2eDgAANoy5BerW2uNJfibJTUnuyuLTPO6sqrdX1asnq/18kjdU1V8kuTbJ32+tLb8tBAAA1q1N89x4a+3GLH7ZcHreW6d+/kSSM+dZAwAAzJPflAgAAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0GGugbqqzqmqu6vqnqq6bIV1/l5VfaKq7qyq/zzPegAAYH/bNK8NV9VCkncnOTvJjiQfraobWmufmFrn5CRvTnJma+3LVXXcvOoBAIB5mOcn1C9Nck9r7d7W2qNJ3pfk/GXrvCHJu1trX06S1trDc6wHAAD2u3kG6q1JHpia3jGZN+37knxfVX24qj5SVefM2lBVvbGqtlfV9p07d86pXAAA2HvzDNQ1Y15bNr0pyclJzkpycZLfqKqj92jU2jWttW2ttW1btmzZ74UCAMC+mmeg3pHkhKnp45M8NGOd/9Jae6y1dl+Su7MYsAEAYEOYZ6D+aJKTq+qkqjosyUVJbli2zvVJfjRJqurYLN4Ccu8cawIAgP1qboG6tfZ4kp9JclOSu5Jc11q7s6reXlWvnqx2U5IvVtUnknwoyaWttS/OqyYAANjfqrXltzWvb9u2bWvbt29f6zIAADjIVdWtrbVtY+v5TYkAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAECHTUMLq+rrSdpKy1trR+33igAAYAMZDNSttSOTpKrenuRzSX47SSV5XZIj514dAACsc6u95eOVrbVfba19vbX2tdbav0vyE/MsDAAANoLVBuonqup1VbVQVYdU1euSPDHPwgAAYCNYbaD+35P8vSSfn/z5yck8AAB4Shu8h3pJa+3+JOfPtxQAANh4VvUJdVV9X1V9sKo+Ppl+UVW9Zb6lAQDA+rfaWz5+PcmbkzyWJK2125NcNK+iAABgo1htoH56a+3Pls17fH8XAwAAG81qA/UXqup5mfySl6q6MMln51YVAABsEKv6UmKSn05yTZLnV9WDSe7L4i93AQCAp7TVBupPt9Z+rKqOSHJIa+3r8ywKAAA2itXe8nFfVV2T5Iwk35hjPQAAsKGsNlCfkuR/ZPHWj/uq6leq6m/NrywAANgYVhWoW2uPtNaua629JslpSY5K8idzrQwAADaA1X5Cnar621X1q0n+PMnhWfxV5AAA8JS2qi8lVtV9ST6W5Lokl7bWvjnXqgAAYINY7VM+Xtxa+9pcKwEAgA1oMFBX1T9vrf1Skiuqqi1f3lr7ublVBgAAG8DYJ9R3Tf7ePu9CAABgIxoM1K21/zr58fbW2m0HoB4AANhQVvuUj3dW1Ser6her6gfmWhEAAGwgq30O9Y8mOSvJziTXVNUdVfWWeRYGAAAbwaqfQ91a+1xr7d8k+UdZfITeW+dWFQAAbBCrCtRV9f1V9S+q6uNJfiXJ/5fk+LlWBgAAG8Bqn0P9H5Jcm+TvtNYemmM9AACwoYwG6qpaSPKp1tq7DkA9AACwoYze8tFaeyLJM6vqsANQDwAAbCirveXj00k+XFU3JPnm0szW2jvnUhUAAGwQqw3UD03+HJLkyPmVAwAAG8uqAnVr7W3zLgQAADaiVQXqqvpQkrZ8fmvt5fu9IgAA2EBWe8vHP5v6+fAkP5Hk8f1fDgAAbCyrveXj1mWzPlxVfzKHegAAYENZ7S0fx0xNHpJkW5K/OZeKAABgA1ntLR+35sl7qB9Pcn+S18+jIAAA2EgGA3VV/W9JHmitnTSZ/qks3j99f5JPzL06AABY58Z+U+KvJXk0SarqR5JcmeQ3k3w1yTXzLQ0AANa/sVs+FlprX5r8/Nok17TWfjfJ71bVx+ZbGgAArH9jn1AvVNVS6H5Fkj+aWrba+68BAOCgNRaKr03yJ1X1hSSPJPlfSVJV35vF2z4AAOApbTBQt9auqKoPJvnuJP+9tbb0pI9DkvzsvIsDAID1bvS2jdbaR2bM+8v5lAMAABvL2D3UAADAAIEaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQYa6BuqrOqaq7q+qeqrpsYL0Lq6pV1bZ51gMAAPvb3AJ1VS0keXeSVyV5QZKLq+oFM9Y7MsnPJbllXrUAAMC8zPMT6pcmuae1dm9r7dEk70ty/oz1fjHJLyX59hxrAQCAuZhnoN6a5IGp6R2TebtU1WlJTmit/cHQhqrqjVW1vaq279y5c/9XCgAA+2iegbpmzGu7FlYdkuRfJ/n5sQ211q5prW1rrW3bsmXLfiwRAAD6zDNQ70hywtT08Ukempo+MskLk/xxVd2f5IwkN/hiIgAAG8k8A/VHk5xcVSdV1WFJLkpyw9LC1tpXW2vHttZObK2dmOQjSV7dWts+x5oAAGC/mlugbq09nuRnktyU5K4k17XW7qyqt1fVq+e1XwAAOJA2zXPjrbUbk9y4bN5bV1j3rHnWAgAA8+A3JQIAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdJhroK6qc6rq7qq6p6oum7H8TVX1iaq6vao+WFXfM896AABgf5tboK6qhSTvTvKqJC9IcnFVvWDZarcl2dZae1GS9yf5pXnVAwAA8zDPT6hfmuSe1tq9rbVHk7wvyfnTK7TWPtRa+9Zk8iNJjp9jPQAAsN/NM1BvTfLA1PSOybyVvD7Jf5u1oKreWFXbq2r7zp0792OJAADQZ56BumbMazNXrLokybYkV89a3lq7prW2rbW2bcuWLfuxRAAA6LNpjtvekeSEqenjkzy0fKWq+rEklyf5262178yxHgAA2O/m+Qn1R5OcXFUnVdVhSS5KcsP0ClV1WpJfS/Lq1trDc6wFAADmYm6BurX2eJKfSXJTkruSXNdau7Oq3l5Vr56sdnWSZyT5nar6WFXdsMLmAABgXZrnLR9prd2Y5MZl89469fOPzXP/AAAwb35TIgAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAh03z3HhVnZPkXUkWkvxGa+2qZcufluS3kvxQki8meW1r7f551rQ/vOX6O3LtLQ/kidayUJWLTz8h77jg1F3Ln3/5jfn2E23X9OELlU9ecW6S5PrbHszVN92dh77ySJ599OZc+spTcsFpW3et+7pf/9N8+FNf2jV95vOOyXvf8LJV1TW27bG6h5a/6Bf+MF/7zhO71j3qaQu5/W3nrKptzzGNWb7tk487It969K939cGPPn9LPvTJnSv2yYmXfWCPbd5/1XlJhl/HsbZjy2ct23r05l11PviVRw7otle777FjHuqzWW0XqnadM0+0tsfysWNeqV0luW+q7UmXfSDL1xo65un2Y20//9VH8vjUCqs5TyrJs4/enK8/8uhu76tnHXlYbrn87F3TY++75bVN1738tagkh0z19xGHHbLbtk8+7ojc/Kazdk0PvZZnv/OP81cPf3PFtmN1L9/2koWqHPuMQ/P5rz+6a97y8eJ73/yB3fp7UyX3XLl4zGNjzdgYePoVN++27+nXY2jZavY9ZGzby8f25ePaic/cnI/c++WZx9Uz7o8tH2vb+3oM6bnmjF0r52l53Wc897ty/xcfyUNfeSSbDz0kjzz+12kte90fY9bymJ8qqs24GO2XDVctJPnLJGcn2ZHko0kubq19Ymqdf5zkRa21f1RVFyX5u6211w5td9u2bW379u1zqXk13nL9HflPH/nMHvMvOeM5eccFp654oTh8oXLVhS/Om3/vjjzy2JMXms2HLuTK15yaC07buscgsGQ1A/P1tz04uO2xuoeW33Dbg7tdHJcsXSSH2t638xv7fExjVuqvIdN9MivoLDl8oVZ8HT95xbmDbe+/6rzB5T3mue0e91913uC5P2v+3mx7X455KVzOCsSrbZ9kn9qu5jxZyVKYWh5Klyy971Y6rkrytH3s86VgPPRanvDMp+8Wppe3Hat7pW0PWRovlofpJZsqOf25xwyONWNj4PJQu+RZRx6WJCsuu+Xys7vG7qH93nL52TPH9tW45IznJMk+j/tjy8e2PdYnY/se0nPNGbtWztNKdQ9ZTX+MWctjPhhU1a2ttW1j683zlo+XJrmntXZva+3RJO9Lcv6ydc5P8puTn9+f5BVVVVnHrr3lgcH5K10ovv1Ey9U33b3HoPjIY0/k6pvuTpIVw+FqQuPYtsfqHlo+6+KYZNf8obY9xzRmX7Yx3SdDhl5HZltvfdaW/b0v7fe1bc8xL4WrsffdSntoHftfCspDr+WsMD3ddqzufalt6b0+K0wvzR8ba8bGwFmhdmn+0LLpfay07yFj2541tq/Gtbc80DXujy0fa9v7egzpueaMXSvnaTXHtj/aLLeWx/xUMs9AvTXJ9JmwYzJv5jqttceTfDXJM5dvqKreWFXbq2r7zp0751Tu6sz67+Wh+dMemvFf6UPz98bYtsfq7jmunrZrYX/0N7CxbLRxasm+jldPtNY97g8t7+3PtbrmzPM6PGZfzrX9cX6u5TE/lcwzUM/6pHn5mbGaddJau6a1tq21tm3Lli37pbh9tbDCB+grzZ/27KM379X8vTG27bG6e46rp+1a2B/9DWwsG22cWrKv49VCVfe4P7S8tz/X6pozz+vwmH051/bH+bmWx/xUMs9AvSPJCVPTxyd5aKV1qmpTkr+RpP9egDm6+PQTBucfvjD75D98oXLpK0/J5kMXdpu/+dCFXPrKU5Is3uM1y0rzp41te6zuoeVHPW1h5rKl+UNte45pzL5sY7pPhgy9jsy23vqslv29L+33tW3PMS/dszv2vltpD9Wx/5OPOyIZaH/4Qu1aZ6W2Y3XvS21L7/VNKzTdVOPj59gYuNTvyz3ryMMGl03vY6V9Dxnb9qyxfTUuPv2ErnF/bPlY297XY0jPNWfsWjlPqzm2/dFmubU85qeSeQbqjyY5uapOqqrDklyU5IZl69yQ5KcmP1+Y5I/avL4luZ+844JTc8kZz9ntX/DTXxr45BXn7nHBWPqC0gWnbc2Vrzk1W4/enMrikwKmvxTw3je8bI/BYLVf3hvb9ljdQ8tvf9s5e1wkp7+1P9S255jGzNr2yccdsVsfXHLGc1bsk+knSEy7/6rzBl/HsbZjy1daNl3ngd72avY9dsxDfbZS27FPwsaOeaV200+7uO+q82aGz6FjXmq/mrbLQ95qzpOltsvfV9NPdxh7382qbanuWa9FZff+Xr7t6Sd1DL2WN7/prD1C9XTbsbpnbXvJQtUeAXN6vLjnyvP26O+lp3yMjTVjY+Atl5+9x76XXo+hZUnf2D227Vlj+/Jx7cznHTPzuHrG/bHlY217X48hPdecsWvlPM2q+8znHbOrlqcfekiWhrS96Y8xa3nMTyVze8pHklTVuUl+OYuPzXtPa+2Kqnp7ku2ttRuq6vAkv53ktCx+Mn1Ra+3eoW2u9VM+AAB4aljtUz7m+hzq1tqNSW5cNu+tUz9/O8lPzrMGAACYJ78pEQAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6VGttrWvYK1W1M8mnD9Dujk3yhQO0r4OFPts7+mvv6bO9o7/2nj7bO/pr7+mzvbOW/fU9rbUtYyttuEB9IFXV9tbatrWuYyPRZ3tHf+09fbZ39Nfe02d7R3/tPX22dzZCf7nlAwAAOgjUAADQQaAeds1aF7AB6bO9o7/2nj7bO/pr7+mzvaO/9p4+2zvrvr/cQw0AAB18Qg0AAB0EagAA6CBQr6Cqzqmqu6vqnqq6bK3rWY+q6j1V9XBVfXxq3jFVdXNV/dXk7+9ayxrXk6o6oao+VFV3VdWdVfVPJvP12QxVdXhV/VlV/cWkv942mX9SVd0y6a//p6oOW+ta15OqWqiq26rqDybT+mtAVd1fVXdU1ceqavtknvdUleBJAAAGDElEQVTkgKo6uqreX1WfnIxnL9Nns1XVKZNza+nP16rqn+qvYVX1f07G/Y9X1bWT68G6HssE6hmqaiHJu5O8KskLklxcVS9Y26rWpf+Y5Jxl8y5L8sHW2slJPjiZZtHjSX6+tfb9Sc5I8tOT80qfzfadJC9vrb04yUuSnFNVZyT5l0n+9aS/vpzk9WtY43r0T5LcNTWtv8b9aGvtJVPPufWeHPauJH/YWnt+khdn8XzTZzO01u6enFsvSfJDSb6V5Pejv1ZUVVuT/FySba21FyZZSHJR1vlYJlDP9tIk97TW7m2tPZrkfUnOX+Oa1p3W2v9M8qVls89P8puTn38zyQUHtKh1rLX22dban09+/noWL0Jbo89maou+MZk8dPKnJXl5kvdP5uuvKVV1fJLzkvzGZLqiv/aF9+QKquqoJD+S5N8nSWvt0dbaV6LPVuMVST7VWvt09NeYTUk2V9WmJE9P8tms87FMoJ5ta5IHpqZ3TOYx7lmttc8miwEyyXFrXM+6VFUnJjktyS3RZyua3L7wsSQPJ7k5yaeSfKW19vhkFe/N3f1ykn+e5K8n08+M/hrTkvz3qrq1qt44mec9ubLnJtmZ5D9Mbi36jao6IvpsNS5Kcu3kZ/21gtbag0n+VZLPZDFIfzXJrVnnY5lAPVvNmOf5guwXVfWMJL+b5J+21r621vWsZ621Jyb/VXp8Fv/n6PtnrXZgq1qfqurHkzzcWrt1evaMVfXX7s5srf1gFm/x++mq+pG1Lmid25TkB5P8u9baaUm+GbcrjJrc7/vqJL+z1rWsd5P7yc9PclKSZyc5Iovvz+XW1VgmUM+2I8kJU9PHJ3lojWrZaD5fVd+dJJO/H17jetaVqjo0i2H6va2135vM1mcjJv+l/MdZvPf86Ml/Aybem9POTPLqqro/i7epvTyLn1jrrwGttYcmfz+cxXtbXxrvySE7kuxord0ymX5/FgO2Phv2qiR/3lr7/GRaf63sx5Lc11rb2Vp7LMnvJfnhrPOxTKCe7aNJTp58o/SwLP43zQ1rXNNGcUOSn5r8/FNJ/ssa1rKuTO5n/fdJ7mqtvXNqkT6boaq2VNXRk583Z3GQvSvJh5JcOFlNf0201t7cWju+tXZiFsesP2qtvS76a0VVdURVHbn0c5K/k+Tj8Z5cUWvtc0keqKpTJrNekeQT0WdjLs6Tt3sk+mvIZ5KcUVVPn1w3l86xdT2W+U2JK6iqc7P46c5Ckve01q5Y45LWnaq6NslZSY5N8vkkv5Dk+iTXJXlOFt8UP9laW/7FxaekqvpbSf5Xkjvy5D2u/1cW76PWZ8tU1Yuy+MWThSz+4/+61trbq+q5WfwE9pgktyW5pLX2nbWrdP2pqrOS/LPW2o/rr5VN+ub3J5Obkvzn1toVVfXMeE+uqKpeksUvvh6W5N4k/yCT92j02R6q6ulZ/F7Wc1trX53Mc44NmDwm9bVZfDrWbUn+YRbvmV63Y5lADQAAHdzyAQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBjgIVNXfrapWVc9f61oAnmoEaoCDw8VJ/t8s/lIXAA4ggRpgg6uqZ2TxV4+/PpNAXVWHVNWvVtWdVfUHVXVjVV04WfZDVfUnVXVrVd209CuQAdg3AjXAxndBkj9srf1lki9V1Q8meU2SE5OcmsXfMvayJKmqQ5P82yQXttZ+KMl7kvhNsAAdNq11AQB0uzjJL09+ft9k+tAkv9Na++skn6uqD02Wn5LkhUlurqpk8Ve7f/bAlgtwcBGoATawqnpmkpcneWFVtSwG5Jbk91dqkuTO1trLDlCJAAc9t3wAbGwXJvmt1tr3tNZObK2dkOS+JF9I8hOTe6mfleSsyfp3J9lSVbtuAamqH1iLwgEOFgI1wMZ2cfb8NPp3kzw7yY4kH0/ya0luSfLV1tqjWQzh/7Kq/iLJx5L88IErF+DgU621ta4BgDmoqme01r4xuS3kz5Kc2Vr73FrXBXCwcQ81wMHrD6rq6CSHJflFYRpgPnxCDQAAHdxDDQAAHQRqAADoIFADAEAHgRoAADoI1AAA0OH/BxInDnRxRYc7AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "plt.scatter(titanic_df['Age'], titanic_df['Survived'])\n", + "plt.xlabel('Age')\n", + "plt.ylabel('Survived')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Survived')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "plt.scatter(titanic_df['Fare'], titanic_df['Survived'])\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Survived')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Survived01
    Sex
    female64195
    male36093
    \n", + "
    " + ], + "text/plain": [ + "Survived 0 1\n", + "Sex \n", + "female 64 195\n", + "male 360 93" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(titanic_df['Sex'], titanic_df['Survived'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    Survived01
    Pclass
    164120
    29083
    327085
    \n", + "
    " + ], + "text/plain": [ + "Survived 0 1\n", + "Pclass \n", + "1 64 120\n", + "2 90 83\n", + "3 270 85" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.crosstab(titanic_df['Pclass'], titanic_df['Survived'])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassAgeSibSpParchFare
    Survived1.000000-0.356462-0.082446-0.0155230.0952650.266100
    Pclass-0.3564621.000000-0.3659020.0651870.023666-0.552893
    Age-0.082446-0.3659021.000000-0.307351-0.1878960.093143
    SibSp-0.0155230.065187-0.3073511.0000000.3833380.139860
    Parch0.0952650.023666-0.1878960.3833381.0000000.206624
    Fare0.266100-0.5528930.0931430.1398600.2066241.000000
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Age SibSp Parch Fare\n", + "Survived 1.000000 -0.356462 -0.082446 -0.015523 0.095265 0.266100\n", + "Pclass -0.356462 1.000000 -0.365902 0.065187 0.023666 -0.552893\n", + "Age -0.082446 -0.365902 1.000000 -0.307351 -0.187896 0.093143\n", + "SibSp -0.015523 0.065187 -0.307351 1.000000 0.383338 0.139860\n", + "Parch 0.095265 0.023666 -0.187896 0.383338 1.000000 0.206624\n", + "Fare 0.266100 -0.552893 0.093143 0.139860 0.206624 1.000000" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_data_corr = titanic_df.corr()\n", + "titanic_data_corr" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 10))\n", + "sns.heatmap(titanic_data_corr, annot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked
    003122.0107.2500S
    111038.01071.2833C
    213026.0007.9250S
    311035.01053.1000S
    403135.0008.0500S
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked\n", + "0 0 3 1 22.0 1 0 7.2500 S\n", + "1 1 1 0 38.0 1 0 71.2833 C\n", + "2 1 3 0 26.0 0 0 7.9250 S\n", + "3 1 1 0 35.0 1 0 53.1000 S\n", + "4 0 3 1 35.0 0 0 8.0500 S" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import preprocessing\n", + "\n", + "label_encoding = preprocessing.LabelEncoder()\n", + "titanic_df['Sex'] = label_encoding.fit_transform(titanic_df['Sex'].astype(str))\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['female', 'male'], dtype=object)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "label_encoding.classes_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### C = Cherbourg, Q = Queenstown, S = Southampton" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_S
    003122.0107.2500001
    111038.01071.2833100
    213026.0007.9250001
    311035.01053.1000001
    403135.0008.0500001
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked_C Embarked_Q \\\n", + "0 0 3 1 22.0 1 0 7.2500 0 0 \n", + "1 1 1 0 38.0 1 0 71.2833 1 0 \n", + "2 1 3 0 26.0 0 0 7.9250 0 0 \n", + "3 1 1 0 35.0 1 0 53.1000 0 0 \n", + "4 0 3 1 35.0 0 0 8.0500 0 0 \n", + "\n", + " Embarked_S \n", + "0 1 \n", + "1 0 \n", + "2 1 \n", + "3 1 \n", + "4 1 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = pd.get_dummies(titanic_df, columns=['Embarked'])\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_S
    003014.0007.8542001
    111128.00026.5500001
    211036.012120.0000001
    303117.0107.0542001
    40314.04231.2750001
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked_C \\\n", + "0 0 3 0 14.0 0 0 7.8542 0 \n", + "1 1 1 1 28.0 0 0 26.5500 0 \n", + "2 1 1 0 36.0 1 2 120.0000 0 \n", + "3 0 3 1 17.0 1 0 7.0542 0 \n", + "4 0 3 1 4.0 4 2 31.2750 0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = titanic_df.sample(frac=1).reset_index(drop=True)\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "titanic_df.to_csv('datasets/titanic_processed.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fashion-mnist_train.csv titanic_processed.csv titanic_train.csv\r\n" + ] + } + ], + "source": [ + "!ls datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Machine Learning/02-BinaryClassification_LogisticRegression_Titanic-Copy1.ipynb b/Machine Learning/02-BinaryClassification_LogisticRegression_Titanic-Copy1.ipynb new file mode 100644 index 00000000..774410b9 --- /dev/null +++ b/Machine Learning/02-BinaryClassification_LogisticRegression_Titanic-Copy1.ipynb @@ -0,0 +1,834 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_S
    003014.0007.8542001
    111128.00026.5500001
    211036.012120.0000001
    303117.0107.0542001
    40314.04231.2750001
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked_C \\\n", + "0 0 3 0 14.0 0 0 7.8542 0 \n", + "1 1 1 1 28.0 0 0 26.5500 0 \n", + "2 1 1 0 36.0 1 2 120.0000 0 \n", + "3 0 3 1 17.0 1 0 7.0542 0 \n", + "4 0 3 1 4.0 4 2 31.2750 0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = pd.read_csv('titanic_processed.csv')\n", + "\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(712, 10)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X = titanic_df.drop('Survived', axis=1)\n", + "Y = titanic_df['Survived']\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((569, 9), (569,))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape, y_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((143, 9), (143,))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_test.shape, y_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic regression for classification\n", + "\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "logistic_model = LogisticRegression(penalty='l2', C=1.0, solver='liblinear').fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = logistic_model.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pred_results = pd.DataFrame({'y_test': y_test,\n", + " 'y_pred': y_pred})" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_testy_pred
    67311
    25500
    64311
    30511
    62400
    \n", + "
    " + ], + "text/plain": [ + " y_test y_pred\n", + "673 1 1\n", + "255 0 0\n", + "643 1 1\n", + "305 1 1\n", + "624 0 0" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_results.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_test01
    y_pred
    07019
    11143
    \n", + "
    " + ], + "text/plain": [ + "y_test 0 1\n", + "y_pred \n", + "0 70 19\n", + "1 11 43" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_crosstab = pd.crosstab(pred_results.y_pred, pred_results.y_test)\n", + "\n", + "titanic_crosstab" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision-recall scores\n", + "\n", + "When we use these for multiclass classification we need to specify an averaging method to determine how the precision and recall scores for different labels should be weighted\n", + "\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import recall_score" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy_score : 0.7902097902097902\n", + "precision_score : 0.7962962962962963\n", + "recall_score : 0.6935483870967742\n" + ] + } + ], + "source": [ + "acc = accuracy_score(y_test, y_pred)\n", + "prec = precision_score(y_test, y_pred)\n", + "recall = recall_score(y_test, y_pred)\n", + "\n", + "print(\"accuracy_score : \", acc)\n", + "print(\"precision_score : \", prec)\n", + "print(\"recall_score : \", recall)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_test01
    y_pred
    07019
    11143
    \n", + "
    " + ], + "text/plain": [ + "y_test 0 1\n", + "y_pred \n", + "0 70 19\n", + "1 11 43" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_crosstab" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "TP = titanic_crosstab[1][1]\n", + "TN = titanic_crosstab[0][0]\n", + "FP = titanic_crosstab[0][1]\n", + "FN = titanic_crosstab[1][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7902097902097902" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_score_verified = (TP + TN) / (TP + FP + TN + FN)\n", + "\n", + "accuracy_score_verified" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7962962962962963" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "precision_score_survived = TP / (TP + FP)\n", + "\n", + "precision_score_survived" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6935483870967742" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recall_score_survived = TP / (TP + FN)\n", + "recall_score_survived" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(titanic_df)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.svm import LinearSVC" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearSVC(dual=False, tol=0.001)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = LinearSVC(C=1.0, max_iter=1000, tol=1e-3, dual=False)\n", + "model.fit(x_train, y_train) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_train = model.predict(x_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7943760984182777\n", + "0.7658536585365854\n", + "0.6946902654867256\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_train, y_pred_train, normalize=True))\n", + "print(precision_score(y_train, y_pred_train))\n", + "print(recall_score(y_train, y_pred_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = model.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7762237762237763\n", + "0.7777777777777778\n", + "0.6774193548387096\n" + ] + } + ], + "source": [ + "print(accuracy_score(y_test, y_pred, normalize=True))\n", + "print(precision_score(y_test, y_pred))\n", + "print(recall_score(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_test01
    y_pred
    06920
    11242
    \n", + "
    " + ], + "text/plain": [ + "y_test 0 1\n", + "y_pred \n", + "0 69 20\n", + "1 12 42" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "pd.crosstab(pd.DataFrame({'y_test': y_test,'y_pred': y_pred}).y_pred, pred_results.y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Machine Learning/02-BinaryClassification_LogisticRegression_Titanic.ipynb b/Machine Learning/02-BinaryClassification_LogisticRegression_Titanic.ipynb new file mode 100644 index 00000000..3197cb2f --- /dev/null +++ b/Machine Learning/02-BinaryClassification_LogisticRegression_Titanic.ipynb @@ -0,0 +1,679 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_S
    003014.0007.8542001
    111128.00026.5500001
    211036.012120.0000001
    303117.0107.0542001
    40314.04231.2750001
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked_C \\\n", + "0 0 3 0 14.0 0 0 7.8542 0 \n", + "1 1 1 1 28.0 0 0 26.5500 0 \n", + "2 1 1 0 36.0 1 2 120.0000 0 \n", + "3 0 3 1 17.0 1 0 7.0542 0 \n", + "4 0 3 1 4.0 4 2 31.2750 0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = pd.read_csv('titanic_processed.csv')\n", + "\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(712, 10)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X = titanic_df.drop('Survived', axis=1)\n", + "Y = titanic_df['Survived']\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((569, 9), (569,))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_train.shape, y_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((143, 9), (143,))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_test.shape, y_test.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Logistic regression for classification\n", + "\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "logistic_model = LogisticRegression(penalty='l2', C=1.0, solver='liblinear').fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = logistic_model.predict(x_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "pred_results = pd.DataFrame({'y_test': y_test,\n", + " 'y_pred': y_pred})" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_testy_pred
    23801
    36711
    20100
    20000
    63300
    \n", + "
    " + ], + "text/plain": [ + " y_test y_pred\n", + "238 0 1\n", + "367 1 1\n", + "201 0 0\n", + "200 0 0\n", + "633 0 0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred_results.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_test01
    y_pred
    08316
    11133
    \n", + "
    " + ], + "text/plain": [ + "y_test 0 1\n", + "y_pred \n", + "0 83 16\n", + "1 11 33" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_crosstab = pd.crosstab(pred_results.y_pred, pred_results.y_test)\n", + "\n", + "titanic_crosstab" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Precision-recall scores\n", + "\n", + "When we use these for multiclass classification we need to specify an averaging method to determine how the precision and recall scores for different labels should be weighted\n", + "\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import recall_score" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy_score : 0.8111888111888111\n", + "precision_score : 0.75\n", + "recall_score : 0.673469387755102\n" + ] + } + ], + "source": [ + "acc = accuracy_score(y_test, y_pred)\n", + "prec = precision_score(y_test, y_pred)\n", + "recall = recall_score(y_test, y_pred)\n", + "\n", + "print(\"accuracy_score : \", acc)\n", + "print(\"precision_score : \", prec)\n", + "print(\"recall_score : \", recall)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    y_test01
    y_pred
    08316
    11133
    \n", + "
    " + ], + "text/plain": [ + "y_test 0 1\n", + "y_pred \n", + "0 83 16\n", + "1 11 33" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_crosstab" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "TP = titanic_crosstab[1][1]\n", + "TN = titanic_crosstab[0][0]\n", + "FP = titanic_crosstab[0][1]\n", + "FN = titanic_crosstab[1][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8111888111888111" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_score_verified = (TP + TN) / (TP + FP + TN + FN)\n", + "\n", + "accuracy_score_verified" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.75" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "precision_score_survived = TP / (TP + FP)\n", + "\n", + "precision_score_survived" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.673469387755102" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recall_score_survived = TP / (TP + FN)\n", + "recall_score_survived" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABvoAAAb8CAYAAADV0EZZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdfZRcd33n+c/31kN3qVuy9dAioJZjY4wdLyM/qAO7ZIY4QLJO4MDZWBiDNU5YjonNQ1jCGDxnchiGmTmL8UkYGLCFnQnBsQePkc9uPOABZgkOWRwSJGO0wWAwfkBtM6gltWypu7qrbt3v/lEPqq6ualW3qm7Xr/V+nXOPqu7D7/e9v/rW/d3ur6ra3F0AAAAAAAAAAAAAwhKtdgAAAAAAAAAAAAAAlo9CHwAAAAAAAAAAABAgCn0AAAAAAAAAAABAgCj0AQAAAAAAAAAAAAGi0AcAAAAAAAAAAAAEKOhC35VXXumSWFh6uaSG/GXpw5Ia8pelD0tqyF+WPiypIX9Z+rCkhvxl6cOSGvKXpQ9Lashflj4sqSF/WfqwpILcZenT0lbQhb7Dhw+vdgjAipG/CBn5i5CRvwgZ+YuQkb8IGfmLkJG/CBn5i1CRu0hT0IU+AAAAAAAAAAAA4ExFoQ8AAAAAAAAAAAAIEIU+AAAAAAAAAAAAIEAU+gAAAAAAAAAAAIAAUegDAAAAAAAAAAAAApRNoxMz+3NJb5R0yN1f0Wa7SfqUpN+RNCvp9939kTRiAwbV3FysI8WS4sSVjUybC3kND6fylh0YjEG4YxBq3OidkHNgbi7W9FxZLpe7FEWSu6lcSZSJTLnIZCbFFVc2E6mSJEpcqrgrY6bIJJdpXT7SzHxFw7lIc+VElcSVqR0rSRkzzcWJspFpKBupVEnkLlUS11A2UuKSzJUkUlxbZ5Lm4kS5yJTNRJJc5Yo3xnk4G2m+1k4USZ5I5dq2bGSajxPlMpEik+bjpBGPu1TIRzoxX1HGqusiM0VmKlWqseci04ZCpJl5VzlxVRJXPhNp43BOZcV6oZg02qokrqg+VpEUx74ojmxkykTVMchnIm0q5HR4tpozuUykbGQqlivKZSKtq8VmUm38TFtHh5TN9uf/rCWJ68hMSaW4onw2o80jeUWR9aWvXkvrvZdGP2mdS6kUa2rmZD9jI3nl873vp1yu6NCJ+UY/W0eHlMtlet5PyPrxmvcrj/rRbmg5EvK1stfqY7Eu5yqWpVKcqFyb99cPRToxnzRe19HhSHFFqiTV+bLUdH+RjUyq3WPU59pCNpJMKlVcSW2OzGWr9yX1e4RsZBrORYorLtfJOb6Qq/Y1F1eUiUz5THXerM+vp5pLW3NycyG/IO+3jg4pk4kW5MGGfKYxn/fzmjqIQnsP14V8347eCTUPQo0bvRVqHoQaN3qrV3mQVub8haTPSLqrw/bflnRBbXmVpNtr//bEuTd/ZdnHPP3xN/Sqe2DZ5uZi/eTIjG68e78mp4sa31jQ7bt36oLNI2fMBZ8xCHcMQo0bvRNyDszNxXriyIw+/Y0f6/defZ6+8PBTetdrztf/8V8ebZzLrbt2aPNoXvfvO6jXX/xLymUjvfc/f6+x/ZardugLDz+l9772Av3oued10UvO0rvveWTR9nf82nn6xFcf19SJed1+7eWSpBvveURjo0P60JUX6vPffkq/9+rz9OH7Dyzou37Mp665VOuHs/rf/2LfyXG+9nLlsqY//fqP9c5/+lJ98Evfb3ts8+PmeO/+u2f08JNH9Jm3X6ZcZHphLtZNe0/2//l3/KqOzZT0gftOtvu5f75TQ9lIn/jqjxbF+9m3X6ZMFOmGplxoF8crzz1bb7x0fEHONO9327WXN2Krx/uHr3u5LnrR+p4X+5LE9fgvjuv6u06O653XTejCF60f+F9gp/XeS6OftM6lVIr1+NTifi4cG+npL6bL5Yp+dOjEon4u2joaxC+B09CP17xfedSPdkPLkZCvlb1WH4tzNuZ1eLaiF2bLurE273/0jRdp53lbFr2u4xuH9OyxuQX3B7fu2qEto3kN5TJ6drqom/Ye0NjokD76pos1W6osmI8/dc2lykS24P7j8+/4VZ2Yi/W+L1bX/dbFW/W+117QiKXex7p8Rh994DFNnZjXnt07O86lrTnZ6Vy2jOb0lj3f0eR0UX/wz85dNJ/345o6iEJ7D9eFfN+O3gk1D0KNG70Vah6EGjd6q5d5kMpXd7r7tyQdXWKXN0u6y6u+I+lsM3txGrEBg+hIsdR4g0vS5HRRN969X0eKpVWOLD2MQbhjEGrc6J2Qc+BIsaQb7t6vq3Zu14fvP6Crdm5vFPmk6rnctPeAnp2e066Jc/SB+76v6Znygu314959zyN69QVjjV/itW6/ae8B3XDF+dXxuecRHT5R0uR0UTdccb5u2nugEUNr3/Vj3n/vo3p2em7hON/ziLJRRlft3N4o8rU7tvlxc7zXv+almpwuanqmrEPHS41fKtbbmDxabBT56uv+4C/36+DRYtt4j86UG0W+peLYNXHOopxp3q85tnq8N9y9X4dOzPc+B2ZKjV9c12O5/q59OjITRv6m8d5Lo5+0zmVqpn0/Uz1+vQ+dmG/bTz9yOFT9eM37lUf9aDe0HAn5Wtlr9bE4VkxUjr1RWJOk11784rav6+x8suj+oDrnzqkUe2P+veGK83V0prxoPn7/vY8uuv+YPFpsFPkk6aqd2xfEUu/j6Ey5Mb8uNZe25mSnc4kraqxrN5/345o6iEJ7D9eFfN+O3gk1D0KNG70Vah6EGjd6q5d5MCh/o2+bpINNzydr6xYxs3eZ2T4z2zc1NZVKcECvdJu/ceKNN3jd5HRRceL9DnFgMAaDNwbkL7o1iDmw3Pw9u5Bb8G+zyemi1uUzykTWeNy6vX5cpcNYNLff3KakU/bd7pjm7ZGpq2NbH09OF5WpfQpjXT6jdfnMojbaravH0a7PTvu39l0fy6X2q8fWHG9cSdRrpbjSNpZSXOl5X90atOtvGv2spXNJs59BtJr5269xDynWfhnEa2U/dJO/9bGIE1dkWjAuiXd+XTvNp81tnF3ILTn3Nmvdb6l7mOb5tdNc2hpjp3NJ/GSOdprPBzWPe2kQ38Pd5O8gxo30DWIekL/o1qDlwaD97IbB1ss8GJRCX7vv9mh7Nu5+h7tPuPvE2NhYn8MCeqvb/M1GpvGNhQXrxjcWqn+z4QzBGAzeGJC/6NYg5sBy8/dYsbzg32bjGwuaLVVUSbzxuHV7/bhMh7Fobr+5TUmn7LvdMc3bE1dXx7Y+Ht9YUKV2Mzlbqmi2VFnURrt19Tja9dlp/9a+62O51H712JrjzWZ6fyubz2baxpLPrt7Xbg3a9TeNftbSuaTZzyBazfzt17iHFGu/DOK1sh+6yd/6WGQjU+JaMC6RdX5dO82nzW0cK5aXnHubte631D1M8/zaaS5tjbHTuUR2Mkc7zeeDmse9NIjv4W7ydxDjRvoGMQ/IX3Rr0PJg0H52w2DrZR4MSqFvUtL2pufjkp5bpViAVbe5kK/97YbqG73+/bybC/lVjiw9jEG4YxBq3OidkHNgcyGvPbt36v79B3XLVTt0//6D+g9vvXTBudy6a4e2bRzW3n0/0yevvkQbR3ILttePu+3ay/XwT6Z027WXt91+664d2vPQT6vjc+3l2jKa1/jGgvY89FPdumtHI4bWvuvHfOqaS7Vt4/DCcb72csVJRffvP6g/ecslHY9tftwc753felLjGwvaOJLT1vV53bprYf/jmwr65NUL2/3cP9+p7ZsKbePdNJLTnpZcaBfH3n0/W5Qzzfs1x1aPd8/undo6OtT7HBjJ687rJhbEcud1E9o8Ekb+pvHeS6OftM5lbKR9P2M9fr23jg617acfORyqfrzm/cqjfrQbWo6EfK3stfpYnF2IlMuabm+a9//6sZ+3fV3XDUWL7g+qc+6w8llrzL97HvqpNo3kFs3Hn7rm0kX3H+ObCvqPb7usse7+/QcXxFLvY9NIrjG/LjWXtuZkp3PJZk4WJtvN5/24pg6i0N7DdSHft6N3Qs2DUONGb4WaB6HGjd7qZR6YezofBzWzcyV92d1f0WbbGyS9V9LvSHqVpE+7+ytP1ebExITv27fvlH2fe/NXlhuunv74G5Z9DNaE1P7bxKnyd24u1pFiSXHiykamzYX8GffHWBmDZY8B+YuBsYIcGKj8nZ4rS3IlLkWR5G6KK4miyJSLTGZSXHFlM5EqnihJpIq7MmaKTHKZ1uUjzcxXNJyLNFdOVElcmai+XcqYaS5OlI1MQ9lI5UqixKv/G34oGylxSebVthNXPhvJJM3FiXKR1f4Hvqtc8cY4D2cjzVcSeT3upPpVEJnIlI1M83GiXCZSZNJ8JVHGqufiLhVq8Ua1czAzRWYqVaqx5yLThkKkmXlXOXEliSuXibRxOKeyYr1QTBptVRI/OVaRFMe+KI5sZMo0xbSpkNPh2WrO5DKRspFprlxRNhM1xlJSY3y3jg4pm+3P/1lLEteRmZJKcUX5bEabR/KKlv4fdQOVv2lcf9PoJ61zKZViTc2c7GdsJK98vvf9lMsVHTox3+hn6+iQcrmB+PTTms7ffuVRP9od4BxpawXXyn4YiPytj8W6nKtYlkpxonJt3ls/FOnEfNJ4XUeHI8UVqVKb38tN9xfZyKTaPUY5cVUSVyEbSSaVKifn3lzWVK49r99fDOcixRWXq9p/FJkKuWpf83FFUWTK1z69V59fTzWXtubk5kJ+Qd5vHR1SJhMtyIMN+UxjPu/nNXUQreA9PBD5y89ukML9+Y38hTS4+TsoP7thsPUqf1PJHDP7oqQrJG0xs0lJ/1pSTpLcfY+kB1Ut8j0haVbSO9KICxhkw8NZbTvDL+6MQbhjEGrc6J2Qc2B4OKsX9yj2s9f1pJnUbBo59T6jw4vXDSur9W3WL9e2oc7jnuZYRpFpbP1g/w/8TtJ676XRT1rnks9ntS2FX0Lnchlt2xjYRSFl/XjN+5VH/Wg3tBwJ+VrZa81jMdJmPjwrnJd1gXY52S7vW/Ngqfl8LQvtPVwX8n07eifUPAg1bvRWqHkQatzorV7lQSqZ5O5vO8V2l/SeNGIBAAAAAAAAAAAA1oJB+Rt9AAAAAAAAAAAAAJaBQh8AAAAAAAAAAAAQIAp9AAAAAAAAAAAAQIAo9AEAAAAAAAAAAAABotAHAAAAAAAAAAAABIhCHwAAAAAAAAAAABAgCn0AAAAAAAAAAABAgCj0AQAAAAAAAAAAAAGi0AcAAAAAAAAAAAAEiEIfAAAAAAAAAAAAECAKfQAAAAAAAAAAAECAKPQBAAAAAAAAAAAAAaLQBwAAAAAAAAAAAASIQh8AAAAAAAAAAAAQIAp9AAAAAAAAAAAAQIAo9AEAAAAAAAAAAAABotAHAAAAAAAAAAAABIhCHwAAAAAAAAAAABCg1Ap9ZnalmT1uZk+Y2c1ttp9jZt80s++Z2QEz+520YgMAAAAAAAAAAABCk0qhz8wykj4r6bclXSzpbWZ2cctufyzpPne/TNI1km5LIzYAAAAAAAAAAAAgRGl9ou+Vkp5w9yfdvSTpXklvbtnHJW2oPT5L0nMpxQYAAAAAAAAAAAAEJ5tSP9skHWx6PinpVS37fFTS183sfZJGJL0+ndAAAAAAAAAAAACA8KT1iT5rs85bnr9N0l+4+7ik35H0l2a2KD4ze5eZ7TOzfVNTU30IFegf8hchI38RMvIXISN/ETLyFyEjfxEy8hchI38RKnIXqyWtQt+kpO1Nz8e1+Ks53ynpPkly97+TNCxpS2tD7n6Hu0+4+8TY2FifwgX6g/xFyMhfhIz8RcjIX4SM/EXIyF+EjPxFyMhfhIrcxWpJq9D3XUkXmNl5ZpaXdI2kB1r2+Zmk10mSmf2KqoU+yt4AAAAAAAAAAABAG6kU+tw9lvReSV+T9ENJ97n7D8zsY2b2ptpuH5R0vZl9X9IXJf2+u7d+vScAAAAAAAAAAAAASdm0OnL3ByU92LLuI02PH5P0a2nFAwAAAAAAAAAAAIQsra/uBAAAAAAAAAAAANBDFPoAAAAAAAAAAACAAFHoAwAAAAAAAAAAAAJEoQ8AAAAAAAAAAAAIEIU+AAAAAAAAAAAAIEAU+gAAAAAAAAAAAIAAUegDAAAAAAAAAAAAAkShDwAAAAAAAAAAAAgQhT4AAAAAAAAAAAAgQBT6AAAAAAAAAAAAgABR6AMAAAAAAAAAAAACRKEPAAAAAAAAAAAACFB2tQMAVuLcm7+y7GOe/vgb+hAJAAAAAAAAAADA6uATfQAAAAAAAAAAAECAKPQBAAAAAAAAAAAAAaLQBwAAAAAAAAAAAASIQh8AAAAAAAAAAAAQoGy3O5rZcUneabu7b+hJRAAAAAAAAAAAAABOqetP9Ln7+lox7z9IulnSNknjkj4s6d+d6ngzu9LMHjezJ8zs5g77XG1mj5nZD8zsP3cbGwAAAAAAAAAAAHCm6foTfU3+V3d/VdPz283s7yV9otMBZpaR9FlJvylpUtJ3zewBd3+saZ8LJP1LSb/m7tNmtnUFsQEAAAAAAAAAAABnhJX8jb6KmV1rZhkzi8zsWkmVUxzzSklPuPuT7l6SdK+kN7fsc72kz7r7tCS5+6EVxAYAAAAAAAAAAACcEVZS6Hu7pKsl/aK2vKW2binbJB1sej5ZW9fs5ZJebmbfNrPvmNmVK4gNAAAAAAAAAAAAOCMs+6s73f1pLf403qlYu6baxHKBpCtU/dt/f2tmr3D3YwsaMnuXpHdJ0jnnnLPMMIDVRf4iZOQvQkb+ImTkL0JG/iJk5C9CRv4iZOQvQkXuYrUs+xN9ZvZyM/uGmf1j7fkOM/vjUxw2KWl70/NxSc+12eev3L3s7k9JelzVwt8C7n6Hu0+4+8TY2NhywwdWFfmLkJG/CBn5i5CRvwgZ+YuQkb8IGfmLkJG/CBW5i9Wykq/uvFPSv5RUliR3PyDpmlMc811JF5jZeWaWr+3/QMs+/7ek35AkM9ui6ld5PrmC+AAAAAAAAAAAAIA1byWFvnXu/g8t6+KlDnD3WNJ7JX1N0g8l3efuPzCzj5nZm2q7fU3SETN7TNI3Jd3k7kdWEB8AAAAAAAAAAACw5i37b/RJOmxm56v2N/bMbJekn5/qIHd/UNKDLes+0vTYJf1RbQEAAAAAAAAAAACwhJUU+t4j6Q5JF5nZs5KeknRtT6MCAAAAAAAAAAAAsKSVFPqecffXm9mIpMjdj/c6KAAAAAAAAAAAAABLW8nf6HvKzO6Q9D9LOtHjeAAAAAAAAAAAAAB0YSWFvgsl/T+qfoXnU2b2GTP7p70NCwAAAAAAAAAAAMBSll3oc/eiu9/n7r8r6TJJGyT9Tc8jAwAAAAAAAAAAANDRSj7RJzP7dTO7TdIjkoYlXd3TqAAAAAAAAAAAAAAsKbvcA8zsKUmPSrpP0k3uPtPzqAAAAAAAAAAAAAAsadmFPkmXuPsLPY8EAAAAAAAAAAAAQNe6LvSZ2Yfc/ROS/r2Zeet2d//DnkYGAAAAAAAAAAAAoKPlfKLvh7V/9/UjEAAAAAAAAAAAAADd67rQ5+7/tfbwgLt/r0/xAAAAAAAAAAAAAOhCtIJj/tTMfmRm/9bM/qeeRwQAAAAAAAAAAADglJZd6HP335B0haQpSXeY2f9nZn/c68AAAAAAAAAAAAAAdLaST/TJ3f+Hu39a0g2SHpX0kZ5GBQAAAAAAAAAAAGBJyy70mdmvmNlHzewfJX1G0sOSxnseGQAAAAAAAAAAAICOsis45vOSvijpt9z9uR7HAwAAAAAAAAAAAKALyyr0mVlG0k/d/VN9igcAAAAAAAAAAABAF5b11Z3uXpG02czyfYoHAAAAAAAAAAAAQBdW8tWdz0j6tpk9IGmmvtLd/7RnUQEAAAAAAAAAAABY0rI+0VfznKQv145d37QsycyuNLPHzewJM7t5if12mZmb2cQKYgMAAAAAAAAAAADOCMv+RJ+7/5vlHlP7236flfSbkiYlfdfMHnD3x1r2Wy/pDyX9/XL7AAAAAAAAAAAAAM4kyy70mdk3JXnrend/7RKHvVLSE+7+ZK2NeyW9WdJjLfv9W0mfkPQvlhsXAAAAAAAAAAAAcCZZyd/oay7CDUu6SlJ8imO2STrY9HxS0quadzCzyyRtd/cvm1nHQp+ZvUvSuyTpnHPOWUbYwOojfxEy8hchI38RMvIXISN/ETLyFyEjfxEy8hehInexWpb9N/rcfX/T8m13/yO1FO3asHZNNTaaRZI+KemDXfR/h7tPuPvE2NjYsmIHVhv5i5CRvwgZ+YuQkb8IGfmLkJG/CBn5i5CRvwgVuYvVspKv7tzU9DSSNCHpl05x2KSk7U3PxyU91/R8vaRXSHrIzFRr7wEze5O771tujAAAAAAAAAAAAMBat5Kv7tyvk5/GiyU9Lemdpzjmu5IuMLPzJD0r6RpJb69vdPfnJW2pPzezhyT9C4p8AAAAAAAAAAAAQHtdf3Wnmf2qmf2Su5/n7i+V9G8k/ai2PLbUse4eS3qvpK9J+qGk+9z9B2b2MTN708rDBwAAAAAAAAAAAM5My/lE3+ckvV6SzOw1kv5PSe+TdKmkOyTtWupgd39Q0oMt6z7SYd8rlhEXAAAAAAAAAAAAcMZZTqEv4+5Ha4/fKukOd79f0v1m9mjvQwMAAAAAAAAAAADQSddf3SkpY2b1wuDrJP1107aV/K0/AAAAAAAAAAAAACu0nALdFyX9jZkdllSU9LeSZGYvk/R8H2IDAAAAAAAAAAAA0EHXhT53//dm9g1JL5b0dXf32qZI1b/VBwAAAAAAAAAAACAly/rKTXf/Tpt1P+5dOAAAAAAAAAAAAAC6sZy/0QcAAAAAAAAAAABgQFDoAwAAAAAAAAAAAAJEoQ8AAAAAAAAAAAAIEIU+AAAAAAAAAAAAIEAU+gAAAAAAAAAAAIAAUegDAAAAAAAAAAAAAkShDwAAAAAAAAAAAAgQhT4AAAAAAAAAAAAgQBT6AAAAAAAAAAAAgABR6AMAAAAAAAAAAAACRKEPAAAAAAAAAAAACBCFPgAAAAAAAAAAACBAFPoAAAAAAAAAAACAAKVW6DOzK83scTN7wsxubrP9j8zsMTM7YGbfMLNfTis2AAAAAAAAAAAAIDSpFPrMLCPps5J+W9LFkt5mZhe37PY9SRPuvkPSXkmfSCM2AAAAAAAAAAAAIERpfaLvlZKecPcn3b0k6V5Jb27ewd2/6e6ztaffkTSeUmwAAAAAAAAAAABAcNIq9G2TdLDp+WRtXSfvlPTf2m0ws3eZ2T4z2zc1NdXDEIH+I38RMvIXISN/ETLyFyEjfxEy8hchI38RMvIXoSJ3sVrSKvRZm3Xedkez3ZImJN3abru73+HuE+4+MTY21sMQgf4jfxEy8hchI38RMvIXISN/ETLyFyEjfxEy8hehInexWrIp9TMpaXvT83FJz7XuZGavl/SvJP26u8+nFBsAAAAAAAAAAAAQnLQ+0fddSReY2Xlmlpd0jaQHmncws8skfU7Sm9z9UEpxAQAAAAAAAAAAAEFKpdDn7rGk90r6mqQfSrrP3X9gZh8zszfVdrtV0qikL5nZo2b2QIfmAAAAAAAAAAAAgDNeWl/dKXd/UNKDLes+0vT49WnFAgAAAAAAAAAAAIQura/uBAAAAAAAAAAAANBDFPoAAAAAAAAAAACAAFHoAwAAAAAAAAAAAAJEoQ8AAAAAAAAAAAAIEIU+AAAAAAAAAAAAIEAU+gAAAAAAAAAAAIAAUegDAAAAAAAAAAAAAkShDwAAAAAAAAAAAAgQhT4AAAAAAAAAAAAgQBT6AAAAAAAAAAAAgABR6AMAAAAAAAAAAAACRKEPAAAAAAAAAAAACBCFPgAAAAAAAAAAACBAFPoAAAAAAAAAAACAAFHoAwAAAAAAAAAAAAJEoQ8AAAAAAAAAAAAIEIU+AAAAAAAAAAAAIEAU+gAAAAAAAAAAAIAAZdPqyMyulPQpSRlJf+buH2/ZPiTpLkk7JR2R9FZ3fzqt+IBBMzcX60ixpDhxZSPT5kJew8OpvWUHAmMQ7hiEGjd6J/QciONEs+WSjs8ljXMYHY50bLaiXCbSlnU5HS/HKpYSRSa5SxV3ZaNIGZPMpHLFG8duHR2SJB2eKalUSbRhOKPZUrXt5sfZyHRWIdJsSSpVEiWJKxOZzCSTKTJpLk6UjUy5jKlccQ1lI82UKspGtuBxLmuKZJqPE1US13A2UpxUY8pE1bYiM8WJN9qqxzCcizRXTjQyFCmuSIm7yhVXpenYikvZyJSNqseWKokykSmfiSR3FWtxRiYlLg3nIkWmBeeay5gSl5LElW2JYV0+UrG2b73PfDbSfOwqVxaOQSEfqZKYNo/kFUWmOE50dLY61pVGX5E2ratuPzJTUimuKJ/NNI5ZK9J676XRz1o6lzT7mZ+PdXj2ZD9b1uU1NBTG9bcfY9Svce9Hu3Gc6NCJeZUriXKZSFtHh5TNnv7/zS2VYk3NnIx1bCSvfP70x6Af7SaJB3mNrufDhkKk43OubLTwPqCQj3RirqIoMhVykebLicqJK1ebR+cqSeMeIpErkqmcVOfdfCaSyyWXhvMZbRjK6WixpEqSKEmkSuLKZSKN1fKldQzPGsoseJ22jg4pl8t0dV7dvB6t+5w9nNXUTKnneYz+Cf2+Hb0Rah6EGjd6K9Q8CDVu9Fav8iCVzDGzjKTPSvpNSZOSvmtmD7j7Y027vVPStLu/zMyukXSLpLemER8waObmYv3kyIxuvHu/JqeLGt9Y0O27d+qCzSNnzAWfMQh3DEKNG70Teg7EcaLn50t67tj8onP4H8dm9aX9k/rQlRdp6vi8Pv/tp/R7rz5PH77/QGO/z+2+XIlLN97zyIJjzypk9fY7/15v3TmuK37lRbrx7v0LHk9OF/VbF2/VTVdepMPH53XT3pNt3nLVDn3h4af0jl87T5/46uOaOjGv2669XF/5/rN6wyXbdPffPaOHnzyi2669vPH4878/ofnYdcPd+zU2OqQPXXnhgjb/5C2XaDgX6cEDz+kNl2zTu1vi3f/UYe08b4teclZeh14o6YamsajH857feJkyUYOE1FEAACAASURBVLRg2627dmhdPqOPPvCYpk7MN/b9V2+8WC8U4wVj+ue/P6Hjc7G+/o8/bxvDf/zGj/X1xw5pfGNBf/Z7O1WOfcG41sfgjZeOa/9Th/Wq88f0si0jemZ6VlMtY/jJqy/RC3NlRWa67s//obH+zusmdOGL1gfxi+RTSeu9l0Y/a+lc0uxnfj7Wjw8v7uflW0YGvtjXjzHq17j3o904TvSjXxxfcD3ds3unLnrR+tMqkpRKsR6fWhzrhWMjp1WU60e7SeJ6/BfHdf1d+4K6RtfzIWuJpmcj/ddHJ9vOaQ/98Bf6L/snddu1l+szf/2Txvx2664djbn9k1dfovWFrKZnygvmsE9fc5n2/M0T+oNfP19nFXL6xFd/tOj+Y8/unbpw66ieODzTGMM/+Gfn6o2Xji96nS7aOnrKYl83r0e7fVrn717kMfon9Pt29EaoeRBq3OitUPMg1LjRW73Mg7TutF4p6Ql3f9LdS5LulfTmln3eLOkLtcd7Jb3OzAb3bh7ooyPFUuMNLkmT00XdePd+HSmWVjmy9DAG4Y5BqHGjd0LPgUMn5jVXStqew8UvOUtX7dyug0eLumnvAV21c3vjl2z1/Q4dLzWKUc3HlmLX5HRRb7785C/cmh9L0lU7t2uy1nbz8R++v9rXTXsP6IYrztfkdFHvvucR7Zo4R+++5xFd/5qXNtbVH09OzzV+YXzDFecvavODX/q+js6UG220xvvai1+sG+/er7myN9ppjefoTHnRtpv2Hqiur8VZ37cc+6IxfXZ6Tu+/99GOMVy1c3vjeTbKLBrX+hjU473+rn06dGK+8fo07/uB+76vg0eLeubI7IL119+1T0dmwsjNU0nrvZdGP2vpXNLs5/Bs+34Ozw5+jvdjjPo17v1o99CJ+UXX0xvu3q9DJ+ZPK9apmfaxTp3mda8f7R6ZKTUKRvU2Q7hG1/NhQ2FIN9y9v+Oc9ubLxxtzV/P81jy3f+C+7ysbZRbNYX947/d01c7tev+9j+rg0WLb+496vjSPYX2ObI2lm7zq5vVot0/r/N2LPEb/hH7fjt4INQ9CjRu9FWoehBo3equXeZBWoW+bpINNzydr69ru4+6xpOclbW5tyMzeZWb7zGzf1NRUn8IF+qPb/I0Tb7zB6yani4oT73eIA4MxGLwxIH/RrUHMgeXcP5QryZLncHYhp3X5jCanizq7kFu0X31b67H1DyMkfrLt5seSFrTdeny9r7MLuca6TGSNf5vXtcbRLs7J6aLW5TONNlq3eS22TmOxVKzr8pkFcZ5dyCkydRyrTjHU25DU9vjmY5vjXSqudfnMovWluKJBNmjX3zT6WUvnshb7WY7VzN9+jUc/2i1XkvZtVpIVtymFNQaluNK2zdW8RneTv/WxqP+71Lxaf9w8v7XO7Z3mu/pcXp9jO41/8/pOsXTzOnXzenTap/X8TjePsTLLyd9mqz1vIH2DmAfkL7o1aHkwaD+7YbD1Mg/SKvS1+2Rea7Td7CN3v8PdJ9x9YmxsrCfBAWnpNn+zkWl8Y2HBuvGNBWUH+Ctreo0xGLwxIH/RrUHMgeXcP+Qy0ZLncKxY1mypovGNBR0rlhftV9/Wemz9Pi2yk203P5a0oO3W4+t9HSuWG+sqiTf+bV7XGke7OMc3FjRbqjTaaN1mtdg6jcVSsc6WKgviPFYsK3F1HKtOMdTbkNT2+OZjm+NdKq7ZUmXR+ny2u79VtFoG7fqbRj9r6VzWYj/LsZr526/x6Ee7uUzUvs3M6f3IHtIY5LOZtm2u5jW6m/ytj0X936Xm1frj5vmtdW7vNN/V5/L6HNtp/JvXd4qlm9epm9ej0z6t53e6eYyVWU7+NlvteQPpG8Q8IH/RrUHLg0H72Q2DrZd5kNbd1qSk7U3PxyU912kfM8tKOkvS0VSiAwbM5kJet+/e2Xij17+fd3Mhv8qRpYcxCHcMQo0bvRN6DmwdHdJwPmp7Do8997zu339Q2zdV/6bO/fsP6pardizYb+v6vG6/9vJFx+az1Ru4v3pkstF282NJun//QY3X2m4+/parqn3dumuH9jz0U41vrP59ur37fqbbrr1cd37ryca6+uPxjcPaU2t7z0M/XdTmn7zlEm0ayTXaaI33rx/7uW7fvVPDOWu00xrPppHcom237tpRXV+Ls75vLmuLxnTbxmF96ppLO8Zw//6DjedxUlk0rvUxqMd753UT2jo61Hh9mvf95NWXaPumgn5587oF6++8bkKbR8LIzVNJ672XRj9r6VzS7GfLuvb9bFk3+DnejzHq17j3o92to0OLrqd7du/U1tGh04p1bKR9rGOned3rR7ubR/K687qJ4K7R9Xx4oTivPbt3dpzT/uqRycbc1Ty/Nc/tn7z6EsVJZdEc9ulrLtP9+w/qU9dcqu2bCm3vP+r50jyG9TmyNZZu8qqb16PdPq3zdy/yGP0T+n07eiPUPAg1bvRWqHkQatzorV7mgdW/PqKfaoW7H0t6naRnJX1X0tvd/QdN+7xH0j9x9xvM7BpJv+vuVy/V7sTEhO/bt++U/Z9781eWHfPTH3/Dso9Bevr4mqb23yZOlb9zc7GOFEuKE1c2Mm0u5M+4P8bKGCx7DMhfDIwV5MDA5K8kxXGi2XJJx+eSxjmMDkc6NltRLhNpy7qcjpdjFUuJMlb9tFnFXdkoUsYkM6lc8cax9V9wHZ4pqVxJtH44o9lSte0NTY+zkemsQqTZklSqJEoSVyYymUkmU2TSXJwoG5lyWVM5dg1lI82UKspGtuBxLmuKZJqPE1US13A2Upy4YndlrNpWZKY4ceUytiDe4VykuXKikaFIcaX6FaPliqtSiycyqeLV/32WjarHliuJosiUz0SSu+bipLpvJCWJNJyLFJkWnGsuY0pcShJXtiWGdflIxdq+9T7z2UjzcbWv+vHliquQj1RJTJtH8ooiUxwnOjpbWjCGuUykTeuq24/MlFSKK8pnM41jTtPA5G9a1980+llL55JmP/PzsQ7Pnuxny7q8hobCuP72Y4z6Ne79aDeOEx06Ma+4kiibibR1dEjZ7On/39xSKdbUzMlYx0byyudPfwz60W6S+HKv0QORv/V82FCIdHzOlY0W3gcU8pFOzFUURaZCLtJ8OVE5ceVq8+hcJWncQyRyRTKVE1eSuHKZSJLLXRrOZ7RhKKejxZIqSaIkqX5qL5eJNFbLl9YxPGsos+B12jo6pFyuu09JdvN6tO5z9nC22l+P83iNGqj85We3M1uoP7+Rv5AGN38H5Wc3DLZe5W8qmePusZm9V9LXJGUk/bm7/8DMPiZpn7s/IOk/SfpLM3tC1U/yXZNGbMCgGh7OatsZfnFnDMIdg1DjRu+EngPZbKQN2WFtWPgNCjqr6fnmfFYaWV67Lz77ZAMbm47d2NLO6PDy2t3S4fEgaj3XXu3bLJuNtHVD50EcW792P1mQ1nsvjX7W0rmk2c/QUFbbli7sDax+jFG/xr0f7WazkV5yduHUOy5TPp/Vth4U9tJoN4osyGt0cz6s7zD9bFrhnNbO1k6dqP0YrvR16ub1aLdPP/IY/RP6fTt6I9Q8CDVu9FaoeRBq3OitXuVBapnk7g9KerBl3UeaHs9Jekta8QAAAAAAAAAAAAAh4/sTAAAAAAAAAAAAgABR6AMAAAAAAAAAAAACRKEPAAAAAAAAAAAACBCFPgAAAAAAAAAAACBA5u6rHcOKmdmUpGe62HWLpMN9DmfQMQbdjcFhd78yjWDI32VhDMjfkJ3pY9Dt+Q9a/obwug16jIMen9S7GActf6Uwxr9ba+lcpME7H/L39BBrf3D/sPo4l/4jfwcPY8D1N2SMwYDl7xq99+0XxuA08zfoQl+3zGyfu0+sdhyriTEIdwxCjbuXGINwxyDUuHvpTB+DUM8/hLgHPcZBj08KI8aVWkvntpbORVp759MPIY0RsfZHSLE2CzXudjiXMw/jxBhI4Y5BqHH3EmMQ7hiEGncvMQanPwZ8dScAAAAAAAAAAAAQIAp9AAAAAAAAAAAAQIDOlELfHasdwABgDMIdg1Dj7iXGINwxCDXuXjrTxyDU8w8h7kGPcdDjk8KIcaXW0rmtpXOR1t759ENIY0Ss/RFSrM1CjbsdzuXMwzgxBlK4YxBq3L3EGIQ7BqHG3UuMwWmOwRnxN/oAAAAAAAAAAACAteZM+UQfAAAAAAAAAAAAsKZQ6AMAAAAAAAAAAAACRKEPAAAAAAAAAAAACBCFPgAAAAAAAAAAACBAFPoAAAAAAAAAAACAAFHoAwAAAAAAAAAAAAJEoQ8AAAAAAAAAAAAIEIU+AAAAAAAAAAAAIEAU+gAAAAAAAAAAAIAAUegDAAAAAAAAAAAAAkShDwAAAAAAAAAAAAgQhT4AAAAAAAAAAAAgQBT6AAAAAAAAAAAAgABR6AMAAAAAAAAAAAACRKEPAAAAAAAAAAAACFDQhb4rr7zSJbGw9HJJDfnL0oclNeQvSx+W1JC/LH1YUkP+svRhSQ35y9KHJTXkL0sfltSQvyx9WFJD/rL0YUkFucvSp6WtoAt9hw8fXu0QgBUjfxEy8hchI38RMvIXISN/ETLyFyEjfxEy8hehIneRpqALfQAAAAAAAAAAAMCZikIfAAAAAAAAAAAAECAKfQAAAAAAAAAAAECAKPQBAAAAAAAAAAAAAaLQBwAAAAAAAAAAAAQom0YnZjYs6VuShmp97nX3f92yz5CkuyTtlHRE0lvd/enT6XduLtaRYklx4spGps2FvIaHUzllDBDyAIPm3Ju/suxjnv74G/oQSf/wvkPIOVAuVzRbLsslzcwnihPXcDZS4lK5kigTmaJI8kSKIlPiLncpcVdkJjMpY6ZK4ionrlxkymYizccV5TOR4sSVy5jKFVecuEaHMporJ42x2lCIdHzOVa4kGs5Gi9oplivKRqZcxhQnroyZ5uJE2cg0lI00U6pU982a4ko15mxkykam+bgafzYyzVcSZZriTVyKk6RxDu7SUDaSS3J3lSuuSuLV8zep4lIuMo0ORXphLlGpNja5yJSJTIlc5dgb57V1dEi5XEaSlCSuIzMlleKK8tmMNo/kq2PZtL6QzyhOXOU4UT6b0cZCTtPFcttt9eObddNHp2NDltZ7L41+1tK5pNnP/Hysw7Mn+9myLq+hoTCuv/0Yo36Nez/aLZcrOnRivu11cxDb7UeuhXqNLpViTRfLGh0yzZSq9wRxUp0385lIuYxptlRRVJsnC3nTiblE5drYjQxFer5Ynd/HRvLK58N4z2JtCPm+Hb0Tah6EGjd6K9Q8CDVu9Fav8iCtzJmX9Fp3P2FmOUn/r5n9N3f/TtM+75Q07e4vM7NrJN0i6a0r7XBuLtZPjszoxrv3a3K6qPGNBd2+e6cu2DzCG+YMQh4A6eN9h5BzoFyuaHquJEn6xQsl3Xj3fo2NDulDV16om/YeaJzPLVft0Bcefkrv+Y2Xaa6c6INf+n5j22fefpnKcaIP3Hdy3a27duj/euRZ/W+Xb9NDP/qF3nDJNr37nkf06pdu1u7/5Zf17nse0eR0Ub918Va973Uv79jvrbt26BNffVxTJ+Z127WX6yvff1ZXXPSiBevu/rtndKxYarTT7tjmx+3irZ/fe197gbaM5nR0JtYNTW01n38mihZsu3XXDm0ZzSuXjbT7z/5hQQ5ctHVUmUykx39xXNffta+x7c7rJnTB2Kh+MnVC19+1r+2579m9U5/+xo81dby0aNud103owhetb/wyOEn8lH10OjZkab330uhnLZ1Lmv3Mz8f68eHF/bx8y8jAF/v6MUb9Gvd+tFsuV/SjQycWtXnR1tHTKsr1q91+5Fqna/egX6NLpVjPHCvqJRty+vkLZc3Mx5otVRbNYXc9/LQefvJIdZ5cP6QfPXdM77v3QGPsfvjsMX36mz/V7bt36sKxEYp9SEXI9+3onVDzINS40Vuh5kGocaO3epkHqXx1p1edqD3N1RZv2e3Nkr5Qe7xX0uvMbMV380eKpcYASdLkdFE33r1fR4qllTaJAJEHQPp43yHkHDh0Yl6l2FWKvXEON1xxfuOXdVL1fD58/wFdtXO7js6UG0W++rbpmXKjaFZfd9PeA7r+NS/VTXsPaNfEOY3C3vWveWnjsSRdtXP7kv3etPeAbrjifE1OF/Xuex7RrolzFq27/jUvXdBOu2ObH7eLt35+777nEVUSaxTy2p1/67ZqzHOKK1qUA4dOzOvITKnxS9z6tuvv2qdDJ+Yb69ud+w1379dVO7e33Xb9Xft0ZOZkfnXTR6djQ5bWey+NftbSuaTZz+HZ9v0cnh38HO/HGPVr3PvR7qET823bPHRi/rRi7Ve7/ci1TtfuQb9GT82UNHm0qGPFRAePFnV0ptx2Drv+NS89OU8eLeqyX97c2H7j3fv16gvGGo+nBvycsXaEfN+O3gk1D0KNG70Vah6EGjd6q5d5kNrf6DOzjJk9KumQpP/u7n/fsss2SQclyd1jSc9L2tymnXeZ2T4z2zc1NdWxvzjxxgDVTU4XFSet9UWsZYOWB93mLzCIuP6iW4OYA8vJ30ptqZ/D2YVc2/M5u5DTunxm0bZ26yani8pEtuBfSQset/a1VL+tbbau6+bY+uNO8dbbqHj717PT+U9OF7Uun1Hrhy/qOVCKK+3zo5J0de6dtpXiSuN5N310OnYQDdr1N41+1tK5rMV+lmM187df43Gmx9qvdjtdu1fzGt1N/saJa13tK6XX5TNL3gfUH6/LZ1RpGqvJ6WLj+Wq/Z7F2dJu/gzZvIH2DmAfkL7o1aHkwaD+7YbD1Mg9SK/S5e8XdL5U0LumVZvaKll3afXpv0Rm5+x3uPuHuE2NjYx37y0am8Y2FBevGNxaUHeCv/EDvDVoedJu/wCDi+otuDWIOLCd/M7Wlfg7HiuW253OsWNZsqbJoW7t14xsLqiS+4F9JCx639rVUv61ttq7r5tj6407x1tvIWPvXs9P5j28saLZUUet9aT0H8tlM+/zIRF2de6dt+ezJr6Drpo9Oxw6iQbv+ptHPWjqXtdjPcqxm/vZrPM70WPvVbqdr92peo7vJ32xU/ft79X+Xug+oP54tVRqFv/q6+vPVfs9i7eg2fwdt3kD6BjEPyF90a9DyYNB+dsNg62UepFboq3P3Y5IeknRly6ZJSdslycyyks6SdHSl/Wwu5HX77p2Ngap/v+nmQn6lTSJA5AGQPt53CDkHto4OKZ815bPWOIc9D/1Ut+7aseB8brlqh+7ff1CbRnL6k7dcsmDbxpGcPnn1wnW37tqhO7/1pG7dtUN79/1Mt117efVvD33rycZjSbp//8El+7111w7teeinGt9Y0G3XXq69+362aN2d33pyQTvtjm1+3C7e+vnddu3lykSuPS1tNZ9/67ZqzMPKZrQoB7aODmnzSF53XjexYNud101o6+hQY327c9+ze6fu33+w7bY7r5vQ5pGT+dVNH52ODVla7700+llL55JmP1vWte9ny7rBz/F+jFG/xr0f7W4dHWrb5tbRodOKtV/t9iPXOl27B/0aPTaS1/imgs4uRNq+qaBNI7m2c9id33ry5Dy5qaDvPXOksf323Tv18E+mGo/HBvycsXaEfN+O3gk1D0KNG70Vah6EGjd6q5d5YO79/ziomY1JKrv7MTMrSPq6pFvc/ctN+7xH0j9x9xvM7BpJv+vuVy/V7sTEhO/bt6/j9rm5WEeKJcWJKxuZNhfy/DHLM9Ay8yC1/zZxqvzF2nXuzV9Z9jFPf/wN3ew2MPnL9RcryIGByd9yuaLZclkuaWY+UZy4hrOREpfKlUSZyBRFkidSFJncXYlLibsiM0UmRWaqJK5y4spFpmwm0nxcUT4TKU5cuYypXHHFiWt0KKO5ctIYqw2FSMfnXOVKouFstKidYrn6qYFcxhQnroyZ5uJE2cg0lI0anyrIZk1xRYprMWcj03x88nGpkigyk5mUMVPiUpycXOcuDWUjuSR3V7lS/UrTTFQ9x4pLucg0OhTphblE5UqiKDLlap+ITOQqx944r62jQ8rlqp/KSBLXkZmSSnFF+WxGm0fyiiJbsL5Q+xq0cpwon81oYyGn6WK57bb68c266aPTsSswMPmb1vU3jX7W0rmk2c/8fKzDsyf72bIur6GhMK6//Rijfo17P9otlys6dGK+7XVzENtdQa6d0gqu0QORv6VSrOliWaNDpplS9Z4gTlxJ4splIuUyptlyRZFV58lC3nRiLlG5NnYjQ5GeL1bn77GRvPJ57pvPEAORv/zsBincn9/IX0iDm7+D8rMbBluv8jetzHmxpC+YWUbVTxHe5+5fNrOPSdrn7g9I+k+S/tLMnlD1k3zXnG6nw8NZbePNccYjD4D08b5DyDmQy2V0Vu0XoGevW50Y1g+vTr8rNbLMeKPINLZ+8adJOq2vW2pbr/oIXVrvvTT6WUvnkmY/Q0NZbTvNYstq6ccY9Wvc+9FuLpfRto29n3j61W4/ci3Ua3Q+n9WLasW5bufEDQu/pWnV7jmAkO/b0Tuh5kGocaO3Qs2DUONGb/UqD1LJJHc/IOmyNus/0vR4TtJb0ogHAAAAAAAAAAAACF3qf6MPAAAAAAAAAAAAwOmj0AcAAAAAAAAAAAAEiEIfAAAAAAAAAAAAECAKfQAAAAAAAAAAAECAKPQBAAAAAAAAAAAAAaLQBwAAAAAAAAAAAASIQh8AAAAAAAAAAAAQIAp9AAAAAAAAAAAAQIAo9AEAAAAAAAAAAAABotAHAAAAAAAAAAAABIhCHwAAAAAAAAAAABAgCn0AAAAAAAAAAABAgCj0AQAAAAAAAAAAAAGi0AcAAAAAAAAAAAAEiEIfAAAAAAAAAAAAECAKfQAAAAAAAAAAAECAKPQBAAAAAAAAAAAAAaLQBwAAAAAAAAAAAASIQh8AAAAAAAAAAAAQIAp9AAAAAAAAAAAAQIAo9AEAAAAAAAAAAAABotAHAAAAAAAAAAAABIhCHwAAAAAAAAAAABAgCn0AAAAAAAAAAABAgFIp9JnZdjP7ppn90Mx+YGbvb7PPFWb2vJk9Wls+kkZsAAAAAAAAAAAAQIiyKfUTS/qguz9iZusl7Tez/+7uj7Xs97fu/saUYgIAAAAAAAAAAACClcon+tz95+7+SO3xcUk/lLQtjb4BAAAAAAAAAP8/e/cfH9dd3/n+/TlzZqSxbMe2LKfBcsgPAmmWTYilhiXsZXPpXW4g3HK3MZSs3QDtmnXCj6XlkcJ272273dvHA5qlLBBiN2kJuDawEPe2PCAL3Qc00JbCIhmSbgMhP8ECLpZlh8TSSDPnnO/9YzSjGc2MNJJnjuZrvZ6Pxzw0c358z+d8z+ec7xl9NCMAwPko9f/RZ2aXSLpW0jebzH6ZmT1kZv/NzP5JqoEBAAAAAAAAAAAAHkm10GdmGyUdk/Qu59yzi2Yfl/R859w1kj4i6S9atPFWMxszs7HJycnuBgx0GPkLn5G/8Bn5C5+Rv/AZ+Qufkb/wGfkLn5G/8BW5i7WSWqHPzLIqF/mOOuf+fPF859yzzrmz888fkJQ1s+1NlrvHOTfqnBsdGhrqetxAJ5G/8Bn5C5+Rv/AZ+Qufkb/wGfkLn5G/8Bn5C1+Ru1grqRT6zMwk/amk7zrn/qjFMj83v5zM7Lr52KbSiA8AAAAAAAAAAADwTZjSdl4u6Vcl/YOZfWd+2m9LuliSnHOHJO2RdJuZRZIKkt7onHMpxQcAAAAAAAAAAAB4JZVCn3PubyXZMsvcJemuNOIBAAAAAAAAAAAAfJfa/+gDAAAAAAAAAAAA0DkU+gAAAAAAAAAAAAAPUegDAAAAAAAAAAAAPEShDwAAAAAAAAAAAPAQhT4AAAAAAAAAAADAQxT6AAAAAAAAAAAAAA9R6AMAAAAAAAAAAAA8RKEPAAAAAAAAAAAA8BCFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwEIU+AAAAAAAAAAAAwEMU+gAAAAAAAAAAAAAPUegDAAAAAAAAAAAAPEShDwAAAAAAAAAAAPDQigt9Zna5mfXNP7/BzN5pZls6HxoAAAAAAAAAAACAVlbzib5jkmIze4GkP5V0qaRPdjQqAAAAAAAAAAAAAEtaTaEvcc5Fkv6VpP/inPsNSRd1NiwAAAAAAAAAAAAAS1lNoa9kZrdIepOkz89Py3YuJAAAAAAAAAAAAADLWU2h7y2SXibpD5xzT5nZpZKOdDYsAAAAAAAAAAAAAEsJV7qCc+4RSe+UJDPbKmmTc+59nQ4MAAAAAAAAAAAAQGsr/kSfmT1oZpvNbJukhyTdZ2Z/1PnQAAAAAAAAAAAAALSymq/uvMA596ykX5Z0n3NuRNL/1tmwAAAAAAAAAAAAACxlNYW+0MwukvQGSZ/vcDwAAAAAAAAAAAAA2rDi/9En6fclfUnS3zrnvmVml0l6bKkVzGyXpMOSfk5SIuke59yHFi1jkj4k6TWSZiS92Tl3fBXxVc3ORpoqFBUlTmFgGszn1N+/ml2Gz8gDIH2cd/A5B0qlWJPTRUlOzkmBSU6mUpwoE5hymUCJcwrMFMWJLJCSRIoTpzATaMfGPoXhwt9SJYnT1HRRxSiWmSljUvm2R5otxeoPA5USp9g5ZcxkJvVnAxWKSbX/8rlAJmmmZlo2Y4qTcnxzUaIwMAWBaS5KlA1MfblAs8VEceLUFwYyMxXjRMn8drIZUyl2ihOnTGDakAvkpLrt9mcDRbHT7Hz7m/OBni0szO8LA0XJQl+UEqd8NlN9Hgamjf2Bzs6W47ggn9HZufr9cpKKJadinCiXCZTLmmaLSbWvsvPbKEWJcmFGgwM5BYE19G3tvFbT14O0zr00tpPWvqznfOk13Tjm3cqjbrQbRYlOnp1TKU6UbTKewA+zs5HOliLNReXxbmMuo9loYey7YH4srYyTOzb2FvVbHgAAIABJREFUKZvNVI9/X2iaLS0sv31DTn19ftxDwU8+37ejc3zNA1/jRmf5eg9F/kLqXB6seA3n3Gclfbbm9ZOSbl5mtUjSu51zx81sk6RxM/vvzrlHapZ5taQr5h8vlXRw/ueqzM5GemxqWrcdGdfEmYKGt+Z1cN+Irhgc4IRZR8gDIH2cd/A5B0qlWI+ePKsPf/n7etP1l+oTX39Kv/7PL9O7P/tQdV/u3HO1NueziuJEH/3rx/Wm6y/Ve449XJ1/aN+Irrxwk8IwUJI4PfrT57T/8Fh1/vtvvlqf+PpTesvLL9X/e/xH+le7d+qO+xfW/+NfHZFz0oGa/ju6/6V6thDV9ende3frCw/9SDdceaH+8IuPavLsnO7cc3X1+aF9I/rwl7+vyeeK+r1fukozxbhuO3fv3a27vvKY/uqRk9W4+7OB3nzft+qW6QtN7z32P3XdJVv02pcMN8SQzwaaKcZ62ye/raGNffqtG19Ut52D+0Y0/tQpbciF+vmdW+rWv+8tv6Czs5He8alv1/VvZR8+8Ppr1J8N9LZPLsy/99ZRvejCTZLU0Lf33jqqK4Y26rHJsw3TX3ThpvO+eJPWuZfGdtLal2bn6HrJl17TjWPerTzqRrtRlOh7P32u7tpfO57AD7OzkX46M6efzZR0+9Hjuv6yQe172fN1+9Hjdbny+e9M6I//5unq6xduH9Bjp6Y19tQpjVy6vSG3Xrh9gGIfusLn+3Z0jq954Gvc6Cxf76HIX0idzYMVZ7uZ9ZvZ28zsbjP7WOWx1DrOuZ9UPp3nnHtO0ncl7Vy02OskHXZl35C0Zf4rQldlqlCsdpAkTZwp6LYj45oqFFfbJDxEHgDp47yDzzlw8uycDhwZ180ju/SeYw/r5pFd1SKfVN6XO+5/WCefndPp6VJ1udr5B46M6+TZOUnS1HSxWkCozK+0e8f9D2v/Ky6rFsUq808+O1d9k1KZVopcQ5/efvS49oxerDvuf1gHbri8GlvleWU/DtxwuU5Plxq2c/vR47p5ZFdd3CdOFxqWyQQZHbjhcu0ZvbhpDGaBTk+Xym3ccHnDdm47Mq5XXnWRrr9iqGH9idOFapGvtn8r+/Duzz5Ubbsyf//hMU1NF5v27f7DYzp5dq7p9Knp3s+/c5XWuZfGdlLblxZ5tB7ypdd045h3K4+60W5l/Gk1nsAPU4WiSpGrFvb2v+Ky6nNpIVf2jF5c9/rUTFEH5sfLZrl1aoZrErrD5/t2dI6veeBr3OgsX++hyF9Inc2D1ZS1/0zlr+D83yV9VdKwpOfaXdnMLpF0raRvLpq1U9KJmtcTaiwGyszeamZjZjY2OTnZcjtR4qodVG3wTEFR4toNFeeBXsuDdvMX6EVcf9GuXsyBlebvlny27metiTMFbchltCGXaTk/ihNJUjGKm86vrJcJrGH+hlymYVpgatpOZf0t+Wxd27XPt+SzTdusXbZ2vxYvE5i0JZ9tGmtlfmW9Vv3hXPkrQtvZ18X70CymYhS37NtSnDSdXoxi+arXrr9pbCetfWmVRz7nS69Zy/ztVh51o91W167KeIK1sdL3b+Wvs14Ys1uNnZmaTwxXcmfiTEGJ6717KPirnfztxft2pK8X84D8Rbt67R6q1967obd1Mg9WU+h7gXPu/5Y07Zz7hKSbJP3TdlY0s42Sjkl6l3Pu2cWzm6zSsEfOuXucc6POudGhoaGW2woD0/DWfN204a15hXwFz7rSa3nQbv7WuuS9X1jxA+gGrr9oVy/mwErz95lCqe5nreGtec0UY80U45bzw0z5FisXZprOr6wXJ65h/kwxbpiWODVtp7L+M4VSXdu1z58plJq2Wbts7X4tXiZx0jOFUtNYK/Mr67XqDzNTpkleLBdXq5hyYaZl32YzQdPpubC+YOiTXrv+prGdtPalVR75nC+9Zi3zt1t51I12W127KuMJ1sZK37+FgdWN2a3GzrjmlzeV3BnemldgvXcPBX+1k7+9eN+O9PViHpC/aFev3UP12ns39LZO5sFqMr7yG6FnzOzFki6QdMlyK5lZVuUi31Hn3J83WWRC0q6a18OSfryK+CRJg/mcDu4bqXZU5ftNB/O51TYJD5EHQPo47+BzDuzY2KdD+0Z0bPyE3n/z1To2fkIfeP01dfty556rtWNzn7YNZKvL1c4/tG9EOzb2SZIGB3K699bRuvmVdu/cc7Xu/dqTunNP/fo7NpdjqJ2WDa2hT+/eu1v3j/1Qd+65WocefKIaW+V5ZT8OPfiEtg1kG7Zz997dOjZ+oi7uXdvyDcvESaxDDz6h+8d+2DQG5xJtG8iW23jwiYbtHNw3oq888hN9/bHJhvWHt+X1kVuubejfyj584PXXVNuuzL/31lENDuSa9u29t45qx8a+ptMHB3o//85VWudeGttJbV9a5NF6yJde041j3q086ka7lfGn1XgCPwzmc8qGprv37i5fT772ZPW5tJAr94/9sO719g05HZofL5vl1vYNXJPQHT7ft6NzfM0DX+NGZ/l6D0X+QupsHphzK/sYoJn9G5ULdldLuk/SRkm/45w7tMQ6JukTkk47597VYpmbJL1d0mskvVTSh51z1y0Vy+joqBsbG2s5f3Y20lShqChxCgPTYD7HP7Nch1aYB6n92cRy+Vuxmk/oPf2+m1YTElLSxWPaM/nL9ReryIGeyd9SKdbkdFGSk3NSEEjOmaI4URCYcplAzjmZladZYEqS8ldThplAOzb21f3T7yRxmpouqhjF5U+2mVS+NZJmS7H6w0ClxCl2ThkzmUn92UCFYlLtv3wukEmaqZmWDU1xXP5az7k4UWimIDDNRYmygakvF2i2mCh2Tn2ZQGamYpwomd9ONmMqxQvb3ZAL5KS67fZnA0Wx02yUKAxMm/OBni0szO8LA8XJQl+UEqd8NlN9Hgamjf2Bzs4mihOnC/IZnZ2r3y8nqVhyKsWJsplAuaxptphU+yobBooSp1KUKBdmNDiQUzD/1221fVs7r9X0LuqZ/E3r+pvGdtLalzXIl15zXudvt/KoG+1GUaKTZ+cUxUnT8QRN9Uz+VszORjpbijQXlce7jbmMZqOFse+CmrE0E5h2bOxTNpupHv++0DRbWlh++4ac+vq4jz5P9UT+8t4Nkr/v38hfSKu6h0olf3vlvRt6W6euvyvOHOfcn8w//aqky9pc7eWSflXSP5jZd+an/baki+fbPCTpAZWLfI9LmpH0lpXGtlh/f6idnBzrHnkApI/zDj7nQDab0fO25JdfsE1BYBratIq/JhxonLS1ybSVtnGu62zqX3mTF9R05wUbzj2GilZ9u+o+Pw+kde6lsZ209mU950uv6cYx71YedaPdMAw6Ov5gbfT3h8v+km5jk7GU44+14vN9OzrH1zzwNW50lq9jKPkLqXN50HYLZvabS813zv3REvP+VstUyl35o4VvazceAAAAAAAAAAAAYD1bSalwU9eiAIAl8PWlAAAAAAAAAAA0arvQ55z7j90MBAAAAAAAAAAAAED7Vvyfvc3sE2a2peb1VjP7WGfDAgAAAAAAAAAAALCUFRf6JF3tnHum8sI5d0bStZ0LCQAAAAAAAAAAAMByVlPoC8xsa+WFmW3Tyv7XHwAAAAAAAAAAAIBztJoC3Qck/b2ZfVaSk/QGSX/Q0agAAAAAAAAAAAAALGnFhT7n3GEzG5P0Skkm6Zedc490PDIAAAAAAAAAAAAALbVd6DOzfkkHJL1A0j9IOuSci7oVGAAAAAAAAAAAAIDWVvI/+j4haVTlIt+rJf3nrkQEAAAAAAAAAAAAYFkr+erOq5xz/1SSzOxPJf2P7oQEAAAAAAAAAAAAYDkr+URfqfKEr+wEAAAAAAAAAAAA1tZKPtF3jZk9O//cJOXnX5sk55zb3PHoAAAAAAAAAAAAADTVdqHPOZfpZiAAAAAAAAAAAAAA2reSr+4EAAAAAAAAAAAA0CMo9AEAAAAAAAAAAAAeotAHAAAAAAAAAAAAeIhCHwAAAAAAAAAAAOAhCn0AAAAAAAAAAACAhyj0AQAAAAAAAAAAAB6i0AcAAAAAAAAAAAB4iEIfAAAAAAAAAAAA4CEKfQAAAAAAAAAAAICHKPQBAAAAAAAAAAAAHqLQBwAAAAAAAAAAAHiIQh8AAAAAAAAAAADgoVQKfWb2MTM7aWb/s8X8G8zsZ2b2nfnH76QRFwAAAAAAAAAAAOCrMKXtfFzSXZIOL7HM3zjnXptOOAAAAAAAAAAAAIDfUvlEn3Pua5JOp7EtAAAAAAAAAAAAYD3opf/R9zIze8jM/puZ/ZNWC5nZW81szMzGJicn04wPOGfkL3xG/sJn5C98Rv7CZ+QvfEb+wmfkL3xG/sJX5C7WSq8U+o5Ler5z7hpJH5H0F60WdM7d45wbdc6NDg0NpRYg0AnkL3xG/sJn5C98Rv7CZ+QvfEb+wmfkL3xG/sJX5C7WSk8U+pxzzzrnzs4/f0BS1sy2r3FYAAAAAAAAAAAAQM/qiUKfmf2cmdn88+tUjmtqbaMCAAAAAAAAAAAAeleYxkbM7FOSbpC03cwmJP2upKwkOecOSdoj6TYziyQVJL3ROefSiA0AAAAAAAAAAADwUSqFPufcLcvMv0vSXWnEAgAAAAAAAAAAAJwPeuKrOwEAAAAAAAAAAACsDIU+AAAAAAAAAAAAwEMU+gAAAAAAAAAAAAAPUegDAAAAAAAAAAAAPEShDwAAAAAAAAAAAPAQhT4AAAAAAAAAAADAQxT6AAAAAAAAAAAAAA9R6AMAAAAAAAAAAAA8RKEPAAAAAAAAAAAA8BCFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwEIU+AAAAAAAAAAAAwEMU+gAAAAAAAAAAAAAPUegDAAAAAAAAAAAAPEShDwAAAAAAAAAAAPAQhT4AAAAAAAAAAADAQxT6AAAAAAAAAAAAAA9R6AMAAAAAAAAAAAA8RKEPAAAAAAAAAAAA8BCFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwUJjGRszsY5JeK+mkc+7FTeabpA9Jeo2kGUlvds4dP9ftzs5GmioUFSVOYWAazOfU35/KLqOHkAdA+jjv4HMOJInT1HRRSZIodpJzTtkwUBiYCsVYuTCjwYGcgsCWbaMYLSwvSaem5zRbirUhm9FclFT7py8MNF2MFQambMbknOQkOTkliRQlTrkwUCBpNkqUDUxhJlCcJEqcFM+3EwSmYpQoE5gCkwIzRYlTnDhlApOZ1BcGmi2Vt90fBooSp8BUbSczH0MUO/XnAs0UE8WJUzYwbcoHmplzKs23mc0E6s+W+6FQTGQmufl2gsCUDUwWSFFUXicMTGFgmosShZlAgUlz8/uzLZ+ry5kNuUDTlW1nAklOuUxQ12/ZjClKpB0b+xSGQUP/m1x1+WwmaFhuJcdvqePdS9I699LYztxcpFMzC9vYviGnvr7O70upFOvk2bnqdnZs7FM2m+n4drC8buRVt3LVp3HO52uaLxZfR/rCQE6mwYGcisW4YXybKSYKAknOVIzL43Z2foycXnSfkA1MfdlAhVKipHo/YJI5lWJX124xcnIqj62ZwNQfBgqC8hhdu9yzs3Fb42KxGGlyuj7Pa/dlx8Y+mZlOnp1TKU6UzQTa1p/15tzoNF/HE5+uZ+geX/PA17jRWVGU1I1F7b7vW2vkL6TO5UFamfNxSXdJOtxi/qslXTH/eKmkg/M/V212NtJjU9O67ci4Js4UNLw1r4P7RnTF4AAnzDpCHgDp47yDzzmQJE6P/vQ5ffC/P6o3XX+p3nPs4eo+3Lnnav3hFx/V5Nk53XvrqF504aamvyittLH/8Fh13cO/dp3mSon2/9mYrr9sUPte9nzdfvR4df7de3fryN//QF9/ckr3vXlUz85G+uOvPrFkDB98wzXKhoHe/slvN51/17++VqUo0W985qGF47B3tyTptqPHNbSxT79144t039891bCdu/fu1vGnp7T7ksG6OO97yy/omeliXZuH9o1ocz7U//P5Rxra+ei/vlaZINCBmlyojbHy/NdefrEuGdrckDMf+fL39VePnCxv+82jmiolum1Rv33hoR/p/3jJsK68cJPCMKj2/18cP6GbrtlZF/+hfSPV5ZbLgdrjt9Tx7iVpnXtpbGduLtL3TzVu44XbBzpa7CuVYn3v5NmG7Vy5Y6MXv5w9n3Qjr7qVqz6Ncz5f03zR7DpSGZ/e+YuX68mpuYZceXryWV20ZUDv+q/fqRsfBzfm9J0fnNGVz7ugOn696qodevsrr6gbzz5yy7Ua6Mvo1z4+VjfGZTOmX//EWHW9/3DTz+tnhahu3YP7RvTgd3+q/zo+seS4WCxGenRyIc9/77VXauTS7XX7cmjfiPqzgd5837dajt+9em50mq/jiU/XM3SPr3nga9zorChK9L2fPlf3nrOd931rjfyF1Nk8SCXbnXNfk3R6iUVeJ+mwK/uGpC1mdtG5bHOqUKx2kCRNnCnotiPjmioUz6VZeIY8ANLHeQefc2Bquqj9h8d088iuasFKKu/DHfc/rAM3XK6JMwXtPzymqenm+1Npo3bdH0zNaP+flaftf8Vl1V+4VebffvS49r/iMk2cKWjizKz+3ae/s2wMv/GZh3RmutRy/pnpUrUgV5l/6myxWig7cMPluuP+h5tu5/ajx/XKqy5qiHPidKGhzQNHxlWMXNN2Tk+Xqm+4msVYeX7t8web5szNI7uqr82Cauy1ce4ZvVgHjozr5Nm5uv7fM3pxQ/y1yy2XA7XrLXW8e0la514a2zk103wbp2Y6uy8nz8413c5yeYLO60ZedStXfRrnfL6m+aLZdaQyPj1TSJrmyrXPH6wW+SrT77j/Yf3ozKyuv2Kobvy6eWRXw3j2jk99Wz86M9swxv34mdm69UqxGta97ci4Xrd7eNlxcXK6Ps9fedVFDfty4Mi4TpwuLDl+9+q50Wm+jic+Xc/QPb7mga9xo7NOnp1reM/Zzvu+tUb+QupsHvRKWXunpBM1ryfmpzUws7ea2ZiZjU1OTrZsMEpctYOqjZ4pKEpcB8KFL3otD9rNX6AXcf1Fu3oxB9rN32IUa+JMQVvy2ab7sCWfrT4vRvGSbdTakMtUp2UCa9p2Zv6TFZVl24lhQy7Tcn7tNpvFUWm/1XYS13gcm7U5caagwNS0nVbL1+7DlnxWcYucqSwnSYGpZb9NnCkoihNJC/3fqp8ry7XS7PgtdbzT0GvX3zS2cz7ty3q3lvnbrePrU9704jXNJ+3kb6t8yATWcl6rcW9DLtMwr9U43eweoHbalny25djpnKs+bzUuLo692X3Bcvci1W304LnRab14XTiX/F0PxwwLejEPyF+0qxQnq3rf1y299t4Nva2TedArhb5m3xnSdG+cc/c450adc6NDQ0MtGwwD0/DWfN204a15hXw9ybrSa3nQbv4CvYjrL9rViznQbv7mwoyGt+b1TKHUdB+eKZSqz3Nh869hqrRRa6YYV6fFiWvadjx/I1dZtp0YZopxy/m122wWR6X9VtsJrPE4NmtzeGteiVPTdlotX7sPzxRKyrTImcpyUvl/CLbqt+GteYWZ8m1tpf9b9XNluVaaHb+ljncaeu36m8Z2zqd9We/WMn+7dXx9yptevKb5pJ38bZUPlf+f22xeq3Fvphg3zGs1Tje7B6id9kyh1HLsNLPq81bj4uLYm90XLHcvUt1GD54bndaL14Vzyd/1cMywoBfzgPxFu7KZYFXv+7ql1967obd1Mg96pdA3IWlXzethST8+lwYH8zkd3DdS7ajK95sO5nPn0iw8Qx4A6eO8g885MDiQ0723jurY+Am9/+ar6/bhzj1X69CDT1T/v9HgQPP9qbRRu+7zBzfo3l8tT7v3a0/q7r276+bfvXe37v3akxremtfw1n596I0vWTaGD77hGm0dyLacv3Ugqw++4Zq6+ds35nRwftuHHnxCd+65uul27t67W1955CcNcQ5vyze0eWjfiHKhNW1n20BWhxblQm2Mleff/sFU05w5Nn6i+tq5pBp7bZz3j/1Qh/aNaMfGvrr+v3/shw3x1y63XA7UrrfU8e4laZ17aWxn+4bm29i+obP7smNjX9PtLJcn6Lxu5FW3ctWncc7na5ovml1HKuPTlnzQNFe+/YMp/ZdfeUnD+Lhza7++/thk3fh1bPxEw3j2kVuu1c6t/Q1j3PO29Netl82oYd2D+0b0l8cnlh0Xhwbq8/wrj/ykYV8O7RvRrm35JcfvXj03Os3X8cSn6xm6x9c88DVudNaOjX0N7znbed+31shfSJ3NA6t8ZUO3mdklkj7vnHtxk3k3SXq7pNdIeqmkDzvnrluuzdHRUTc2NtZy/uxspKlCUdH8X9IN5nP8M8t1aIV5kNqfTSyXvxWXvPcLK2776ffdtJqQetb51gdd3J+eyV+uv1hFDvRM/iaJ09R0UUmSKHaSc07ZMFAYmArFWLkwo8GBnIIl/sKq0kYxWlhekk5Nz2m2lGhDNtBclFT7py8MNF2MFQambMbkXPmrDZyckqT8KcBsGCiQNBslygamMBModkl5vnMKzRQEpmKcKGOmwMp/fR8lTnHilAlMZlJfGGi2lChOnPrCQFHiFFj5E3OV5bIZUxQ79ecCzRTLy2YD06Z8oJk5p1LilCROYSZQf7bcD4ViIjPJzbcTBKZsYLJAiiKnaL7tMDDNxYnCIFBg0tz8/mzL5+pyZkMu0HQxqW7H5JTN1PdbNmOKkvKbu9p/tF7pf5PTXFSOP8wEDcut5PgtdbzVQ/mb1vU3je3MzUU6NbOwje0bcurr6/y+lEqxTp6dq25nx8Y+ZbPr6tNO53X+ditXfbrXWcU1zSc9kb+LryN9YSAn0+BATsVi3DC+zRQTBYEkZyrFSXXMDAPTTClWPpupjnfZwNSXDVQolcfEXBgokEnmVIpdXbvFyMlJKkblNvvDQEFQHqNrl3tuNm5rXCwWI01O1+d57b7s2NgnMyvve5wozATa1p/15tzotFWMJz2Rvz5dz9A9vr5/I38hSVGU1I1FbbzvSyV/e+W9G3pbp66/qWSOmX1K0g2StpvZhKTflZSVJOfcIUkPqFzke1zSjKS3dGK7/f2hdnJyrHvkAZA+zjv4nANBYBra1OKv/wbOrY0dm/pbrrO9vaZTt23RPrfchTb7ZimLc2brKttc8himsP5aSuvcS2M7fX2hdnahsLdYNpvRzq0bur4dLK8bedWtXPVpnPP5muaLpa4jzXJlqfFtsJOBVSzaXrvjay4XameuPvZmef+8Lflll1kPfB1PfLqeoXt8zQNf40ZnhWHQMBb5gPyF1Lk8SCWTnHO3LDPfSXpbGrEAAAAAAAAAAAAA54Ne+R99AAAAAAAAAAAAAFaAQh8AAAAAAAAAAADgIQp9AAAAAAAAAAAAgIco9AEAAAAAAAAAAAAeMufcWsewamY2KekHbSy6XdKpLofT6+iD9vrglHPuxjSCIX9XhD4gf3223vug3f3vtfz14bj1eoy9Hp/UuRh7LX8lP/q/XefTvki9tz/k77kh1u7g/mHtsS/dR/72HvqA66/P6IMey9/z9N63W+iDc8xfrwt97TKzMefc6FrHsZboA3/7wNe4O4k+8LcPfI27k9Z7H/i6/z7E3esx9np8kh8xrtb5tG/n075I59/+dINPfUSs3eFTrLV8jbsZ9mX9oZ/oA8nfPvA17k6iD/ztA1/j7iT64Nz7gK/uBAAAAAAAAAAAADxEoQ8AAAAAAAAAAADw0Hop9N2z1gH0APrA3z7wNe5Oog/87QNf4+6k9d4Hvu6/D3H3eoy9Hp/kR4yrdT7t2/m0L9L5tz/d4FMfEWt3+BRrLV/jboZ9WX/oJ/pA8rcPfI27k+gDf/vA17g7iT44xz5YF/+jDwAAAAAAAAAAADjfrJdP9AEAAAAAAAAAAADnFQp9AAAAAAAAAAAAgIco9AEAAAAAAAAAAAAeotAHAAAAAAAAAAAAeIhCHwAAAAAAAAAAAOAhCn0AAAAAAAAAAACAhyj0AQAAAAAAAAAAAB6i0AcAAAAAAAAAAAB4iEIfAAAAAAAAAAAA4CEKfQAAAAAAAAAAAICHKPQBAAAAAAAAAAAAHqLQBwAAAAAAAAAAAHiIQh8AAAAAAAAAAADgIQp9AAAAAAAAAAAAgIco9AEAAAAAAAAAAAAe8rrQd+ONNzpJPHh08pEa8pdHFx6pIX95dOGRGvKXRxceqSF/eXThkRryl0cXHqkhf3l04ZEa8pdHFx6pIX95dOGRCnKXR5ceTXld6Dt16tRahwCsGvkLn5G/8Bn5C5+Rv/AZ+Qufkb/wGfkLn5G/8BW5izR5XegDAAAAAAAAAAAA1isKfQAAAAAAAAAAAICHKPQBAAAAAAAAAAAAHqLQBwAAAAAAAAAAAHiIQh8AAAAAAAAAAADgoTCNjZjZxyS9VtJJ59yLm8w3SR+S9BpJM5Le7Jw7fq7bnZ2NNFUoKkqcwsA0mM+pvz+VXUYP8TUPfI27k+gD+gC95ZL3fmHF6zz9vpu6EEl3zc1FOl0oSXJKnBSYlDhVz8P+MNBANtRsEmt6LlbsnDJmymZMUexUml8uDEyzUaIwMOVzgZykYuRUjBJt6s+oUEwUJU6b+zOamX8eBqbN+UCFolSMEyWJUyYwmUkmU2CqtpnNmKJECk0qzE8b6Av0s0JcjjMXyCXSXJQoTpz6w0BR4hTNtxmYFJgpSpyyGVMpdgv7mA00W0o00BdoruQUOyfnpMQ55TIL7WQzgXIZU5iRpucW9qEvDFSKEyVOiivbC6RcptxuNB9P7JxKsVM2MG3oC+rayOcCFYpJdX2z8vrF2KkUL/RBKXbK5wLFiWlwIKcgMEVRotMzRRXj8vrlZQNt21Dd2lDPAAAgAElEQVSePzVdVDGKlQsz1XVqJYlbdplelda4kcZ20tqXYjHS5PTCdoYGcsrl/OwzKb396YZu9FG3+r0b7Xbr2hNFiU6enVMpTpTNBNqxsU9heO5/8+vztbLTKvmwOR/ouVknqTxuxonTQF+mOvZVxvlnC0l1HM3UjO392UDFOFEgUylxipPyuFu5JwkDU74v0GzRKXaJkmTh/iSbMcWJ1J8zzcwlip1Tfzajrf1ZTU4XV3X82znGi5fZ0h+uenu+69a51m2854Tkbx74Gjc6y9c88DVudFan8iCtzPm4pLskHW4x/9WSrph/vFTSwfmfqzY7G+mxqWnddmRcE2cKGt6a18F9I7picIATZh3xNQ98jbuT6AP6AFgLc3ORHjs1rQ9/+ft60/WX6hNff0pvuv5SvefYw9Xz8O69uzW0KaepsyUdmD8/X3XVDr3jlVfotqPHq8vduedq/eEXH9Xk2Tkd2jeiMJD+zeFxXX/ZoPa97Pm6/ehx/crIsG74+Qur5/mrrtqh37rxSk0+N6c77l/Y5vtvvlqf+PpTesvLL622effe3frCQz/SDVdeWJ12cN+IvvujZ/Thv35C973lFzRXSnTgyLiGNvbpt258UV2bH3j9NerPBnrg4R/rpmt26vaa2A/uG9H4U6c0cul2bd0Q6kdnZvXuzz7UtJ1D+0a0KR9q773fXFh/725JquuPP3nTiEqR021Hjze082//l0v02pcMN1zvPvLl7+uvHjnZsH7tsfjCQz/Sa18yrPGnTumllw/pBdsH9IMzMw19+ME3XKNnZ0sKzHTrx/5Hdfq9t47qRRduqv7iMkmcHv3pc9p/eKzlMr0qrXEjje2ktS/FYqRHJxu386KhgY4Wx863/emGbvRRt/q9G+1269oTRYm+99PnquNV5bp95YWbzqkA4fO1stMq+RBaojMzQfUe4j3HHq4b8xePsb/3+e813C8c3LtbG/sz+v9+Vj+GffiN1+rQVx/Xv/0Xl2tzPqs7v/i9hvuTg3t3a/zpKe2+ZLC6vVddtUPv+MUX1uVqu8e/nWPcbJnF43cn8s0H3TrXuo33nJD8zQNf40Zn+ZoHvsaNzupkHqRyt+Gc+5qk00ss8jpJh13ZNyRtMbOLzmWbU4VitYMkaeJMQbcdGddUoXguzcIzvuaBr3F3En1AHwBr4dRMUQeOjOvmkV16z7GHqz9rz8Pbjx5XFKv6ixxJunlkV7UAVVnujvsf1oEbLtfEmYIOHBnXT342p4kzBe1/xWXVX8C9bvdw3Xl+88gunThdqP5yr9JWJZbaNm8/elx7Ri+um3bbkXFdf8WQJs4UNHG6UI3xwA2XN7T57s8+pNPTJe0ZvbgaT2XebUfG9cqrLtJtR8Ylmd792YdatnPgyLhKkatf/+hxnTpbrJsWBplqHy1uZ8/oxU2vdzeP7Gq6fu2xqKz7yqsu0v7DYzp5dq5pH/7GZx7SidMF/WBqpm76/sNjmppeuK5OTRerv7BstUyvSmvcSGM7ae3L5HTz7Ux2+Hifb/vTDd3oo271e1di7dK15+TZubrxqnLdPnl27pza9fla2WmVfNic76u7h1g85kv1Y2zldd04fvS4wiDTMIa989Pf1s0ju/TvPv0dTZwuNL0/ue3ocb3yqovqtnfzyK6GXG33+LdzjJsts3j87kS++aBb51q38Z4Tkr954Gvc6Cxf88DXuNFZncyDXvmzop2STtS8npif1sDM3mpmY2Y2Njk52bLBKFn4hVO10TMFRYnrQLjwRa/lAfnbPvqg9/qg3fwFetFKr79b8tm6n7UmzhSUuPrzs9VyW/LZ6vMNuYwkKRNYddlm7WzIZVq2tbjNSlu10+L5a0RtO63i25DL1MVTO8/NxxbXXItatbP4wxu1+1sRmFq20yqGyn4tXr92mcq6lXijxLXsww25TENcE2cKKkZx9XUxipuuW7tM2nrt/iGN7ZxP+3I+bmcl1jJ/u9Uf3Wi3W9eeUpw0jzVOzqndXrxWdkM7+VvJh8X3EFLr8c05V/e6bhx3zfOr0u6GXKbleOzavD9p5/i3c4xbLVM7fnci33zQrXPtXKwkf2ut9biB9PViHpC/aFev5UGvvXdDb+tkHvRKoa/Zd3s03Rvn3D3OuVHn3OjQ0FDLBsPANLw1XzdteGte4Tr7GpH1rtfygPxtH33Qe33Qbv4CvWil199nCqW6n7WGt+YVWP352Wq5Zwql6vOZYvkXY3Hiqss2a2emGLdsa3GblbZqp2XmrxG17bSKb6YY18VTO8/mY8vUXItatbP4HrR2fysSp5bttIqhsl+L169dprJuJd4wsJZ9OFOMG+Ia3ppXLlwo/uXCTNN1a5dJW6/dP6SxnfNpX87H7azEWuZvt/qjG+1269qTzQTNY82c268CevFa2Q3t5G8lHxbfQ0itxzczq3tdN45b8/yqtDtTjFuOx9bm/Uk7x7+dY9xqmdrxuxP55oNunWvnYiX5W2utxw2krxfzgPxFu3otD3rtvRt6WyfzoFfutiYk7ap5PSzpx+fS4GA+p4P7RqodVfl+08F87lyahWd8zQNf4+4k+oA+ANbC9g05Hdo3omPjJ/T+m6+u/qw9D+/eu1thRjpUc34eGz+hg3t31y13556rdejBJzS8tfw/Wi66oE/DW/O692tP6u75Zf/y+ETdeX5s/IR2bSuvW9tWJZbaNu/eu1v3j/2wbtrBfSP6+mOTGt6a1/C2fDXGQw8+0dDmB15/jbYNZHX/2A+r8VTmHdw3oq888hMd3DciyekDr7+mZTuH9o0oG1r9+nt3a/vGXN20KImrfbS4nfvHftj0ends/ETT9WuPRWXdrzzyE91766h2bOxr2ocffMM12rUtr+cPbqibfu+toxocWLiuDg7kdO+to0su06vSGjfS2E5a+zI00Hw7Qx0+3ufb/nRDN/qoW/3elVi7dO3ZsbGvbryqXLd3bOw7p3Z9vlZ2WiUfni3M1d1DLB7zpfoxtvK6bhzfu1tREjeMYR9+47U6Nn5CH3rjSzS8Ld/0/uTg3t36yiM/qdvesfETDbna7vFv5xg3W2bx+N2JfPNBt861buM9JyR/88DXuNFZvuaBr3GjszqZB1b7lRHdZGaXSPq8c+7FTebdJOntkl4j6aWSPuycu265NkdHR93Y2FjL+bOzkaYKRUWJUxiYBvM5/pnlOrTCPEjtzybI3+XRB/7mL85Pl7z3Cyte5+n33dTuoj2Tv3NzkU4XSpKcEicFgZQk5b/KzwSm/jDQQDbUbBJrei5W7JwyZspmTFHsFM0vFwamuShRJjDlc4GcpGLkVIoSbezPqFBMFCVOm/szmpl/HgamzflAhaJUjBMl822ZSSZTYNJslCgMTNnQFMVSaFJhftpAX6CfFWKFgak/F8gl0lyUKE6c+sNAUeIUzccbWPkThVHilM2YSvOxh4GpPxtotpRooC/QXMkpdk7OSc45ZTPlduLEKcwEymVMYUaanlvYh74wUClOlDhV+ycIpFym3G40H0/snEqxUzYwbegL6trI5wIVikm13wMr/7V8MXYqxfN9MB93PhcoTkyDAzkFgSmKEp2eKdb1YTYTaNuG8vyp6aKKUaxcmKmuUytJ3LLLLNIz+ZvW2JnGdtLal2Ix0uT0wnaGBnLK5fzsM2lV+3Ne52+3+r0b7a7i2tOWKEp08uycojhRmAm0Y2OfwvDc/+a3W/GuUE/kbyUfNucDPTfrZPP3EHHiNNCXqY59lXH+2UIyP/4GytSM7f3ZQMU4USBTKXFK5peROSVJ+a++832BZotOsUvq7k+yGVOcSP0508xcothJ/dlAW/uz5WvCKo5/O8d48TJb+sNVb893qzjXeip/1/P7bqwqD8hf9Ixezd9eee+G3tap/E0lc8zsU5JukLTdzCYk/a6krCQ55w5JekDlIt/jkmYkvaUT2+3vD7WTk2Pd8zUPfI27k+gD+gBYC319oS7qW/6861eoLRvOYUMDC0+3DtTP2tS/+mbPKaZz0IntNrQx0HSxZYVhoB2bW3fi0Kal/7o+CGzZZXpVWuNGGttJa19yuVA7u1DYW+x8259u6EYfdavfu9Fut649YRjoeVvyyy+4Qj5fKzutNh/aGcPPZZyXpCWGOEnS1kXj6WqPfzvHuNky3cg3H3TrXOs23nNC8jcPfI0bneVrHvgaNzqrU3mQSiY5525ZZr6T9LY0YgEAAAAAAAAAAADOB+vj+xMAAAAAAAAAAACA8wyFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwEIU+AAAAAAAAAAAAwEMU+gAAAAAAAAAAAAAPUegDAAAAAAAAAAAAPEShDwAAAAAAAAAAAPAQhT4AAAAAAAAAAADAQxT6AAAAAAAAAAAAAA9R6AMAAAAAAAAAAAA8RKEPAAAAAAAAAAAA8BCFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwEIU+AAAAAAAAAAAAwEOpFfrM7EYze9TMHjez9zaZf7GZ/bWZfdvMHjaz16QVGwAAAAAAAAAAAOCbVAp9ZpaR9FFJr5Z0laRbzOyqRYv9X5I+45y7VtIbJd2dRmwAAAAAAAAAAACAj9L6RN91kh53zj3pnCtK+rSk1y1axknaPP/8Akk/Tik2AAAAAAAAAAAAwDtpFfp2SjpR83piflqt35O0z8wmJD0g6R3NGjKzt5rZmJmNTU5OdiNWoGvIX/iM/IXPyF/4jPyFz8hf+Iz8hc/IX/iM/IWvyF2slbQKfdZkmlv0+hZJH3fODUt6jaQ/M7OG+Jxz9zjnRp1zo0NDQ10IFege8hc+I3/hM/IXPiN/4TPyFz4jf+Ez8hc+I3/hK3IXayWtQt+EpF01r4fV+NWcvy7pM5LknPt7Sf2StqcSHQAAAAAAAAAAAOCZtAp935J0hZldamY5SW+U9LlFy/xQ0i9Kkpn9vMqFPj7fCgAAAAAAAAAAADSRSqHPORdJerukL0n6rqTPOOf+0cx+38x+aX6xd0vab2YPSfqUpDc75xZ/vScAAAAAAAAAAAAASWFaG3LOPSDpgUXTfqfm+SOSXp5WPAAAAAAAAAAAAIDP0vrqTgAAAAAAAAAAAAAdRKEPAAAAAAAAAAAA8BCFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwEIU+AAAAAAAAAAAAwEMU+gAAAAAAAAAAAAAPUegDAAAAAAAAAAAAPEShDwAAAAAAAAAAAPAQhT4AAAAAAAAAAADAQxT6AAAAAAAAAAAAAA9R6AMAAAAAAAAAAAA8RKEPAAAAAAAAAAAA8BCFPgAAAAAAAAAAAMBDFPoAAAAAAAAAAAAAD1HoAwAAAAAAAAAAADxEoQ8AAAAAAAAAAADwUGqFPjO70cweNbPHzey9LZZ5g5k9Ymb/aGafTCs2AAAAAAAAAAAAwDdhGhsxs4ykj0r6l5ImJH3LzD7nnHukZpkrJP17SS93zp0xsx1pxAYAAAAAAAAAAAD4KK1P9F0n6XHn3JPOuaKkT0t63aJl9kv6qHPujCQ5506mFBsAAAAAAAAAAADgnbQKfTslnah5PTE/rdYLJb3QzP7OzL5hZjc2a8jM3mpmY2Y2Njk52aVwge4gf+Ez8hc+I3/hM/IXPiN/4TPyFz4jf+Ez8he+InexVtIq9FmTaW7R61DSFZJukHSLpD8xsy0NKzl3j3Nu1Dk3OjQ01PFAgW4if+Ez8hc+I3/hM/IXPiN/4TPyFz4jf+Ez8he+InexVtIq9E1I2lXzeljSj5ss85fOuZJz7ilJj6pc+AMAAAAAAAAAAACwSFqFvm9JusLMLjWznKQ3SvrcomX+QtL/Kklmtl3lr/J8MqX4AAAAAAAAAAAAAK+kUuhzzkWS3i7pS5K+K+kzzrl/NLPfN7Nfml/sS5KmzOwRSX8t6Q7n3FQa8QEAAAAAAAAAAAC+CdPakHPuAUkPLJr2OzXPnaTfnH8AAAAAAAAAAAAAWEJaX90JAAAAAAAAAAAAoINS+0QfAGD9ueS9X1jxOk+/76YuRAIAAAAAAAAA558Vf6LPzP6TmYU1rzeb2X2dDQsAAAAAAAAAAADAUlbz1Z2hpG+a2dVm9ipJ35I03tmwAAAAAAAAAAAAACxlxV/d6Zz792b2ZUnflHRG0iucc493PDIAAAAAAAAAAAAALa3mqztfIelDkn5f0oOS7jKz53U4LgAAAAAAAAAAAABLWPEn+iT9Z0mvd849Iklm9suSviLpyk4GBgAAAAAAAAAAAKC11RT6XuaciysvnHN/bmZf7WBMAAAAAAAAAAAAAJax4q/ulLTdzP7UzL4oSWZ2laT/s7NhAQAAAAAAAAAAAFjKagp9H5f0JUkXzb/+vqR3dSogAAAAAAAAAAAAAMtb1Sf6nHOfkZRIknMukhQvvQoAAAAAAAAAAACATlpNoW/azAYlOUkys38m6WcdjQoAAAAAAAAAAADAksJVrPObkj4n6XIz+ztJQ5L2dDQqAAAAAAAAAAAAAEtq+xN9ZvYLZvZzzrnjkv6FpN+WNCfpryRNdCk+AAAAAAAAAAAAAE2s5Ks7/1hScf759ZL+g6SPSjoj6Z4OxwUAAAAAAAAAAABgCSv56s6Mc+70/PNfkXSPc+6YpGNm9p3OhwYAAAAAAAAAAACglZV8oi9jZpXC4C9K+krNvNX8rz8AAAAAAAAAAAAAq7SSAt2nJH3VzE5JKkj6G0kysxdI+lkXYgMAAAAAAAAAAADQQtuf6HPO/YGkd0v6uKR/7pxzNW28Y7n1zexGM3vUzB43s/cusdweM3NmNtpubAAAAAAAAAAAAMB6s6Kv3HTOfaPJtO8vt56ZZSR9VNK/lDQh6Vtm9jnn3COLltsk6Z2SvrmSuAAAAAAAAAAAAID1ZiX/o+9cXCfpcefck865oqRPS3pdk+X+k6Q/lDSbUlwAAAAAAAAAAACAl9Iq9O2UdKLm9cT8tCozu1bSLufc55dqyMzeamZjZjY2OTnZ+UiBLiJ/4TPyFz4jf+Ez8hc+I3/hM/IXPiN/4TPyF74id7FW0ir0WZNprjrTLJD0QZX/B+CSnHP3OOdGnXOjQ0NDHQwR6D7yFz4jf+Ez8hc+I3/hM/IXPiN/4TPyFz4jf+ErchdrJa1C34SkXTWvhyX9uOb1JkkvlvSgmT0t6Z9J+pyZjaYUHwAAAAAAAAAAAOCVtAp935J0hZldama5/5+9+4+Po7rvf/8+M7MrrSWBhSy7ie0UkhBzXWrAEtSE+03T0B+00FBqQyg2BCexDW7SftOUkvv91k0fpb034PCg+eUfuK3ND9OYmHLJTfIlaUlo7oUQsE2gqRNBwy+bJLYsbLDklXZ35tw/JC1aaRfL1uzuHO3ryUMPrNXszNnZz/kx58ycI+lqSV8b/aO19nVr7Sxr7enW2tMlPSHpg9baXTVKHwAAAAAAAAAAAOCUmgz0WWsLkj4u6VuSfizpfmvtfxpj/sYY88FapAEAAAAAAAAAAACYToJaHcha+01J3xz32l9V2Pb9tUgTAAAAAAAAAAAA4KpaTd0JAAAAAAAAAAAAIEYM9AEAAAAAAAAAAAAOYqAPAAAAAAAAAAAAcBADfQAAAAAAAAAAAICDGOgDAAAAAAAAAAAAHMRAHwAAAAAAAAAAAOAgBvoAAAAAAAAAAAAABzHQBwAAAAAAAAAAADiIgT4AAAAAAAAAAADAQQz0AQAAAAAAAAAAAA5ioA8AAAAAAAAAAABwEAN9AAAAAAAAAAAAgIMY6AMAAAAAAAAAAAAcxEAfAAAAAAAAAAAA4CAG+gAAAAAAAAAAAAAHMdAHAAAAAAAAAAAAOIiBPgAAAAAAAAAAAMBBDPQBAAAAAAAAAAAADqrZQJ8x5hJjTI8x5r+MMZ8u8/c/M8bsNcY8a4x5xBjzy7VKGwAAAAAAAAAAAOCamgz0GWN8SV+W9LuSFkr6I2PMwnGbPS2p21q7SNJOSbfVIm0AAAAAAAAAAACAi2r1RN8Fkv7LWvuCtTYn6SuSLh+7gbX2u9baYyO/PiFpXo3SBgAAAAAAAAAAADinVgN9cyXtG/P7/pHXKvmopP9V7g/GmNXGmF3GmF29vb0xJhGoPuIXLiN+4TLiFy4jfuEy4hcuI37hMuIXLiN+4SpiF/VSq4E+U+Y1W3ZDY1ZI6pa0vtzfrbV3Wmu7rbXdnZ2dMSYRqD7iFy4jfuEy4hcuI37hMuIXLiN+4TLiFy4jfuEqYhf1EtToOPslzR/z+zxJPxu/kTHmNyX9T0m/bq0dqlHaAAAAAAAAAAAAAOfU6om+pySdaYw5wxiTlnS1pK+N3cAYc56kzZI+aK09WKN0AQAAAAAAAAAAAE6qyUCftbYg6eOSviXpx5Lut9b+pzHmb4wxHxzZbL2kVklfNcb80BjztQq7AwAAAAAAAAAAABperabulLX2m5K+Oe61vxrz79+sVVoAAAAAAAAAAAAA19Vq6k4AAAAAAAAAAAAAMWKgDwAAAAAAAAAAAHAQA30AAAAAAAAAAACAgxjoAwAAAAAAAAAAABzEQB8AAAAAAAAAAADgIAb6AAAAAAAAAAAAAAcx0AcAAAAAAAAAAAA4iIE+AAAAAAAAAAAAwEEM9AEAAAAAAAAAAAAOYqAPAAAAAAAAAAAAcBADfQAAAAAAAAAAAICDGOgDAAAAAAAAAAAAHMRAHwAAAAAAAAAAAOAgBvoAAAAAAAAAAAAABzHQBwAAAAAAAAAAADiIgT4AAAAAAAAAAADAQQz0AQAAAAAAAAAAAA5ioA8AAAAAAAAAAABwUM0G+owxlxhjeowx/2WM+XSZvzcZY3aM/P0HxpjTa5U2AAAAAAAAAAAAwDVBLQ5ijPElfVnSb0naL+kpY8zXrLV7x2z2UUmHrbXvNsZcLelWSR+aynEHBwvqy+ZUiKwCz6gjk1Zzc00+MhLE1ThwNd1x4hxwDoB6KBQivT6Yk+9JA0ORCpFVyvfkG2mwECnwjFK+USG08jwjz0j50BbzaTrwlA8jSUbWDr9XkgbzoXzPyBjJaPh9g4VIzYGnyEq5cHjfnif5xlMhjGSMFFkV0xAYKVuIlPKMAt9TNh8W06OR7QLfKB9aeUayVsqPpCvwjAYLkdK+J89IQ4WomB5rpaaRdI99T1PgabAQKRw9vi/lC29+1ubAU0sq0JGhvIZGzk3gGQ0VInmeUdozam4y6h+0yoelf/c9o1RgZKPhzxj4RrnCm+e7KTAazEdKB746WtKKIquD/UPKh5FSvqfZrU0KAianGK9W9UYtjjOdPkstjxNFVn0DOeUKYTH/eJ6J/TjVUI1zVK3zXo395nIF9Q68uc/OlrTS6amntVCIqlJ+ViO9LsevNBwXb+QLspFVPrIKI6v0SBsiO1JPNgfD9elg3iq0VvnQyh+pHz1J/kgbY/T9mcCTjJQLraKROjIdGOXCSFGkN+vklKcwkiJri/VsJuXJWpXU5U2BUf9QOKlYyOdDHewfKonzsXE/u7VJvu+VfGenpH0dOhZ/HLvA1fjlmhOSu3HgaroRL1fjwNV0I15xxUGtIucCSf9lrX1BkowxX5F0uaSxA32XS/rrkX/vlPQlY4yx1tqTOeDgYEHP9w3oxnt3a//hrOa1Z7RxRZfO7GghwzQQV+PA1XTHiXPAOQDqoVCI9LM3smoOjH72er4k/61ftki3Pdyj3v4hbVi+WN945lV98Ny5iqx04/Y9xe3+6fpuvZEt6L/v+GHZ9966dJHuevxFrbzoDD2451VdsXiubtr5bHHb2688R80pT1/+7n/pw+89Qzc/8GzZ/Yz998bli5UOjP5l935des5cfek7z+uj//s79amvPnPc946m5xMfOFPNKU8rt+3S/sNZ/fbC2fr4B87U2jGfbePyxfrid57Xt/ceLH7WX4RDWnNP+fP05WvOk+95uqHCedy4fLE8I33th6/q0nPmlhxr7HZbru1WKjC6futTxb9vWtGls+a0Mdg3Rq3qjVocZzp9lloeJ4qseg4c1aq7dxWPs+W6bi2Y05b4zuZqnKNqnfdq7DeXK6ind+I+F3S2TGmQpFCI9JMDR0vK4TjKz2qk1+X4lYbjYv8bWQ3lQ70xWCip28e3ITrb0jp0NFfSfli/bJFOa0nJ9zz1Hh3STTufVWdrk/76gwt1LBeW7O/zV58r3zP6+H1PF1/buvJ89Q8W9Il/frpYj990yVk6NLKvsXX5Pd9/WY+/0PeWsZDPh/rJwf7id/zXl52lrjNmTfjOZ7WmdOWmJ7T/cFZr/tvpuuzcebHHsQtcjV+uOSG5GweuphvxcjUOXE034hVnHNSqZ2SupH1jft8/8lrZbay1BUmvS+o42QP2ZXPFEyRJ+w9ndeO9u9WXzZ3sLuEgV+PA1XTHiXPAOQDq4WD/kHIFq1yoCfnvpp3P6ob3v0v7D2e1dvseLet+hw6O6aQb3e7Vw4PFQb5y7735gWe1tGu+btr5rFa9753FzrfRbT/11Wf02kBeS7vmFwf5yu1n7L9v3L5HvudrWfc7tHb7Hi3tml8c5Dvee0fTc+P2PTLGK75nadf84sDb6D5uHNn32M86OshX7jivDeSLncvl/n7j9j06eDRXTHel7Vbds0v7XsuW/P2Ge3frYP9QFaPBPbWqN2pxnOn0WWp6nIFcsZN59Dir7t6lvoHktx2qcY6qdd6rsd/egfL77J3id3ewf2hCORxH+VmN9Locv9JwXOx7LauDR3MT6vbxbYhCqAnth5t2Pivf87XvtWzx/Te8/116bSA/YX9/+pUf6vBAvuS1/a9li4N80nA9vn/Mvka3u3H7Hq163zuPGwsH+4dKvuMPLHxb2e+8EKr42rLud1Qljl3gavxyzQnJ3ThwNd2Il6tx4Gq6EVpTqI4AACAASURBVK8446BWA33lbl8a/6TeZLaRMWa1MWaXMWZXb29vxQMWIls8QaP2H86qEJ3UA4JwVNLigPidPM5B8s7BZOMXSKLJxm8+jOQZKayQ/2ZmUsV/+57RjLQ/Ybtyr41/78xMqriPctvOSPvFbY63n9F/e0bF/Z3oe0e3H3vD+fH2MZnPOplzMSPtVzwP47cb//dCGKkRJK39UIvjTKfPUsvj5Aph2ePkCmGsxzkR9Yzfap13l9KaD6Py+51i+VmN9Locv9LwOZmR9idV94W2/PnzTGndOTOTqri/8fXi+O3e6r3+SIX/VrEw/juOKqQ5GjMJU6X6vBGu4VyN36Rdc6I+khgHxC8mK2lxkLRrNyRbnHFQq4G+/ZLmj/l9nqSfVdrGGBNIOlXSa+N3ZK2901rbba3t7uzsrHjAwDOa154peW1ee0ZBgqdMQPySFgfE7+RxDpJ3DiYbv0ASTTZ+U/7wenl+hfx3JJsv/juMrI7lwgnblXtt/HuPZPPFfZTb9lguLG5zvP2M/juyKu7vRN87uv3YtuTx9jGZzzqZc3EsF1Y8D+O3G//3wG+MaTuT1n6oxXGm02ep5XHSgV/2OOnAr/CO6qtn/FbrvLuU1pTvld/vFMvPaqTX5fiVhs/JsVw4qbrPN+XPX2RL684j2XzF/Y2vF8dv91bvDUcq/LeKhfHfsVchzZ558zuvVJ83wjWcq/GbtGtO1EcS44D4xWQlLQ6Sdu2GZIszDmrVO/KUpDONMWcYY9KSrpb0tXHbfE3Sh0f+vUzSd052fT5J6siktXFFV/FEjc5v2pFJn+wu4SBX48DVdMeJc8A5AOphdmuT0oFR2teE/Ld+2SJtevSnmtee0Ybli7Vz1yua3ZbWxuWLS7ab296sv//QuRXfe+vSRXpg9z6tX7ZIW773gtYvW1Sy7e1XnqPTWlJ6YPc+3bp0UcX9jP33xuWLFUahdu56RRuWL9YDu/fp9ivPmdR7R9OzcfliWRsV3/PA7n3aMO6zbRzZ99jPuvnayufptJaUNr3Fedy4fLFmt6WL6a603ZZruzX/tEzJ3zet6NLs1qYqRoN7alVv1OI40+mz1PQ4LWltua675DhbrutWR0vy2w7VOEfVOu/V2G9nS/l9dk7xu5vd2jShHI6j/KxGel2OX2k4LuafltHstvSEun18GyLwNaH9sH7ZIoVRqPmnZYrv3/ToT3VaS2rC/j5/9blqb0mVvDbvtIy++EfnldTj88bsa3S7jcsXa8v3XjhuLMxubSr5jr+z9+dlv/PAV/G1nbteqUocu8DV+OWaE5K7ceBquhEvV+PA1XQjXnHGgZnCWNqJHciY35P095J8Sf9krf07Y8zfSNplrf2aMaZZ0j2SztPwk3xXW2tfeKt9dnd32127dlX8++BgQX3ZnAqRVeAZdWTSLGbZgE4wDmp22wTxe3ycA3fjd9Tpn/7GCe/7pc9eejJJQg1U+ftMTPwWCpFeH8zJ96SBoUiFyCrle/KNNFiIFHhGKd+oEFp5npFnpHxoi/k0HXgqhJGsjKwdfq8kDeZD+SPbS8P/HyxEag6GnyLMh9Hw3z3JN8P7MEbFJ/UC31NgpGwhUsozCnxP2XxYTI/s8LQPgW+UD608I9mR13zPKPCMhgqRUr4nz0hDYSTfGJmR7ZoCT/kwKnlPU+BpsBApGj2+L+ULVuHI35sDTy2pQEeG8soVopLjeJ5R2jNqbjLqH7QqhKV/9z2jVODJRlaRlVL+8Ouj57spMBrMR0oHvjpa0ooiq4P9QyqEkQLf0+zWJgVBYp7oS0z81qrurMVxptNnqeVxosiqbyCnXCEs5h/vre8IndbxW63zXo395nIF9Q68uc/OlrTS6amntVCIqlJ+ViO9LsevNBwXb+QLspFVPhquL9MjbYjsSBuiOfAUBNJgziq0ttieCDwjT5I/0sYYfX8m8CQj5UKraKSOTAem+HuxTk55CqPhKTZzI/VwJuXJ2uH2Rjimfh0YCicVC/l8OBw7Y+J8bNzPbm2S73sl39kpaV+HjsUfxy5wNX657oZ0UnFA/CIxkhq/Sbl2Q7LFFb81ixxr7TclfXPca3815t+Dkq6M85jNzYHmkjkanqtx4Gq648Q54BwA9RAEnjpamyVJM2fUOTGO+KXjlFOnNE/9GJ5n9PaZmeNv2OBqVW/U4jjT6bPU8jieZ9TZ5ubTrtU4R9U679XYbzodaG4VBkSCwKtK+VmN9Locv9JwXEy2g+7UOlZpHa2T2y6V8jW3vbQxVC7ux39nc5sa8/rF1fjlmhOSu3HgaroRL1fjwNV0I15xxUFiboMGAAAAAAAAAAAAMHkM9AEAAAAAAAAAAAAOYqAPAAAAAAAAAAAAcBADfQAAAAAAAAAAAICDGOgDAAAAAAAAAAAAHGSstfVOw0kzxvRKenkSm86SdKjKyUk6zsHkzsEha+0ltUgM8XtCOAfEr8sa/RxM9vMnLX5d+N6Snsakp0+KL41Ji1/JjfM/WdPps0jJ+zzE79SQ1uqg/VB/fJbqI36Th3NA+esyzkHC4neatn2rhXMwxfh1eqBvsowxu6y13fVORz1xDtw9B66mO06cA3fPgavpjlOjnwNXP78L6U56GpOePsmNNJ6s6fTZptNnkabf56kGl84Raa0Ol9I6lqvpLofP0ng4T5wDyd1z4Gq648Q5cPccuJruOHEOpn4OmLoTAAAAAAAAAAAAcBADfQAAAAAAAAAAAICDGmWg7856JyABOAfungNX0x0nzoG758DVdMep0c+Bq5/fhXQnPY1JT5/kRhpP1nT6bNPps0jT7/NUg0vniLRWh0tpHcvVdJfDZ2k8nCfOgeTuOXA13XHiHLh7DlxNd5w4B1M8Bw2xRh8AAAAAAAAAAAAw3TTKE30AAAAAAAAAAADAtMJAHwAAAAAAAAAAAOAgBvoAAAAAAAAAAAAABzHQBwAAAAAAAAAAADiIgT4AAAAAAAAAAADAQQz0AQAAAAAAAAAAAA5ioA8AAAAAAAAAAABwEAN9AAAAAAAAAAAAgIMY6AMAAAAAAAAAAAAcxEAfAAAAAAAAAAAA4CAG+gAAAAAAAAAAAAAHMdAHAAAAAAAAAAAAOIiBPgAAAAAAAAAAAMBBDPQBAAAAAAAAAAAADnJ6oO+SSy6xkvjhJ86fmiF++anCT80Qv/xU4admiF9+qvBTM8QvP1X4qRnil58q/NQM8ctPFX5qhvjlpwo/NUP88lOFn5ogdvmp0k9ZTg/0HTp0qN5JAE4a8QuXEb9wGfELlxG/cBnxC5cRv3AZ8QuXEb9wFbGLWnJ6oA8AAAAAAAAAAABoVAz0AQAAAAAAAAAAAA5ioA8AAAAAAAAAAABwEAN9AAAAAAAAAAAAgIMY6AMAAAAAAAAAAAAcFNTjoMaYT0r6mCQr6T8krZT0NklfkXSapD2SrrXW5uqRPkwvUWTVN5BTrhAqHfjqaEnL80y9k3VcrqYbACTKMKBeyHtwGfGL6YrYRpIRn3AZ8QvAdXGVYzUf6DPGzJX0J5IWWmuzxpj7JV0t6fck3WGt/YoxZpOkj0raWOv0YXqJIqueA0e16u5d2n84q3ntGW25rlsL5rQluuJ3Nd0AIFGGnajTP/2NE37PS5+9tAopgevIe3AZ8YvpithGkhGfcBnxC8B1cZZj9Zq6M5CUMcYEkmZI+rmkD0jaOfL3uyT9QZ3ShmmkbyBXzCiStP9wVqvu3qW+gWQ/LOpqugFAogwD6oW8B5cRv5iuiG0kGfEJlxG/AFwXZzlW84E+a+2rkj4n6RUND/C9Lmm3pCPW2sLIZvslzS33fmPMamPMLmPMrt7e3lokGQ7LFcJiRhm1/3BWuUJYl/RMNn6Tlm5AovzF5CWxDCN+4TLaD3AZ8QuXxdF+ILZRL5OJX+ITSUX8wlX0PeBExFmO1XygzxjTLulySWdIerukFkm/W2ZTW+791to7rbXd1truzs7O6iUU00I68DWvPVPy2rz2jNKBX5f0TDZ+k5ZuQKL8xeQlsQwjfuEy2g9wGfELl8XRfiC2US+TiV/iE0lF/MJV9D3gRMRZjtVj6s7flPSitbbXWpuX9C+S3itp5shUnpI0T9LP6pA2TDMdLWltua67mGFG57ntaEnXOWVvzdV0A4BEGQbUC3kPLiN+MV0R20gy4hMuI34BuC7Ociw4/iaxe0XSEmPMDElZSRdL2iXpu5KWSfqKpA9LeqgOacM043lGC+a06cG1FylXCJUOfHW0pBO/KK+r6QYAiTIMqBfyHlxG/GK6IraRZMQnXEb8AnBdnOVYzQf6rLU/MMbslLRHUkHS05LulPQNSV8xxvztyGv/WOu0YXryPKPOtqZ6J+OEuZpuAJAow6rt9E9/44Tf89JnL61CSpA05D24jPjFdEVsI8mIT7iM+AXgurjKsXo80Sdr7WckfWbcyy9IuqAOyQEAAAAAAAAAAACcU481+gAAAAAAAAAAAABMEQN9AAAAAAAAAAAAgIPqMnVnrUSRVd9AjgVZATiLcgyuInaB+iDvwWXEL1xHDMNFxC1cRvwCcF1c5di0HeiLIqueA0e16u5d2n84q3ntGW25rlsL5rRR4ANwAuUYXEXsAvVB3oPLiF+4jhiGi4hbuIz4BeC6OMuxaTt1Z99ArniCJGn/4axW3b1LfQO5OqcMACaHcgyuInaB+iDvwWXEL1xHDMNFxC1cRvwCcF2c5di0HejLFcLiCRq1/3BWuUJYpxQBwImhHIOriF2gPsh7cBnxC9cRw3ARcQuXEb8AXBdnOTZtB/rSga957ZmS1+a1Z5QO/DqlCABODOUYXEXsAvVB3oPLiF+4jhiGi4hbuIz4BeC6OMuxabtGX0dLWnd/5AK93HdMM9K+juVC/XLHDHW0pOudNGBSCoVIB/uHlA8jpXxPs1ubFATTdmweZXS0pLXluu4J8zRTjiHpqIOB+iDvwWXEL1x3vLZ7FFn1DeSUK4RKB77aMykdzuaLv3e0pFlTCjXHNSdGudgHRdsBrnMx3yFecdbD03agL4qsAt9o/mkz5BkpslLgG0WRpfGMxCsUIv3kwFHdcO/uYibftKJLZ81pa6gCf/zFcKNd/Hqe0Zmdrbp/zYUllX4jnQO4iToYqI9a5r1Gr6MRv2rFL7GKWhltu391zYXKhZF8zyiTHr52iyKrngNHi504v71wtv7k4veUXO9tua5bZ3a2MviHmvI8o3d1zNCO1UtUiKwCz6iTuGs4rvZBRZFVZG3pa9Zy3dmAXGzvFQqRXnptQPteyxYHqo+dVtDpp7UkOt8hXp5n9O5ZLSX18Mn2/U7bgb7XB3M6ciyvtdv3FCupDcsXqyXtq6O1ud7JA97Swf6hYgNLGp6b94Z7d+v+NRfq7TMzx3n39DD+Ynj04nfBnLbEV9ZxiSKr53v7G/ocwE3UwUB91CrvUUejGqoRv8Qqaqlc2339skWac0pBM2ekiq9L0tKu+ROu91bdvUv3fezXdM0//IB4Rc3k86F6egd045gBno0runTW7FalUkx/2Chc7YN67VhOvUeHtO6hH5WUu6c0pzT7FK47G4Wr7T3iF9LwgG/Pwf5YbrSYtsPDg/moeJEoDVdSa7fv0WA+qnPKgOPLh1HZhTjzYePEb99AruRiePTit28gV+eU1Q7nAK6iDgbqo1Z5j/oJ1VCN+CVWUUvl4u2mnc/q5b5jyubCkuu7mZlU2eu9g0eHiFfU1MH+oeIgnzQcdzfeu1sH+4fqnDLUUqU+qELC+6ByYaSbdj47odzNJTzdiJer7T3iF1LlGy1Oph6etgN9hciWr6QiW+EdQHIEnim7EGeQ4DtR4pYrhGXzcK4Q1ilFtcc5gKuog4H6qFXeo35CNVQjfolV1FKleJuR9hValVzfHcnmy17vje+UJF5RbbTbIUkp3yvfB+Unu9s4rBC/EfHbUFxt7xG/kOK90SLZJfYUMFACl7U0+dqwfHExhotTFzU1ztQZ6cAvm4fTAeegkc4B3EQdDNRHpU6aVMydNNRPqIZqxC+xilqqFG/HcqGaU562XNdd/PsDu/dp04qukuu9zdd26YHd+ya8n3hFNdFuhyTNbm2aUCZtWtGl2a1NdU7ZW0sHFdoOrG/WUFxt72VS5dPdzLTJDSXOGy2mbck3u7VJG8dVUhsdqKQASTqlOa32GSltW3mBvvOpX9e2lReofUZKpzSn6520muloSZdcDI/Osd3RwjlopHMAN1EHA/VRq04a6idUQzXil1hFLZWLt/XLFumXO2ZoVkuTFsxp04NrL9JjN/+G/u6KRTprzO8Prr1IC2a36ZO/tYB4RU3RbockBYGns+a06f41F+p7N71f96+58KTWh6q1zpYmbR5/08SKLnW2EL+NxNX23qzWJm25dly6r+3WLMrfhhLnNZCx1t3HQbu7u+2uXbsq/j2fD3Wwf0iFyCrwjGa3NrGYMI6nZretHS9+o8iqbyCnXCFUOvDV0ZJO9CKy1cA5OOFzkJj4BU6iDm7Y+D3909+oyXFe+uylNTlOg0pM/BYK0XDeCyMFvqfZrU1V6aShjp5WpnX8EqvTXmLiVxqOt0MDQxrMR/KNlEn7mpmZfMwRrw0nEfFL3xlOUiLit1ZtXyTbSdSfNYlf+n4xGSdRjpUNkqA6yUsG3/eGH9MdySx+wueWBsbyPKPONu7iaHTEAVxFHQzUh+cZpXxP1lqlfK9qF4rUT6iGasQvsYpa8jyjWS1NxU67E11ehXhFPdBuh8tq1fZFsrlaf7qabsQrrnJs2g70RZFVz4GjWnX3Lu0/nC0+trtgThuFPuAA8jDgLvIvUB/kPbiM+MV0QBzDNcQsXEb8AnBdnOXYtB3o6xvI6Qc/7dV9q5YoslaeMfrO3p9rVmsTI+Vwwuhju/kwUqoBpx/oG8gVCzlJ2n84q1V379KDay9qqDzMY/xwEXUwUB/kPbisbyCn/3vPPm29/nz5nlEYWe3c9Yo+9r53Tyl+aUuhlo5kc/rF64O6/cpzdCSb16ZHf6o7/rVHf/3Bs2WtrRiDxCnqpVplL9zjYjlE/GKUi/Er0feL4XLsjn/t0brLFmpmJqUj2bzu+Nce/d0Vi064HJu2A32+Z9V1xixds+WJ4mjoxhVd8j131yRE4ygUIv3kwFHdcO/uYvxuWtHlxGLIcckVwuIg36j9h7PKFcI6paj2uDsNrqIOBuqDvAeXGVldes5crdz2VDF+NyxfLKOTj1/aUqilKLL6+ZFBrXvoR8V4u/3Kc9SU8nTV5u9XjEHiFPVUjbIX7nG1HCJ+Ibkbv/T9QpKiKNKH33uGbn7g2WIc3Lp0kaLoBOd/1zQe6MvmIn39h/sn3NVx3XvPkFrqnTrgrR3sH9L/UyZ+T7vonXr7zEy9k1cT6cDXvPZMyWDfvPbM8NoBDYKnGuEq6mCgPrK5SF985LmSuwG/+Mhz+szv/0rsec/Vu2aRXEOFSF/6zvMl8ful7zw/HL8nibbUMPJrbfQN5LRmpLNOGo63T331Gd1x1blveZf2dItT4s0tQ4VI33jm1fLtdjQMV8sh4heSu/F7sH+oOMgnDaf7hnt36/41FzZM3y+k0Erf6zkwoRw7Y9Y7T3hf03agL+Wbsnd1pHwamI3GxcegA09l4zfhyY5VR0taW67rnnBHTkdLut5JqxmXn2p0Md8hPtTBQH0Yo7J3A5qYs56rd80i2SrF71RCyuW2VFzIr7VTLt46W5vUnPJ0y9f3VrxLu1KcZvOhXj18zKnBMuLNPbTbIblbXxK/kNyN33wYlU13ITzxJ7ngrnSFcix9EuXYtO11zYdWa7fvKRkVX7t9j/Ihj283ktHHoK/a/H39+vpHddXm7+snB46qUEh2oUn8Sp5ntGBOmx5ce5Eeu/k39ODaixruAjEVeJrXXnoXz7z2jFIJHzBzNd8hPpRhQH1Yq+IgiTSc925+4FnZmLPeoYGhsnfNHhoYivdAaCiV4jeaQvyOzhAxFjNEDOfXvoFcnVM2/ZSLtz+5+EzdOK5NdPMDz2psk6hSnP70YL8uuvW7umLDY+o5cFTRVDJDjRBv7qHdDsnd+pL4heRu/Kb88n1+gZ/sPj/Ea6gQlS3Hhk6iD3XaRk4hsuVHxR1oHCM+B/uH9IWRKax2rF6idZct1BceeU4H+5PdEUX8QpICz2j9skXFin9ee0brly1SkPDBzkrTDyQ93yE+lGFAfUS2fN6LO+sN5svfNTuY54YOnLywYvyefACPzhAxti3FDBFu3OXuoo6WtDZf21USb6fPmlH2/NsxcV0uTtcvW6QvPPJ8cXtXBsuIN/fQbofkbn1J/EJyN35ntzZp04rSdsOmFV2a3Zrc6UYRvzjLsWk8dadXdn2vFKPiDcaWn8Iq4QvzBp4pG79JH+CJE9O+SNlcqNse7ilZ0+O2h3v0pWvOS/Q6Z0w/AOpgoD4Cr3zei7v94Jvy7RRmScJUpCrG78nXHWNniGjU9cJY97p2PM9oTluT7v7IBXptIKe+gZyODYXHbRONj1NJ+vh9T+vpfUeK27gyWEa8uYe+B0ju1pfELySH4zfwdNacNt2/5kIVwkgBy940pDjLsWkbOU2B0cbli0tGxTcuX6ymINmZHPGqxhRAtdAUeBXid9pm2QmY9mX4QrmzrfQOpM62dOIvlJl+AGm/fB18MnOMA5g8z0h3XHVOSd6746pzprTGWTmZtF/2ifNMOtn1E5KtWvHreUadbU2a2z5DnW1Nie/0iZurd7m7KrLSy33HFI5ccJ6SCSaUlxuXL5a1tmRa+7Fxmg589Y6bCcOVwTLizT30PWCUi/Ul8YtRLsavNDzY9/aZGb2jo0Vvn5lhkK8BxVmO1eWJPmPMTEn/IOlsSVbSRyT1SNoh6XRJL0m6ylp7+GSPkStESgVG21ZeIM8MN7gLUagca0Q1FCvpve/s0Kr3vVO+ZxRGVlu+90K9k3VcuTBSc8oriV9rI+Ub6Ikopn2R2jMp/cnF7ylOgzn6GH97JlXvpL2l2a1N2rbyfO17LasZaV/HcqHmn5Zh+oEGkgvL18GNVIYB9RBaq1Tg6ZbLzy6Wv6nAm9LUh+XMzKT1rtkt+srqJQojK98zSgdGMzN05OLkhdaqOeWXxG9zyp9y/BYKkQ72DykfRko14J3Srt7l7qIosuo9OqR1D/2o2Ha/56MX6LaHe/TZP/xVvW1mRq/0HdNfPfSf6u0f0qYVXVowu1WpVOkAXnsmpX9etURDhUi+kQ7159TRmnZisIx4cw/tdoxysb6k7wyuczHfIV5x1sP1mrrz85IettYuM8akJc2Q9D8kPWKt/awx5tOSPi3p5pM9QGSlj921e8JjjztWL5li0uGS1iZfKy78Za3c9lTxYmvD8sVqaUr63ZBGK7ftmhC/96+5sI5pqq1UUGHqvwaq8A5n82XXuntw7UXqbEvuoJnnGeULtqSTY8u13VzgNxBLHQzUhbXD071VO++FYaQDb+R045gbUTau6FJ7c1qel/Q2FhLLSjeOWYheGmn/TiF+C4VIPzlwdMJNU2fNaWuoTpTRu9xRXX0DOa0Z13Z/6dAx9fYPaSAX6sP/9GRJfN9w725t/9ivaX77jGI7OYqsnu/tL1m+YP2yRXX5PCeLeHML7XZIDteXVuX7zohfOMDZfIdYxVkP1zxqjDGnSHqfpH+UJGttzlp7RNLlku4a2ewuSX8wleOEFRYyDJM+ZyNidSwXae2YDoP9h7Nau32PjuWSfXcPa5wNz1H85WvO09brz9eO1Uu09frz9eVrzmuoudZdfaqxbyCnVfeMm3b1nsaadrXRUQcD9VGrvHewf6g4yDd6jBvv3a2D46aaA05EPsaF6Ecd7B8qe9MUsYpqGNt2P2/+TG2+tkszZ6S0/WO/pl86pblsfPceHSppI5dbvuCmnc/q5b5jtKVRFbTbIblbX1aj7QDUiqv5DvGKsx6ux/DwOyX1StpqjHnaGPMPxpgWSXOstT+XpJH/zy73ZmPMamPMLmPMrt7e3ooHGV3IcCwWZG08SRswm2z8+hXit5GeiMoXIg3mI6176Ef60J1PaN1DP9JgPlK+gabfHX2qcax6PtU42fh1dYAS8alUhvl1LMMmG79AEiWt/VugUwUnoJ7t36RdC8A9J9J+GG27nzd/pv78dxbolq/v1RUbHtfyf/iBZs5IlY3vvoFcSRu5Ujt6RtqnLY0TNpn4pe8MUjLry8nEL31nSKLJth2SmO9Qe3HWw/WYujOQtFjSJ6y1PzDGfF7D03ROirX2Tkl3SlJ3d3fF3oTAM9q8YrEOHs0V13iY3ZamsdJgUr6n3144W0u75mtmJqUj2bwe2L1PgV+fgZLJxm+qQvymGih+Qyt96qvPlNzZ8qmvPtNQ05cGntH6ZYt0085nS6buqVc5Ntn4TQd+2XyXDpjOrVEksQ6ebPwCSTTp9q/v6R+u69LPXx8q5r23ndoUe7tn9GJk/PQitLNRzom0f++46hx98v5niu2eO646Z0rt32peC0SRLQ7SsA7Z9HUi7YfRtvtgPtLNDzxbch1z3xMvafOKruLUnvPaM7p16SLd9fiLWvyON6fmTAd+2fL1WC6c0JaeTAxGkdWhgSEN5kP5xiiT9jUzQ6w2isnEb1Ch7KVObywp39Oa/3a6lnW/Q75nFEZWO3e9Ure+M2ly8UvfGZJo0m3fBOY71F6c9XA9Bvr2S9pvrf3ByO87NTzQd8AY8zZr7c+NMW+TdHBKR/GG1+kbu0bUxuWL6/MMI+qmI5PSJy5+z4Q1ZDoyqXon7S2d2pRSb3+uJH43rejSqU3JTnecrC3/tIC1jdM/n82Fuu3hHq27bGGxc+q2h3v0pWvOk1rqnbrKTm3yy+a7UxO/NibiYirUwYY6GKiqVCAVIk1oP6RibvHPbm3SxhVdE8r52a2syYSTFwRGM1vSuuXys4uddTNb0gqCk++s62xJl22TdLakp5TWKLLqOXC0ZB21Ldd1a8Gc0RJjWgAAIABJREFUNgZQGtho2339lYtKrmPOmz9T71swR59/5Dmtu2yhOlrS6mhJ6ytPvqxP/tYCdYyJx46WtLZc1z1hjb45pzSXbDeZGCy3zei+Tu9oIVYhSQpllQq8krI3FXgK1TjX3RjuO7vs3Hlaue0pp/rOmtNGVmZC27c5TfmG5HM13yFecdbDNR/os9b+whizzxizwFrbI+liSXtHfj4s6bMj/39oKsfJF2zJYu77D2d14/Y9LCjcYA4dy5VdQ2bH6iWa21SPce7J6cvmys7TvGP1Es1tTm6648RTYcPnoLd/SGvu2V18bV57JvHnoHfgLfJdujHit9FRBwP1cWwoqth+aJ8R33FSKV9nzW7VjtVLVIisAs9odmuTUqlk109ItsFcpJVbnyq/EP1J3uB0ZLBQtk3y4NqL1DmFeC23jtqqu3cN77eNAe9GNdp27z06pK3Xn68ZaV9Hsnm1pP3iE37f3jt8P/O89ozuX3OhfumU5pIBN88zWjCnTf+y9r0azEfyjco+hTeZGKy03t8tl5+ttuYUsQpJUhRJH7/v6fJlLxqGq31n/YOV276nZo7zZkwrLs604Gq+Q7zirIfrFTWfkLTdGJOW9IKklRp+1u5+Y8xHJb0i6cqpHIC1QyC5GweupjtOM5uDsndgz2yQgU6p/B29W67rLrmbN4mIXxADQH3UMu+lUr7mxjl6iIZXjfit1rrBrEeMcjpa0rr7Ixfo8LFcydT7d33kgoozlZTrhPQ8o9ltzcVOy2wuVBjlSjotJxODrPeHyQgrzKQTNtBMOnD3+s3VdCNers60QPxCircersskWtbaH1pru621i6y1f2CtPWyt7bPWXmytPXPk/69N5RgsKAzJ3ThwNd1xqvRUWO9Ars4pqx3PMzqzs1X3r7lQ/37T+3X/mgt1ZmdrohsqEvELYgCoF/IeXFaN+E0FXtl9poKpXQaPrqM2fr9Jn3UB1eV5Rq1Ngf70Kz8suYZ5pe9Y2XgJI6uoQmfeaKflFRse00W3fldXbHhMPQeOFrefTAxW2qbcen9oXL4pX/b6hrZDI3G1DelquhGvSk+59yW8/5D4hRRvPTxtV8tJ+UYbli8unqh57RltWL5YKZ/M0kiaU17ZOGhOJTv0W5o8bVzRVZLujSu61NKU7HTHKR9G5e9sCaM6paj2osjq+d5+XbX5+/r19Y/qqs3f1/O9/RU7BJIiFVQof6fYqQZ3NAXly94mYgCoqhnp8u2HGWnyHpKvGtdvgWe0ftmikn2uX7Zoyh0oo7MujN2vC7MuoLqiyOpYbuJTdF945HltGlc237p0kf72G3srdkIer9NyMjFYbpv1yxbplztmEKsoMka6demiCfFJP3NjyVRoQ2YS3oak7QvJ3ZkWXM13iJdXqR4+iTCYtnPg5UOrbzzzqrZef758zyiMrHbuekXXvfeMeicNNTQwFOre779cEgdbvveCPv6Bd6ujtd6pq+z1bKgfv3pEX1m9RGFk5XtGjz/fq1ObZ2lmg8ySlfaH78AeP0dxym+cCs/V9V8KhfLl74cpfxvGQK5y2QugegZykV7qfaOk/fD0y31qn9Gh9pNc4wyolVyF67drp9B+yOZC3fZwj9ZdtrC45vNtD/foS9ecd9Lr/klvrqP24NqLnFoLBtXVN5DTi4cGJlzD9PYPqaM1XRKHn/tWj57ed0Sf+f3ynZDH67ScTAxOdr0/NDZrpbsef7EkPu96/EV95vd/pd5JQw0dHQz16I8P6L5VS2StlTFGD+3Zrw+eN1enJbgNSdsX0ptPsI/vP0z60+uu5jvEK4qxHp62A32BZ/TbZ79NK7c9VZyf9/NXn8vjrw3G94yOZHN64dBAMbMcyebkJzwOAs/o335yUKe2NBXT/W8/OaiLzuysd9JqJp0y+uIfnadP/PPTxTz8xT86T+lUsr+7OLl6V1Lge/rguXOLa4Acy4X64LlzFTTQIG2jCzyjx1/o0/279xdfm9ee0Z/+5pl1TBUw/aU8o1ltGV195xPFunP9skVKVaHdk8sV1DuQUyGyCjyjzpa00ulpe2mBGkh5Rr+36O0l7YffW/T2KcVvOvDV2Vb65FJnWzqWjh/PM7HfeDW6JhuDh27KFUJ94ZHntWH5Yq3dvqdYDm++tktG0i1f3zvxJsbA02sDQ8Pr8Fmr5pSvWS1Namny9W9/9uvyjIo3TD3+Ql9J7E4mBkfX+wMqCTyjG9//7gnX3fSdNZbAM/qPn72u97ztlGIf1H/87HX9Yde8eiftLaU8o3d0tOr5A/3FtsM7Olqr0vZFco0+wT5+jb6kP72e8r2y+W5p9/x6Jw01FGc9PG2vxo0ntTUHuuXys4uFfVtzIEM/c0PJpD194uL3FNd6c+Ux6HTg6RMfOFM3jrlA3Lh8sdINNO2dkdQ6Lg+3NgdqpOaaq3clGSNZGa176EfF+N20oovpXxpI88gUFOPL3uaEl72A64yROlrTJXVnR2tacS+zk8sV1NM7MCGPL+hsiXWwj0GPxpIOjHzPm9B+SAcn/523Z1L6k4vfoxvGxOqmFV1qz6RiTHk8RtdkG99JtWBOG3HviHTgq7d/SGnfaN1lCzW7rUkdrWkN5kP91UM/0q1LF+nmB54tGQCUtXr18GDJdd+Wa7uVCoyu3/rmTcsbli/WJy5+d+I7LeGewDc6rTWtbSsvkGeGnyxIBUYBy940lFkz0mX7zmbNSHaZ05wq33ZobqAbxOHuTAsdmVTZfNeRwHYqqifOenja9rjlC1Yf2bZLK7c9pQ/d+YRWbntKH9m2S/lCste2QrwGc1GxwJSGn4a68d7dGswle523fCEqXuxJI+nevkf5QrLTHadsLtLKrU+V5OGVW59SNuHfXZxcXf8lX4iKHWrScPzecO9u5RoofhvdYC7SFx95TusuW6gdq5do3WUL9cVHnkt82Qu4Lh9afe5bPcqNrGebCyN97ls9yofxtn97B3Jl21e9MS54PzroccWGx3TRrd/VFRseU8+Bo4lfpxYnL5sr336YStvvcDZfdp+Hs/lY0hyn463JhuQbbbsHvq9bvr5XB48OKTfSL/HtvQf1uW8NTyO784YL9ZXVS9QUePrPnx2dcN236p5d2vdatuS1tdv3yPe8xHdawj2F0Opnh7O6fuuT+sDt/67rtz6pnx3OqhBz2wHJ1pfNl23b9SWwvhxrYKh822FgiOvORjP6lPvc9hnqbGtyor50Nd8hXnHWw9P2ib5CZMtOeVegc6Ch5COrztamknluNz36U+UTHgeV0t1I8UsedveuJFfzHeJTiKy+vfegvr33YMnr//PShXVKEdAYwgp57y8vizfvFWrQTnF1nVqcvHyFtt9U2g+5Qlg2VpM4DbqrU7bjTaNt9wNHs7p16SI1BZ48o+L3+vS+I1pzz25J0v9382/o+q1P6fYrzyn7vc+ckdLma7uKcfvI3gOKrNWrh485c00AN+Qjq62Pla4NtPWxF/VXrNHXUPJhVL7/JUz2gBl9D3CZq/kO8YqzHp62A32BZ8pOecc8442lKfD0F5cs0E0735wiZf2y4YuuJKuU7kaaupM8PKwa679Um6v5DvEh/wL1EfhehbwXb/nbXKGcb46xnGfQo/H4FeqOqayt3ZyuEKsJnEra1SnbUcrzjHzj6a7HX9RNv3OWIquy32s4MrB9JJsv+/dTM6niWi2/vXC2Pv6BM0vWX2VaV8TFM9KH33tGybSyty5dJGbubCzpCm3IlJ+8+nIs+h7gMlfzHeIVZz08bSNnxsj6QGOnvNu4okszEnhRh+qJIlus8KXhDqKbdj6b+GmfXE13nFqbyufh1qbGysP5fKhXDx/Ty30DevXwMeXzye/gjKzKx2/jhG/Da0552rh8cWn+Xb5YzanGyr9ArQWe0R1XnVOS9+646pzYB9lrUc6PDnqMxaDH9Jb2Pa1ftqgkftcvW6T0FDo7cvnybepcPnmNElenbEepfD5UPoz0Py5dqMA3SvmaeE2zfLEOD+SG15J69Ke6dWlp3G9e0aUdT75cjNulXfO1dvz0nkzriphYq2LnojQcXzc/wLVbo0mnyrch0wlf646+B7jM1XyHeMVZD0/bJ/oiK81qTemfVy1RZK08YxT4orBvMBWnf7TJDgRX0x2n17OhbBRqx+olKkRWgWfU1z+o17OhTp1R79TVRj4f6icH+ycszHvW7FalUsnt6Cww/UDDy4WRmlNeyWLC1kbFdcNQH6d/+hsn/J6XPntpFVKCagltpLZMUJL3ClGo0Mab93IVyvk48/jooMfo9J0MejQCq862Jt1y+dmakfZ1LBcOz2pgTr79W2lKpHwC6yNXp2zHm8q13betPF+nlimX7/l/X9bmFV1ac+9ufe5bPbrl8rN1+qwZslba8eTLet+COXrypSN6et8RzcykysZxNldQ71GVxEkUWfUN5GoaQ/U4JuITVuh7CBuo7wFSoWA1qy1d0odaiEIVCsmOg0JUoe8hSl49D4znar5DvOKsh6ftQF8YSS8dOjbh8e13dbbWO2mooYrTx5lkX3jUauqtJGtr9vX6oK8PjZmiZuOKLrU1J3eAK24H+4fKLsy7Y/USzW1P7mhniukHGp6NpJXbdk2IgftXL6ljqoDpz5PR4YG8btq5u6T9256Jd3CsVuV8U+CVDPowDdP0Vgitbnv4J1raNV8z5CsXRrrt4Z/or6ewTlTFNnVC2yQuTtmON5Vru+97Lat1D/1oQgzecvnZ+qWZTfqXte/VwFColw4N6M92PKPe/iHdunSR7nr8Rd3w/ndpzT27K07v+eNfHNUtX99bnMZTknoOHJ1wg0Q1p/iMIlvzYyJeFadNTnifCeIV+EZHj4Zau/2pYl7esHyxTmlO1Ttpbykwbvb5AZK7+Q7xirMeTuYVTgxyYVR+mpYE3r2J6vEqTGGV9IsOz6h8upOd7Fgdy0VlB7mO5RonD1d8sjPhjyYTv8g7GruA68Yu5L1j9RKtu2yhtj72ovIx5z3fqOwUi3Gu59M3kNN1//SkVm57Sh+68wmt3PaUrvunJ5mqbhrLR1a9R0u/396juSnVHV6FWKVNgrhFkS3bdp+R9tXZ2qTN13Zpx+ol2nxtlzpbm3T6rBkyI//9X9/cq1wY6dO/e5bWXbZQdz3+olZedIbeM6dVO1Yv0dtObdbmcdN/3rp0kTY9+tPiNJ6/eGNQhwaGigNuUm2m+OwbyNX8mIiXMdLtV5Zeu91+JddujWYwH02YInjt9j0azCe7/8XVPj9AcjffIV5x1sPT+Im+Co890snYUCJrlRp3N3gq8GQTPg1FpXRHCU93nFwd5IpTxSdSE95oDYnfhlfpjiQuuIDqinMh77cyFEa67eEerbtsoWZmUjqSzeu2h3v0+T86N7Zj5Aph+elBC8lfqxYnpynw9BeXLJgwI0t6Ck9yDhUqxOrV8cUqMPpUW2tTMKH94xlTNq6bAk/ZXKjAMxPK7S9dc558z9O1//hk8bUvX3OePvuHv6q3z8zo+YP9+ty3evT0viOShsvGnx3JalZrU83LTcpq9/nGqDlVeu3WnPLk8URUQ3G1/4W+M7jM1XyHeMVZD0/bgb6UZ/TbC2dradf84kXdA7v3KUUnY0OxVvr4fU9P6GzekfDp41xNd5xcHeSKU3PK070fu0CFUMU1PQJ/+PUkI36R9ow2r1isg0dzxYbK7La00g2Uf4F6qLSQd9zlr2+MLjh9pt45q0W+Z3RaS1oXnD4z1mm+0oFfth2QDhpnCu9GE0W27IwsU5n2OeV76u0f0pp7dhdfi2vqzmqsS8ZaZ24afarty9ecp20rz9e+17LF9s+cU5t0zZYfTIjrf161pDgdca4Q6fYrz9GRbF6bHv2pDg/kS6b73H84qz++72mtu2yhnj/Yr1u+vndC2dg3kNPRwULNy03KaveFVvrjMtdu96+5sI6pQq252v9C3wNc5mq+Q7zirIeT3Vs8BadkPH3i4vfolq/v1YfufEK3fH2vPnHxe3RKZtp+ZJQR2vJ3RyT97p7I0XTHqSOT1sZxU9RsXNGljpjXGUoyY6Sjg6Gu3/qkPnD7v+v6rU/q6GCopN9cWSnfsaB740iljKyM1j30I33ozie07qEfycoolUp48AKOq9R+iHsmg+aUp8vOnaeV257SB27/d63c9pQuO3derDeidLSkteW67pJ2wJbrutXR0jjtgEZTjfZD2jfauHxxaXty+WKlp/iY6+gTXFdseEwX3fpdXbHhMfUcOKpoCndgV2OfqI3Rp9q2P/GKBvNRSfun0kxDB94Y1L7Dx/TGYL64/S1f36s//50FmtWaLvuemZmUNj36U926tHQ62tFpPL/wyPMTpvisdrlJWe2+MIrK9z1ETB3XSFK+0YZx9eWG5YuVintaiJjRdwaXuZrvEK846+Fp+0Tf69lIX3zkuZJpWr74yHP6zO//ilqb65061IpfYWHepE9D4Tma7ji9Npgvm4f/+oNn6+3N07boKpHNVS7H1FLv1FXmm/JPVLOge+M4NhTphnFrbN5w727tWL1E7TPqnDhgGqvUfjAxl7+D+beon2LieUYL5rTpwbUX8XRTg/AqtB+m0v61Mtr9Up/uW7VE1loZY/SdvT/XpefMm1JaK61L9uDai9TZ1pSYfaI2Rp9qu3jhnGL757z5M3XD+98lI6Ot15+vLzzyfHGqzdEn8G75+l7dcvnZE57C3rbygrJl+ZFsXk/vO6K7Hn9RO1Yv0c9fH1TfQK44jee89ozeNrO5puUmZbX7KpW9cbcdkGz50Oobz7yqrdefL98zCiOrnbte0XXvPaPeSXtL1Wg7ALXiar5DvOKsh6dtb7mpsEYJZX1jMUa6deki5+LA1XTHKR9G+vbeg/r23oMlr//lpY1zZ6Gr5VjKN/r4B84sLirMXUmNh7nmgfqoVfuhVvWT5xkGOBpINdoP7ZmUus+YpWu2PFHc56YVXWrPpKaU1mqsS8ZaZ+4afaptYKhQHOT7899ZUFJGrl+2SLc93KPe/iHdunSRPvetHu0/PDzF51j7D2eV8o3+/kPn6r/v+GHx/XdcdY7+z2/+RPPaM/rkby3QnLZmvTFYKNlmy3Xdmpmp/SAbZbXbuHaDVDpbw2gcbFzRlfhlQ4hfuMzVfId4xVmOTduBvlqtUYJks1a66/EXS+44v+vxF2O947waXE13nFK+V36u6hjWVHGFq+VYPrTFCkoaTvfa7XsSn27Eh7nmgfqoVfvB1foJyVaN9sPhbL7sE+ZTfUquGuuSsdaZu0afavv568OdMze8/10Tysibdj6rez56gZ470F/yBN6xXOlA7rz2jF7oHVB7S0qfu/IczWpNa99rWZ06I6UNKxYr8LziE3M8SYc4cO0GSQojKZMy2rbyAnlGGr4/M1KY8PusiV+4zNV8h3jFWY5N24E+5mmGJLU2e/qLS87SvteGYyHtD//e2pzswaJM2tNfXrZQuYKVZ6SO1ib95WULlUknO91xCoz05WvO02sD+eJi9qe1pBQ00LVrpTU9kr7WHeUvWpo8bV/1a8qPlGGRlVKBUUtT45RhQD00pzzddMlZ2j+m3XPTJWfFfleoq2sgI9kia9XZ2lQyUL3p0Z9OKa5yhVAf6pqnyxfPU2StPGP00J79U35KbvQJrtGpNuNYl6wa+xwVRVZ9AzkGhKrI84wCz2jryvNlZErKyOI0nsYoPXLT4ujTpYUoKg7wjj4d/blvDT/5d/dHLtCmR3+qixfO0RvZglqbUiXf3ck8SUcsYLxqlL1w020P90yYOu5v/+BX652st0T8wnUu5jvEK85ybNoO9FVam401ohqLZ0xxQfSx0/W4MF/369mCbhy5A3n08e3Wpsa5ozeUnfDd3X7lOWqkyYuCSk81eskeLKn0NGaqgZ7GbHSFUHrjWF43jpl6YOPyxWpJNU4ZBtRDGElHBnIldecdV52jU5unNk3heJWe2vXpLMYUNPme/uKSBbppZ+l0h01TaD+0Nvt6//82p2Tqzo0rutTaPLX6qBpPU1XrCa0osuo5cHTCAOKCOW0M8MQsHRjl+iMdPDpULCPLTeO5cfliDeYjnZIJ9Ldf36t7PnKBDh4d0pFsvvi0nzQ8UH35eXNL3juV745YQDlNgV++7OVp4oYS2qjstOxJv8mY+IXLXM13iFec5di07XX1PKM7rjpH89ozklTs6KAB21jeyIZlp+t5I5vs4aJsLioO8knD6b7x3t3K5hrn+e0okr7z419o6/Xn6zuf+vXh///4F4oaaI2vwKhsOZb0pxqNpM9ffW5Juj9/9blKeLIRo6FCVBzkk0bKsO17NFRonDIMqIdcGOmT9z9Tkvc+ef8zysU8/4tvyrez476hLoqseo8O6dXDx9R7dKih2gCNqBDZ4gWu9OZ0h1NZ37V/MNQXH3lO6y5bqB2rl2jdZQv1xUeeU//g1K8FRp+mmts+Q51tTbFcZ1Zjn30DueLAjjR8XlfdvUt9A7kp7xuljuUiff6R59Sc8rRx+eKK03jeuH2PXjuWUxhZ/cnF79Gh/pyO5ULNzKR0w/vfpfPmzxy5ecKb8N5Vd+/SoYGhk0ofsYByCmFUvuxl7riGEkXS93oOlPS/fK/nQOLbXsQvRrl43RBF0vO/eF33rVqiR296v+5btUTP/+J1J9KO+MRZjk3bJ/oiazVzRqpknltrIx7fbjCuTi3l6pSNcUr5RpeeM7dkUdpGW1S5UKEcKyQ8DoyRTmkOdMvlZxenXT2lOZADD9IiJpRhQH3YCu0eG3PeC61Vc8ovKeebU36seZwnTxpPoUL8TiWujFHZO6UbKYRyhbDseZ3q9KXV4vLUksZIa3/j3To8kFdrk7Rt5QVK+abs+T+9Y8bwenutnl7rlz55/w9L7uLubGvSwFD57+7YUKioxZ7weTmZWHD5+8DkFCq025N+zYl4pYIK/S9Bsp8PqdTnx3VnY3H1uqE57anrjFkTZp5obqBlmxBvPTxtIycwRof6c7p+65P6wO3/ruu3PqlD/TkF9DQ3lNEpXMea155J/NSdvlc+3Y009WylxUjzYeM02Fwtxwqh1cptu7Ry21P60J1PaOW2p7Ry2y4VGui7a3RBhTIs6bELuK5Suyf29oOVbty+p6Scv3H7HinGYp4nTxpPUIX4tVYTnoi6+YFn1Ug3SqcDv+x5TSdwWrPRjrorNjymi279rq7Y8Jh6Dhx15s523xhlc6HWPfQj/f6XHtP1W5+UpLLnv6Up0B9teULP7n9Da8bN5HLTzmd1SnNKnW1NZd/74qGBkyoLTzQWXP8+MDm02yFJ+UKF/peEz8hSs7YvEs3V64bBCrO5DTbQbG6Itx6e0kCfMWaOMeYfjTH/a+T3hcaYj05ln3HJVZj6JUejtKEYI91+ZenUUrdfeU7inyxyNd1xqnhHQwPlYVfLsXw0vJDs5mu7tGP1Em2+tkudrU3KJzzdiI/nGa1ftqikDFu/bFGi76YDpoNaTV2fr0Ed7dpTSJi6asRvxSfMG6hN0tGS1pbrukvO65brutXRkq5zyiZytaNuVL5M2/3vvrFXX75mccn5v3XpIr2ezWv/4axmZlJlYzQfRvqlU5q1eUXXhPd+4ZHnT6osPNFYKPd93PGvPfrFG4NOTY2Gt0a7HdJw/0u5a/ik15cs2wTJ3euGSvmukfo9EW89PNWpO7dJ2irpf478/pykHZL+cYr7nTIu6iAN393TnPLGTS3lJf7uHlfTHafUyB0NY/PxvPaMUg3UYHO1HGsKvAoLyU7bh8gxzlAh0m0P92jdZQs1M5PSkWxetz3co89ffW69kwZMa5G1SgWl7YdU4MU+dadfoY6Os1Nl9MmT8cdI4lNIiEc14jeoEKtBA7UnPc9owZw2Pbj2osRPv+hqR92ocm33b+89qD/9zfeUtInuevxFLe2aL0k6ks1XLOs8z+htM5uLeeJINq/PfatHvf1DJ1UWnmgsjP8+zps/Ux9+7xm6avP3nZoaDW+NdjskKZPyy17DN6eS3e6qVdsXyebqdUNzhb6zZvrOGkqc9fBUB/pmWWvvN8b8H5JkrS0YYxLRCmeQANLwmmZ/fN/TE+Lgq2surGOqjs/VdMcpHXjasHxxcfqI0Tni0w1U4blajkVWZZ9EvL+B4rfRpXxPvf1DWnPP7uJr89ozCvzGyb9APVgrfbxM+2HH6iWxHic1ctfh+IvSOOun0SdPxq+1kcSnkBCPasRv4HtlYzWO+siltcv+f/bePb6q6sz//6y997klJyEhJAgkykVAkYbLAQzQWpWO41TUsaAoBAQtVy+tX6s4345jp3z7+xWo4wWVANOCgigoOp3ijNUvyNjRWiGgdBpFimhBLQmRYC4n57L3+v5xcjbZOXtjyFnnnL2zn/fr1VebU3L2yjnPetaz1nqezyNJDKUFPuHvK/ozcOpBXRKPLJmOP+hTsGJnnW6DNdUhPL7rIwBAzZ4jWDmj0tBHcl11SPd1RQEvzuvj77Yv/Lrv5Fxsoev3seTyYSlSuAuf2YeXl03NiH0R2YHidiKJ2R7+paVTcjyqs5Ot2JewN07dNzDGHDnvCLGIXIfTvehrZYyVoKMjB2OsCsDp7vwiY0wGsA/AZ5zz6YyxIQCeB9AXwH4AcznnPdboUGSGtXPGY2mnS4K1c8ZDke25+SIyQ1TVLKVQ7IxTxy2S1qiKLb//FBvnT4QsMagax4Y3P8YdV16IfrkeXJaw9GOKvf1YXDO3X1Vzj/0SHI/OGosfbntPt91HZ42F0AZeBEGkYFkJLjirWeUcAa9syJ4OeGXhz/F1ydCmyvDeTSbsN6qaZ8iumT0urbEme5d1PVByU2VTJj4Dpx7UJVEkYF11SO+5l2y/8O8HPsNzC6sQUzV4ZQkffHEad08bgbovmnHgWBOefvsonrltEk6HY6hvjqBvvkf/DM+lCk/0d9L1+yjJ9zq64pIwR5GAmuoQlnSy25rqEGjJdRelbSSvAAAgAElEQVQxh55Bqdw8dtCoos9VSBLD8NIgti+ejLiqQZEllAV9to/JnDrvCLGIXIfTvej7XwD+HcAwxthbAEoBzOzm7/4AwAcACjt+XgngEc7584yxGgC3A1jb04HFVI5PTjbj+UVVUDUOWWI48Gkj+jpkk0CIwUquR7a5s3fquEWiSAxN4Sg+PtmqH8w0haOuklpyqh9LNsROkXRzkfSs2+EceP1PXxgu6l/c9xfMmzIk10MjiF6NlaSmaOlvzoH3Pv0SV44aAI1zSIxhd90X+JtLBgh7RmNrFPN+9W7K30KVI72XTNivxBhKC4xxU2mBN+2Y5GRrxLSX3EvLpqCswJ/We2eCTFQfWvXTS2eOOklmtCuaxsE50DfowS9uHIPSAh+8sgQGjqph/XD8VBtu2fAHlBcHsGnBJOR5JTy/qAp/Pd2OxtYo7t3+Pg4ca0J5cSBFBaO7VXhW38lLy6aAgZ3zZ9r1+2AWMb5TKi4Jc2Iqx9GGr1L2nMV5JbkeGpFFrOY3s/kens4eCCCxBv/lVBs+bWzTEwTbS1QMLsm3dQzh1HlHiCWmcuw7ehJbF1aBcw6Wxt46rRwdzvl+AN8GMAXAYgCXcM4Pft3vMcbKAVwD4F87fmYArgTwYsc/eRrA36cztjyvhEuHGet+Lh3WD3k+SktyE5IErJxhbGi5ckYlJJubgSwxPHyjsaHwwzeOcdVFX9Av4cfTR2FYaRClBT4MKw3ix9NHIei3+ZcnEL9HQuX5fdEe06BqHO0xDZXn94XfY+/PQGIwtV8Xma/rKQxIuPWbQ+D3JPrL+D0ybv3mEBQG7G27BOF0JAY8fvM4g/99/OZxwv1vScCLqSPKcKS+BSdOt+NIfQumjihDSUBcIorTe3UR544kAY/OGmuw30dnjU0rbvd7GO6aNgIrdtZh1vp3sGJnHe6aNgJ+T3qToj1mbp/tMftlYCervG546i1MXfkGbnjqLRw60QwtzZ7P0biKWaFyvHn/Fdhz3+V48/4rMCtUnvYcTV5qDSrOQ2mB/bPxgcRn/EljK1SNIxrnGNDHj5X/+QG+teoN3LzhD/AqDBV987Dnvsvx3MIqBH0yHn39MH76mz/B55GxYmedfsm3tjqE0o6kvnhcw+dNYXza2IrPm8KIx7WU5zY0R/DZqTY0NEcQsfCbbRG1x99/5+/jvEI/NsybYJijTqq4JMzJ89HZGZGIIU3PzmzugiXJIvYl83UVTeEomttjhtea2xPFAnaGMW56dsYYVaS6Cb9XwlXfGACGhAYWA3DVNwYg4M2ydCdj7HtdXhrBGDsN4I+c8/qz/OqjAO4HUNDxcwmAJs55vOPn4wAGpTc24MRXUSztVPa4tjqEimLKAHYTXAOefvtoSvPzn1x7Sa6HdlYkxuD3GOWq/B7JVVlJsThwui2W0qMv3+bNoEXCuTM/A7JfIq4CjS2xlDU4oNAaTBCZxOeRUBBQDP63IKDAJzhB5HQkhpPNETz46/8x9D0r9Cnw+9MVDEng9F5dxLnjkSQUdrHfwoACTxqnde0xrq9FQOLSY+mW2rT7BltVENixS0QmKu8AIOiXcfnF/TF7wzuGtT7od98cbQpHceKrdkMvyJUzKtHQnDhgjGscN69/x+AvF142BBvePIo1uz7CtkVViMQ1XQFBGV+BC/vl41B9S4qM00X9C6AokqlM57Pfv9TULo+ebBXy/Tu54pKwhoHOzghA4xZnZ9eNzvXQzopPsYh9SXvWVcTiGtqiasreJBa3XwJWZyRYnJ2B1lU3wWB+fjao6NzX4XR34rcDmAzgjY6fLwfwDhIXfj/lnG/u+guMsekA6jnntYyxy5Mvm7y36fU1Y2wRgEUAcP7551sOrDWiobaj7LGzpFDfvAEoyuveH0c4H0liWPitobhn+/v6ZHnkpjE524x0137jqob/OPg5Zk443yB7d6uLZO8icU2/4AISm9Jlz+53VVPlSFzDE7sPG4LtJ3YfxkM5uqjutv1qHHdQQ2xX0xa1WIMvGYDi/NyMqbv2SxB2pLv2G45qeOujenNJTYFzL6Zx08bxzwv0807v1UWcobv2G4lr2LHvWEr8m47sc0zVUBr0GWKpmj1H0u59EvDKWD2z0nCxs3pmJQJe+11yZao6tqVdNb1E3baoCn0Cab21reiO/YajKja+deaAXOuQXVp9YyU4B1b/9sMUf7ni+tFYeNlQ/M0jb+KOK4bj+iffAgCMqyhC1bBSFPgV/ZIv+XtLOj7fAX0Cphe4P3ulDk/OHo87thr7e2/+/adYNzdkmANUHe0OumO/dHZGAIDMYHp2lssElu753+zEvoS9ycbe5FygszPiXAif5fzsXP1Yuhd9GoCLOecnAIAx1h+JvnqXAngTQMpFH4CpAK5jjH0XgB+JHn2PAihijCkdVX3lAD43eyDnfD2A9QAwYcIEy1pWv0dCaEi/lAxDu0veEWLROIdHMWZHeBQJPEeNebtrv4rMcM2YQViwaa+hkkuxY5pwhohr5k2V42nKDDkJxoBbpwzB8h3G7OBcFcZ1136tvjvVRd+d27HjGtxd+yUIO2K3+Fe18PPpSgF2hipHeg/dtV+PRfzrSSP+9SkS7r96ZMqFXLqZ/kUBL/oX+g17jP6FfhQJlK8VRaaqY90Sq3fHfiXpTMxeGvTh/qtH4kcvvG+I3xuaozhwrAlA4nPK88p6X8q/ftUOIHHJ96O/HYnlOw7i4RvHmH6+X5xux1ftcRT6lZT//7W6evzgOyMMF9sSY7hh/CAhl9JmVYQb5k3AyP4F5JttSnfs145xO5F9VIuzMzVHZ2cA2S/RfTRusTex+dmv1Z6Kzs7chUg/lq7nG5y85OugHsAIzvmXAGJmv8A5/wfOeTnnfDCAmwHs5pzPQaIqcGbHP7sVwK/TGVh7TDPNMLRj3wQic3AO3Ln1ABZs2otZ69/Bgk17cefWA7C7z4yp3LSaLabafOACUTo2vp0pLw5AcdEmknPol3xAwg6W7ziIHMba3cJD353roTWYIHJDtuaeV5Zw1agyrJsbwrZFVVg3N4SrRpXBI9OhCtFzMhH/ahymGd7p7gUkieH84jyMPK8A5/XxY+R5BTi/OM+Wlx3J6ljRfdUoVj8D50yP2ZdcPizF5pbvOIgllw/T/315cQBtURUaBzYtmIgR/YPYfe+38dgt4/DmoRM4fiqMpnDM9PNNVvIxZv75F/gUQ0/KwoDHdA705ELWSga2sdXePZCIs0NxOwFYn53Z/eyB7JcAAL9HNl0T/TZveyNbxFKyC2MpNyPSj6Vb0fc7xthOAC90/DwDwJuMsXwATef4XssBPM8Y+z8ADgD4ZToDc0uGIXF2VM4xZWgJFl42VJcA2vDmxznL6ugulNUBBLwSNi2YiGNfhvWMsoq+gR41I3UqdstK6i6MARvnTwBjEiSWOGTjXMtZJSKRfWgNJojckK25F/Qz3D1tRErvqKBfnKOnyhH3kYn4V8tQ9ammcRyub8bCzWfmwIa5IYw8r9B29pmp6tiAV8La6lBqP14XxepJeKeYvSjgMbW5kecV4Dd3TsXJligGFvkQ8HrgkYEvW2JY3PkznDMeV39jABRZxpbbL8X/9x91eK2uXq8M/MVvD+H4qTBkhhR545rqEPweGb+5awraIhpUzsFhPgd60rcoUzKwvQlN42hsjTqqEp3idgKwPjvLZUVfdyD7JQCgX77PVPK/X769e41KErB2znicbInq5579gl6k0Z6acCAi/Vi6F313APgegG92/PwugAGc81YAV3zdL3PO9wDY0/G/PwYwKc3x6CQzDLtKlLgxw9DNBH0yqidfkCIBlO9zRlZHV/t1W1ZHe0wzNNOtqQ7lekhZRZEkCz9m71XfI0uJjJRn9xkOLYrz7D1uQhy0BhNEbsjW3Gtp56a9o15YPBkFfjHPsKoceXnZVJQW2HvTTvSMTMS/ksV7pnvw3tAS0S/5gA773FyLHUunoH+hoEkgEEliwueNqjG9nwjv6Em3u+4LnDemXOhznIBHOROzJyvxutrc0YZWeBUJz737Ke6aNgIP7/wTZoQqsGJnnTGD+9n9WHH9aCzY9HuUFwfw5Ozx+MF3RiDgkVGz5wgOHGvqsGEJI/sX4KWlU9AaVfHJyVY8+G//g9ICL+6aNkK/gN04f6Iw6dZMycD2FpyaoEJxOwEA+V6LszMb9p7tDNkvAThX8t/boYbS+dxz7Zzx+uuEOxDpx9KyHJ5odHYECZnOGwBMA/BBOu8pCo/M8NSc8QaJknR7PBDOIxzV8MTuw3hw+ihsW1SFB6ePwhO7DyMctXcZv8SAlTMqDfa7ckYlbL5GCSUc1UwPEe3+3YlEYsAjN40x2MEjN42xvR1E4hrWdJl3a3YfRqQHmcOEM6E1mCByQ7LCpvPcy0SFTVTVzCtEVHF+nipH3Ecm4t9MxVLtMXP7jMTcY58l+V5cOqwUsze8g2+v3oPZG97BpcNK05YEdSKKxLB6ZsJ2d9WdSImBVs6oxOO7DmP5joOYEarA0i21mBGqsKz+y+s4WD9+Kow7tu7HV+EYPm1sw7IrLsTzi6rwzG2T9APMuMYx95d/wIJNe3HgWBNmhCqwZtdHehzu90gpc6Cn0q2ZkoHtLThV2pTidgJIJFmbyWfbXQKT7JdIkkxqGlSch9ICn+0v+YAOycYu826pA+YdIRaRfqxHFX2MsRFI9Ne7BUAjgG0AGOf8a6v4skVM5Xjl/c+wcf5Evez8xX1/wbwpQ3I9NCKLMHamMXrnZuh2lxDUOPD020cNjdSffvsoHrr2klwPLWtYyjfZXDpCJHZsiN0dnDrvCHHQGkwQuSES0+BXGDYtmGSQThadaGGVdShSeYAqR9wHh3n8+5Preh7/ahaxVLoy6FbVh044VBKFU7PnM0E4qmLVq4fwwpLJONkc0RPeSvK96Jvv1SvxgDPSnkkbN7OjzkkTpUEfgj4F9724v5NM7AT9/491SbwY2MefEoc/MXscti+qAgfS+p7oOz87EYcmqMQ187j91qkUt7sJp0pgkv0STsap844Qi0g/1lPpzg8B/A7AtZzzPwMAY+yeHr5XRlAkhu9WDtQz4tqiKr5bOZDKt10G59A3OcCZZujbFlXleGRnR5EYFkwdojdOLy8OYPXMSlfZrywxXDWqTM92bQrHsKP2GGQX3RYlG2J33fzb3X6dOu8IcdAaTBC5QePA6t8eSlk7RScKJatXMhmnJCtHukqgUeVI70VmDHdccSG+bI0BSMgZ3XHFhWnFfpmKpTwWc8DjsnUuE5KgTsSryGhoiSCucj0zv6E5iiWXDwMALLxsKA7Xt6ChJaJf7jWFY6jZcwQrZ1QaLuVWz6w0vPfd04anZPsv3HxGxtgjG6X+/R455d/fufUAti+ejEFFgbT/VvrOrWGA6cWt3ZGZedzupn034VwJTLJfwsk4dd4RYhHpx3p60TcDiYq+NxhjrwJ4Hom4xjZonKf093r4xjFpZ28SzkLVOEqDPkNmcM2eI7aviGqPa1j16iHDuFe9egiP3Tw210PLGh6Z4c4rh+vyEW6UYFC5uf3a3Y85dd4R4qA1mCByg1VFtei9YsQqTrlFXJySzcoRTeNobI1ShUqOUTlHTOWGtePRWWPTih84h2mmdLrLUXHAg4HFAUP1rEdhKA540ntjwpEkExM4T2Tmj6sowgN/dxHufeF93ZYfuWkMPIqEp974M9ZWh7Bm10c4cKwJT799FM/cNgmnwzHUN0ew6tVD+PE1FwNIHPYN7pd31iqxsqAPNdUhveVBW9S8qoxTDJZxWIf8cKbXYNFkwvcSziMpHee08xeyXyKJE+N5p847Qiwi/ViPLvo45y8DeJkxlg/g7wHcA6A/Y2wtgJc556/15H1FonHogTWQCG7vfeF9qihxGT5Fwv1Xj0zJtvXZvLGpIjE0tESweHOt/ppoSSy7E1O5qUa8m+awTza3X7s35nXqvCPEQWswQeSGbFVUy1ZxiuDs6WxUjmgax6ETzSmVgyP7F9j+cKDXwYEfbnvPYL8/3PYetqdhvxIzr7BJ96uVJAkt7XEs3lyr2826uSFIhfav3iHEk0xM+OJ0whbuvWpEShx0z/b3sXXhpbhl0gWQAPzDdy/GA393MT5tbMO929/XpT3LiwMYWBTAW8uvgCIxtMc1Uxv2KIm4WlEkXNS/ANsXT0Zc1SxlZUn2OBswC/nh0bke2NnJgO8lnEdcg4V03NBcD+3skP0ScG4879h5R4hFoB9L69SVc97KOX+Wcz4dQDmA9wA8kM57ioL6exFA4rA5edkAJGzgvhcPwu5yx36PhLVdGnGunTMefo97LkpUTnM4rnFT+7W7XrdT5x0hDlqDCSI3WK2doqtp/Ypk2jDcr4iNUzSNo6E5gs9OtaGhOQItAwtJY2tUPxQAOmTxntmHxtao8GcRZycTfUqkDonNzra6emZl2oc+9R0X3Z3tZvHmWtS3RNJ6X8K5SBIDY8Cjs8bivD4BU1uOxTn8HglxTYMiMZwOR+H3SGjosBs9qU9hCHhlxDWOlkgcT85O9bedZb0URcLAogDOL8nHgD6Jw83O/55kj7NDWdCHu6eNwIqddZi1/h2s2FmHu6eNQFnQ3lKn1COKAACZQZeOa2iO4PipcId0XK5HdnbIfgkgEc//2/5j2Dh/Inbf+21snD8R/7b/mO3jeZkB140dZJh3140dZPt5R4hFpB/rqXRnCpzzLwGs6/hPzrHUuSWdZlfRtTk50LHJ6tTg3I5E4onxrbh+tK7P2/l1NyAx8zksuWgOW16W2DxojTp03hHioDWYIHKDIkmmc0+WxF7AtURVbPn9p4bs0w1vfow7rrwQJYKeka3M3GjcXOYuKYtHZA/JYu1I5/uOZkgO32qPEadYx9XIjMEjM/gUc1/s60ja/Mm/1+HH11yMojwvfvqb91Psc83scfiiKVEFePxUGFeNKjPIezIA4agK5KeOIZuyx4SRrtWViiyhLOiDIjgJRjSZ8L2E81AtWi/YPVGT7JcAAAaOa8YMwoJNew0SmAz2tl/WIf/eed6tnTMedGziLkT6MWEXfXZDsWiQTg0t3YUsMVw1qgwzQhX65mlH7THbS2ByDkMTdSAxyd0ke+e1mMNem393InGq/SoOHTchDlqDCSI3yAx4cvY4fNka0xOF+uZ7hGeFKhJDcZ5xG1Gcpwid41aVdi8vmypUztOryCRzZxM8EjO1X08adqXIEiYNLsLQfvmQJYa++V5MGlwEJU05cY8smcY66b4v4WxUjeOOrQfw5OxxeOa2SfiyNYrG1ih21B7DgqlDoHQkRpQWeNHYGkV5cQB3TxuOPK+s97RuaImgPabpl3wA8FpdPeq+aMaD00dhxc46rLh+NM7rE7DsR3Q22WMn9jByEsnqSieRCd9LOA/u0NYLZL8EkCiKeGL3YUPizBO7D+Ohay/J9dDOSkzlhrPf46fCWOqylkWEWD/Way/62jOUvUk4C4/CcOeVw1Mbmyr2XvS1LElv2RlZZuhX4DNUNfYr8EF2UQ27R7awX5t/Bk6dd4Q4aA0miNyQrWzsPK+E6WPLDVmza6tDyPOJu+TIVqVdSb4XG+ZNSKkcJJm77MNYQtFBZFaz38NMbdXvSS8mKc334q5pI7B0S63hfUvJblxNTOMoDfrQHtNwx9Z3DXFwYUDBv7z2Ed7+uBFrq0OoPXoSA4sCBntfPbMSJUEvwjFz/1eS78XqmZXoX+hHccBzzlXPTu1hRGQWJln4XspbcBVOPYPKROxAOA/GgNu/OVS/rE7ugey+tDlVxYsQi8h1uNcu3bLE0NDRO2HW+neweHMtGloiVFHiMmJxrl82AAmHuezZ/YjF7e00k7KVnXGbbGV7TMPqVz9EtEMCKaomfm6PuUcSKaZa2K9qb/t16rwjxEFrMEHkBqtsbNFnNG1RTb/gSD5n6ZZatEXErdHJSrvOZKLSTpIYhpcGsX3xZLx53+XYvngyhpcG6dA7B1hlNacT94QtbDUcTc9Wm9rjpu/b1B5P630JZ6NIDHdPG57ih5c9ux9fheOYNqo/Hr5xDE42R3D1Nwam2NB9Lx6EIkkI+sz934A+fgwry0efPAX1LRG0RuJ4cPoojKso6lZ/UepJSpgRi1v4Xtq7uQqnnkFlInYgnAjDL//7Yzw4fRS2LarCg9NH4Zf//TE47G2/smQ+7+jcxF2IXId77UWf3yOhpjpkaEJdUx2C39Nr/2TCBMvsCJtnJXlkhrVzjE3X1zqgkkskGudoaDZuOhuao7bPKBOJ6tCsOqfOO0Icfo+EdV3W4HW0BhNExslWNnZc45gytASv33MZdt/7bbx+z2WYMrSkRw3DrUhW2nX2I5motNM0jsMNLbhp3e9x2eo9uGnd73G4oQUaZdJmnUzYr8jm9p1xWm9HTeNoaI7gs1NtaGiOkH1nCL9HwuB+eaa20SfggVeW8PP//BAP/vp/cLIlitKgUV6zNOgDY+Z7wZUzKvHPv/kTPm9qx8f1rbhp3e8xs+b3WLGzDj/625H6Zd/ZbLAndku20/tx6p6TEEueV8LaLvu3tdUh5HntvX9zaiUiIRYGjmVXXAhvh4S6V5aw7IoLwZi97cDvMZ93dG7iLkSuw71WuhMAfAozyP75SDbOdSSzI7r2XZFtnpXEeSJg6Wy/GufCM/LtjE+Rcf/VI1N6fPlc1DNHkSRz+5Xsveg7dd4R4pAY4PVIBh/m9Ui2l84gCKeTzMZOaeQt2P8GfTKqJ1+Q0vA+6BO3RksSw8j+BXh52dSM9pLKVi9A4uuxinuUNOIexSImSbefpEcxH6tHsV+MRnKN2UNVuaUf/vCvzVixsw4rZ1TiF789hCVbarHi+tFYsGkvAGBcRRHuv3ok5v4yIfl51agyPPv9S9HUFsNfv2rHL357CAeONeGWSRfo0k5Awmct33FQ7993tqrnc+1JSrbjDjwZ8L2E8wjHONbs+sjQemHNro/wk+tGozjXgzsLskPPTAixSIwhHFVT5LAlm1f0RePm8+6frxud66ERWUTk2W+v9XztMQ0LNu3Dgk17MWv9O1iwaS8WbNrnKtk/IqHTvHJGZUo2pN33JbGORu6d7feOrQcQc1EGZVzV9Es+4IycTVx1zxyWGLB6ptF+V8+0v/06dd4R4miLaliwca9xDd64F21pSqURBHF2rPyv6DyL9phmKtHsxDjbaZVZvZlMxD1+j2SqkpFuprQiMdOxpnuBmAlIrjF7RDWOn71SZ+qHa/Yc0S/lllw+DMdPhTGkNF//d3dPG27Y+7xWV485//oH/PWrdizeXIsDx5oAAHle2bJ/39dVPZ9rpTTZjjtw6p6TEEtM1fBaXb2h9cJrdfW2P3+RLezXRWJYBBJnqGbnh3Y/Q41azLuYzecdIRaRfqzXVvRlSqaFcBYaB55++6ghO+Lpt4/in669JNdDOytW0odukkqJ0RxGNK5h1auHDPa76tVDeOzmsbke2lnhFvPuIZvPO0IctAb3HgY/8Mo5/ftPfn5NhkZCdIdsxT3ZmOPZqiQ51woXInO0W8Q9j6YR9yR7PXeuME+8np6thqOq6VifmD0OyE/rrYVDl9nZQ9M4XqurR0NzFA9OH4XhZUEcrm/Rq/GAxGdfFPCgvDgABuClpZPRElGhcVhe4HWmLaqa+qyBRQGcV+g/q38810ppsh13kAnfSzgPK1Ueu1fvkv0SgHMlXJ067wixiPRjvfaiT5EYFn9rMGZOOB+yxKBqHC/u+4stsyyJzOGRGO69agRkSYbEgJKgD/deNQIem9uBR5Zw1agyzAhV6JN8R+0xKHKvLcJNQZGY6Wfgpqa0ssQwaXARhvbLhywx9M33YtLgItt/Bj5Fwp1XDterPZKSbj4bylkRmUGRGNbcXIlxF5RA1ThkieHAp420BhNEhvFIDP/w3YugSDJUzjGwKIAR371IeNxjtUaLnOPZktRMVrh0vVAU3QuQ+HoUieG2qefjkoGFUDWOQcUB3Db1/LTsStNgaG4PJA5Qti2qSmusXkU2jdHseEFMl9nZQ+5yBqHIEvZ/0ggAWDc3hKKAB21RFRJjeHL2ePzslTr88/Wj8WljGyr6BrBx/kQ8vuuwfilYXhxA/0K//npDSwQDi/x45KYxuGf7+waf9XWXfEkkiXXbh3oV2dTXk+30LjLhewnn4ZFYim955KYxtj87I/slAMDvMY91/B57r1eeDoWIri2L7D7vCLGI9GO99qIv6JMwfWy5oXfI2uoQgj46aHYTfi9DtAVY+vS7Bjvwe+3tNBUJuOvK4frBRFJmyE33JB6ZmV4WeVykwZBn4cfybO7HvApwXh8fnl9UpV/yeBUGb69dcYiu9AlIGFxaiJvXv2Ow3T4Be9suQTgdr8JQ36Jh6RbjulEUELt2FgYk3DVtBJZuqTU8p1DgHM9WJUm2egESX08m1g6r5vZqmhneRX7FNEYr8tsv2KHL7Ozh95jH7t8LleP2p8/4y3XVIez+4AQamqNobImm9BRa9eohNLRE8NSc8WhqS8hkrpr5DZxsiaLQr6C0wIeXlk1BLK5l1GcVBzy4e9oILOnk62uqQygOeIQ/i8gdhRa+V+SaTtgfn4ehKN9rqIAvyvfC57F3PET2SwBAv3yfaazTL9/e/bb9XoZ+BT7DvOtX4LP9mTUhlgK/uR8r8FOPPp2WiKYfPgCJDd3SLbVoiZDOrZtoabewg3Z720Fc5Ybs4+Onwlj67H7E05QZchIxlZv2/4m56DNos/BjbTb3Y9E4cOJ0BDevfwffXr0HN69/BydORxCN53pkRLY4HTa33dNhe9suQTidtqjFuiG4P+ZXFnP8K4Fz3KNIep+CJOXFAXgykPWUrHAZVJyH0gIfXfLliEysHTJjpnYkp9m4sqE1ajrWBhv2Lut8mf3W8ivw8rKpwiVwiQTtMXMbliXZ8NriLbUYMaAQd08brl+iJf+/+148iMdvGfHhlmoAACAASURBVIdnbpuEJ3YfxrVPvIUHf/0/ONkShSJJUGQZffN9KCvwZ9xnnQrHUsa3ZEstToVjGXkekRuysaYT9qc1Yt5jvdXmZw9kvwTg3FinpV3D6lc/1KXmo2riZ7ufWRNiaba4u2jugR3YL+VQEHGNY8rQEiy8bKgu3bnhzY+pP5DLcGqfKOpPl+hTWBr0GTSKa/YcSTsD20nELT4Du9tBJK6ZXlSnK5NFOAen2i5BOJ1sxT3ZmOOKhZQNSTH1XjJhv5IEPHzjGNz7whkpsodvHJP2wU9M1UznQEy158HMucg1Ej1D03iKDY+rKMKSy4dBkRnWzQ2hZs8RHDjWhNKgDyP6ByExhgenj9JfB870FJr3q3dTLgCfW1gFTdPQ0Bw5pyo+TeNobI2ec9Uy9ehzB049MyHE4tT9m1PHTRBAwn4bmo1JYg3NUbJflyFyHe61F335XhnVky8wyGY8NWc88r321uclxKJYNDa1+yERNWRN9Hm7/+qRKQd8Phf1KfRbfAZ+m2u40maRCFjYbsDmtksQTidbcU825ng4qpo2JX9i9jggX9hjCBvhsbDfdPqUyGDweySDJJLfIyHdHSGtc0RnNI3j0IlmFPoV3YbHVRThR387Est3nLGRlTMq8esDn+GG8YMw95fvGl7/xW8P4cCxpsSejzHTWDqmavjWqv/SJcm6U62QHFtXObPu/C71d3QHmfC9hPNw6rrm1HETYklnrcslZL8EIHYd7rUXfZG4hlfe/wwb50/UK/pe3PcXzJsyJNdDI7KI3yPhV/Mn4LNT7frmflCxH36PvZ2mV5bw5Oxx+LI1po+7b74HXhddcmkc2PPhiZQ5PHjq0FwPLWs49TNQJIarRpVhRqhCP5zdUXvM9hfshDjiFrZ7gc1tlyCcTsArYfuSKqhqojeZzBhkGcITROIc+oYUOFNtsn3xZGHP8CqyaVPyTBww97TahRCLIkvYtGACAAkSS8RBgAYljfg3zoH/OPg5Zk4437Ae3ZrmepSpORCPa6hviSCmavDIEsqCPih02GN7GlujWPjMPvzbsin63nNYWT6icY41t4wDwNEnkOiJePd3hmPz20cNtrN8x0E8OH0UVuysw9o54+GTzQ98vuyQhj1+KoyFz+zDy8umfm2lZnJsnZ/X3d/tbn/Hrj60OODBqXCMfKpDUGQJWxdOAoOkxw48Td9LOI9sxHaZIM6BQ1+cxtaFVdA4h8QYdtd9gQtKKCvMTaSz1uUSp847Qiwi1+Fee9HnkRmuGTMopaLPo1CA6Sb8ChCNc0OD85rqEPw2t3yfB5AlKWXcPhf1PZclmM5h2UUJpE79DIoCEu6aNkLXmE42ki2ihtiuQbGwXUoAJ4jM4pWBz5piKf53SInYDW5M1SyrTURR6JVNm5IXClbncGoGcG9EloFwjGPplncN33lxGmd1lutRmiFJXDOfA3Gt53MgHtfw4YlmvSdaMv6/qH8BXfbZnKTEpcY5onGO5979FLdOGYLlOw6iNOjD/VePxF3PvWuwwVNtcWyvPQ4gYTsj+gfx7PcvRXssji++ak+RLn7kpjHwKRLGVRThwLGmxPM6ZDzPdqGWjvxm555HVs8w86E11SE8vusjvFZXTz7VAXgU4ESzmuJ7C4ptfmhCCEW1WNfUNNa1bODzMISG9MPsDcZ40echf+MmnCo17dR5R4hF5Drca3cMMZVjWZceUcue3Y9YnKTj3ERTWDNtIN5k88a8Le3m43ZTQ1aaw879DJosGmLbfd4R4nCq7RKE0zlt4X9PC/a/SYnQzoiWCD3ZFjX9W062Rb/mN8/xOa0R0wzgk60Roc8hvp72qLn9tkd7br+W65Ga3nokM/M5ILOez4H6lohp/F/fQrZod5ISlzGVY8mWWswIVeiSnUsuH5aSrb/s2f1YeNmZqtLy4gBkScLHDa34vCmCO7Ye0KWLty2qworrRyOucSx9dj+WXD4MAHDVqDKcbI3ihqfewtSVb+CGp97CoRPN0LpI5SfH1plzkd9M9nccVJyH0gJfymWdWRVF8jNI/kw+1d60Rcx9b1uE9m5uQrJY16Q01rVskInYgXAe6a51ucKp844Qi8h1uNem6FBDVgJwrh04ddwioT5vzrUDp46bEAfZAEHkhrjGMWVoCRZeNlSXKdzw5sfC555HZnhqznj9AkVXzpDFbUqzFQe0x8wzgNtjdECUbTKxdmTKjhgDnpg9Dqc6yewX53uQzrmMVaVsXGClLJEZkhKXKk/44FEDCvHwjWPQFI6hrMBn+r3KEsO4iiLcPW04BvfLg8SAC8vy8XlTO46fCuP4qTAWb67Vf2fboiocPxVGUcCD8uIA/vGaUZj9r38wlSkryffqUpoeRcIzt03CvF+dyRI3k9/sKVZVFEUBj+Fn8qn2hfbdBJCZdS0b0L6TABLr8DO3TcKnjW26/V5QkidsrcsUTp13hFhErsO99qLPb9HQUnSPEsLeONUOnDpukSgWzUjd1Oct4LFozGvzHpPUUJggH0YQuSHolVE9+YIUmcKgYLnLmMoz3gs7W3FAsjKr63ME3lkS3SQTa0em7EhmDLG4ZpDZf+SmMWlV9HlkyXys1CfL9iQlLr9sjaB68gW4pZOE3DO3TTL9XgNeGT+9/hIs7ZQwUVMd0rP7u/77pnAM5cUBlBcHdClNs0MhTdNMpTQfuWksNM7RFlXhExiPJasozMbb+WfyqfbFY+EnPS7adxOZWdeyAZ09EEkiXex3w7wJuR7S1+LUeUeIReQ63Gsv+jQObHzrqCGrY+NbR/HP143O9dCILMJh3tj0xSX2bmyqwXzcL9h83CLJ90lYWx1K6TOU73NPwBZXuXlj3kVVOR7Z2aGGwgStwQSRG9rjmqlM4TbB64YiMVx+UX/DheLqmZVCL+GCfvM4IOgXGwcEvHJKL6zVMysREHw5Snw9mkX88EIa8YNPkfCr+RPw2al2PVN6ULE/7YsOjQP3bH/fMNZ7tr+f1ljLgj7UVIdSevSVBcX22CQygyQxREx88M//84OUCui11SFEYpp+yZf8t0u21GLL7Zfil7eGEPAqYEgkUkgS8OzvP8GGeRMwoE8AksTQ0BwxPRRSOUylNB+cPkqvEExeFpYWpG9byWrGzheLa+eMx5rdh/VnPXLTGPKpNkaRJTw5exy+7FRR0jffQ0kGLkODxbpm8zMoOnsggISM9L/tP5aShPj9yy4UstZlCqfOO0IsItfhrF/0McYqADwD4DwkbHo95/wxxlhfANsADAbwCYCbOOenevocDq43wE4GnCtnVIKDyrfdRCRuLoETjdtbOiRqMe6YzcctEg6gMKBg04JJkFjiQMWjMFfN4JhF+XbM5jIUJD1F0BpMELnBSvZDFbxuROKa3j8qeZm/6tVDeOzmscKeEZAV9AmohjjAqzAEZLHbl6KAF/0L/Vhx/Wh9Y9W/0I+igL2lfnojUYv4IZpG/BBTNXwVjhsypR+dNRYFvvTsyCrWiaUxVkWRcFH/AmxfPBlxVYMiSygL+qBQVYJjMPPBr9XV45+vuwTbFlUhrnGoGkfNniO4aWKFqQ2FYyoUWcJnHYfVSbtdNzeEC/vl6z3yzC7YNsybAM7N14GuUprRuCrkb05WMyarDD2yhPa4ilsmXYDbvzm0Q4bMi0Kf5+vfjMgJcU1De8xYUfLwjWMQ12jv5iaszqDsfnZGZw8EADBwXDNmUIqqid1PEJ067wixiFyHc1HRFwdwL+d8P2OsAEAtY+x1APMB7OKc/5wx9gCABwAs7+lDOId+wAgkJsryHQeFZzQT9sZKrke2uQyFU8ctkraIhjkb/pDyGWxbVIXivBwOLIs4Vb6U7JegNZggckO2/K8sMTS0RAz9o0Q/p6E1itkWccAgr7gtjCQxDC7JR4Hfg2hchVeRUZLv1Q/TieyRibhH48APt71nWI9+uO29tNcjOUNzTVEkDCwKpPUeRO6wsuH2uAZZkjDnX/+AB6ePwvba45g2qr/pv833KThS36If9gDQ+/VtXzxZt4+uF2xJ39XYGu2WlKZXEVdhJ0lMr5hoaI6Y+m5RFYSEeDgH7n3BWFFy7wvvU9zuMujsgXAyZhX1mVA1EY1T5x0hFpHrcNYv+jjnXwD4ouN/NzPGPgAwCMD1AC7v+GdPA9iDNC76VItMNo3b+zafEIskAStnVKZUlUg2T4x16rhFQk3BAb/XXLbM77W3IUjMwn4pVnENvWUNHvzAK+f8O5/8/JoMjIQguke24gePwrBx/gQc7ySHWF7sh0cR5+gpDnAffo9F3JNGb2IOmNpRulZEsQ7RlXhcg1dhKfKrq2dWoqU9juJ8L46fCmNYaT7GVRShZs+RFBtaWx0CA0eeV7asUPnsVJshIaHr5ZlZpV9NdQiP7/oIAPTKv5L8zFQtR+MqSoM+Q8V3zZ4jwioICfH0lridSI/CgPkaXBiw+dkDnZ0RyJ6qiWicOu8IsYhch3Pao48xNhjAOAB/ANC/4xIQnPMvGGNl6by3bNHEWqKGlu6CMzz9trFP1NNv279PFIP5uH96vb3HLRLKbAHaoxrW7PrIYAdrdn2Eh669BMjP9eis4Rym9vuTay/J9dCILEFrMEHkBk0z978PCfa/MmNoj3ODvMja6hD6Cpzj2YoDNI3j0InmFPm7kf0LqKovy7THzhL39BCr9UhO21aduccgMoOmcXx+OowjDa0YNaBAlwJOyho3tETw/KIqlBcHcOzLMH70tyPxi98ewtNvH8Xzi6rw19PtaGyNYud7xzFvypCO5IlUuz3S0IoFm/ae1U+ZVfoVBzz42Q2VeOjazFctB7wy7r96JPU9dRAUtxMA8FXYeg0u8Od6dNZkK/Yl7I1Hlsz3DTbvNerUeUeIReQ6nLOLPsZYEMAOAD/knH/Fujl4xtgiAIsA4Pzzz7f8d5TVQQCJbNsFU4ekbDRydW7TXftlABZ/exh+8Px7+rgfu3ks3BRqB7xSSkZsTXUIAZtXs4lE1Theq6vHa3X1htf/8ZpRORlPd+1XkRnuuOJCfNmakAjyyhLuuOJCKLKbLNjd2HEN7q79EoQdOZf416w/pui51x7T9MxTIJFxuHRLrVB5nLKgzzTDtSwoVvqtsTWqX/IBib9l4TP7SGZOIN21X5Wbxz0PTu953CMx4MnZ43HH1v26HT05e3zaewFZYlh6+YW467kD+vuuuWUcSYX1Qrpjv42tUdQ3R/D4rsN47JaxWLBpb8q/iWscK2dU4he/TVz8rbh+NEoLfPjpb/6UYvMzJ56P1TMrDXvYtdUhbH77EwAJP/XI64fwk+tGg3OecnlnVuknyp9pGkdja9RS6jiucWx8y3jovvGto/g/N3xDyPOJc6M79mvHuJ3IPnGNo6E5anitoTmaUyWF7tpvNmJfwt74FIZfzZ+AzzqpjQwq9sMnUG3kXOhu7GvHeUdkH5HrcE4u+hhjHiQu+Z7lnL/U8fIJxtiAjmq+AQDqzX6Xc74ewHoAmDBhgrXlO7SSixBLJK5h1auHDHaw6tVDeOzmsTkZT3ftlzGgwK/o2aBtURUFfgVuSqprj2lgDIbPgLHE625BkS2qGXJ0YdZd+1U5N20kq5L8i2uwrOq8LneZld2OHwjChtgt/lWt5HEE+nnGGPoEFGxaMAkSS/Ra8yoM3U0O7C7RuGr6t5DMnDi6a78eySIbO43TOq+HIc8nG+LJPJ8Mryc9O5IlINglVg/6Fdg8cZzoAd2x32hcRWNrFJMGF4FzmNqxR2L4xW8P4cCxJgBARd8AfIqUcsm37nef4LZvDoVfkfD8oirENY6TzRGs2fURbp0yBIfrWwAkDrZvWvf7rFYid6cCmmvc9NCd06FlTuiW/6WzMwJAwGNRjevJXTUu2S/RXSJxDV+F44YzqEdnjUWBLzf1Td2Nfe0474gcINCPZd3iWWJ3/ksAH3DO/6XT//XvAG4F8POO//51Os+xWyUXkRtkiaGhJYLFm2v115zQmDemcty2aV/KBtHujWRFwjmweHOtqz8DhbGUbN7VMyuh2PzGV9OoobvbURgzXYPtbrsE4XSyFf/KFrKa6cshnqG+JYLZG/6Q8oztiydjYFFA2HO8imz6t3gV8Rvsr6uEcTsSg2nck85H1BbRsGDjXtN4sjiv5+/bHrN+X8J9eBUZO2qP4Z+uvQQ//c2fUrKyn5ozHk3hmH7Jl5TwLC8OoLw4gNKgD0suH4aigAdtURV/+TKML9uiWLGzzmBjdV806xWuy3ccNPTC++vpdvQv9KFvfuYqkbtTAa1y6H978t8s33EQ2xdPzti4iPSgszMCAOKqptsAkJi79714ENttvq6R/RJA4vzwh9veM9jvD7e9Z/u4zKnzjhCLSD+Wi6vtqQDmAvgjY+y9jtf+NxIXfNsZY7cD+AuAG9N5SLtFJdejOarkInIDY8DaOeNxsiWqZ9v2C3ptv+jHNY4pQ0uw8LKhkCUGVePY8ObHrirfpqbgQNihfsypjZAJcTjVdgnC6WQr/mXMXF5E5F1+TNVM15K4KrayvyTfiw3zJqRUqJTke4U+R9M4PmlsxaeNbXpMekFJHgaX5Au/7HPqhWIm7DeuccNlSFM4hpo9R9KOqTP1vk797txOSb4X9/zNSF12v6E5arANBiAcTVQJXzWqDD/ukOGXJYZti6oQiWv4+X9+gNfq6nUljIF9/Dh+KoxxFUX6JWBTOIaBffyIxDWUBn340d+ONPjhddUhFAUyZzPdqYDmFns47qI9nNOgszMCAGIWe/iYzffwZL8EAHDA1H7tjlPnHSEWkX4s6xd9nPP/BixbjU0T9RzFopJLoY2Sq/B26Od0Lt9eO2c8PDbX1cn3yqiefAEWbNpryATNd1ETc8WiGanIagG741Q/5rVohGz3eUeIw6m2K4LBD7yS6yEQLiZbc89Knveha8XJ83os1hJF8FoiSQwj+xfg5WVTM3q50hSO4sRX7YaYdPXMShTleYRW4HRHWs+uZMJ+MyWJ5Fck0/f1Kz23Tyd/d24n6Ue+OJ343g4ca9LtuLw4gBXXj0Z5cQC/uXMqOIA5//oHQ5LE028fxa1ThqChOYoDx5pw7wvvY+P8ibhqVFmKDOa66hAqigO4e9rwlMq5xVtqM9pftDsV0NmskibE4Oa4nTiDYqHWYHc7IPslAOeeQTl13hFiEenH7G3xaZDnlbC2OoTy4oS0T7KBdZ631/7JhAmRuIalz+43bICWPrsfkbi9+7xF4hqWdRn3MgeMWySSlJCt7DyHV8+sdNVBh98j4ak54w2fwVNzxsPvsbcfS0pvpXx37vnqXA+twe5l8AOvnPN/CHEELOZeQPDc88gMd145HCt21mHW+newYmcd7rxyODwCe8jmec3XwEz4EUliKC3wYVBxHkoLfBmJNcJR1VSaJ1nlIworab3G1qjQ52SCTNivlSRSupWhGofp+6aTgO3k745I+JF8n4SaLja8emYlygq8uP/FgzjZEk3Z4y3fcRAzQhVYvuMgllw+TH+9Larigb+72PQyT9U4zi/Jy0p/UU3jaGiO4LNTbZAlYMO8CYa/r2sFdLJK+mz/hrAX2YodCHvj95jbgd3PHsh+iSRPzh6HjfMnYtuiKmycPxFPzh6X6yF9LQGvhI0LJhrGvXHBRLJflyHSj+WmK2UW+KpdxZ4PTmDrwipwzsEYw6/3H8d14wahOD/XoyOyhZWEoN0lMJ06bpFESIIBLREVW37/KTbOn2iQcL3jygtREsz16Kwh+QyC1mCCyA3NZ5l7fQXOvajK8cr7nxnWpxf3/QVzpwwR9oxWizXwB98ZjqI0eqvlCitJclVwaNcdaT27kgn7zZQkUtRCWjaWxgWik787IkFbVEO/oAfP3DYJssQgMYav2mP4KhzDwzeNgWbhB4oCHv2/gcQBT7+gF6rVnpADDc2RjFfOmVWZPnPbJLy0bApicc20AjpbVdKEOLIVOxD2pj2mofboSYMd7K77An0vGZDroZ0Vsl8CAOKahvaYZlDOePjGMVA1exdLtMc0NIfjhnE/fvM4BF2k5kaI9WO99qJPkRi21R7Hw//3sP5aeXEA3wuV53BURLZRJIarRpVhRqhCv3DYUXvM9mXQVL6dKL0vLTBmfpYWeHU5VjegSAxvf9yI7bXH9dfKiwP4wXeG53BUX48iMdPvzk3263ZoDSaI3JCtueeVJXy3ciCOnwrr/ea+WzlQ6BrtVWTTNfA+5SJhz8gmfo+5nJ3oTHkny+YpEsMfPz+NEQMK9bj9j5+fTst+MxVTByy/z55/zk7+7ogEnAMf/LVFP7BLkqzs6xf04cUlk9HYGkXNniM4cKwJ5cUBNIVjhv9ePbMSAa8MVYOl3ygr8GH1zErc9+JBlAZ9uHvacAzplw8ODk3jQi7WzKpM5/3qXby8bCoGFVtnXCSrpAlnQHE7ASQqSr45ogxH6lv02O6bI8psX1mUidiBcB6cA/e+8L5hvbr3hfexbVFVjkd2djgHav7rz8aez//1Z6HtEAj7I3Id7rUXfX0CibLHpVtqz/Rmqw6hT8DeixQhFr9Xwn1XX4TjXyacvVdO/Oy3ebCSLNvtar92D7JE0i/fa/rd9XOR7ItHYXhqznhd4icpW+ZR7H1hlmcx7/J87rFft0NrMEHkBn+H/MvxL89cwJX3DQiPe7wKIEuSIfu0pjoEr8CdRVL+rWu/MqfKv/UNeFFTHcKSTn6xpjqEvgGxf09xwGP6nOKOSiE74/dKuGvaiJS1Ix379Xss5kSaF6zFAY/pOpfO59zbbN6NqJzj8V2HsXJGJZbvSL2A+///4wO8Vlef0pvv6bePoqY6BEUCVlw/Gv0L/Sjq8A1mNtEv34d++T4U5XmwY8lkNLREDXNeVG9HqjJ1B3k+cz9Jezd3oWpIqYiqqQ4hTaXrjFMUMD97KKJ9p6vggOl6ZXckCSm9eFfOqIRE5usqigLm63BP/FivvehrCpuXnRfnDUDQn+vREdkiHuc42RwxBCurZ1ai0Gdv02+Pcex873iKJNatU4fmemhZozkaN/3uSvK96JtGtrSTiMfNpdFuFSiNlgmiDp13hDhOhzVTHzZvyhBagwkig6hxjpZ2o/zLmlvGoY9g/9sa0fRDZSCxkV6ypRbbFlUJk9XsbfJvp8IxPL7rI0PG7uO7PsLPbqgUWvmSredkgvaopl+cAR29tTvsCj2U34qr5nMi3ZikoTWKNV0+5zW7PsJPrhuNgUWBHr1nb7N5N6IwhoaWCH7x20NYPbMSQZ+i94tPHt41NEdx4FgTlu84iK0Lq3C6LYoZoQo8vusjPHTtJSgJ+g3f+9lsom++Dw3NkRR/vPCZfXh52dS05zxVmbqDaIyjLaIa/OSTs8cjSns3VxGLm8d2221eEdUaMT976ONTkE/7Ttfgc+h6pWlI6cW7fMdB21ciEmJpiXCEo8Z1+Kk549ESOXc/1mtXblXj+MnOD/GTnR8aXr/y4vNyNCIiF8Q0jvteNDrN+148iOdt7jQ551j3u0+w7nefGF6fZ/MLHpGEo6rpd5fOYY/TkCSGyy/qjwWb9hqCVrsf+Dh13hHiUDVzHzananBOxkMQbiHOgbueO2Dwv3c9dwDbF08W+5ws9RLuTfJv0biK1+rq8VpdveH1h64VWxmTredkAqt+ZGoadqVmaE7EVM30c/7Ha9IrfehNNu9GFFnS5TS/ao+nxMPLdxzEg9NHYfHmxGH6F01hzFr/jv77D117Scr3/3U2kcmqO6oydQdxjeOOrfsNtnrH1v100OwyMtXTNtNE6eyBgHPXq0zEvoTziGlcV3IDEjaw7Nn9PfJjvfaizyNL5v0YXNTfi3Cu06TsyYT0jel3Z++vTiiRuIZVrx4yZIuvevUQHrt5bK6HdlacOu8IcVitwR5agwkio8RUzfyQRrDuEvUSPneyFds5OYaULOwqnQSnuGY+J9K9lKa9JmFGe1zVY/fhZUFT2yvqkHdN9uRL0tN5msk5T1Wm7sAqeYf2bu5CtliDZZvPdzp7IADnrleZiH0J52Hlx7Qe+LFee9FXmu/F1oWXIhrnkBigccCrMJTa/DafEItTD5udmo0iEr/HfNOabk8VJ+GVJZQWGL/z0gKv7e3XK0u4alQZZoQq9AvKHbXH4LX5uAlxeD0M6+eFcOJ0RNcY79/HB6+HAlaCyCTZinuCftm0P1nQb//LpFxRku/FM7dNwqeNbbpfvKAkT3hsl63nZAJvp2qozkoG6cQPmYony4I+071mWZCq8dyM3yOjoSWCxZtrsW5uyGB74yqKcPe04SgJerFx/kSUF/vR1BbHtkVVac3TTO8bqcq09+P3yKZ7N59L2mUQCTKxBmcDj8XZg93PTAjxOHG9cuq8I8Qich3utRd9AHA6HE85gBhQkOtREdnE72GoqQ4ZmpPXVIfgd8Bhs0+RsOL60fohjU9xl6Pvl++zbD7vFgJehrumjUjxYwGvve23X77XdNz9HHDISIiBIaE337WZu70tlyCcT2m+1/QCTnSiW3tUQ9An4bmFVdA4h8QY4pqK9qiGPj1rT+YKInHN4Bc3zJvg6OeIRmJAv6DXEP/2C3qRTlJzpuJJzrnFXpMqCNxMv3wf1lWHsHhLLWr2HNEP70qDPtx/9UjDQV5NdQgbfncEr9XVpz1P3b5vJNKj2K+Y7t2K/b36uJDoQibW4GwQ9JufmQT9Nh84QcC5844Qi8h1uNdGgPUtEdNm7vUtkRyPjMgm7TGO37x3HBvnT8Tue7+NjfMn4jfvHUd7zN6b8MbWKOb96l0s2LQXs9a/gwWb9mLer95FY2s010PLGp1L799afgVeXjYVI/sXuKqEvTWimfqx1ohYCTbRNLXHTcfd1B7P8ciIbBGOmjdzD0ftbbsE4XSa2uOoPXoSWxdW4b/uuxxbF1ah9uhJ4f5X5cDcX+7Ft1a9gW+v3oNvrXoDc3+517Hy2prG0dAcwWen2tDQHOmRTMrXQTlKzgAAIABJREFU0dga1S+bgIRfXPjMPuGxXbaekwmiKsfq3x5CtENqNqpqHT/3/PvIVDxJe03CDEliGFDkx4rrR2P1jZVQJAk//9438MissSk9pJZsqcWMUIX+c0/nKe0biXRpaI2a+rMGsiFXkYk1OBs0h83PTJrDtO8k7I9T5x0hFpHrcK9N0bHSGU+3HwPhLDjnONVmPNw61RYH5/a2g0w2VXcSTiy9F4lT/RjZLxHXOEqDPkN/yZo9R6hXAkFkGE3TcPHAIsze8I6eDfjwjWOgaWIPOzjnmDK0BAsvGwpZYlA1jg1vfiw8vtI0jsbWaEZ7bWgax6ETzSkVX6KTi7K1Njp5Deac47W6erxWV294/aFr07MrTeOIqRriGgdTNWgaT/u7dWqMRmSeooAX5/XxJ/53ngce2QvGcNZ+fcmfezJPnTznCXtA/owAEmtwQ7PxULmhOWr7szOyX8LJOHXeEWIR6cd67UWfYtHQUnFRNRAB5PtkVE++AAs27dUPb56aMx75PnvrzWeyqTrhHDwWfsxjcz/GmPm4GbP3uAlx+BUpRaJq9cxKkpIiiAyjceDeF943ZAPe+8L7eGHxZKHPyUZ8la0LuJOtEdMKuJeWTUFZgV/Yc7LVP9GjWDzHAf43E/FvPK7hwxPNKTL+F/UvgJLGZ0J7TcIKSWK4sF8+Pqxv0bOzEz35Uu2lKRwz/NyTeUr7RiJdnLrnJMQS8Mqm+7eA196+hOyXcDJOnXeEWET6Mfvv+HqIR2ZYO2c8yosTjULKiwNYO2c8PDI5ezfRFtXwxO7DeHD6KGxbVIUHp4/CE7sPo83m8nHJpuqd7VdkU3WnEI9r+LwpjE8bW/F5UxjxuL2/N9HIEsMjN40x2MEjN42BbPuglePRWWMN43501lgwUFaSW4hrPEWi6r4XD1JmJUFkmJimoTTow7q5IWxbVIV1c0MoDfoQE1zR1xbVsOzZ/YY5vuzZ/ULjq2xJULbHVNPPrD0mPuYwW9NFo0gMq2dWGp6zemalIy6gMhH/1rdE8Piujwx7gcd3fZS2xCbtNYmz0VmCaVxFEYryFNRUhwz28tSc8dhRe0z/uafzlPaNRLrIEsNjNxv3bo/dPNYBe05CJE7dv5H9EkmyIcUvGqfOO0IsIv1Yr63o4zzR1LJzQ0uJJV4n3AMDx61ThmD5jjPZEStnVNr+wqFzP5FMylXZmUxlYDsJlXN4FMngxzyKBM3mjswjSygMKIZxFwYUKIKrFgj7YiU9QNKdBJFZfLJFNa1g/xtTNXN5EVXc5Vi25OisPzOxMVdc00zXdFXwJWw4qmLVq4cM0smrXj2EJ2aPA/KFPko4mYl/M7MXoL0mcTaSPnJcRREe+LuLcNdz76E06MOK60fjgpI8SIxhz4d/xf1XX4x/+O7F+OxUuMfzlPaNRLowBhT4jXu3Ar8CEmNxF7G4eWwXs3myNdkvAWRPCUQ0Tp13hFhE+rFee9GncmDxlv0pZY/bBUsXEfZG49A39kDCYS7fcRDbFlXleGRfj9v709W3RPRLPuBM0/rtiydjYFEgx6PLDpwDd249kOLH7G6/0biG2zbtc9y4CXFYSQ84oaKEIJyMxmGaFSpautNKtlBk9nS25Oiy9ZlxDjz1xp8xI1SBPMiIqhqeeuPPeOjaS4Q+x6vIaGiJYPHmWv01J8n4iY5/eYb2ArTXJM5GUqp3yeXDdDnl46fCWLBpL8qLA1hx/WhccfF5+OHz76GhJYIHp49CQ0ukx/PU7ftGIj1iKqe9G+FYGWCyXwJIKIE88rox0e2R1w/hZzdU2np9ZIzhqlFlmBGq0Me9o/YYtb1xGSL9WK8tr4hrFpnGgrNmCXujcouqEkq3tT3xLFQL2B3Nwn7tXtFHDbEJxULSTCFJM4LIKFZxj+h1w6dIeKrLHH9qznihfTizJUeXrbVWlhhu/+ZQrNhZh1nr38GKnXW4/ZtDhUtLkYyfkUzNCc0i1nGCTBSRecqCPtRUh1CS7zW1kzyvrNvK8VNhnFfod/U8JXIL7d0IwLnxA9kvAQCapuHWKUMMcfatU4ZAs/kdgFdmuPPK4YZx33nlcHjp3MRViPRjvbaiT2YWmcZ0K+4qyA6ci5yFagG7I1nYr2Rz+7Wq9KBqLvcQUznWdPRHTWamrdl9WHjlCkEQRqzWDdFZoRwMr7z/GTbOnwhZYlA1jhf3/QXfv+xCYc/IlhxdNisHk5U9QGLzdu8L7wuvACMZPyNWe4F0YynJItZx6+dMGJEkhj4BBX0CHlM7aYuq+KSxDfdeNQIPvPRH9At6MaAP2Q+RG2jvRgDOjR/IfgkgobRgpuBgd6WFqMpN+57bfdyEWET6sV5b0ccYsHKGsRH9yhmVIF/vLiQGPDF7HDbOn4hti6qwcf5EPDF7HKRea/m9B0mymMMumsSMAQ/fOMbwGTx84xjb+zGPRTWXh7KSXIPKOV6rq8fizbWYtf4dLN5ci9fq6qmamiAyDjddO0X3Ji7J9+Lvx1dgwaa9uPLh/8KCTXvx9+MrbJ/1bUa2MthjqobSoA/r5oawbVEV1s0NoTToy4hSQVLGb1BxHkoLfI6KnTSNo6E5gs9OtaGhOZJ2hZxktSdMcy/AGDfdY9g8F4vIEo2tUazYWYeTLe1YVx1KiYkvLMvHf/7xCwzok7BHRWKOmqdE74L2boSTIfslAIBbKDhwm58/OHXchFhE+rFeW9GnceDpt48aqgmefvso/omqCVyFLEmIxTU8+Ov/0RuyPnLTGMiMbvrsDtfM5/BPXDSHJcbg90iGhqx+j2R7vW6NJw7WOo9bYonXCXdA1dQEkRu4RfybiWpan2Jcn0TKdgKJC59DJ5qx8Jl9egy3Yd4EjOxfIPRAPFsZ7D5Fwv1Xj9T7AZYXB7B6ZiW8gj83J5OJ79xqT5junJDATPcY9G0SQEJC7PZvDsVdz72HNbeMxbPfvxQa5/jkZBv+6dd/QkNLBKtnViLPJ+Ppt4/iZzdU5nrIhIvhFns3Omd2F9mKu0RDZw8E4Nwekx5FMh23h/YHrkKkH+u1F30Br4QFU4ekbKYDXposbiKuavi/dX9NkZa6dcqQXA/ta9E0jsbWqKNkE0SiyBLuuOJCfNkaAwB4O35WZPfMYU3jePKNP2NGqAJ5kBFVNTz5xp9tf9mpaRz//t5nmDnhfMfNO0IM+T4ZNdUhLNlSq6/BNdUh5PvsHWgTuWHwA6+c8+988vNrMjAS56NIzDT+FS1f1NgaxZuHTuDKUQOgcQ6JMeyu+wIFY8qFNbxvbI3qh01AIrt14TP78PKyqcKekU04h/69AIm/574XD+IFkubRycR37lMk/Hj6KMTiHBIDSoI+/Hj6qLQvplUO3LPdKMV6z3bxUqyEM2GMoS2qYu2c8Sgt8CGuccRiXM/MTs7/zbdPwg++M8Kygtjt+0EiO6gc5nu3qUNzPTQiizS2RvHI64cMiTGPvH4IP7uh0tZxF509EMAZhY6uF9V2VxtRJIZHbhqjx5TJxDGSnnUXIv1Yr73oK/AoKC3wGW5DSwt8KPD02j+ZMEGRGWZMqNCbnrdFVcyYUAHF5mX8Ts2mEonGecqlniJLriphZxJw65Qhuta4LsFm87tORWa4ZswgLNi0Vx/3U3PG237eEeLwSzJKgh48t7AKKueQGYMiJ14nCCJzxFSOVa8aD2lWvXoIa24ZJ/Q5ssQxdUQZjtS36PHV1BFlkCVxa3Q0rppK2UTjqrBnANmLuVQLaR6SND5DJr5zRQaiYQ3HvzyzFyjvG0Aff3rrUVzVTMeaCSlWwlkk5WffOdKA74UqcPRkKza+dRQzQgl543+5aQzW7jmC7bXHITOGPgEPNI2n+BvaDxLZQpFgcWaS65ER2UTTNCy74kKc6pRoveyKC6Fp9l7XLM/8FPKTbiPTaiOZIBbXUBBQsGnBJL2CK66piMXtPe8IsYj0Y7321utkWwyrXv3QUAmz6tUP8ZPrRmOgt9f+2UQXOAcaW6IGWZ3VMyvRx+/J9dDOSm/LYu8JEmP4KhxL+e5Kg+74+wFA08wbCm9bVJXjkZ2dmEVDYbuPmxBHayyOhuaobgfJy16vJMHvpzWYIDIFY0BDSwSLN9fqr5UXB4T3DYvFOU42R1LW6EKfuPnNLCSARctXn2yNmMZcLy2bgrICv7DnSBZ/j0SSxjqZkF2KxDJjq4psLrXkJuUJwpzG1ige2/URHvi7i/FpYxuee/fTlMS9J2ePR1M4ir982YYHXvoj1s0N4eLzCg0XeLQfJLKFU89MCLEwxhCOqil2YPe2IWS/BJBYM+f96t2UuMwJa+ap1hjue7HWYL/FAXtXIhJiEenHeu1OJKZquDFUjtEDC3FeHz9GDyzEjaFyxCjL0lXENG4qkxSzuWB3NK6iNOjDurkhbFtUhXVzQygN+oRnsduZqKqZfndRF81hVbPI/re5/cY1bmq/cZuPmxBHe1wzvextp8w0gsgojAErZ1QaGnmvnFEp/qIvC/GVbPG3iC4Ob4+pmDK0BK/fcxl23/ttvH7PZZgytATtMbH+SmLAYzePNfw9j908FlSYc4ak7FLnzyhd2aVM2arMgPXzQtg4fyK2LarCxvkTsX5eSLh9Es4jGlcxI1SBL1ujyPPKmBGqSEncu2Prfvz4mlGo6JuH0qAPizfXorE1mvI+oipck1WGn51qQ0NzBBrF5EQnnHpmQoglZnH+YvczVLJfAsieEohoyH4JQKwd9Nq0+qI8GecV5WHW+nf029C11SEU5ZH+gJuwuiix++Ym4JVx/9UjTXpMusd+nfrdicRjkS3usXm2uF+RTO3X7wDpBEIMcYdeUhOE0+EcePrtowbpzqffPoqHBPd21bKwRkuSZPq3/OyGSmHPAIA8j4zqyRekyE3necSuWRIDCvyKQVKowK/QRV8nJIlhZP8CvLxsqrCeZJmKJxWZQdNgyLytqQ6RTDkBjyKhJN+LxtYovHLif5vaIOf4f+zdf3hb133n+c+5F7ggCCoRTZFuKiq24zpy3T5ybHLSNNn1OM608a6zk+kjNWlirpJ2R67laabNdFxnt0+n2+l2G0eTSds0lmo1v1yl+VFpu+0TZ9N0m3rcxE1j0km8O44V1z9FN2tSFBWLJAjg3nv2D5IQQAIkSF6Q9wjv1/PgEXV/nPu9B1+cc3APcPG7X/qu/v1b9+o//dXpFRcjk/qGK7cAxVq2ok9H+jW/xfg2BdQi8hfSQt/b8NpZyq9Bcd0TUrLtWLozfhNm5mMdPjFWNxt6+MSYZubT/WkUJGtpoqSWC7fVCZvM5nfSN6Kynmn83HXQG1LPSEcO1H+b4ciBfam/KBhZNczftL9JQHIyTV6/ftqTF3BcV9bTr9/2o7q6v0f9O3K6ur9Hv37bj6or4Ukrfwte432FQL/19h/Tda96hS5/ZZeue9Ur9Ftv/7FNfburkVKTbyCXEv4GciWy+oVPjernP/Wo3nn/N/Tzn3pUv/CpUVXa0Dm6/O0dzzPq35HT7t5u9e/IbXoiol3vBeYrse5c9l7zzhNjiX8TFO7JeEZ9PYFOjZ1RbyGrywpBwxx87uxc9dt+//Yt16y4GJnUN1yb3QJ0+TcI0bm2ok9H+nVlff30dQN1d+X56esGEh9DJo38hbTQ9za6dpb264dBxmv4ukv7BCWSlWQ7lqrMMcbcaow5bYz5R2PMBzZTVrNvE3TSRAkkY6x+7531t0n6vXe+Tp5Jdx5Uwrhh/nbSD7IaT/rwz15f99x9+Gevl0lVq9Ve82GsD335tH7jbdfp83e8Qb/xtuv0oS+fTv3tDytRk/xN+W0/kBzPa3zLPa+DXr/Adnl5PtJ7P/lN3fLh/6L3fvKbenk++VvWbMUtQqMo1tmZit51/Bu6+chDetfxb+jsTEVRwn3JVn0Deavemyx9e+dn7vu63nTv3+pn7vu6Tr90wanJvmQ1fi8gba4+eK+JZiphrCi2et8t1+i+v/1HdQee/mhkaEV7+Qd/85R25rMany7qyl3dmpkP616ntd9w/fo9b9af3/WmDX0Lz9XbmWHrbNVtv5Furwwyet9bXqvf/uITeuf939Bvf/EJve8tr9Urg3TfCI78hSQVy1HDa2fFcrr7unzWNHzd5bMkcCdJsh1LTYttjPElfUzST0kal/SoMeYvrbVPbKS8pW8TrPiB9JTP5iNZcSzd//DTdbd9uv/hpxO/hVXSjGmcv2n/IeQkxbH08a89U/fcffxrz6T+uUtSxjOanCnpF/9krLrMhXaM9hdxvDW3DwRQb77S+I4Wn7/jDYkeZytuEToxU2p6Lrt7uxM7TrPbZCd994et6hubfXvnz+96k/p35BI9lgusbc97AcY6aCa20ns/+aiOHNin33jbj6lYiTVXjqq37T1frOg//dVpTc6UdL5YWcwbT+86/o0Vr9Olb7huBu8rsZatuu030m1qrtxw3PWFO96gH86l5tLxCuQvpIXbXTe6drbe211vtblS4/duX7jjDdqZ3NsdpFyS7ViaWuvXS/pHa+0zkmSM+Zykt0va0ERfT87T0ZGh6gtm6Tf6enJ8naCTGCO9541XVX8A3ZVP9/iLs/nL4+6kn/1w9blLUnfQuB3rDtLdjrkaN5LT7PXL9U+gvbbqW0ZZ3+iXbrmmesvLpd+1yyY4UNmqcxnoyenYyFD1NoxLv7U20JPspFgu4+m+229cUWe5hG/Nw7d36nVlPb3vLa9dMSbZ7K3IurKNxzppv8UZ2q8SL9zZwjNG5+fKiqzVzkJWYRzrV//sO9V8+fDPXq+Pf+0Z3bt/nyTbttcp7yuxlp5c43aSa2edpdJk3FVJ+TfVyV9IF293vfz3aJO+5X/SXH3dIVlJtmNpmujbLelMzf/HJf3ERgubLVtNzxT1+TveoDC2ynhGT0+8rL5CoFcyK94xXP10j+d5DeP+nZ/Zt92hbRlXn7skzZVjPTf5sj53xxsUxVa+Z/St56d0WXefegvbHV1zrsaN5PD6BbbHVn3LqBJZPfidF/XJ9/4z+Z5RFFudHH1BB994VWLH2KpzyWQ8XXv5Dn3hF39SYRQr43sa6Mkpk/AEnJVpWGf/+qYfSfQ4QcZvWG9p/0Rzu8xXYo09e1Z/eugNstbKGKOvPvF9XfZjr0pluXCfv/gNuvPFiubKkT77zef167ddp0Iuo88eeoNiuzA2ni2Fuvut1+rIXz2pu996bdtep7yvxFpmSnHDa2eXdWe5dtZB/CbjrrT/1h35C6n+dtflMFKQ8dVXCDb9W8/t5urrDslKsh1L00RfoyxeMYVtjLlD0h2S9OpXv7ppYQM9OZ2bzeud93+jrZ/ORbq9Mt94VvyV+e35dE+r+dtXCPT+n9rr3KdRkrSrO2j43O3q7pw62NHl6cr+V+jnatqxoyND2tGV7vztLwQN4+7voPztdDubtL07t6ntlVrPXyCNWs3f7lzjb411J/yp5l3dgd72ukH9/KcebVsfPdCTa/iNqXaM5TMZTz+8M7/2hpvQVwj0r27cU1dn7RjbpfETzdvZ/u7o8jR01S69+3iyY6m+fNCw3L48Y51LzXrzNx/4OnJgnz759Wd115t/RD//pqv0Ow8+ofe88aq6tvne/fv06Uee1S/dco1Ojr7Qttcp7ys7Wyv525cPdK6n/toZ7VnnyWc9HTmwT3efvPjt3yMH9im/jd9UbyV/d+Y99TbI3+1834ntkcTtrpPS6tghja87bL0k+2FjbTq+DmqM+UlJ/6u19q2L//+fJcla+7vN9hkeHrajo6NNywzDWBMzpbZ+OhfpFsdWF0olzczH1Vnxni5PO3K5Zp/s2LKPTayVv3FsNTVbdurTKEkrlUKdnStXn7td3YFyKb4/fNLi2Ors7LzKoa1+My7IGO0qdKU+f8vlUJOzF5+7/kKgIOU/5I3kxLHVTLmkC8WLbe+OvKeeoGnbK6Uof5dc+YEHtyAabJXnPnhbO4tPRf7GsdWL5+dUCq08s/BbUbmM0e6d3YmPIbaij65UooWx/OIxBnpyymbd/WbaVo3tNnCcVORvO2xgLNWy+flQU8WLr4G+fKCuLsY62yBV+RvHVs9Nzer5qTnt6gn0yu6sPGPkm4Vv9sbWymjhNufWLtzW18q09b0e7ytTLRX5S3uGOLZ67uysnj83p+7A11w50hWXdevKXYVUv3+LY6vZckkv17zvfEXeU2H1952AtEX5u9Z7tw287nAJ2kA/3DBB0tRzPyrpGmPMVZJelPRzkt69mQK34tO5SDfPM9qRy6kclqXFNzY7cm68sUnTp1G2Sy6X0e4OmthbzvMWLkS5+MY8CDLazcRex/I8o54gp1LlYtvbE7iRu4DLPG9hUm8r+o2t6KOzWV+7ey+d+y5t1diOMeRF7RxLdXVltJsL4VjG84yu7CtoR1c2NeN32gSshfYMnmd05a6CduTT03a1wvOMCkFO8zXvOwu874QjXH3dIXlJ9cOp6cmttaEx5pck/ZUkX9InrLX/dZvDwiWANzZwGfkLV5G7wPbgtQfU4zWBrUbOAXCRq22Xq3EDEvmLZKVmok+SrLVfkvSl7Y4DAAAAAAAAAAAASDt+sA4AAAAAAAAAAABwEBN9AAAAAAAAAAAAgINSdetOAAAAoB2u/MCD697nuQ/e1oZIAAAAAAAAksM3+gAAAAAAAAAAAAAHGWvtdsewYcaYSUnPt7DpLkln2xxO2lEHrdXBWWvtrVsRDPm7LtQB+euyTq+DVs8/bfnrwvOW9hjTHp+UXIxpy1/Jjfpv1aV0LlL6zof83RxibQ/GD9uPc2k/8jd9qAPaX5dRBynL30t07Nsu1MEm89fpib5WGWNGrbXD2x3HdqIO3K0DV+NOEnXgbh24GneSOr0OXD1/F+JOe4xpj09yI8aNupTO7VI6F+nSO592cKmOiLU9XIq1lqtxN8K5dB7qiTqQ3K0DV+NOEnXgbh24GneSqIPN1wG37gQAAAAAAAAAAAAcxEQfAAAAAAAAAAAA4KBOmei7f7sDSAHqwN06cDXuJFEH7taBq3EnqdPrwNXzdyHutMeY9vgkN2LcqEvp3C6lc5EuvfNpB5fqiFjbw6VYa7kadyOcS+ehnqgDyd06cDXuJFEH7taBq3EniTrYZB10xG/0AQAAAAAAAAAAAJeaTvlGHwAAAAAAAAAAAHBJYaIPAAAAAAAAAAAAcBATfQAAAAAAAAAAAICDmOgDAAAAAAAAAAAAHMREHwAAAAAAAAAAAOAgJvoAAAAAAAAAAAAABzHRBwAAAAAAAAAAADiIiT4AAAAAAAAAAADAQUz0AQAAAAAAAAAAAA5iog8AAAAAAAAAAABwEBN9AAAAAAAAAAAAgIOY6AMAAAAAAAAAAAAcxEQfAAAAAAAAAAAA4CAm+gAAAAAAAAAAAAAHMdEHAAAAAAAAAAAAOMjpib5bb73VSuLBI8nHliF/ebThsWXIXx5teGwZ8pdHGx5bhvzl0YbHliF/ebThsWXIXx5teGwZ8pdHGx5bhvzl0YbHliB3ebTp0ZDTE31nz57d7hCADSN/4TLyFy4jf+Ey8hcuI3/hMvIXLiN/4TLyF64id7GVnJ7oAwAAAAAAAAAAADoVE30AAAAAAAAAAACAg5joAwAAAAAAAAAAABzERB8AAAAAAAAAAADgICb6AAAAAAAAAAAAAAdltjuA5Ywxz0m6ICmSFFprhzdaVqkU6uxcWWFslfGMdnUHyuVSd8oA0JSr7VgcW03NllUOIwUZX32FQJ5ntjssbKFyOdTk7MXc7S8ECoL05+6SjeRw7T75wFcYW1XCuLq/pFXX8xpBkq78wIPr3ue5D97WhkiAS5OrYzSkA/mDNCEf4TLyF4DrkmrH0tryvdlae3YzBZRKob53dlaHT4xpfLqowd68jo4M6bW7CjT4AJzgajsWx1anX7qgQw+MVuM+fnBYey/fwURGhyiXQ52eXJm7e/sLTkz2bSSHa/fp78np127dq7tPPl63fy7j6eAnvtl0Pa8RAHCDq2M0pAP5gzQhH+Ey8heA65Jsxy7ZW3eenStXK0iSxqeLOnxiTGfnytscGQC0xtV2bGq2XJ0gkRbiPvTAqKZm0x03kjM52zh3Jx3JgY3kcO0+d958dXUSr3b/56fmVl3PawQA3ODqGA3pQP4gTchHuIz8BeC6JNuxNE70WUlfMcaMGWPuWL7SGHOHMWbUGDM6OTnZtJAwttUKWjI+XVQY28QDBlrVav4CUvrasVbztxxGDeMuh1G7Q0RKpC13pfW1vxvJ4dp9duazDffvDvxV1/MaQTOMH+CySzF/09jPoT3akb/kD7ZKK/lLPiKtyF+46lIc+6J9kmzH0jjR9yZr7Y2S/jtJ/8YYc1PtSmvt/dbaYWvtcH9/f9NCMp7RYG++btlgb14ZbomFbdRq/gJS+tqxVvM3yPgN4w4yfrtDREqkLXel9bW/G8nh2n3OFysN958rR6uu5zWCZhg/wGWXYv6msZ9De7Qjf8kfbJVW8pd8RFqRv3DVpTj2Rfsk2Y6lbqLPWvtPi/9OSPpzSa/fSDm7ugMdHRmqVtTS/U13dQeJxQoA7eRqO9ZXCHT84HBd3McPDquvkO64kZz+QuPc7XckBzaSw7X7HHvoaR05sG/F/lf0da+6ntcIALjB1TEa0oH8QZqQj3AZ+QvAdUm2Y8ba9Hyd2RhTkORZay8s/v3Xkv6jtfbLjbYfHh62o6OjTcsrlUKdnSsrjK0yntGu7oAfY8VatuxjP2vlLyCtux1LTf7GsdXUbFnlMFKQ8dVXCOTxqbqOUi6Hmpy9mLv9hUBBsGofnJr8lTaWw7X75ANfYWxVCePq/pJWXc9rxGmpyl9JuvIDD6677Oc+eNtGQoL7Upe/LuC9Zmo4mb/kDxalIn/JR2wQ+QuXbUn+XkpjX7TPBtqxhvmbtpbvckl/boyRFmL702aTfK3I5TLaTeMOwGGutmOeZ9S/I7fdYWAbBUFGu1ef2EtvPH9JAAAgAElEQVS1jeRwK/vwugCAS4OrYzSkA/mDNCEf4TLyF4DrkmrHUtUSWmufkXT9dscBAAAAAAAAAAAApF3qfqMPAAAAAAAAAAAAwNqY6AMAAAAAAAAAAAAcxEQfAAAAAAAAAAAA4CAm+gAAAAAAAAAAAAAHMdEHAAAAAAAAAAAAOIiJPgAAAAAAAAAAAMBBTPQBAAAAAAAAAAAADmKiDwAAAAAAAAAAAHAQE30AAAAAAAAAAACAg5joAwAAAAAAAAAAABzERB8AAAAAAAAAAADgICb6AAAAAAAAAAAAAAcx0QcAAAAAAAAAAAA4iIk+AAAAAAAAAAAAwEFM9AEAAAAAAAAAAAAOYqIPAAAAAAAAAAAAcBATfQAAAAAAAAAAAICDmOgDAAAAAAAAAAAAHJTZ7gCWM8b4kkYlvWitfdtmypqfDzVVLCuMrTKeUV8+UFdX6k4ZbVapRJqYKVXzYKAnp2zW3+6wgJa42o65GjeS43oOxLHVD+ZLmivF1XPI+kax1eLDyjdGxkilMFbW9+QZKYqtjJEy3sJnqSpRrCDjq68QSJKmZssqh5Hyga8wtqqEsYwx8o3keZ76CoE8z2znqQPAJaVd/ZHr/RzaI45tta9f6v89z6zIl+7A01w51q7uQLkceYPtRXsGl5G/kKRyOdTk7MU86C8ECoL054GrcSNZSbVjacycX5b0XUmv2Ewh8/Ohnpqa1eETYxqfLmqwN6+jI0O6pq9Ag99BKpVIT07MrMiDawd6mOxD6rnajrkaN5Ljeg7EsdWL5+c0PVfRXZ95rHoOn3jvsC7Mh/rlz327uuzIgX360JdPa3KmpA//7PX6+Nee0V1v/hFVwljv/8J3qtsdPzisXMbTwU98U/09Of3arXt198nHq+vv3b9Pn37kWb3/p/Zq7+U7mOwDgAS0qz9yvZ9De8Sx1emXLujQA6N1/f9Vvd0N8+W5yZd1Zf8r9NpdBSb7sG1oz+Ay8hfSwmTZ6cmVebC3v5DqSTNX40aykmzHUnXrTmPMoKTbJP3xZsuaKparFSRJ49NFHT4xpqliebNFwyETM6WGeTAxU9rmyIC1udqOuRo3kuN6DkzNllUKbXWST1o4hxen56uTfEvL7j75uO68+WqNTxf1q3/2He0f2qPp2Up1km9pu0MPjOr5qTmNTxd1581XVyf5ltbfc+px7R/ao0MPjGpq1o16AoC0a1d/5Ho/h/aYmi1XJ/mki/1/s3y54Yo+HT4xprNz5A22D+0ZXEb+QpImZxvnwWTK31e7GjeSlWQ7lqqJPkm/J+nXJMXNNjDG3GGMGTXGjE5OTjYtKIxttYKWjE8XFcY2qVjhgLTlQav5C0ju5m/a4sbWS2MOrKf9LYeRPKMV59Ad+A3Pa2c+W/d3s+26g4Vvku/MZ5uWMz5dVDmMNnSOuHQxfoDLtjN/29UfpbGfQ3usd/zQLC8aLY8Wl5M3aJdW8pf2DGlF/qJVacsDrp1hPZLMg9RM9Blj3iZpwlo7ttp21tr7rbXD1trh/v7+pttlPKPB3nzdssHevDLcCqujpC0PWs1fQHI3f9MWN7ZeGnNgPe1vkPEVW604h7ly1PC8zhcrdX83226uvDCBd75YaVrOYG9eQYZbS6Me4we4bDvzt139URr7ObTHescPzfKi0XJ/cTl5g3ZpJX9pz5BW5C9albY84NoZ1iPJPEjNRJ+kN0n6l8aY5yR9TtItxpgTGy2sLx/o6MhQtaKW7m/alw8SCRZuGOjJNcyDgZ7cNkcGrM3VdszVuJEc13OgrxAolzG67/Yb685hd2+Xfv/nXle37MiBfTr20NMa7M3rwz97vU6NnVFvIauPvOP6uu2OHxzWFX3dGuzN69hDT+vIgX116+/dv0+nxs7o+MFh9RXcqCcASLt29Ueu93Noj75CoOMHh1f0/83y5VvPT+noyJB2dZM32D60Z3AZ+QtJ6i80zoP+lL+vdjVuJCvJdsxYm76vgxpjbpb07621b1ttu+HhYTs6Otp0/fx8qKliWWFslfGM+vIBP8bagSqVSBMzpWoeDPTklM02/bbEln1sYq38BaR1t2OpyV/aX2wgB1KTv5IUx1Y/mC9prhRXzyHrG8VWiw8r3xgZI5XDWBnfk2ekKLbyjOR7C5+lqkSxgoxfnbybmi2rHEbKB77C2KoSxjLGyDeS53nqKwTy+ASfi1KVv5J05QceXHfZz33wto2EBPelLn+T1K4xCWOd1EhV/saxrfb1S/2/55kV+dIdeJorx9rVHSiXI286WCryl/YMG0T+IjXK5VCTsxfzoL8QKAi2//rDWmOHDcSNS1BS188u6czp6spoN417x8tmfe3u7d7uMIANcbUdczVuJMf1HPA8o97uLiXdffTv4BvlALCV2tUfud7PoT08zzTs6xvlS29hq6ICVkd7BpeRv5CkIMhot4MTZK7GjWQl1Y6lMpOstQ9JemibwwAAAAAAAAAAAABSK02/0QcAAAAAAAAAAACgRUz0AQAAAAAAAAAAAA5iog8AAAAAAAAAAABwEBN9AAAAAAAAAAAAgIOY6AMAAAAAAAAAAAAcxEQfAAAAAAAAAAAA4CAm+gAAAAAAAAAAAAAHMdEHAAAAAAAAAAAAOIiJPgAAAAAAAAAAAMBBTPQBAAAAAAAAAAAADmKiDwAAAAAAAAAAAHAQE30AAAAAAAAAAACAg5joAwAAAAAAAAAAABzERB8AAAAAAAAAAADgICb6AAAAAAAAAAAAAAcx0QcAAAAAAAAAAAA4iIk+AAAAAAAAAAAAwEFM9AEAAAAAAAAAAAAOymx3ALWMMV2SHpaU00JsJ621v7nR8ubnQ00Vywpjq4xn1JcP1NWVqlPGFiiXQ03OXsyD/kKgIEh/HlQqkSZmStW4B3pyymb97Q5rS8Wx1dRsWeUwUpDx1VcI5Hlmu8PaUq62Y67GjeS42vbWCsNYEzMlVaJYGc8ol/E0V4nUlfW1q5Crtke1bZVnjDwjlaJ4xXZradbmLS2P41iRlay1q7aJjcqR1PHtKYD0q213s76ngZ6cMpnNfTa1XWNqxjpYzVJfXKpEMkbyjBRbyRjJWjk5LmoV72PdQ3sGl5G/AFyXVDuWtpavJOkWa+2MMSYr6WvGmP/LWvuN9RY0Px/qqalZHT4xpvHpogZ78zo6MqRr+go0+B2kXA51enJlHuztL6T6jVWlEunJiZkVcV870NMxb5Li2Or0Sxd06IHRah0cPzisvZfv6JiL0662Y67GjeS42vbWCsNYT750QXfWnMN9t9+oE3//vB55ZqraHkla0VYdObBPH/ryaU3OlFput5q1edf09+ipyRl95K9P6z1vvEr3nHp81TaxUTkP/MLrVQrjjm5PAaRfo3b32MiQrr18x4Yn+9o1pmasg9U06ovv3b9Pn37kWb3njVfp0488q/e95bVOjYtaxftY99CewWXkLwDXJdmOperWnXbBzOJ/s4sPu5GyporlagVJ0vh0UYdPjGmqWE4mWDhhcrZxHkzOpjsPJmZKDeOemCltc2RbZ2q2XH1zLC3UwaEHRjWV8ucuSa62Y67GjeS42vbWmpgpVS82SwvncNdnHtOhm15T1x41aqvuPvm47rz56nW1W83avImZkg49MKr9Q3uqk3y165eX3aic56fmOr49BZB+jdrdOzc5/m3XmJqxDlbTqC++59Tj1b58/9Ae58ZFreJ9rHtoz+Ay8heA65Jsx1I10SdJxhjfGPNtSROS/tpa+w/L1t9hjBk1xoxOTk42LSeMbbWCloxPFxXGG5o3hKPSlgfkb+vKYdSwDsphtE0Rbb205QH5i1alMQdazd8llShueA7+4jfgltqjZm3Vzny2bru1NCsnXIxjZz7bUpvYqJzuwO/49tR1681fIE1azd9m7W4YxRs+drv6ozT2c2iPjbS/q40Nav+9FPOF10a6tJK/PGdIK/IXruK9G9YjyXYsdRN91trIWvs6SYOSXm+M+fFl6++31g5ba4f7+/ublpPxjAZ783XLBnvzynCLqo6Stjwgf1sXZPyGdRBkOueWL2nLA/IXrUpjDrSav0uyvtfwHKLFwdZSe9SsrTpfrNRtt5Zm5WQW4zhfrLTUJjYqZ64cdXx76rr15i+QJq3mb7N2N+Nv/C1ru/qjNPZzaI+NtL+rjQ1q/70U84XXRrq0kr88Z0gr8heu4r0b1iPJdqxtE33GmB8yxvxLY8z/YIz5ofXub609L+khSbdu5Ph9+UBHR4aqFbV0f9O+fLCR4uCo/kLjPOgvpDsPBnpyDeMe6Mltc2Rbp68Q6PjB4bo6OH5wWH0pf+6S5Go75mrcSI6rbW+tgZ6cji07h/tuv1HHH36mrj1q1FYdObBPxx56el3tVrM2b6Anp+MHh3Vq7Izu3b9vzTaxUTlX9HV3fHsKIP0atbvHNjn+bdeYmrEOVtOoL753/75qX35q7Ixz46JW8T7WPbRncBn5C8B1SbZjxtrkv85sjPnXkv6DpK9KMpL+uaT/aK39xBr79UuqWGvPG2Pykr4i6V5r7RcbbT88PGxHR0ebljc/H2qqWFYYW2U8o758wI+xdqByOdTk7MU86C8Eq/3o+ZZ97Get/K1UIk3MlKpxD/TkOu4HzOPYamq2rHIYKcj46isE8jrsk1nrbMdSk7+0v1hn2yulKH+XhGG80A5HsXzPKJfxNFeJ1ZX1tKuQq7ZHtW2VZ4w8I5Uiu2K7tTRr85aWx3GsyErW2lXbxEblSOr49rTNUpe/V37gwXWX/dwHb9tISHBfavK3tt3N+J4GenLKZDb32dR2jakZ66RGavK31lJfXAojGUmeJ8WxZIxkrVoZFzmL97Hrkor8pT3DBpG/cNmW5O96xg7oXBtoxxrmb7tavrsl3WCtnZIkY0yfpEckrTrRJ+lVkj5tjPG18G3DLzSb5GtFV1dGu2ncO14QZLTbwTdR2ayv3b3d2x3GtvI8o/4dnf3pT1fbMVfjRnJcbXtrZTKefnhnfs3tkmqrmpWz3vKbbd/p7SmA9Gu13V2Pdo2pGetgNZ38Pob3se6hPYPLyF8ArkuqHWtXSzgu6ULN/y9IOrPWTtbaxyXd0KaYAAAAAAAAAAAAgEtGuyb6XpT0D8aYv5BkJb1d0jeNMf9Okqy1/7lNxwUAAAAAAAAAAAA6Qrsm+p5efCz5i8V/d7TpeAAAAAAAAAAAAEBHactEn7X2t5b+Nsb0SjpvrbXtOBYAAAAAAAAAAADQibwkCzPG/AdjzLWLf+eMMV/Vwjf7XjLG/IskjwUAAAAAAAAAAAB0skQn+iS9U9Lpxb/fs1h+v6R/Lul/T/hYAAAAAAAAAAAAQMdKeqKvXHOLzrdK+qy1NrLWflft+z1AAAAAAAAAAAAAoOMkPdFXMsb8uDGmX9KbJX2lZl13wscCAAAAAAAAAAAAOlbS37L7ZUkntXC7zo9Ya5+VJGPMfy/pWwkfCwAAAAAAAAAAAOhYiU70WWv/QdK1DZZ/SdKXkjwWAAAAAAAAAAAA0MmSvnWnJMkY02eM+QNjzGPGmDFjzO8bY/racSwAAAAAAAAAAACgE7Vlok/S5yRNStov6cDi359v07EAAAAAAAAAAACAjpP0b/Qtucxa+9s1///fjDH/qk3HAgAAAAAAAAAAADpOu77R97fGmJ8zxniLj3dIerBNxwIAAAAAAAAAAAA6TqLf6DPGXJBkJRlJ/07Snyyu8iXNSPrNJI8HAAAAAAAAAAAAdKpEJ/qstTuSLA8AAAAAAAAAAABAY0l/o+9aa+2TxpgbG6231j6W5PEAAAAAAAAAAACATpXoRJ8Wbtd5h6QP1yyzNX/fkvDxAAAAAAAAAAAAgI7kJVzeHxtjfsha+2Zr7ZslfUoLv833/0o6kPCxAAAAAAAAAAAAgI6V9Df6jkn6F5JkjLlJ0u9Kep+k10m6X6tM9hlj9kh6QNIPSYol3W+t/f3NBDM/H2qqWFYYW2U8o758oK6upE8ZaedqHpRKoc7OXYx7V3egXC79cSfJ1ecuSa7WgatxIzku50AYxjo3V1Y5ihXFVvmsr8u6A00XK4rjWJGVrLUKMr76CoE8z0iS4thqaraschitWLdc7baFnK+5cqxKFCvrexroyclaq4mZUrX+erp8RbFVsRxXl+3I+7pQXDhWbz6r6WJlxbHXimm19Y3WSVrzHDe6HwC0o+9oV39ULoeanL1Ybn8hUBBsrtz19CNIpzCMNTlTUjmKlfGMMp6RMVIlstVcyfpGlciqO/BUiaRKFNc935VKVDcG6C8E+kEpWvf4YrtyKAxjTcyU6sY1mUzSnzFHklwetyM5rl6DIn8hpaP/2wjyF1JyeZB05vjW2nOLf79TC5N1pySdMsZ8e419Q0m/aq19zBizQ9KYMeavrbVPbCSQ+flQT03N6vCJMY1PFzXYm9fRkSFd01fgBdNBXM2DUinU986ujPu1uwpODLSS4OpzlyRX68DVuJEcl3MgDGM9d25WkxdKuvvk49X4/2hkSH/57XHdtPdy3XPq4vLjB4e19/IdkqTTL13QoQdGV6xrNBG2tO0bX9OnkZ+8Qnd95rHqfn966Cf0g2JYV3+Nlh0dGdJ3Xzyv//vJCf3bt7xWd9asO35wWNf09+ipyZmmMdXG0cr5PPALr1cpjFc9x0ZltrIfALSj72hXf1Quhzo9ubLcvf2FDU/2rdYm01a6IQxjPfnShbr++OPvGVI5tDpc08/fd/uNeuy5KQ1dtasuh44fHNbVfd0Nc+uL3x7XH/3dcy2PL7YrhxrVwbGRIV17+Q4m+1LK5XE7kuPqNSjyF1I6+r+NIH8hJZsHSY+0fGPMUgRvkfTVmnWrRmat/b619rHFvy9I+q6k3RsNZKpYrlaQJI1PF3X4xJimiuWNFgkHuZoHZ+cax312Lt1xJ8nV5y5JrtaBq3EjOS7nwMRMSWfOFauTfNJC/L94YkwHhl9dneRbWn7ogVFNzZY1NVuuvrFYvm652m0P3fSa6iTf0n7l0K6ov0bLDp8Y0xuv6df+oT3VC2q1x56YKa0a02oxN1r3/NTcmue40f0AoB19R7v6o8nZxuVObqJdW08/gnSamCmt6I99z69O8i0tu+szj+mW6161IocOPTDaNLcODL+6bru1xhdrbdsujergzhNjmpgpbVkMWB+Xx+1IjqvXoMhfSOno/zaC/IWUbB4kPdH3WUn/xRjzF5KKkv5OkowxPyLpB60WYoy5UtINkv6hwbo7jDGjxpjRycnJpmWEsa1W0JLx6aLC2LYaBi4BacsD8rd11EH66oD8RavSmAOt5m8litUd+A3j9z3TcHk5XLidVrN1y9Vu26hMz6ilZePTRUWx1c58tuG6ShSvGtNqMTda16xeas9xo/thda3mL5BG2zl+aFd/1I5y19OPYOusp/1t1O8267+tbZ5DzcYgtf9fa3yx1rbt0mzsEUbxlsWAi1rJ3zSO27H10pgH5C9alYb+rxbXzrAeSeZBohN91trfkfSrkj4l6b+x1i5F5Gnht/rWZIzpkXRK0q9Ya19ucIz7rbXD1trh/v7+puVkPKPB3nzdssHevDIp/soukpe2PCB/W0cdpK8OyF+0Ko050Gr+Zn1Pc+WoYfxRbBsuDzK+gozfdN1ytds2KjO2amnZYG9evmd0vlhpuC7re6vGtFrMjdY1q5fac9zoflhdq/kLpNF2jh/a1R+1o9z19CPYOutpfxv1u836b2Oa51CzMUjt/9caX6y1bbs0G3tkfG7buR1ayd80jtux9dKYB+QvWpWG/q8W186wHknmQeKjLWvtN6y1f26tna1Z9r2l23KuxhiT1cIk32estf/HZuLoywc6OjJUrail+5v25YPNFAvHuJoHu7obx72rO91xJ8nV5y5JrtaBq3EjOS7nwEBPTnsuy+vIgX118f/RyJBOjr6ge/fXLz9+cFh9hUB9hUDHDw43XLdc7bbHH35G991+Y91+QcasqL9Gy46ODOmRpyZ1auyMji1bd/zgsAZ6cqvGtFrMjdZd0de95jludD8AaEff0a7+qL/QuNz+TbRr6+lHkE4DPbkV/XEURzq6rJ+/7/Yb9dUnvr8ih44fHG6aWydHX6jbbq3xxVrbtkujOjg2MqSBntyWxYD1cXncjuS4eg2K/IWUjv5vI8hfSMnmgbn4pbvtZYwxkj4t6Zy19lda2Wd4eNiOjo42XT8/H2qqWFYYW2U8o758wI9ZdqB15sGWfWxirfwtlUKdnbsY967uINU/gtwOvIbdzV+eO2wgB1KTv2EY69xcWeUoVhxbdWV9XdYdaLpYURzHiqxkrVWQ8dVXCKo/8B3HVlOzZZXDaMW65Wq3LeR8zZVjhVGsjO9poCcna60mZkrV+uvp8hXFVsVyXF22I+/rQnHhWL35rKaLlRXHXium1dY3WidpzXPc6H6OS03+LrnyAw+uu+znPnjbRkKC+1KTv+0YP7RrTFIuh5qcvVhufyFQEGyu3PX0I6hKTf5KC2OIyZmSylGsjGeU8YyMkSqRreZK1jeqRFbdgadKtHC7y9rnu1KJ6sYA/YVAPyhF6x5fbFcOhWG8EH/NuCaT4Rt9TaQif3nvBmlD16DIX6TGBvq/Lclfrp2hFUldP0tT5rxJ0v8o6f8xxnx7cdn/Yq390kYL7OrKaDcvjo7nah7kchnt7rCJveVcfe6S5GoduBo3kuNyDmQyngZe0bVief+O1T+N7nlmzW2abbuze+U2u3sbLCzU/7c2zEbHXium1dY3W7fRemi1bgB0rnb0He3qj4Igo92bnNhbbj39CNIpk/H0qp35tTdcRTbrrxgD9LeYa2nIoUzG0w9vsg6wtVwetyM5rl6DIn8hpaP/2wjyF1JyeZCaTLLWfk1b+GkQAAAAAAAAAAAAwGWpmegDAAAA4BZuEQoAAAAAwPbiRukAAAAAAAAAAACAg5joAwAAAAAAAAAAABzERB8AAAAAAAAAAADgICb6AAAAAAAAAAAAAAcx0QcAAAAAAAAAAAA4iIk+AAAAAAAAAAAAwEFM9AEAAAAAAAAAAAAOYqIPAAAAAAAAAAAAcBATfQAAAAAAAAAAAICDmOgDAAAAAAAAAAAAHMREHwAAAAAAAAAAAOAgJvoAAAAAAAAAAAAABzHRBwAAAAAAAAAAADiIiT4AAAAAAAAAAADAQUz0AQAAAAAAAAAAAA5iog8AAAAAAAAAAABwEBN9AAAAAAAAAAAAgIMy2x1ALWPMJyS9TdKEtfbHN1ve/HyoqWJZYWyV8Yz68oG6ulJ1ytgCruaBq3EniTpwtw5cjRvJcTkHKpVI5+bK8jyjchjLGMlaKYqtPM8o6xllM0ZRbNRXCCRJU7NlGVmVwlhhbJX1PXUHnmZLkYwx8o3UFXiamY8W1ntGGd9TFMfK+p5Ky47je0ZdWU9RbBVGVpXYKpfxFMdWmYxRJbTV42SMVAxjBb4nz0jzYayMZ7Qz7+l8Ma4+B7mMp9lypJ7A13wYK1osM4wXygp8Tz1dRjPzVuUolr94rhnPqBTFslbyPCmOV9ZFqVK/j+cZzZUjBb6nfGA0W4qrx6iNsb8QKAhW5kWlEmliplSNfaAnJ9/3dL5YVrEcKbJWXVlfuwo5eZ5ZsX8cW03NllUOIwUZX32FoOF2ANKlHX1Hu/qjdpTbqO3LZv1Nx+pSua633/PzoYpRKCtpvmJViRb6u4xnVAoX+slCzlOxvLAuu6xf7Mp4mq1E8o2R50k2lrKZxfFAbBXFVkHGU9Y3imOrSmSr9d+V9TRfidXT5WlmPq7WXxTF1ecp63vKZYzmK/GG6reV58f157ATuTxuR3JczQNX40ay2jXWaTfyF1JyeZC2zPmUpD+U9MBmC5qfD/XU1KwOnxjT+HRRg715HR0Z0jV9BV4wHcTVPHA17iRRB+7WgatxIzku50ClEum56TnFcaxSaPWHX31K73njVbrn1OPVczlyYJ927cjp69+b0E17L1cpjPV/PnZGt12/W3d95rHqdvfdfqNO/P3zeuSZKf3J//TPNDET19XJR95xvXbtCDQ5U254nKMjQ8pnPb33k4+qvyenX7t1rx568qUVxzlyYJ8+9OXTmpwp1f19dGRIX/z2uP7o756rxvPkP/1A1/7wK3XXZx6rlnn3yYvHPDYypD/4m+/pK09MXDzXnkCRtfrPX/neihg/9u4b5Hue7qw5ryMH9qmvJ9AfP/yszhfLet9bXlt33stj3NtfqJvsq1QiPTkxsyJ/Ln9FoKcnZuviPX5wWHsv31F3ATGOrU6/dEGHHhhddTsA6dKOvqNd/VE7ym3W9l070LOpC1Uulet6+z0/H2q6VFYlsnq5GK7oG2v7vo8u62uX1tWOHe7dv08Pn35J+4f3aGqmXNf/ffRdN6iQ8/ULnxqtq/+xZ89q6Kpdeui7L+nzY+P6szvfoLMzlab98Hrqt5Xnx/XnsBO5PG5HclzNA1fjRrLaNdZpN/IXUrJ5kKpbd1prH5Z0LomyporlagVJ0vh0UYdPjGmqWE6ieDjC1TxwNe4kUQfu1oGrcSM5LufAxExJ4+eK8j1fd33mMe0f2lOd2JIWzuXuk49r/FxRt1z3Kj0/NadDD4zqwPCrq5NvS9vd9ZnHdOim12h8uqiM56+ok/d/4TvKrHKcwyfGdOZcUePTRd1589W6++TjDY9z98nHdefNV6/4+/CJMR0YfnVdPG+8pr+6/1KZtWXdeWJM+4f21J/r9Lwynt8wxnOzleqFzNp9Xpye16GbXqP9Q3tWnPfyGCdn6/NiYqbUMH/KoV0R76EHRjW1bP+p2XL1AuNq2wFIl3b0He3qj9pRbrO2b2KmtKlYXSrX9fZ7qlhWGEnl0DbsG2v7vuV97dK62rHDPacW+v0Xp+dX9H/v++y39OL0/Ir6v+W6V+nwiTG9/cZBjU8XFUZatR9eT/228vy4/hx2IpfH7UiOq3ngatxIVrvGOu1G/kJKNg9SNdHXCmPMHSGgwR0AACAASURBVMaYUWPM6OTkZNPtwthWK2jJ+HRRYWzbHSJSJG15QP62jjpIXx2Qv2hVGnNgPfnbHfjyzELMO/PZhufSHfiydmHb8emifM803M5f/PR6ZBvXSby4fLXjSKqub3acnflsw7/9mk/Pj08XFdU8N82OubR/bQyeabz90vk3itv3zJrHaJQXzfInarK8HEZ1y8ph1NJ2Lmk1f4E02s7xQ7v6o06PtV3lprH9Xk/7G8ZWkbXVMUStZn11o3VLfffS36v1tcuX2cVxhbULz0Oz8Uft8Vqt31aenzQ+h52slfxN47gdWy+NeUD+olVpywOunWE9kswD5yb6rLX3W2uHrbXD/f39TbfLeEaDvfm6ZYO9eWW4XURHSVsekL+tow7SVwfkL1qVxhxYT/7OlSPFdiHm88VKw3OZKy/89t5cOdJgb15RbBtuFy0OznzTuE68xeWrHUdSdX2z45wvVhr+HdUMDgd78/Jrnptmx1zavzaG2Dbefun8G8UdxXbNYzTKi2b54zdZHmTqL3QGGb+l7VzSav4CabSd44d29UedHmu7yk1j+72e9jfjGfnGVMcQtZr11Y3WLfXdS3+v1tcuX2YWxxXGLDwPzcYftcdrtX5beX7S+Bx2slbyN43jdmy9NOYB+YtWpS0PuHaG9UgyD5yb6GtVXz7Q0ZGhakUt3d+0Lx9sc2TYSq7mgatxJ4k6cLcOXI0byXE5BwZ6chq8LK8ojnTf7Tfq1NgZ3bt/X925HDmwT4OX5fXVJ76vK/q6dfzgsE6OvqD7br+xbrv7br9Rxx9+RoO9eYVxtKJOPvKO6xWucpyjI0Pac1leg715HXvoaR05sK/hcY4c2KdjDz294u+jI0M6OfpCXTyPPDVZ3X+pzNqyjo0M6dTYmfpz7e1SGEcNY7yskNWxZed15MA+7e7t0vGHn9GpsTMrznt5jP2F+rwY6Mk1zJ8gY1bEe/zgsPqW7d9XCHT84PCa2wFIl3b0He3qj9pRbrO2b6Ant6lYXSrX9fa7Lx8o40tBxjTsG2v7vuV97dK62rHDvfsX+v3dvV0r+r+PvusG7e7tWlH/X33i+zo6MqS/eGx84SKRr1X74fXUbyvPj+vPYSdyedyO5LiaB67GjWS1a6zTbuQvpGTzwCzd0iEtjDFXSvqitfbH19p2eHjYjo6ONl0/Px8u3Cc/tsp4Rn35gB+z7EDrzIMt+9gE+bs26oD8hbs2kAOpyd9KJdK5ubI8z6gcxjJGslaKYivPM8p6RtmMURSb6oWrqdmyjKxKYawotsr4nroDT7OlhW/++UbqCjzNzEcKY6usZ5TxPUVxrKzvqVR7HGvlG6OurKcotgojq0pslct4imOrTMaoEtqFcnxPGSMVw1iB78kz0nwYK+MZ7cx7Ol+Mq89BLuNpthypJ/A1H8aKrFXO9xZuNbZYVk+X0cy8VSWKq+ea8YxKUSxrJc+T4nhlXZQq9ft4nlGxHCnre8oHRrOlhTiWx9hfCBQEK/OiUok0MVOqxj7Qk5PvezpfLKtYjhRZqSvraVchJ6/BJ93i2GpqtqxyGCnI+OorBA23S1Bq8nfJlR94cN1lP/fB29a9z1YdB22Vmvxtx/ihXWOSdpTbqO3LZjf/TSiXyt1A+52a/JUW8qIYhbKS5itWYRTLr+lLfWNUyHkqlhf6zeyyfrEr42muEskzRp4n2VjKZhbHA7FVHFtlM56yvlFsbXU8kPEWxg3zlVg9XZ5m5uNq/UVRXH2esr6nXMZovhJvqH9s5fnZhj7YZanIX967QXL3/Rv5C2lDY5ItyV+unaEVSbW/qcocY8xnJd0saZcxZlzSb1prP77R8rq6MtrNi6PjuZoHrsadJOrA3TpwNW4kx+UcyGZ9Xf7K/Nob1ujf0fjTgju76/+/zmI3rdBV//9dLeyzo2vtbdZreT2sJZv1tbt35U6XFXJSYe39Pc80fU4ApFc7+o529UftKLdZ29dJ5brefnd1ZdTVwmWW1frFvgTiqB1veF5yz1Mrz4/rz2EncnncjuS4mgeuxo1ktWus027kL6Tk8iBVmWStfdd2xwAAAAAAAAAAAAC44JL9jT4AAAAAAAAAAADgUsZEHwAAAAAAAAAAAOAgJvoAAAAAAAAAAAAABzHRBwAAAAAAAAAAADiIiT4AAAAAAAAAAADAQUz0AQAAAAAAAAAAAA5iog8AAAAAAAAAAABwEBN9AAAAAAAAAAAAgIOY6AMAAAAAAAAAAAAcxEQfAAAAAAAAAAAA4CAm+gAAAAAAAAAAAAAHMdEHAAAAAAAAAAAAOIiJPgAAAAAAAAAAAMBBTPQBAAAAAAAAAAAADmKiDwAAAAAAAAAAAHAQE30AAAAAAAAAAACAg5joAwAAAAAAAAAAABzERB8AAAAAAAAAAADgoMx2B1DLGHOrpN+X5Ev6Y2vtBzdT3vx8qKliWWFslfGM+vKBurpSdcrYAq7mgatxJ4k6cLcOXI0byXE5B8Iw1uRMSeUolu8ZBZ5RLms0U4pljGTtwiPrG4WxlTGSZBRbK98YeUYqRbGynifPSLmsp7lyrEoUK+t7yhjJzxjNl2OFsdWOLl/Fxb8zntEr8p6KZS0c30ixVXVdV8bTTDlSxjPKB56sleYrF/fNB57mK7GslbzFuJbOoyvjqRxZhXEs35jquSzFWBtDV3ahnJ6cpwulWHFs5XtGnid5WjjvMLYKfE8Z38j3pGL5Yv1EsZXnGWW9hXqJreR5km+8hX2jWLmMV60/a6XunKe50sUYdnR5mplf+L/vLcTrG7NYH7G8mnPIB56i2KivEMjzjOLY6nyxrGI5qpaX9T1d1r2wfmq2rHIYKcj41X1qxbFdcxsAyWtH39Gu/qgd5bar7WlXuWEYa2KmVO3fBnpyymQ687PES/nwyrynubJUiS72X/msp1JoVYliZTyjnpynmVIsz5PiWIpqxg+xlXKLdVgK6/vlUiVWV+BJi/2wXeyLo9gqs1j/nmd0dqakYiWq9v2SNB/GimKrrO8pnzWaK8ct5UK5HGpytj7Pz81X6p5zSeTBokol0sRMqVpfAz05ZbP+doe1JpfH7UiOq3ngatxIlqt54GrcSFZSeZCazDHG+JI+JumnJI1LetQY85fW2ic2Ut78fKinpmZ1+MSYxqeLGuzN6+jIkK7pK/CC6SCu5oGrcSeJOnC3DlyNG8lxOQfCMNaTL13QnTWxHzmwT7t25HTy0Rd0097Ldc+px9Xfk9Ov3bpXn/z6s3rPG6/SPacer9v+Q18+rcmZkj753mHNX4h1+DOPVdd/9F03qKcro5//5KN659Cgbv7Ry6t19dPXDejuW6/V2QulhmXfd/uNOvH3z+uRZ6Z0dGRIfT1ZvfP+b9TVc0/O0+9+6UndcdPV+pXPf7tu3z/86lP6yhMTGuzN6979+/TpR57V3W/dq7OzdsXz9dzky7qy/xV1y//w3TeoEsZ6/xe+U132sXffqO6cryNffrJhXeQDX/f97T/qrjf/SHXf5fV3bqa44lhHR4b00b/5XjXeRsdeOof3veW1Gnv2rH7i6n5d09+jF6bn9NLL87r75MVYPvKO6/XyfEWeMTr4iW9Wlx8/OKy9l++oXuiMY6vTL13QoQdGm24DIHnt6Dva1R+1o9x2tT3tKrdRf3lsZEjXXr6j4yZ5lvKhkJX+v4qn6dlyta/66esG9Eu3XKO7asYBR0eGND1TlO9n6vrMe/fv08OnX9K733CFflAMV+wz9uxZDV+1S11Zow99+fSKPvfYyJC6s54OfvLRi2OKt+7V2ZlyXX949PYb9SeLY4nVcqFcDnV68mKe/+J/e6Xe9rrBurw/NjKkrqyn9y4es5PzoFKJ9OTEzIp24dqBnlRP9rk8bkdyXM0DV+NGslzNA1fjRrKSzIM0jbxeL+kfrbXPWGvLkj4n6e0bLWyqWK5WkCSNTxd1+MSYporlZKKFE1zNA1fjThJ14G4duBo3kuNyDkzMlKoXLaWF2O8++bjGzxV1YPjV1Qtqd958te4++bj2D+2pLqvd/s6br9b4dFHGeNVJvqX17/vstzR+rqjx6aLefuNgXV3tH9qj8XPFpmXf9ZnHdOim11TrNIq0op4znq/9Q3uqk3y1++4f2lP9/z2nFo5hjNfw+brhir4Vy6dnK9WLl0vL/s2fPqbxc8WmdTE9W9H+oT11+y6vv0bHOnxirC7eRsdeOofDJ8Z0y3Wv0qEHRjUxU9LzU3PVi5pL277/C9/RmXNFPT81V7f80AOjmpq9mJtTs+XqBfFm2wBIXjv6jnb1R22JtU1tT7vKbdRf3nliTBMzpU2V66KlfMhlszpzrljXV+0f2lOdsJMu5srVA69Y0Wfec+pxHRh+tSqRGu5zy3Wv0p0nxiR5DfvcO0+M6fnF8cXSscen51f0h4drxhKr5cLkbH2eHxh+9Yq8v/PEmM7UHLOT82BiptSwXUh7Xbg8bkdyXM0DV+NGslzNA1fjRrKSzIM0TfTtlnSm5v/ji8vqGGPuMMaMGmNGJycnmxYWxrZaQdUCp4sKY5tQuHBB2vKA/G0ddZC+OiB/0ao05kCr+VuJ4oaxdwe+fM9U1+3MZzU+Xaz+u3z7nfmspIXbYjYrT5JiW19XO/NZdQf+qmX7i5+6H58uKrZ2xfrY2jXjqv1/sxijBs/jUmyNzqfZMZfW1e67vP4aHWt5vM2OvVSGXazLShSvGudS3dcuL4dR9f/lMGq4b+02W63V/AXSaDvHD+3qj9pRbrvannaV26y/DKN4U+WmTSv5u5QPYWxX9D/N+sZmOeR7pmm/vNTPeaZ5ubV93PK+d/lxlv5ulgvLY6wdBzU7ZvX8LrE8aIWr4980xo2tl8Y8IH/RqrTlAdfOsB5J5kGaJvoa3TdkxRlZa++31g5ba4f7+/ubFpbxjAZ783XLBnvzynDLpY6Stjwgf1tHHaSvDshftCqNOdBq/mZ9r2Hsc+VIUWyr684XKxrszVf/Xb79+WJF0sJv7TQrT5I8U19X54sVzZWjVcuOFgd8g715ecasWO8Zs2Zctf9vFqPf4Hlciq3R+TQ75tK62n2X11+jYy2Pt9mxl8owi3WZ9b1V41yq+9rlQebiRcog4zfct3abrdZq/gJptJ3jh3b1R+0ot11tT7vKbdZfZvw0XWLYvFbydykfMp5Z0f806xub5VAU26b98lI/F9vm5db2ccv73uXHWfq7WS4sj7F2HNTsmNXzu8TyoBWujn/TGDe2XhrzgPxFq9KWB1w7w3okmQdpGn2NS9pT8/9BSf+00cL68oGOjgxVK2rp/qZ9+WBzUcIpruaBq3EniTpwtw5cjRvJcTkHBnpyOrYs9iMH9mnwsrxOjr6ge/fvW/j9mYee1pED+3Rq7Ex1We32xx56WoO9eVkb6+jtN9at/+i7btDgZXkN9ub1F4+N19XVqbEzGrws37Ts+26/UccffqZap76vFfUcxpFOjZ3R773zdSv2PTV2pvr/e/cvHMPauOHz9a3np1Ys7y1k9ZF3XF+37GPvvlGDl+Wb1kVvIatTY2fq9l1ef42OdXRkqC7eRsdeOoejI0P66hPf1/GDwxroyemKvu6F561m24+843rtuSyvK/q665YfPzisvsLF3OwrBDp+cHjVbbC1rvzAg+t+wD3t6Dva1R+1JdY2tT3tKrdRf3lsZEgDPblNleuipXwoVSrac1m+rq86NXZG9y0bBxwdGdLTEy+v6DPv3b9PJ0dfUNZXw32++sT3dWxkSFLcsM89NjKkKxbHF0vHHuztWtEfHq0ZS6yWC/2F+jw/OfrCirw/NjKkPTXH7OQ8GOjJNWwX0l4XLo/bkRxX88DVuJEsV/PA1biRrCTzwFibjq+DGmMykr4n6S2SXpT0qKR3W2v/a7N9hoeH7ejoaNMy5+dDTRXLCmOrjGfUlw/4McsOtM482LKPTZC/a6MOyF+4awM5kJr8DcNYkzMllaNYvmcUeEa5rNFMKZYxkrULj6xvFMZWnpH+f/buNjiu677z/O+ce7sbDYASQRBQbIKObK8eospSNoFkdpNdl0euSXlGrs1MkWtvLIoZz65s0bGdpGYVu+bN5E2qrKhS9pQTkTKTOJapPHjEzU42ymSdxONxEmc9BhRbmVIkP8iyCccjgBBlEWCjb997z75odBMP3UCjebtxD/r7qUIB3X0f/vfc/z3/0326G05GzjlZU//KrWqSqmCtrJFKBatrUao4SRUGVqGRgtBoNUoVp04HhgJV1v4OrdFNZatKpPr+Tf1TgUnqFFijodBqOUoUWqNy0co5abV2fd1y0apaS5W6+teGSka1JJVdWzdKnJI0bcaZOslaqRTaDTEMFaxWa6lGS1ZXq6nStf1bK1nVjztJnQqBVRgYBVaqRNfbJ0mdrDUqWKPUueZ+AmMVp05xmqoU1P9urDNcsrpWvR7DgSGr5dX67cZXmVljlDo1272xbrlolaRG4yNFWWuUpk6vVCJV1j6JGVijQmB1aLj++NJKpChOVAyD5jrrpanbcZlNcpO/Dd1Mdr340Xt3vU4/9tOvYxlgucnfXowfejUm6cV2u+h79nS7cZxqYbnarG+ToyWFYd/fS5yL/G3kw81lq2tR/atNG7WwXLCqxk61JFVojUZLVsvVVNZKaSolzilYV5dLa21YjTfW5Wot1VDRSmt12KWuWY8b7W+t0eXlqlZrSbP2S9JqnDbrdrlgdC1KO8qFKIq1uLIxz19erW0455LykAe5UKsl9bZYa6/J0ZIKhW0/PZur/OW522Dz9fkb+Qspv/nLa2foRFb5m5vMcc7FxpgPSPp/JQWSfnu7Sb5ODA2FOsLFMfB8zQNf484SbeBvG/gaN7Ljcw6EodVrDpa33H/zcPfbPNhq3ZE2f0s6MNR+W+Pdh7G9kdZ3d3zcbdbfjbFN+7pp62noiLVGh0ZKbWOaOLD9u+utNTsuAyB7vagdvapHvdhur/qeXm03DK1e26JeDqL1+TC6TQ1vuJExxU4mb9o5gLEOa3axGOpIcWOev7ZF3pMHdYVCoCObBzMe8Hncjuz4mge+xo1s+ZoHvsaNbGWVB7nKJOfcn0j6k72OAwAAAAB6gU8oAgAAAACylKuJPgAAAADYC0zAAQAAAAB8NJhfnA4AAAAAAAAAAAB4jok+AAAAAAAAAAAAwEN8dScAAAAA5BhfKwoAAAAAaMc45/Y6hq4ZYxYlfaeDRQ9LutzjcPKONuisDS47597ej2DI312hDchfnw16G3R6/HnLXx/OW95jzHt8UnYx5i1/JT/av1P76Vik/B0P+XtjiLU3GD/sPY6l98jf/KEN6H99RhvkLH/36di3V2iDG8xfryf6OmWMmXXOzex1HHuJNvC3DXyNO0u0gb9t4GvcWRr0NvD1+H2IO+8x5j0+yY8Yu7Wfjm0/HYu0/46nF3xqI2LtDZ9iXc/XuFvhWAYP7UQbSP62ga9xZ4k28LcNfI07S7TBjbcB/6MPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHBmWi75N7HUAO0Ab+toGvcWeJNvC3DXyNO0uD3ga+Hr8Pcec9xrzHJ/kRY7f207Htp2OR9t/x9IJPbUSsveFTrOv5GncrHMvgoZ1oA8nfNvA17izRBv62ga9xZ4k2uME2GIj/0QcAAAAAAAAAAADsN4PyiT4AAAAAAAAAAABgX2GiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAh5joAwAAAAAAAAAAADzERB8AAAAAAAAAAADgISb6AAAAAAAAAAAAAA8x0QcAAAAAAAAAAAB4iIk+AAAAAAAAAAAAwENM9AEAAAAAAAAAAAAeYqIPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAADzHRBwAAAAAAAAAAAHjI64m+t7/97U4SP/xk+dM35C8/PfjpG/KXnx789A35y08PfvqG/OWnBz99Q/7y04OfviF/+enBT9+Qv/z04KdvyF9+evDTF+QuPz36acnrib7Lly/vdQhA18hf+Iz8hc/IX/iM/IXPyF/4jPyFz8hf+Iz8ha/IXfST1xN9AAAAAAAAAAAAwKBiog8AAAAAAAAAAADwEBN9AAAAAAAAAAAAgIeY6AMAAAAAAAAAAAA8xEQfAAAAAAAAAAAA4KFwrwNYzxhzUNJvSvpRSU7Sv3LO/U2320tTp6WVSFGcqBgGGh8pylqTVbgA0HO+9mO+xg2gNa5pbEZOoJdu/chTu1r+xY/e26NIgN6gD0WekI/wGfkLwHdZ9WO5muiT9O8k/alz7qQxpihpuNsNpanT8y9d1QOPz2r+SkVTY2WdPz2jO245QIcPwAu+9mO+xg2gNa5pbEZOAED36EORJ+QjfEb+AvBdlv1Ybr660xhzk6S3SPotSXLORc65V7rd3tJK1GwgSZq/UtEDj89qaSXKJF4A6DVf+zFf4wbQGtc0NiMnAKB79KHIE/IRPiN/Afguy34sNxN9kt4gaVHSp4wxf2uM+U1jzMjmhYwx7zXGzBpjZhcXF9tuLIqTZgM1zF+pKIqTrOMGOtZp/gJS/vox+l/4jP63e1zTey9v+UtOYDfylr/AbvQif+lD0S+d5C/5iLwif+Erxr7YjSz7sTxN9IWSjks665x7s6QVSR/ZvJBz7pPOuRnn3MzExETbjRXDQFNj5Q33TY2VVQyDbKMGdqHT/AWk/PVj9L/wGf1v97im917e8pecwG7kLX+B3ehF/tKHol86yV/yEXlF/sJXjH2xG1n2Y3ma6JuXNO+c+/La7SdVn/jryvhIUedPzzQbqvH9puMjxRuPFAD6wNd+zNe4AbTGNY3NyAkA6B59KPKEfITPyF8AvsuyHwuzDq5bzrn/Zoy5ZIy5wzn3vKS3SXq22+1Za3THLQf0h+//SUVxomIYaHykyD9jBeANX/sxX+MG0BrXNDYjJwCge/ShyBPyET4jfwH4Lst+LDcTfWs+KOkJY0xR0guS3nMjG7PWaOJAKZPAAGAv+NqP+Ro3gNa4prEZOQEA3aMPRZ6Qj/AZ+QvAd1n1Y7ma6HPOfVXSzF7HAQAAAAAAAAAAAORdnv5HHwAAAAAAAAAAAIAOMdEHAAAAAAAAAAAAeIiJPgAAAAAAAAAAAMBDTPQBAAAAAAAAAAAAHmKiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAh5joAwAAAAAAAAAAADzERB8AAAAAAAAAAADgISb6AAAAAAAAAAAAAA8x0QcAAAAAAAAAAAB4iIk+AAAAAAAAAAAAwENM9AEAAAAAAAAAAAAeYqIPAAAAAAAAAAAA8FC41wFsZox5UdJVSYmk2Dk3s7cRAQAAAAAAAAAAAPmTu4m+Nf/YOXf5RjcSRbEWVyLFqVNojSZGiioW83rI6JU0dVpaiRTFiYphoPGRoqw1ex0W0JFqNdbla9f7scPDRZVK+e/H6H9B3+uXxvkKrFMlSpvX7uhQoOVVzqFP4jjVwnJVtSRVIbCaHC0pDLP/Eo9aLdHCcrWZK5OjJRUKQeb7AYD9YPPYuFy0SlJDbUUu+PqcE5B47QF1vr7+4GvcyFZW/di+7fmiKNbziys6c2FO81cqmhor6+ypad0xMUKHP0DS1On5l67qgcdnm3lw/vSM7rjlAB0ncq9ajfX1y1v7sdsPj+T6iRf9L+h7/dI4X1/+1qKmX394y7X7hb9/SX8wN8859EAcp3rupat6cN05PHdqWnfeciDTyb5aLdFzC8tbcuXOyVEm+wBgk3Zj47lvX9Y/euMEtRV7ytfnnIDEaw+o8/X1B1/jRray7Mfy+D/6nKTPGWPmjDHv7XYjiytRs4Ekaf5KRWcuzGlxJcoqTnhgaSVqdphSPQ8eeHxWS+QBPHD5Wut+7PK1fOcv/S/oe/3SOF/33PWaltfuTx+f4hx6YmG52pzkk+rn8MELc1pYrma+n1a5kvV+AGA/aDc2vueu11Bbsed8fc4JSLz2gDpfX3/wNW5kK8t+LI8TfT/pnDsu6Z9K+jljzFvWP2iMea8xZtYYM7u4uNh2I3Hqmg3UMH+lojh1vYgZORXFScs8iOJkT+LpNH8BKX/9GP0vOpW3vlei/91O43ylrvW165xr/r2X53CQdZq/tSRt3f8maabx0M9jN+h/4bMs8rddn+nW6i61Fb3SSf5S05FX5C86lbfXHzodO+QtbuyNLPux3E30Oef+Ye33gqQ/lPTjmx7/pHNuxjk3MzEx0XY7oTWaGitvuG9qrKyQj74OlGIYtMyDYrg3XyvVaf4CUv76MfpfdCpvfa9E/7udxvmypvW1a4xp/r2X53CQdZq/hcC27n+DbIf89PPYDfpf+CyL/G3XZ5q1ukttRa90kr/UdOQV+YtO5e31h07HDnmLG3sjy34sVxN9xpgRY8yBxt+SfkrSf+1mWxMjRZ09Nd1sqMb3m06MFDOLF/k3PlLU+dMzG/Lg/OkZjZMH8MDh4db92OHhfOcv/S/oe/3SOF+ff/b7La/d//D0POfQE5OjJZ3bdA7PnZrW5Ggp8/20ypWs9wMA+0G7sfHnn/0+tRV7ztfnnIDEaw+o8/X1B1/jRray7MdM4+uY8sAY8wbVP8UnSaGk33XO/Uq75WdmZtzs7Gzb7UVRrMWVSHHqFFqjiZEi/4x1AKWp09JKpChOVAwDjY8Ut/unpn17289O+QtI9X+Ofvna9X7s8HBxu3+Knpv8pf/FLvteKUf5O4ga5yuwTpUobV67o0OBllc7PoeDLDf5G8epFparipNUYWA1OVpSGGb/3r5aLanvZy1XJkdLKhR496mncpO/Dbd+5KldbffFj97bbUjwX+7yt5XNY+Ny0SpJDbUVucjfXT7nBBpykb+89gApv68/7DR26CJu7ENd9GMtkyRXPZ9z7gVJd2e1vWIx1BE694FnrdHEAd5hDj+VSqGOePgki/4X9L1+rFfgMQAAIABJREFU2XC+RjY+dnN56/LIrzC0eu3B3p+0QiHQkbHhnu8HAPYDxsbIM1+fcwIS/SvqfH39wde4ka2s+rFcfXUnAAAAAAAAAAAAgM4w0QcAAAAAAAAAAAB4iIk+AAAAAAAAAAAAwENM9AEAAAAAAAAAAAAeYqIPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAADzHRBwAAAAAAAAAAAHiIiT4AAAAAAAAAAADAQ0z0AQAAAAAAAAAAAB5iog8AAAAAAAAAAADwEBN9AAAAAAAAAAAAgIfCvQ5gM2NMIGlW0vecc++4kW1FUazFlUhx6hRao4mRoorF3B0y0FKaOi2tRIriRMUw0PhIUdaavQ4LfVarJVpYrjb7scnRkgqFYK/D2lEcp1pYrqqWpCoEVpOjJYUh7y0ZJORA/2VVN3bazl7WJ2rjzvrVRr7WJwDop0afXI0TGUnGSJLZ1+MiarV/eO0MPiN/Afguq34sjz3fz0v6e0k33chGoijW84srOnNhTvNXKpoaK+vsqWndMTFCh4/cS1On51+6qgcen23m7/nTM7rjlgM8SRogtVqi5xaWt/Rjd06O5vrF1DhO9dxLV/XgurjPnZrWnbcc2LcvaGAjcqD/sqobO21nL+sTtXFn/WojX+sTAPRTqz754RPH9OkvfVsfetvt+3JcRK32D6+dwWfkLwDfZdmP5WpUaYyZknSvpN+80W0trkTNBpKk+SsVnbkwp8WV6EY3DfTc0krUfHIk1fP3gcdntUT+DpSF5WrLfmxhubrHkW1vYbnanOCR6nE/6EHcyA450H9Z1Y2dtrOX9YnauLN+tZGv9QkA+qlVn/zhi8/oxPTRfTsuolb7h9fO4DPyF4DvsuzHcjXRJ+njkn5JUtpuAWPMe40xs8aY2cXFxbYbilPXbKCG+SsVxanLKlZg1zrN3yhOWuZvFCe9DhE5krd+rNP8rSVp67iTtl079pk85kCn+eurrOrGTtvZy/o0yLUxb+OHvNUn5Nt+73+xv91I/rbrkw+WC3s+LuqVQa7VedRJ/lLTkVfkL3zF2Be7kWU/lpuJPmPMOyQtOOfmtlvOOfdJ59yMc25mYmKi7XKhNZoaK2+4b2qsrJCvi8Ae6jR/i2HQMn+LIV+HNUjy1o91mr+FwLaOO8hNyUGP5TEHOs1fX2VVN3bazl7Wp0GujXkbP+StPiHf9nv/i/3tRvK3XZ/8SqW25+OiXhnkWp1HneQvNR15Rf7CV4x9sRtZ9mN5Gln+pKT/xRjzoqTfl3SPMeZCtxubGCnq7KnpZkM1vt90YqSYSbBAL42PFHX+9MyG/D1/ekbj5O9AmRwttezHJkdLexzZ9iZHSzq3Ke5zHsSN7JAD/ZdV3dhpO3tZn6iNO+tXG/lanwCgn1r1yQ+fOKaLc5f27biIWu0fXjuDz8hfAL7Lsh8zzuXv48zGmLdK+j+dc+/YbrmZmRk3Ozvb9vEoirW4EilOnUJrNDFS5J+xYid9e9vPTvmbpk5LK5GiOFExDDQ+UuQfmA+gWi3RwnK12Y9NjpZUKLR9R2xu8jeO03rcSaowsJocLSkM8/TeEvRaFzmQm/z1VVZ1Y6ft7GV9ynFtzE3+9quNdlmfkG+5yd+GWz/y1K62++JH7+02JPgvd/m73vo+WZKskZzMvh4b57hW51Eu8pfXztAl8hc+60v+7tfXHpCtLvqxlvm7r3u+YjHUETp3eMpao4kD++9dntidQiHQkbHhvQ5j18LQ6rUHyzsviH2LHOi/rOrGTtvZy/pEbdxZv9rI1/oEAP00iHVrEI/Zd7x2Bp+RvwB8l1U/lsue0Dn3BUlf2OMwAAAAAAAAAAAAgNzan98VAQAAAAAAAAAAAOxzTPQBAAAAAAAAAAAAHmKiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAh5joAwAAAAAAAAAAADzERB8AAAAAAAAAAADgobCXGzfGHJH0w+v345z7Yi/3CQAAAAAAAAAAAAyCnk30GWMelvQuSc9KStbudpKY6AMAAAAAAAAAAABuUC8/0ffPJd3hnKv2cB8AAAAAAAAAAADAQOrl/+h7QVKhh9sHAAAAAAAAAAAABlbmn+gzxnxC9a/ovCbpq8aYv5DU/FSfc+5DWe8TAAAAAAAAAAAAGDS9+OrO2bXfc5L+qAfbBwAAAAAAAAAAAAZe5hN9zrlPS5IxZkTSqnMuWbsdSCplvT8AAAAAAAAAAABgEPXyf/T9haTyuttlSX/ew/0BAAAAAAAAAAAAA6MXX93ZMOScW27ccM4tG2OGt1vBGDMk6Yuqf/IvlPSkc+7fdhtAHKdaWK6qlqQqBFaToyWFYS/nNpFHtVqiheWq4tQptEaToyUVCsFeh4UOcA37m79RFGtxJWrGPTFSVLHYy5KDvElTp6WVSFGcqBgGGh8pylqz12HlRjftk+c+sdPjIS96r19t3I/6RL4A8Fkcp1pcripKUgXWaKRotVpzinJYxzHYeO4Gyd9xV7Ua6/K16/l7eLioUon8hR98ve6QrazqcC97vhVjzHHn3NOSZIyZllTZYZ2qpHvWJgULkv7KGPMfnXP/3253Hsepnnvpqh68MKf5KxVNjZV17tS07rzlAIPpAVKrJXpuYVln1uXB2VPTunNy1IvJkkHGNexv/kZRrOcXV7bEfcfECE8YB0SaOj3/0lU98PhsMwfOn57RHbccYNCq7tonz31ip8dDXvRev9q4H/WJfAHgs1Z1+9H7juvXP/8Nfe7ZhVzVcQw2nrtB8nfcVa3G+vrlrfl7++ERJvuQe75ed8hWlnW4lyPKn5f0740xf2mM+UtJfyDpA9ut4OoanwIsrP24bna+sFxtDqolaf5KRQ9emNPCcrWbzcFTC8vV5oUi1fPgDHngBa5hf/N3cSVqGffiSrTHkaFfllai5mBVqufAA4/PaokckNRd++S5T+z0eMiL3utXG/ejPpEvAHzWqm6//4mndWL6aPN2Xuo4BhvP3SD5O+66fK11/l6+lu+4Acnf6w7ZyrIO92SizxhjJRUl3SnpjKT3S/oR59xcB+sGxpivSlqQ9GfOuS9vevy9xphZY8zs4uJi2+3UkrTZQA3zVyqKk3TXxwN/xalrnQdpV/PHN6zT/AXXsORv/uYtbvRfFCctcyCKkz2KKF/9bzftk+c+sdPjyWNe+KLT/O1XG/ejnydf9o889b/AbnWbv+3q9sFyYcPtPNRx7F+d5C/P3SDlc9xF/sJXeXvuhnzLsh/ryUSfcy6V9GvOuZpz7r865/7OOVfrcN3EOfcmSVOSftwY86ObHv+kc27GOTczMTHRdjuFwGpqrLzhvqmxssKAr8UYJKE1rfNgjz4C3Wn+gmtY8jd/8xY3+q8YBi1zoBju3VfO5qn/7aZ98twndno8ecwLX3Sav/1q43708+TL/pGn/hfYrW7zt13dfqVS23A7D3Uc+1cn+ctzN0j5HHeRv/BV3p67Id+y7Md6Oar8nDHmhDGmq97VOfeKpC9Iens360+OlnTu1HSzoRrfgT85Wupmc/DU5GhJZzflwVnywAtcw/7m78RIsWXcEyPFPY4M/TI+UtT50zMbcuD86RmNkwOSumufPPeJnR4PedF7/WrjftQn8gWAz1rV7UfvO66Lc5eat/NSxzHYeO4Gyd9x1+Hh1vl7eDjfcQOSv9cdspVlHTbO9ebjzMaYq5JGJMWSViUZ1f8N303brDMhqeace8UYU5b0OUkPO+f+uNXyMzMzbnZ2tm0McZxqYbmqOEkVBlaToyX+0fUAqtWSeh6kTqE1mhwtqVBo++6Ivr3tZ6f8Bdew5G/+RlGsxZWoGffESJF/5j5g0tRpaSVSFCcqhoHGR4o7/UPp3ORvP3TRPrnuEzs9nm6O2xO5yd9+tfEu61NX9nG+5E1u8rfh1o88tavtvvjRe7sNCf7LXf42xHGqxeWqakkqa41GilarNadaDus49kwu8pfnbpD8ff5Wrca6fO16/h4eLqpUIn+xo77kb16euyHfuqjDLZOkZz2fc+5AF6u9RtKnjTGB6p82/Gy7Sb5OhKHVaw+Wd14Q+1qhEOjI2PBeh4EucA37m7/FYqgjPDkcaNYaTRzgXertdNM+ee4TOz0e8qL3+tXG/ahP5AsAn4Wh1WtyWreB9XjuBsnfcVepFOoIE3vwlK/XHbKVVR3uaU9ojBmTdJukocZ9zrkvtlveOfeMpDf3MiYAAAAAAAAAAABgP+jZRJ8x5v+Q9POSpiR9VdL/IOlvJN3Tq30CAAAAAAAAAAAAg6KXXwj/85J+TNJ3nHP/WPVP6i32cH8AAAAAAAAAAADAwOjlRN+qc25VkowxJefcc5Lu6OH+AAAAAAAAAAAAgIHRy//RN2+MOSjp/5b0Z8aYK5L+oYf7AwAAAAAAAAAAAAZGzyb6nHP/Yu3PXzbG/CdJN0v6017tDwAAAAAAAAAAABgkmU/0GWOGJD0o6b+T9HeSfss595+z3g8AAAAAAAAAAAAwyHrxP/o+LWlG9Um+fyrp13qwDwAAAAAAAAAAAGCg9eKrO+9yzv33kmSM+S1J/6UH+wAAAAAAAAAAAAAGWi8+0Vdr/OGci3uwfQAAAAAAAAAAAGDg9eITfXcbY15d+9tIKq/dNpKcc+6mHuwTAAAAAAAAAAAAGCiZT/Q554KstwkAAAAAAAAAAABgo158dScAAAAAAAAAAACAHmOiDwAAAAAAAAAAAPBQL/5HX1eMMUclPS7phySlkj7pnPt3N7LN1dVYS5VIceoUWqPxclFDQ7k5ZPSJr3nga9xZog38bQNf40Z2fM6BOE51eaWqQmC0WkubxzBUsIoTp8RJcZIqDKwCI6Vycs4oTZ2sNQqMZIxUS1xz3cnRkowxWliuqpakKhfq33QeJakCY2SNtBqnzf0kqZSkTpJT6up/FwIra6TEOTlX/x0aozCwqtQSFQKr4aLVSjVRMQw0Vi7oSqWmKK4/Jqm53ORoSUmS6vK16+fo8HBRpVL9HKWp0yuVSJUoUeKchgqBDo+UZK1p2WZp6rS0EimK6/seHym2XbadLLaB/l17/dhPFMVaXLm+j4mRoopFP4+ln/upVuO21zaA3mrUz2qcKlqr66XQykmK4vqYYii0SiW5Rj1PnUphoEPDBS2uRKol9fVGh6yuVZ2iJG3W7jC8/l7tOE61sFxVnKQKrJExknPa0lfWakl9ubWxRCmsj286qbXd1Gb6IP/4PG5HdnzNA1/jBiTyF3VZ5UGeMieW9K+dc08bYw5ImjPG/Jlz7tluNra6GusbSys6c2FO81cqmhor6+ypad02PsIFM0B8zQNf484SbeBvG/gaN7Ljcw7EcarnF67qKy9c1vTrD284hnOnpmWt9N7Hr9/3sXferUJo9YHf/dvmfY+dOq7USWeeeHrD8d9cDvXu81/WxGhJv/T2O/TQk880H3/k5DH96p8+r8Xlqj71nh/T8mqss1/4pn72J16vD198Ztv9rV/30fuO68LffEevVCJ96G2368F18a9f7rH7p1UMrd7zqa9siPH2wyMqFAK9uLSil15d3RDj+dMzuuOWA1te4EtTp+dfuqoHHp/dcdl2stgG+nft9WM/URTr+cWt+7hjYiTTyb791GZS/QX2r1/eup/bD4/wQjvQY2nq9OLSiq6uxvq5362PAX7qrkn9m3t/RK9WYr3/iaebY4BP/fW3t9T4x+6f1r/786/rc88uNK/dP/7qvB77yxeb45A7bzmgMLSK41TPvXR1Q51/+MQxffpL39YH33Z7s6+s1RI9t7C8oU9YPx7YrtZ2U5vpg/zj87gd2fE1D3yNG5DIX9RlmQe5+epO59z3nXNPr/19VdLfSzrS7faWKlGzgSRp/kpFZy7MaakSZRIv/OBrHvgad5ZoA3/bwNe4kR2fc2Bhuar3fWZO99z1mi3H8OCFOb30g+qG+37xs1/TlZXahvsWrkbNSb7GfWcuzCmKXX07b31jcwKt8fhDTz6jB9/6Rs1fqWj+5Yo++Ht/qxPTR5svAG63v/Xrvv+Jp/XAW96gE9NHmy/+tVrufZ+Z0/zLlS0xXr4WaWkl0neWrm2J8YHHZ7W0svUcLq1EzRcBd1q2nSy2gf5de/3Yz+JK630sZpwT+6nNJOnytdb7uXyNawnotUb9bEzySdKJ6aOKE+n9a+OCxhigVY1/32fmdGL6aPP2mQtzOjnzuubtBy/MaWG5Kqk+Xtlc5z98sb7d9X3lwnJ1S5+wfjywXa3tpjbTB/nH53E7suNrHvgaNyCRv6jLMg9yM9G3njHmVklvlvTlFo+91xgza4yZXVxcbLuNOHXNBmqYv1JRnLpsg0Wu5S0PyN/O0Qb5awPyF53KYw50mr+1JNX8lYpS1/oYhovBjvcNF4OW6zbe/H6wXGj5+MFyYcP67ZZrFUNj3fkrFQXW7LiPdtuJU6coTtoeQxQn2iyKk46XbSeLbexneet/+7Gf/XQs+3E/u9Fp/gJ5tJv8bVU/D5YLskbN+xr1eac63bgdrPvk3PyViuIklXR9vNJq/fXXfLs+Yf14oF2t7aY257EPGmSd5C/nDFI+84D8ha/y9twN+ZZlHuRuos8YMyrpoqRfcM69uvlx59wnnXMzzrmZiYmJttsJrdHUWHnDfVNjZYV8/dNAyVsekL+dow3y1wbkLzqVxxzoNH8LgdXUWFnWtD6Ga1Gy433XoqTluo1x2iuVWsvHX6nUNqzfbrlWMTTWnRorK0ndjvtot53QGhXDoO0xFMONk4OSVAyDjpdtJ4tt7Gd563/7sZ/9dCz7cT+70Wn+Anm0m/xtVT9fqdSUOjXva9Tnnep043ay7kWeqbGywrX/udsYr7Raf/01365PWD8eaFdru6nNeeyDBlkn+cs5g5TPPCB/4au8PXdDvmWZB7ma6DPGFFSf5HvCOfd/3ci2xstFnT013WyoxvebjpeLGUQKX/iaB77GnSXawN828DVuZMfnHJgcLemx+6f1+We/v+UYzp2a1i03lzbc97F33q2xkcKG+yYPFHX2vuNbjr8Y1gdw577wLT1y8tiGxx85eUznvvAtTY2VNXWorE/8zJt1ce6SHj5xbMf9rV/30fuO6/wXX9DFuUs6tyn+9cs9dv+0pg6Vt8R4eLio8ZGifnh8eEuM50/PaHxk6zkcHynq/OmZjpZtJ4ttoH/XXj/2MzHSeh8TGefEfmozSTo83Ho/h4e5loBea9TP33j39THAxblLCgPp0bVxQWMM0KrGP3b/tC7OXWrePntqWk/Ofrd5+9ypaU2OliTVxyub6/zDJ+rbXd9XTo6WtvQJ68cD29XabmozfZB/fB63Izu+5oGvcQMS+Yu6LPPAOJePj4MaY4ykT0t62Tn3C52sMzMz42ZnZ9s+vroaa6kSKU6dQms0Xi7yzywH0C7zoG9vmyB/d0YbkL/wVxc5kJv8jeNUl1eqKgRGq7W0eQxDBas4cUqcFCepwsAqMFIqyTkpTZ2sNQqMZIxUS1xz3cnRkowxWliuKk5SDRXq74avJamsMbJGWo3T5n6SVGvv4ndKXf3vQmBljZQ4J+fqv0NjFAZWq7VEYWA1XLRaqSYqhoHGygVdqdQUxYkKa58AaCw3OVpSkqS6fO36OTo8XFSpVD9Haer0SiVSJUqUOGmoYHV4pCTb5l1laeq0tBIpiuv7Hh8ptl22nSy2sYdyk7/96n/7sZ8oirW4cn0fEyNFFYt+Hks/91Otxm2v7TZyk78Nt37kqV1t98WP3tttSPBfrvK3UT+rcapanCqwRqXQykmK4vqYYii0SiXJXa/xpTDQoeGCFlci1ZL6eGB0yOpa1dVvr9XuMLz+Xu04TpvjisDWxxKp05a+slZL6sutjSVKYX1800mt7aY2d9EHDbJc5C/P3SD5+/yN/EWX+pK/eXnuhnzLqv/NU+b8pKT7Jf2dMeara/f9G+fcn3S7waGhUEe4OAaer3nga9xZog38bQNf40Z2fM6BMLT6oZvLOy/Yhdce7M121zs4fP3viQOltsuFodWRNi+8WWt0aKQkjXS2T2vNtvvq1zbQv2uvH/spFkMd6cHE3mb7qc0kqVQK217bAHqrWT+7tHmcsN1wJAxtR+OKQiHQkbHhHZdrpZvaTB/kH5/H7ciOr3nga9yARP6iLqs8yE0mOef+Sn18NwgAAAAAAAAAAADgs1z9jz4AAAAAAAAAAAAAnWGiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAh5joAwAAAAAAAAAAADzERB8AAAAAAAAAAADgISb6AAAAAAAAAAAAAA8x0QcAAAAAAAAAAAB4iIk+AAAAAAAAAAAAwENM9AEAAAAAAAAAAAAeYqIPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAAD4V7HcB6xpjflvQOSQvOuR+90e2trsZaqkSKU6fQGo2XixoaytUhow98zQNf484SbeBvG/gaN7Ljcw5Uq7FertRUDI1Wa6mMkSSjWpIqsEYFa1QqGMWJVI1TWSOlTkqcU2BMc/nJ0ZLC0CpNnZZWIkVxImuNrKQwqG87Tp0K1igMrCq1RKE1GilZRbEUJ6kS52TXtlkIrKI4bbZpITCKE6cwsEqdU+rUjLEYWJUK0vLq9eWHQquVKFFxLabUScbUf6wxSp2Upq4eo6kfWzGwCoy0GqcKA6tSaFSN3Yb9HBwK9YNqoihOVAjryyyvJkpSV18mrMedpE6FwKpYMFqNUhVCq9AaVaJExTDQWLmgK5VafTtB/b1olVr970ZbNqxv02IYaHykKGtNx4/vZ/269vqxn34dS62WaGG52tzP5GhJhUKQ+X587hcBdKZWS1SJa1qt1etrLXEKbf1349ovrdXjgjU6VC7qB9Wa4rT++Ppxxkr1eg0fLlpFSX1s0Khraeq0sFxVLUmbtdJas6X+bV5uuGi1Uu2sPsZxumUf6+txp8sMCl/HH9QnSP7mga9xI1u+5oGvcSNbWeVB3jLndyT9uqTHb3RDq6uxvrG0ojMX5jR/paKpsbLOnprWbeMjXDADxNc88DXuLNEG/raBr3EjOz7nQLUa65tLK/rKC5c1/frD+sRffF3vfcsb9Qt/8NXmsTxy8pgOHyipGFr9yh8/q5/9idfrwxefaT7+8Ilj+vSXvq0Pve123TE5qm9eXtEDj882H//Ez7xZo0Oh3vOpr2zY5q/+6fNaXK7qU+/5MV2rJvq53326+fjH3/Um3VQO9a9+5/p2Hr3vuJ762vf0z469VrXEbYjxsVPH5WT04Lpz8Oh9x/Wfn1vQzOsP6aEnr8f76+9+s2pxql/87NdaxtP4e+JAUR982+0bzusjJ49p4kBJf/X1Bf3yHz+n9/3Pt+odb5pqLvNTd03qA/fcpvc/8fSO2/7Q227fEO/65c6dmtadtxxoTpw+/9LVDW16/vSM7rjlgKw1Oz6+n/Xr2uvHfvp1LLVaoucWlrfs587J0Uwn+3zuFwF0plZL9GpU05WVmpI0VS2R/p+vzuveu49sqINn7zuuz/zNd/SlF5b0O+/5MV2Lkg2P/8a736zA2mZN/Km7JrfU3/P3z6gQGv3LdWOJc6emdXM51M+c/3Lzvt974B/pB5V4y3jgwtr+t6uPcZzquZeublh3fT3udJlB4ev4g/oEyd888DVuZMvXPPA1bmQryzzI1cjLOfdFSS9nsa2lStRsIEmav1LRmQtzWqpEWWwenvA1D3yNO0u0gb9t4GvcyI7POXD5WqT3fWZO99z1Gp25MKcT00ebE2hS/VgeevIZzb9cUS12OjF9tDnJ13j8wxef0Ynpo3rwwpwWlqvNF3waj3/w9/5W8y9Xtmzzwbe+UfNXKpp/udKc5Gs8/gt/8FV978rqhvve/8TTOjnzOr28UtsS48LVqPmi2/rlf/r4VHOSr3H/lZVac5KvVTyNv09MH91yXh968hldermie+56jSTp5MzrNixzYvpo88XLnba9Od71yzXaUpKWVqItbfrA47NaWok6enw/69e114/99OtYFparLffTyLes+NwvAujMwnJVq1GqSy9XFNhAD16Y08mZ122pg2eeeFoPvOUNmr9S0aWXK1sef3mltqEmtqq/D3xmVpc2jSUevDCnauw23FeNXcvxQGP/29XHheXqlnUf3NQ/drLMoPB1/EF9guRvHvgaN7Llax74GjeylWUe5GqirxPGmPcaY2aNMbOLi4ttl4vT6wPchvkrFcWp63WIyJG85QH52znaIH9tQP6iU3nMgd3mb+rqvw+WCy2PZbgYyBq1fbxxf7u2GC4GLdeRpOFi0PE6gTUtl2+3Dee2xtNu2UY8jb+3awvn6uc2sGbDMtu1TyfbXr9cnKSSpChOWi4bxUlHj/sob/1vP/azn46ln/vJo07zF8ij3eRv4+s3G2OERp1ude0Ha5/w6qSGb1d/N9+3+YNjjTja7X+7+lhL0tb91lo97nSZQZHH8Ucn+TvI9QnX5TEPyF90Km95kLfnbsi3LPPAu4k+59wnnXMzzrmZiYmJtsuF1mhqrLzhvqmxssIcf2UCspe3PCB/O0cb5K8NyF90Ko85sNv8tab++5VKreWxXIsSpU5tH2/c364trkVJy3Uk6VqUdLxOkrqWy7fbhjFb42m3bCOext/btYWp/2NCJanbsMx27dPJttcvF679375iGLRcthgGHT3uo7z1v/3Yz346ln7uJ486zd8bcetHntr1D9CJ3eRvaI1Ca5pjhEadbnXtJ2sv3nRSw7erv5vv2/yaUCOOdvvfrj4WAtu63wrsrpYZFHkcf3SSv4Ncn3BdHvOA/EWn8pYHeXvuhnzLMg/27ehrvFzU2VPTzYZqfL/peLm4x5Ghn3zNA1/jzhJt4G8b+Bo3suPLz1T2AAAgAElEQVRzDhweLuqx+6f1+We/r7OnpnVx7pI+/q43bTiWR04e09Shsgqh0cW5S3r4xLENjz984pguzl3SuVPTmhwt6fzpmQ2Pf+Jn3qypQ+Ut2zz3hW9paqysqUNl/ca7j294/OPvepOOjA1tuO/R+47rydnv6tBIYUuMkweKOrfpHDx633H9h6fn6/Gvu39spKCPvfPutvE0/r44d2nLeX3k5DEdPVTW55/9viTpydnvbljm4twlPXrf8Y62vTne9cs12lKSxkeKW9r0/OkZjY8UO3p8P+vXtdeP/fTrWCZHSy3308i3rPjcLwLozORoSUNFq6OHykrSROdOTevJ2e9uqYNn7zuu8198QVNjZR09VN7y+KGRwoaa2Kr+nr9/Rkc3jSXOnZpWKTQb7iuFpuV4oLH/7erj5Ghpy7rnNvWPnSwzKHwdf1CfIPmbB77GjWz5mge+xo1sZZkHpvFVS3lhjLlV0h875350p2VnZmbc7Oxs28dXV2MtVSLFqVNojcbLRf6Z5QDaZR707W0T5O/OaAPyF/7qIgdyk7/VaqyXKzUVQ6PVWqr6h9WM4iSVtUYFa1QqGMWJVI1TWSulqZQ6J2uMrJGcjCZHSwpDqzR1WlqJFMWJrDWyksKgvu04dSpYozCwqtQShdZopGQVxVKcpEpc/Wu3jKm/az6K02abFgKjOHEKAyvnnBKnZozFwKpUkJZXry8/FFqtRImKazGlrr5dYyRrjFInpamrx2jqx1YMrAIjrSapQmtVCo2qsVMtSRWs7efgUKgfVBNFcaJCWF9meTVRkjoF1qgUWlXjVEnqVAisigWj1ShVIbQKrVElSlQMA42VC7pSqdW3s/ZJgNVaojCwzbZsWN+mxTDQ+EhRdt073nZ6vAdyk7/96n/7sZ9+HUutlmhhudrcz+RoSYVC9p/AyHFtzE3+NvTjE3cvfvTenu8DfZGr/K3VElXimlZr9fpaS5xCW//duPZLodW1qF7zD5WL+kG1pjh1StZqcGOcsVK9XsOHi1ZRUq/zjbqWpq7edyVps1Zaa7bUv83LDRetVqqd1cc4TrfsY3097nSZQdHF+CMX+Zvj+oQ+8vX5G/kLKb/5m5fnbsi3rPI3V5ljjPk9SW+VdNgYMy/p3zrnfqvb7Q0NhTrCxTHwfM0DX+POEm3gbxv4Gjey43MOlEqhXlPKLnZrjSYO7M0722/e+A0QGu/RfiaKG9tr8363GGn9d6fttFOb7mWb77V+XXv92E+/jqVQCHRkbLjn+/G5XwTQmUIhUKEQ6Kad6uA67V7IObhDt2St0WsPbt3R5vrXarmdtt0QhrblPna7zKDwdfxBfYLkbx74Gjey5Wse+Bo3spVVHuQqk5xzP7PXMQAAAAAAAAAAAAA+GMzvUwAAAAAAAAAAAAA8x0QfAAAAAAAAAAAA4CEm+gAAAAAAAAAAAAAPMdEHAAAAAAAAAAAAeIiJPgAAAAAAAAAAAMBDTPQBAAAAAAAAAAAAHmKiDwAAAAAAAAAAAPBQuNcBAAAAAACQd7d+5Kldr/PiR+/tQSQAAAAAcB2f6AMAAAAAAAAAAAA8xEQfAAAAAAAAAAAA4CEm+gAAAAAAAAAAAAAPMdEHAAAAAAAAAAAAeIiJPgAAAAAAAAAAAMBDuZroM8a83RjzvDHmm8aYj+x1PAAAAAAAAAAAAEBehXsdQIMxJpD0G5L+iaR5SV8xxvyRc+7Zbre5uhprqRIpTp1CazReLmpoKDeHjD7xNQ98jTtLtIG/beBr3MjOfs2BNHVaWokUxYmKYaDxkaKsNdsuVwis4iSVsZJLpVrqVAiMAmO0Gqc6MBSoEqXNtjowZLVSdYqSVEOhVZI61VKnYmBljbQapwqt0VDBKkmlOElVW1t3pGT1g0rSfDyK69sJrVEptFqNU6Vry8pIgTFKnJORlDopSZ0Ca1QIjGqJ03DRaiWqrxNYI2slK6M4dYrXYioVjKqxU7QWV2iNqkmqwFz/uxRYxamTtVKaSnHqVLBGYVDflnNSITSqxa75WKlgVamlStZuN+JNnVRL69s3RnJOKoVWTqZ5PtLU6ZVKpEqUNNu1EFgdGq4/vtM57PQ851G/rr1+7KdfxxLHqRaWq6olqQqB1eRoSWGYq/dDAsi5Rn91c9nqWiQ5V6/dSepUDq0SqVknbypbvVpJlTqn4UKg1fj6GGCoYBUlaXO8EFijcsGqGqeqJU7F0GqkaLS86iRTr59J6hSu9V2SdHmlqmqcKrBmbRwhrcaJAmtUXKuFnfZxm+vhzaVAiyvX++XJ0ZIKhaCHLesXX+vJfh23Y3d8zQNf40a2fM0DX+NGtrLKgzxlzo9L+qZz7gVJMsb8vqSfltTVRN/qaqxvLK3ozIU5zV+paGqsrLOnpnXb+AgXzADxNQ98jTtLtIG/beBr3MjOfs2BNHV6/qWreuDx2eZxnT89oztuObBhEmj9chOjJf3S2+/Qp/762/rf/6c36F//+681133k5DFdWlrRjxw52Gyrn7prUh982+06c2Guue5DTz6zYZ1f/dPntbhc1dlT0yoXjP7lp2Y3tPPff+8V/flzC/rQ227Xg+vOwaP3Hdevf/4b+tyzC5oaK+vj73qTCoHRb/ynb+pnf+L1+vDFZzYs+/SLS5p+/eEN5/HX3/1m1eJUv/jZr23Y5yf+4uvN7a6P8ZGTx/SHT39P/+L4EX3qr7+9ZT8fe+fdKoRW//GZf9C9dx/R+594utkOH7jntubtqbGyzt53XKu1ZMO+Hz5xTJ/+0rf1gXtu01Nf+57++fGjum1iVN+9ck0vvbq6oe0+9s679epqTdYYnf7t/9L2HHZ6nvOoX9deP/bTr2OJ41TPvXR1w7Vy7tS07rzlgBcvzgLYe43+aqQg/beaVbWW6NXVWA89+UzLWn721LRqtZp+/yvf06n/8Ye31LqhgtV7fme2bf1+7P5p/dHfzustd9yyoab+znt+TNU41fs+M9e2lj5y8phePVDSrYdGduzjNtfDX37HnVvGBWdPTevOyVEm++RvPdmv43bsjq954GvcyJaveeBr3MhWlnmQp9HGEUmX1t2eX7uvK0uVqNlAkjR/paIzF+a0VIluLEp4xdc88DXuLNEG/raBr3EjO/s1B5ZWouaLXVL9uB54fFZLK1Hb5R586xv10JPP6MT00eYkX2Pdh558Rj9x28SGtjoxfbR5u7Hu5nUefOsbm20q2S3t/BO3TejE9NHmC02Nx97/xNM6MX20efsX/uCrenmlphPTR5svFK5f9p67XrPlPF5ZqTUn2tbvc/1218f40JPP6IG3vKHZBpv384uf/ZqurNR0cuZ1zRciG+2w/vb8lYouL0db9v3hi880lz058zo98PisFpar+s7StS1t94uf/ZouvVzRd5aubXsOOz3PedSva68f++nXsSwsV7dcKw9emNPCcjXT/QDYvxr9ValQ0KWXK1q4GjVrUKtafubCnG65eVgPvOUNW2rdmSeeljEba/vm+v2+z8zp5MzrttTUSy9XmpN8Uuta+tCTz9Rj7KCP21wPW40LztBfNvlaT/bruB2742se+Bo3suVrHvgaN7KVZR7kaaKv1Vuk3ZaFjHmvMWbWGDO7uLjYdmNx6poN1DB/paI43bJJ7GN5ywPyt3O0Qf7agPxFp/KYA53m73aiOGl5XFGctF3uYLmg+SuV5u/N6yab2mr9cu3WOVguNP/e/AGzxjZ3Wrdxe7gYtF02dVvP43Ax6Gi762MMrNm2DYaLQXOZVu3Qyb7X76eWpG2XHS4GGi4GW+5ffw47Pc/9lLf+tx/76dex1JK09X6SNNP9DLIs+l9gr3SSv43+Kk5ds87sVMvjta/lbPVYq9q+uc62Wndz7duu7nbSx22uh63GBXs9vsuTPNaT3eTvepzXwZPHPCB/0am85UHenrsh37LMgzxN9M1LOrru9pSkf9i8kHPuk865GefczMTERNuNhdZoaqy84b6psXL9f9JgYOQtD8jfztEG+WsD8hedymMOdJq/2ymGQcvjKoZB2+VeqdQ0NVZu/t68brCprdYv126dVyq15t+bx36Nbe60buP2tShpu6w1W8/jtSjpaLvrY0xSt20bXIuS5jKt2qGTfa/fTyGwbZe9FiW6FiVb7l9/Djs9z/2Ut/63H/vp17EUAtt6P0Genib5LYv+F9grneRvo78KrWnWmZ1qeWjNltrXeKxVbd9cZ1utu7n2bVd3O+njNtfDVuOCvR7f5Uke68lu8nc9zuvgyWMekL/oVN7yIG/P3ZBvWeZBnp7BfkXSbcaY1xtjipL+N0l/1O3GxstFnT013WyoxvebjpeL2UQLL/iaB77GnSXawN828DVuZGe/5sD4SFHnT89sOK7zp2c0PlJsu9y5L3xLj5w8potzl/Rr/+vdG9Z95OQxfekbixva6uLcpebtxrqb1zn3hW8121RKt7Tzl76xqItzl3Ru0zl49L7jujh3qXn74+96kw6NFHRx7pIePnFsy7Kff/b7W87j2EhBH3vn3Vv2uX6762N85OQxnf/iC8022Lyfj73zbo2NFPTk7Hf16H3HN7TD+ttTY2UdHi1u2ffDJ441l31y9rs6f3pGk6Ml/fD48Ja2+9g779bRQ2X98Pjwtuew0/OcR/269vqxn34dy+Roacu1cu7UtCZHS5nuB8D+1eivqrWajh4qa/JAsVmDWtXys6em9dIPrun8F1/YUuvO3ndczm2s7Zvr92P3T+vJ2e9uqalHD9Uf266WPnLyWD3GDvq4zfWw1bjgLP1lk6/1ZL+O27E7vuaBr3EjW77mga9xI1tZ5oFxLj8fBzXG/DNJH5cUSPpt59yvbLf8zMyMm52dbfv46mqspUqkOHUKrdF4ucg/sxxAu8yDvr1tgvzdGW1A/sJfXeRAbvJ3O2nqtLQSKYoTFcNA4yNF2RbvtFq/XCGwipNUxkourX81QxgYBcZoNU51YChQJUqbbXWgbLWy6lRLUpVCqyR1qqVOxcDKGmk1ThVao6GCVZJKcZKqtrbuSMnqB5Wk+XgU17cTWKNSaLUap0rXlpWRAmOUOCcjKXVSsvZVYoXAqJY4DRetVqL6OoE1CqxkZBSnTknqVAisSgWjauxUi+v7Ca1RNUkVmOt/lwKrOHWyVkrT+n5CaxQG9W05JxVCo1rsFKdOBWtUKlhVatfjNab+aYLUSXGayhoja+pxl0IrJ9M8H2nq9EolUmXt04L1Y7I6NFx/fKdz2Ol5Xic3+duv/rcf++nXscRxqoXlquIkVRhYTY6WFIZ5ej9kz+Umfxtu/chTPY/lxY/eu+t1uomrm/1gV3KRv43+6uay1bVIcq5eu5PUqRxaJZKitfp9U9nq1Uoq55zKhUCr8fUxwFDBKkrS5njBWqNywaoap4oTp0JoNVI0Wl51kqnXz8Q5hdY2J5Qur1QVxamsNRoK62OFapzIWqPiWi3stI/bXA9vLgVaXLneL0+OllQo7N0n3vOmi3qSq/zludtg8/X5G/kLKb/5m5fnbsi3rPI3V5njnPsTSX+S1faGhkId4eIYeL7mga9xZ4k28LcNfI0b2dmvOWCt0cSBnd+Z3elyTSMbb940tMvA1jk43P26rYyN7LxM3lhrdGiktKVdG3Y6N7s+fznSr2uvH/vp17GEodVrD5Z3XhAA2ljfX412UMMP3ECdl6Sbtumyfujm7PqzVvXwSHH/je+y4ms92a/jduyOr3nga9zIlq954GvcyFZWeTBQb1UFAAAAAAAAAAAA9gsm+gAAAAAAAAAAAAAPMdEHAAAAAAAAAAAAeIgvgQUAAAAAICdu/chTfdnPix+9d9frdBNbN/sBAAAA0Dkm+gAAAAAAQE8wOQgAAAD0lnHO7XUMXTPGLEr6TgeLHpZ0ucfh5B1t0FkbXHbOvb0fwZC/u0IbkL8+G/Q26PT485a/Ppy3vMeY9/ik7GLMW/5KfrR/p/bTsUj5Ox7y98YQa28wfth7HEvvkb/5QxvQ//qMNshZ/u7TsW+v0AY3mL9eT/R1yhgz65yb2es49hJt4G8b+Bp3lmgDf9vA17izNOht4Ovx+xB33mPMe3ySHzF2az8d2346Fmn/HU8v+NRGxNobPsW6nq9xt8KxDB7aiTaQ/G0DX+POEm3gbxv4GneWaIMbbwObZTAAAAAAAAAAAAAA+oOJPgAAAAAAAAAAAMBDgzLR98m9DiAHaAN/28DXuLNEG/jbBr7GnaVBbwNfj9+HuPMeY97jk/yIsVv76dj207FI++94esGnNiLW3vAp1vV8jbsVjmXw0E60geRvG/gad5ZoA3/bwNe4s0Qb3GAbDMT/6AMAAAAAAAAAAAD2m0H5RB8AAAAAAAAAAACwrzDRBwAAAAAAAAAAAHiIiT4AAAAAAAAAAADAQ0z0AQAAAAAAAAAAAB5iog8AAAAAAAAAAADwEBN9AAAAAAAAAAAAgIeY6AMAAAAAAAAAAAA8xEQfAAAAAAAAAAAA4CEm+gAAAAAAAAAA/z97dx4fV3Xf//997mwaLbZlWXLANoFQY+JQByxDDP42paElSeOGUjuQgDEQ6gVn+bbflsDv26+/oaX9FePmSxsSL7iN2VsTHH5Q0pKkJDT5siTYQFziYJaw2EAsWchG62z3/P7QzFhj3ZG13BnN1byej4cekmZGM0f3fs5yz7nnHABAADHQBwAAAAAAAAAAAAQQA30AAAAAAAAAAABAADHQBwAAAAAAAAAAAAQQA30AAAAAAAAAAABAADHQBwAAAAAAAAAAAARQoAf6PvGJT1hJfPHl51fZEL98leCrbIhfvkrwVTbEL18l+Cob4pevEnyVDfHLVwm+yob45asEX2VD/PJVgq+yIX75KsFXWRC7fJXoy1OgB/oOHTo00UkAxoz4RZARvwgy4hdBRvwiyIhfBBnxiyAjfhFkxC+CithFOQV6oA8AAAAAAAAAAACoVgz0AQAAAAAAAAAAAAHEQB8AAAAAAAAAAAAQQAz0AQAAAAAAAAAAAAHEQB8AAAAAAAAAAAAQQOGJTgBQaqlURm3dCaVdq7Bj1FIfUyQSmuhkHVdQ0w1IkutadfQklUxnFA2H1FQXleOYiU4WyiiddtXWnVAq4yoSctRSH1M4zP1FlSB3biQrayUrKUY+nTRoPyDISlF30CYBAOD4gtqGDGq6AYn4hb8Y6MOklkpl9GJbt669Z7cOdPZpdmNcm1e06vSW+oouOIOabkAa6FDbd7BLq+7alY/fbSsXad7MBjrWqkQ67erFg11aO6gM27KiVafPbGCwb4Llzs3XH3tJV553iq7fuYd8OonQfkCQlaLuoE2Ccjv5hu+O+m9ev/lTJUgJAIxcUNuQQU03IBG/8B+9bZjU2roT+QJTkg509unae3ZnZzJUrqCmG5Ckjp5kvkNNGojfVXftUkdPcoJThnJp607kO2qlgRhYSxlWEXLnZlnrnPwgn0Q+nSxoPyDISlF30CYBAOD4gtqGDGq6AYn4hf8Y6MOklnZtvsDMOdDZp7RrJyhFIxPUdAOSlExnPOM3mc5MUIpQbqmM612GZdwJShFycudmWjxCPp2EaD8gyEpRd9AmAQDg+ILahgxqugGJ+IX/SjrQZ4x53RjzX8aY540xu7KPTTfG/MAY83L2e2P2cWOM+box5hVjzB5jzMJSpg3VIewYzW6MFzw2uzGucIUv1RPUdAOSFA2HPOM3GmbpgWoRCTneZViI+4smWu7cHO5LkU8nIdoPCLJS1B20SQAAOL6gtiGDmm5AIn7hv3L0uP2OtfZMa+2i7O83SHrMWjtX0mPZ3yXpk5LmZr9WS9pchrRhkmupj2nzitZ8wZlb77ilPjbBKRteUNMNSFJTXVTbVi4qiN9tKxepqS46wSlDubTUx7TlmDJsC2VYRcidm52792vDsgXk00mG9gOCrBR1B20SAACOL6htyKCmG5CIX/gvPAGfeZGk87M/3ynpcUnXZx+/y1prJT1tjJlmjDnBWvvOBKQRk0QkEtLpLfXasXqx0q5V2DFqqY9V/KamQU03IEmOYzRvZoMeXLdEyXRG0XBITXVROdyVVDXCYUenz2zQ/WvOVTrjKhxy1FIfUzjMjL6Jljs3N376DBlZ7Vi9WJLIp5ME7QcEWSnqDtokAAAcX1DbkEFNNyARv/BfqQf6rKTvG2OspK3W2tslzcwN3llr3zHGtGRfO0vS/kF/eyD7WMFAnzFmtQZm/Omkk04qcfIxGUQiIc1qrJ3oZEgaXfxWUroBaXTx6zhGzQ3chVTNwmFHJ06LH/+FZUL74ahKOzc4PtoPCLLRxG8pyifaJBgP2g8IMuIXo1FpbciRxm+lpRvg2g0TpdS31i+x1i7UwLKcXzDGfHSY13rdVjlk90lr7e3W2kXW2kXNzc1+pRMoC+IXQUb8IsiIXwQZ8YsgI34RZMQvgoz4RZARvwgqYhcTpaQDfdbat7Pf2yQ9KOkcSQeNMSdIUvZ7W/blByTNGfTnsyW9Xcr0AQAAAAAAAAAAAEFVsoE+Y0ydMaYh97OkCyW9IOlhSVdmX3alpIeyPz8saaUZsFjSEfbnAwAAAAAAAAAAALyVco++mZIeNMbkPuc+a+2jxphnJN1vjLlG0puSPpN9/b9J+n1Jr0jqlXR1CdMGAAAAAAAAAAAABFrJBvqstb+S9GGPxzskXeDxuJX0hVKlBwAAAAAAAAAAAJhMSrpHHwAAAAAAAAAAAIDSYKAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAYqAPAAAAAAAAAAAACCAG+gAAAAAAAAAAAIAAKvlAnzEmZIx5zhjzSPb3U4wxPzXGvGyM2WGMiWYfj2V/fyX7/MmlThsAAAAAAAAAAAAQVOWY0fffJf1y0O8bJN1qrZ0rqVPSNdnHr5HUaa39DUm3Zl8HAAAAAAAAAAAAwENJB/qMMbMlfUrSP2Z/N5I+JumB7EvulPSH2Z8vyv6u7PMXZF8PAAAAAAAAAAAA4BilntH395K+IsnN/t4k6bC1Np39/YCkWdmfZ0naL0nZ549kX1/AGLPaGLPLGLOrvb29lGkHfEf8IsiIXwQZ8YsgI34RZMQvgoz4RZARvwgy4hdBRexiopRsoM8Ys1RSm7V29+CHPV5qR/Dc0Qesvd1au8hau6i5udmHlALlQ/wiyIhfBBnxiyAjfhFkxC+CjPhFkBG/CDLiF0FF7GKihEv43kskfdoY8/uSaiRN0cAMv2nGmHB21t5sSW9nX39A0hxJB4wxYUlTJb1bwvQBAAAAAAAAAAAAgVWyGX3W2v/HWjvbWnuypM9K+qG19nJJP5K0PPuyKyU9lP354ezvyj7/Q2vtkBl9AAAAAAAAAAAAAEq/R5+X6yX9D2PMKxrYg++fso//k6Sm7OP/Q9INE5A2AAAAAAAAAAAAIBBKuXRnnrX2cUmPZ3/+laRzPF7TL+kz5UgPAAAAAAAAAAAAEHQTMaMPAAAAAAAAAAAAwDgx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQAAx0AcAAAAAAAAAAAAEEAN9AAAAAAAAAAAAQACVbKDPGFNjjPmZMebnxphfGGP+Mvv4KcaYnxpjXjbG7DDGRLOPx7K/v5J9/uRSpQ0AAAAAAAAAAAAIulLO6EtI+pi19sOSzpT0CWPMYkkbJN1qrZ0rqVPSNdnXXyOp01r7G5Juzb4OAAAAAAAAAAAAgIeSDfTZAd3ZXyPZLyvpY5IeyD5+p6Q/zP58UfZ3ZZ+/wBhjSpU+AAAAAAAAAAAAIMhKukefMSZkjHleUpukH0h6VdJha206+5IDkmZlf54lab8kZZ8/IqmplOkDAAAAAAAAAAAAgqqkA33W2oy19kxJsyWdI+mDXi/LfveavWePfcAYs9oYs8sYs6u9vd2/xAJlQPwiyIhfBBnxiyAjfhFkxC+CjPhFkBG/CDLiF0FF7GKilHSgL8dae1jS45IWS5pmjAlnn5ot6e3szwckzZGk7PNTJb3r8V63W2sXWWsXNTc3lzrpgK+IXwQZ8YsgI34RZMQvgoz4RZARvwgy4hdBRvwiqIhdTJSSDfQZY5qNMdOyP8cl/a6kX0r6kaTl2ZddKemh7M8PZ39X9vkfWmuHzOgDAAAAAAAAAAAAIIWP/xLJGGMkXS7pA9bavzLGnCTpfdbanw3zZydIutMYE9LAgOL91tpHjDF7Jf2LMeavJT0n6Z+yr/8nSXcbY17RwEy+z47tXwIAAAAAAAAAAAAmvxEN9EnaJMmV9DFJfyWpS9JOSWcX+wNr7R5JZ3k8/isN7Nd37OP9kj4zwvQAAAAAAAAAAAAAVW2kA30fsdYuNMY8J0nW2k5jTLSE6QIAAAAAAAAAAAAwjJEO9KWyS3BaaWD/PQ3M8Kto6bSrtu6EUhlXkZCjlvqYwuGSbUuICpVMptXek1TatQo7Rs11UUWjIw39idPfn1ZH39F0N8Wjqqmp/HQDUnDzHfxDHTxyrmvV0ZNUMp2RMUYhIxnHKOwY9SUzioQdhR2jVNpVxkrWWkXDITXVReU4ZqKTX1aDj6/SD2EAACAASURBVFW1HoNKUY5zUa5yJJXKqK07ka+zWupjikRCvn8OJpdEIq1DvUfbOjNqo4rFKrOtQ9kJYDhcu0EK7vUbfWcIsqDmO/jLr3p4pH/xdUkPSmoxxvyNpOWS/teoP62M0mlXLx7s0tp7dutAZ59mN8a1ZUWrTp/ZQIapIslkWvvae3TtoDjYvKJV85rrKrrh2t+f1ssdQ9M9t6mOBgsqXlDzHfxDHTxyrmu172CXVt21K3+sNixboDuffE1XLzlFtzy6T+3dCX3zsrPUn3L1Z9/+ef5121Yu0ryZDVXTWet1rKrtGFSKcpyLcpUjqVRGL7Z1D6mzTm+pZ7APRSUSab10aGhb57QZdRU32EfZCWA4XLtBCu71G31nCLKg5jv4y896eERRY629V9JXJP2tpHck/aG19tujTnkZtXUn8hlFkg509mntPbvV1p2Y4JShnNp7kvmMIg3EwbX37FZ7T3KCUza8jj7vdHf0VXa6ASm4+Q7+oQ4euY6eZL7zVRo4Vtfv3KNlrXN03QN7tPb8U3Wgs0/v9qTyg3y51626a5c6qihfeR2rajsGlaIc56Jc5Uhbd8KzzqK8wnAO9Xq3dQ71Vl55RNkJYDhcu0EK7vUbfWcIsqDmO/jLz3r4uMOCxhhH0h5r7RmSXhz1J0yQVMbNH6CcA519SmcqfsVR+CjtWu84cO0EpWhkgppuQCJ+QR08Gsl0xvNYTYtH8t8lqTYa8nxdMp0pW1onWrFjVU3HoFKU41yUqxyhzsJYBCluKDsBDCdI5RlKJ6jXb8Qvgiyo+Q7+8rMcO+5An7XWNcb83BhzkrX2zVF/wgSJhBxdOL9Fy1rnaFo8osN9Ke3cvV/hEFNfq0nYMZrdGC/IMLMb4wpX+DI1Ycd4x2+FpxuQgpvv4B/q4JGLhkOe+eVwXyr/XZJ6kxnPYxoNV97SgqXaC6rYsarEYzCRyrHPQznORSTkeNclPpcj1FmTXynyRJDihrITwHCCVJ6hdIJ6/UbfGYIsqPkO/vKzHh7pQp8nSPqFMeZnknpyD1prPz3qTyyT5rqovnTBaUPWN22ui0500lBGM2qj2ryidUgczKit7DhoinvHb1O8stMNSMHNd/APdfDINdVFtW3lIs89+jYuX6BbHt2n2Y1xnTgtpi99bK6uvffZgvX7G7Mz/ipFKfeC8jpW21YuUhNxlVeufR7KcS5a6mPasqJ1yP/SUh/z7TNyn+NVZ/n9OZgYpcoTQWrrUHYCGE5znXd5Rru9usyojXhev82oraxrjWPRd4YgC2q+g7/8rIeNtcefBmiM+W2vx621/znqT/TRokWL7K5duzyfa+9K6OJNTwwZDX1w3RI1N3DhXi3auxL6xx+/ouWLTlLIMcq4Vg/selN//NHfKBYHZbvtZ7j4fftwny7Z+tSQ+L1/zbk6cVq8XElE8FRE/I4h32GSGWMdXBHxOxEGz4AzxihkJOMYhR2jvmRGkbCjTMbqMx71QqW1a0rd/irVbEEfVET8lrP9UI5zkZuJlc64CpdodqIkpVKZgc9xrcKOUUt9TJFIVc12qoj4LYVS5olEIq1Dvcl83MyojSoWG+l9tOVVwWWnHyoqfk++4bujft/Xb/7UWJOE4KuI+E0m02rvOVqeNddFFY1WZnmG0hhjfTnh8UvfGcahLPFbKdduqGxjqIc943dENfdED+iNBXsRQBqIg60/eV1bf/J6weMrzztlYhI0QqzTjCALar6Df6iDR8dxTPFBsLqBb2919gbimJb63A97rFDW9kM5zkU47JTlIjcSCWlWY23JPwflV8o8EYuFNatCB/aORdkJYDjRaFizGNirakHtgwpqugGJ+MVRftXDI7ol1hiz2BjzjDGm2xiTNMZkjDHvjfvTSyi3F8Fg7EVQfYIaB7l9aQYrxb40QCkENd/BP8SA/4JyTIOSzsmK9gNQiDwBAMDxBbW+DGq6AYn4hf9GGjnfkPQ5SS9Likv64+xjFSu3F0Euw7AXQXUKahzk9qUZnO5S7EsDlEJQ8x38Qwz4LyjHNCjpnKxoPwCFyBMAABxfUOvLoKYbkIhf+G/EcwKtta8YY0LW2oyk7caYJ0uYrnFzHKN5Mxv04Lolk3UvAoyA4xid2lSrHasXF6xzW+lxEA47mjujriDdM2qjJdmXBvBbUPMd/EMdPDoj2Tup1Me0WBpGu68T535ihcOOTmuuG1L+lqL9MMn3/MIE8Xu/xHDY0ekzG3T/mnNLvtcjAABBFdQ+qKCmG5CIX/hvpAN9vcaYqKTnjTG3SHpH+V1jKhd7ESCVymhfe4+uvWe3DnT2aXZjXJtXtOr0lvpxdRqUWiqV0UuHgpduQApuvoO/qINHxnWt9h3s0qq7duXzy7aVizRvZoPnYF8pjmmxNMxtrtfL7d0jSls50onjS6ddvdTeo7WDyt8tK1p1+swGXy8YRxO3wEilUhm92Nbte/uhXHs9AgAQVEHtgwpqugGJ+IX/RnrFf0X2tV+U1CNpjqRlpUoU4Je27kS+wJQGNjW99p7dautOTHDKhhfUdAMS8QuMRkdPMj9YIg3kl1V37VJHT3LC09DWnZjwtGF02roT+UE+aeCcrS1B+VsJcYvJh/YDAAATI6h1cFDTDUjEL/w37Iw+Y8xJ1to3rbVvZB/ql/SXpU8W4I+0a/MFZs6Bzj6lXTtBKRqZoKYbkIhfYDSS6YxnfkmmMxOehlTGnfC0YXSKnbN0xvX1cyohbjH50H4AAGBiBLUODmq6AYn4hf+ON6Pv/8v9YIzZWeK0+M51rdq7Enqrs1ftXQm5ZJSqE3ZMflPTnNmNcYUrfFmpoKYbkIhfDKAO9nbscYmEHc/8Eg2PbKmOwe/X1tWvd3tGf8yj4ZBnGiIh77QZYzifFarYOQuH/N3noVjMjDRuAS+laj+k067ePtynNzp69PbhPqXT/gx8U88BmCxSqYze6uzVGx09equzV6kUN+5Um6Bewwc13YBE/OIov+rh4131D46sD4zpEyZIbu+Qizc9oSUbfqSLNz2hfQe7uACrMk3xqDavaM0XnLn1jpvi0QlO2fCCmm5AIn5BHVyM13Hp7k9r28pFBfll28pFaqo7fn459v3+aNOT2vfrLn3xvudGdcyb6qKeaWipjw15fMOyBbrx4Rc4nxVqek3Es/ydXhPx9XOKxcxI4hYoprnOu/3QPI64SqddvXiwS5dsfUq/vfFxXbL1Kb14sGvcg33UcwAmi9z+qJfe/rR+e+PjuvT2p/ViWzeDfVWmFHVwOdD3gCALar6Dv/ysh4ddulOSLfJzxSu2d8iD65aouSE2walDuXT0JbX7tUO6b9ViWWtljNEP976j6bURzao5XvhPnKCmG5CIX1AHF+N1XFZ+62d6+ItL9OC6JUqmM4qGQ2qqi8oZwV18Xu933QN7tH7pfK25e/eIj7njGM2b2eCZhnkzG3T/mnP19uE+dfQk9Xff26fn9h/W3ne6qv58VqKOvqQeef6Atl91tkKOUca1emDXm1p53im+lr/DxQwwVkcSGc/2w/um1Kg5Orb4LbZv5f1rztWJ0+LH+eviqOcATBbF9ojasXqxZjXWTnDqUC6lqIPLgb4HBFlQ8x385Wc9fLyo+bAx5j0NzOyLZ39W9ndrrZ1S7A+NMXMk3SXpfZJcSbdba//BGDNd0g5JJ0t6XdIl1tpOY4yR9A+Sfl9Sr6SrrLXPjuq/GYS9QyANrHd84yMv6sZHXix4/Hc++L4JStHIBDXdgET8gjq4mGLHpS+ZGVNHSrH3mxaP5H8e6TF3HOPZOe04RtZaLd/y1JDPqfbzWYnSrtXWn7yurT95veDxyxaf7PtnFYsZYKyS6Yxn++H3PnTCmN+zVPtWUs8BmCzYIwpSaergcqDvAUEW1HwHf/lZDw+7dKe1NmStnWKtbbDWhrM/534vOsiXS6ekP7PWflDSYklfMMbMl3SDpMestXMlPZb9XZI+KWlu9mu1pM2j/m8GYe8QSMFd7zio6QYk4hfUwcX4fVyKvd/hvtS433skn1Pt57MSUf4iyEpR1pRq30rKRQCTBW0HSMGt14hfBFlQ8x385Wc5Nr4rnGFYa9/Jzciz1nZJ+qWkWZIuknRn9mV3SvrD7M8XSbrLDnha0jRjzJiHsNk7BJLUUh/zXO+4pb6y70BnnWYEWVDzHfxDHezN7+Pi9X4bly/Qlsdf9fWYcz6Dg/YDgqwUZU1LfUxbjskTW3xok1AuApgsuHaDFNx6jbYvgiyo+Q7+8rMeNtaWfjq+MeZkST+WdIakN6210wY912mtbTTGPCLpZmvt/80+/pik6621u455r9UamPGnk046qfWNN94o+rmua9XRk2TvkCqXSmXU1p1Q2rUKO0Yt9TFFIkXvjihpgIwmfpPJtNp7kvl0N9dFFWWNZgyvYuJ3lPkOk9AY6uCKid9S8rttMvj9ImFHYceoL+l/u4c21XFVTPzSfsAYVEz8lqKsSafdgTZJxlU45KilPqZwePz3u1IuVoyKiV9JOvmG7476M16/+VNjShsmhYqIX67dIAX3+o22L8aoZPE70W1fBM8Y6mHPICn5QJ8xpl7Sf0r6G2vtd4wxh4sM9H1X0t8eM9D3FWvt7mLvvWjRIrtr165iTwNjUbbSlPhFCRC/CDLiF0FG/CLIiF8EWUXFLwN9GKWKil9glIhfBFlZ4pfYRYl4xm/Jlu6UJGNMRNJOSfdaa7+TffhgbknO7Pe27OMHJM0Z9OezJb1dyvQBAAAAAAAAAAAAQVWygT5jjJH0T5J+aa39P4OeeljSldmfr5T00KDHV5oBiyUdsda+U6r0AQAAAAAAAAAAAEFWykWLl0i6QtJ/GWOezz72PyXdLOl+Y8w1kt6U9Jnsc/8m6fclvSKpV9LVJUwbAAAAAAAAAAAAEGglG+jL7rVXbL3bCzxebyV9oVTpAQAAAAAAAAAAACaTUs7oAypCOu2qrTuhVMZVJOSopT6mcLik21P6IqjpBiTiF6gErmvV0ZNUMp1RNBxSU11UjjP+PcdL9b7wR7nOD3GAoKAsBIDhce2GICN+AQSdX+UYA32Y1NJpVy8e7NLae3brQGefZjfGtWVFq06f2VDRFX9Q0w1IxC9QCVzXat/BLq26a1c+H25buUjzZjaMqyO6VO8Lf5Tr/BAHCArKQgAYHtduCDLiF0DQ+VmOUephUmvrTuQziiQd6OzT2nt2q607McEpG15Q0w1IxC9QCTp6kvkOaGkgH666a5c6epIV+b7wR7nOD3GAoKAsBIDhce2GICN+AQSdn+XYpJ7Rx3IqSGVcNdfHtH7pfE2LR3S4L6Utj7+qdMad6KQNK5Vxdd4HmrTqox9QyDHKuFbbfvyrik83IAU338Ff1METK5nO5BuKOQc6+5RMZzxfP/h8RcKOwo5RX3LouRvt+44GMTN+yXTGs/3gx/k59nNKFQeobn6XA6XKE+QBAJMFfQ8IMuIXQND5WY5N2oE+17Xa9+surbp70HIqVyzSvPexnEo1iUdC+son5um6B/bk42Dj8gWqiYQmOmnDqo+FdMW579fVdzyTT/fmyxeqLlbZ6QakgXx346fn692elCQpGnJ046fnV3y+g3+CvqRZrqPZdV1lrGStnbCBJ69Ob0nH7QiPhkOa3Rgv6Iie3RhXNDw0H3qdr43LF+iWR/epvTtRcO5G876j/T+DHDOVojbmaMUx7YdNly9UbczfRTyi4ZAunN+iZa1z8jd07Ny9f9xxgOpWinKgLhbyzBPjbVOTBwBMFvQ9IMiIXwBB52c5NmmX7jzUncgP8knZ5VTu3qVDTN+uKhnXavsTr2n90vnasXqx1i+dr+1PvKaMayc6acNKpK2uvffZgvi99t5nlUhXdroBSTJGSqZdrX/oBV16+9Na/9ALSqZdGfrqq8ahnoTnkmaHeiq/Ds51NP/Fg3v0SnuPLtn6lJZs+JEu3vSE9h3sklvG+iOXlos3PZFPw+sdPUMe80pXU11U21Yu0uzGuCTlO8xzA4WDeS1Bd90De7T2/FOHLEc3mvcdjY6epG79wb6C+vrWH+xjGbxR6k24WndM+2Hdvc+qN+HvXc1TYyF96YLTdNMje3Xp7U/rpkf26ksXnKapdKpgHA71JDzLgfHUHb3JInkiOb48QR4AMFnQ94Ac17Vq70rorc5etXclynrdM1bEL4IuiPkO/vKzHJu0M/r6Ut7LqfSnWE6lmlhZXXneKbp+59EZfRuWLZBVZRecqYzrGb8sP4AgSKZd/en9Py+opP70/p9rx+rFE5wylEt/0Tq48suw3KDX+qXz83WHdHSw8sF1S9TcECtrWgan4Y2OXq1/6IXjpstxjObNbNCD65Ycdwm8YkvQTYtH8j/nlqMbzfuOhuu6nvW161Z+zFSStGs9z6XfNzi19yR17TH7CFx7z27tWL1Ys6KT9vICJZYpUg6MJ35L1aYmDwCYLIqVkyn6HqpKUFfXoO8MQRbUfAd/+VkPT9oZfSHH5O82z5ndGCejVBlrNaSj9vqde2Qre5yP+EWglaujGZUrZLzLsFAAirDcoNe0eGTC91/yGoCrjYZGnC7HMWpuiGlWY62aG2JF65DccpyDzW6M63BfKv/z4OXoRvq+o5EpUl9nKDZGJVyk/RDyuf1QrJxPU85jHFzXuxwYz53NkZDjmSfCofFdBpMHAEwWxfoe/G47oLJ53WA4eFWPSkXfGYIsqPkO/vKzHp60A33RkKONyxcULC21cfkCRcd5UYdgyVjvi3C3wkf6wo7xjN8wjRUEAI1txKMh3XrJhwvKsFsv+bDi0cpf0iw36HW4L+UZx+Xcfym3B9TWK1q1Y/Vibb2iVU6RQdTRpGvw8iBvH+6TkdXWK1qH1DmP7T2o7VedrXuu+YisbEmXEbFF6mtb4fV1pXEcacOywvbDhmULfC9/iw0o0k7BeLhF2+1jf8+W+pi2rCgs37asaFVL/fhmZpMHAEwW9J1BKr7CRzlvchwL+s4QZEHNd/CXn/XwpF1XpDEe0YyGmG666AzVRkPqTWY0oyGmxuwyVKgOsdBAh+3ggnN2Y1zRUGV3Nocdo9poqCB+a6MhGisIhJrwQCV13QNHl97auHyBasJcLFaLKbGIGuuiBWVYY11UU2KVXwfn9qC79Qf7tGHZgoIl5PzYi240GuMRffmC07Q2uzxcroP6rs+fo5Xf+tmY0uW1PMiGZQv0430Hdd8ff0Qhx8i10nt9KV1x7vvza8WPZhkR17Xq6EmOamnP3ADrkPq6jAOrk4EjR3c+ObA38bR4RIf7Urrzydd000W/6evnNNdFtXlFa37pwtmNcW1e0apmn/PHWGIJwRUt2m4fe/vBcYymxsO64+pz5BjJtVIsbMYdRy31Mc88MN4BRAAot6mxsJqP6TtrbohpamzSdhfCQzg7A/7YOni8M+BLjb4zBFlQ8x385Wc9PGlr7sP9aW189EUta52jWoWUzLja+OiL+puLF6g5QqdR9bCeAw6q8D36ii1xyNKHCIKMaz0b28Rv9Xi3L6mrtj8zpMH6nXXnqaWhZgJTdny5Pej+5uKB/eHuX3OurLUTMsjQ2ZfKD/JJA3f3rb1nt76z7rwx75HntTzI9Tv3aP3S+brsH3+q+9ecq8/d/pTWL52vmx7ZO+o9Cse6z0BugPXYvyvnwOrkYHX1klNK3u45ksho92uHdN+qxbLWyhijH+59R++bUqNmn/YnY8+KauR/u72jJ6nPbfvpkPpovPutRiIhnd5Srx2rFyvtWoUdo5b6mCJcZwIImCPJtL79zJtavugkhRyjjGv17Wfe1DUfPVUtDPZVD1ukDq7w1TXoO0OgBTTfwV9+1sOTttZOpjP6/t42fX9vW8HjX/0Dpr9Wk/60q1se3VdwZ/stj+7T33/2zIlO2rD6065ufHiv1p5/an6g+saH91Z8ugGJ+IXUn/JegqI/FYxN0XN70E20Ykt5pNKuZjXW+vqeuT0J09mNoMe6R2GxfQaO16meG2Ad6wAmBpSr3ZNMZ3TjIy/qxkdeLHj89z50gm+fMdZYQnCVIn5LuSRSJBIac1kMAJWiP5XR1p+8rq0/eb3g8cvPPWViEoQJQd8ZUH5BzXfwl5/18KQd6GMJKEgD0/jbuxNac/fu/GNB2D8jqOkGJOIXUii7j9yxdXCoikLAa8lBSaNahrAUbZli75nbkzC3fEju99F+9ng61StlgDXIylX+RsMh3bj0dH1s/glyrZWTndHnZzubPSuqTynit5TXhMlkWu09yfyMvua6qKI+zWgFgHIJGaML57doWeucfEfzzt37q6rdjuBewwc13YBE/GKAn/XwpF30NbcE1OCNDFkCqvpMiTvavKK1IA42r2jVlHhlh/7UIumeWuHpBiSpKR71jN+mOOVvtaiNOdp0+cKCGNh0+ULVxqqjDMstOXjxpie0ZMOPdPGmJ/R6R8+Qx/Yd7JI7zLIypWjLeL3nhmULtHP3fm1e0aoZtRFtW7lIO3fv14ZlC0b92blO9cG40ap8cnvnHVv++r133pRoSK2nzNBl257W+Rsf12XbnlbrKTM0JerfeSaWqk9DPOQZvw3xsZ/zUl0TJpNp7Wvv0aW3P63f3vi4Lr39ae1r71EymR7X+wJAudXHHH3pgtN00yN7dentT+umR/bqSxecpvoqabdjQLnakH6j7wxBFtR8B3/5WQ9P2lsOHcdobnO97l9zrlIZV5GQo5b6GEtAVZn3+lzPPWSm156gSt4m6kifq87uvoJ9P15te0/TayOqr+B0A5L0bn/KM9811UV1Ys2krXYwSH/S6rs/f0vbrzo7v8b4A7ve1FVLPiBVwSpnXksOvtHRq/UPvTCqZQhzy1l+Z9156k+5ChkpPoqBFK9Zhbn33LF6sQ51J1UbDak/ldGy1jm67bGXdOOnzxjXHoXj2WuvWHoxctFoWKfNqCtoP8yo9X+W0aHepB55/sCQPL7yvFM0y6f9fNi3sfrUR6Oa3WgL4rc25qg+OvZzXqplgdt7krrtsZcKllq67bGX9NU/+JBmMasPQIB0J1xde8ye0Nfes1s7Vi/W1Cpot2NANBrW3KbCNmRTvPJnqtN3hiALar6Dv/yshydt5Liu1ctt3Vp196DOgSsWad77Gug0qiKRkFHryU26bNvT+TjYfPlCRSp8HYqaiKPG+rguvX1Qule0qibCXUmofGFHnvkuTPhWDSur3/vQCbr6jmfyMfD3l54pqTo2lfZacrA2Giq6DOE7h/uUzLgKOUbxqKPGeOGNSR3dySGDHfNmDt+eyc0qLPZ3adfqom8+MeTv/vLThYNtzWPoEJ85JaYdqxcrYwfqsxl1x7/R6njpxci4rtWv3u0t+XGMhIyWfnhWQR73u33Fvo3Vx3Wt3upMaG32Qnd2Y1xbVrSqYeb4zrvrWqUyrtKulcm4cl077jgyRrryvFN0/c49+bRuWLZAhvAEEDBp13q2UdPDrDqBySeVyujljp58Z3OuD+r0lnpFIpW7mgJ9ZwiyoOY7+MvPenjSlnyHuhP5QT4pe+f83bt0qDsxwSlDOaUzVtfe+2zhqPi9zyqdqexGa3/KezS/P+VOcMqA4wtqvoN/jIz+ZMfzBTHwJzuel1QdPaBeSw72JjOeyxCmXavPbH1Kv73xcX329qf10q+79XpHT35JT6/Zgavu2qWOnmTRz3ddq1+/16+eRFrrl87XWXOmDfm7SHYvvsEunN+id3tSo1pe9NjP3XewSzfs3KMX3n5Pbe/1qz+ZGdHfj+X/xFCHehKex/FQj7/t33KV87l9G2c11qq5gZU5Jru27qODfNJAXK29Z7faxnH9lk67evFgly7JlrOXbH1KLx7sUjo9vja1tcoP8uXSev3OPbI0dQAETNgxnm1U9oiqLm3dCc8+qPHUweVA3xmCLKj5Dv7ysx4u2UCfMeZbxpg2Y8wLgx6bboz5gTHm5ez3xuzjxhjzdWPMK8aYPcaYheP9/P7U0LvpD3T2qT+dGe9bI0CCendaUNMNSMQvpFTG9YyBVKY6Lri89oR6f1PtkMe2rmjV33x3b0HD/roH9uiNjt78AJfX7MDcTEAvucG2S7Y+peVbntJNj+zVn398Xn6wL/d3LfUxbTlmP4C/+NR8rTnmQmM0g20dPUnd+oN9uvK8U3TTI3u1fMtTuuwff6p9bccfLBzt/wlviSLt34TPnR0pynmUQLpI3ZF2xx6/pRg8lKSM9c4DLiN9AAKGPaIgBfcaPqjpBiTiFwP8rIdLuXTnHZK+IemuQY/dIOkxa+3Nxpgbsr9fL+mTkuZmvz4iaXP2+5iFsqOhgzPM7Ma4QqynUlWKxkGF350WLpJu7qpDEAQ138E/1R4DxZYclFTwWCqT0ff3thX87YHOPtVGQ/kBrtzswGOPZTTsvZSH18y463fu0fql83XTI3vzfxcOOzp9ZoPuX3Ou0hlX4ZAjt0jH9UgH25Lpgb3+jp3lsubu3cPuRTiW/xPeHOOd9/zOesXyODPuMB6luH4rduNJepw3noSK5jXyAIBgiUbDmtdcuEdUcx17RFWboPZBBTXdgET8YoCf9XDJam5r7Y+NMScf8/BFks7P/nynpMc1MNB3kaS7rLVW0tPGmGnGmBOste+M9fONkTYsWzBk3wTySnUxRvrGZWepsyel2mhIvcmMGusiFR8HkZDRt65apLc6+/PpntVYU/F7CwKS5AQ038E/1MFHlxw81uDH3j7c59mw701m8gNcudmBx+651lTk7q5iM+Ny7zP478JhRydOO7pERHtXYlyDbbkBzWM/v7k+pmQ6o7c6e4vuszba/xPeHKdI3vM584Udo29edpbeHVTOT6+LcFGKcSlWd4xn7CwScnTh/BYta52jafGIDveltHP3foVD41vYJh4NaePyBbrugaNp3bh8geJRbk4AEDyO48gYI8nKGCPHmbS7/KCISMho0+ULtS67NPvsxrg2+bz/cinQd4YgKNpwvQAAIABJREFUC2q+g//8qofLfYvOzNzgnbX2HWNMS/bxWZL2D3rdgexjQwb6jDGrJa2WpJNOOqnoB4UdR3c++ZrWL52fv6i788nX9Nd/+Js+/SsIgmjYUca1Wv/QC/lC8x8+e6Yi4YlpuI40fiWjnkSmIN23fe4sTYvT4YmJM9L4LZbvohOU71B+lVgHj7z8LZ/c8plrB22+vXH5AjU3xBRyBpbh9Jod2BiPqKMnWTBbMDeQEwk7noN1J06Lqybi6J0jfYpHQ0q7Vqm0WzDbMORIW1e05pfv9Bpsc11b9LOb6qLqT2UKPv+sOdP0lU/M06W3P13wnvNmNhQMPhWbBckMsQEjjd+QKU/ei4WNQo5TUM5vWdGqWNjf8zVcvCE4RtP+/fG+g9p+1dkKOUYZ1+qBXW/qlBkfGPNnN9dF9aULTsvvf+LXknTT4lHNnFKjmy46I9+xOHNKDW31SagS2w/ASI0kftNpV/vaurTm7qPl5NYrWjWvpUFhrt+qRtqVvvvzt4bUwVcuGXsdPF4jK3/pO8OAdNpVW3dCqYyrSMhRS31swsqwkbYdKjHfofzSaVf7DnYV9MNsXdGqeTNHXw8bW8J9BLIz+h6x1p6R/f2wtXbaoOc7rbWNxpjvSvpba+3/zT7+mKSvWGt3D/f+ixYtsrt27fJ8Lrfx+uDOsy0rWnX6GA4SguvXR/q0fMtTQzo8H1h7rt43Ne71J2XrPRoufseQbkAiflEhEom0XjrUM6Rj9bQZdYrFit5jVBHxWy65AYxEOiOjgZmwaVf69ZF+/b//9ku1dyc8B8Rye/AdO/Mt97p3exJ6ta1bf3r/z/PP33rJh/X+plr94u0uzaiPykoFdw1uW7lIsbCjld/6mZrrY/ryBXN1yow61cZCmlEXy3/+8T7bda1e7+jRwff687Nctl91dv7CO2d2Y/y4S3kGUEXE77s9CXX2JmWtkWMk10rGWDXWRjW9zr/jffBIv5ZteXLIed259jzNnFrjy2ccL97gq4qI357+fr3WkRhy/XZKU0x1NWOLq/auhC7e9ERJyiAGoitGRcRvzsk3fHfU7/v6zZ8aa5IQfBMev1y7QRq4fnv5UM+QOnhuhV+/Eb+QxjwGUJb4Ha7t0N+f1isdQ/PdbzTVqaaG5ZOrxTtH+vQZj3Ls22vP1QnFyzHP+C131BzMLclpjDlBUm5jmgOS5gx63WxJb4/ngzr7Utr12iHdt2qxXGvlGKMf7n1HM6fUTLaOJQwjmfbelyOZHt++HKUW1HQDEvEL6VBvUrs96uDptRHNKn6hWNH87ND1GsDYekWr/uE/XirYs2/VXbsKOqNd1+rX7/WrJ5HW+qXzteXxV/Xc/sNaddcuPfzFJcq4UiqT0ZR4pGCWSUM8rINdSa1/6IX8Xn2D99Bbddcu3XTRGTrQ2TewzGbG1aHuhE4MFzYqc/v/NdfH8jPGfn2kXzOnxDS9LqaOnmR+sDD3fFN9rOi+f3SSl4JVX9IdcrHYWOvvjX39RZaITYxwP8eR8Npv8tg8gcnlvX6bj11p4JyvvWe3vr3mXNWNcfy42HLGI917FAAmO67dIA1cv73W/p7+ZfViZVyrkGP03Bsdaqzw6zfiF5LU1p3wbEPev+bcgq0qKk1H3zD5joG+qpEqUo6lxlCOlTtqHpZ0paSbs98fGvT4F40x/yLpI5KOjGd/PkkysjpvbrNebevOd3SdN7dZRqWbwYjK4xTZ2LTSOxKDmm5AIn4xsNb8kmPq4CVzmwO71rzfM4u8BjDW3L1bd3/+HC1rnaMtj78qSVp7/qnqTab19mFXzXVRvXKoJ/93F85v0f+55MM63JdSTyKtd3uSumr7M0MG8iQVzKqbFo94NiJPaqrVJa2zddFZswr2xxr8fybTGTXXx/TnH59X8JotK1qzx2mggZr7fyRp6xWtnuWBlfT2kT7d/eRr2vqT1z1nBx47CJg7dgwMFtefdPWvzx8YuvzLeadIdf59TqgM5TwDNNUnmSlykZsZe2ddJOS9nHFknHv05WYwv9HRm6/n3t9Uq5Ob6iiXAAQK126QBq7fTp7RoM8OWm5/cwD2CiN+IUmpIm3I9DjakOUQ1HwHf/lZjpVsoM8Y88+Szpc0wxhzQNJXNTDAd78x5hpJb0r6TPbl/ybp9yW9IqlX0tXj/fyMaz3XaZ5aw0BfNYk4RhuXL8gvIZbb/yhS4ZV+Tdjx3JC1psqWnU0k0jrUm1TatQo7RjNqo8MtG4EKEdR8B/9YK3V71MFTaiITnbQx8XtmUbEBjLauhG56ZK82X75QqYzVl//luYIZfw8/d0Drl87XiVNr5Frpim/9rOCCYOPyBaqPhQve+6w503RyU62+9pmBQcFUxvVsRL7Z0au155+qldn3HPx/fmfdeWppqFE0HNKXL5ibH+TLvWbtPbt100VnqKUhpjW/dbIWntyU3x/u2dc7huxDuOnyhfr6f7ysJ3/VoU2XL1Rnb1r37z6QP6ZNdVHPDnRJ+fSxjKO3cMjoD86cravveKZgIDbs88ViOcr5aDikNb91spYvOqlg0DIaDvn2GagsYcfowvktWtY6J1+G7Ny9X6FxxFUkLG1e0TpkKenIOJuTh/uSOvhef0E9t3H5Ak2rjfi6TC4AlFpN2NHmyxfq2kF9D5ursO+h2qUyVo947BW28rxTJjppwyJ+IRW/sSs8zhu7Si2o+Q7+8rMcK1mPubX2c0WeusDjtVbSF/z8fNdKX/rn5wo6or70z8/p22vO9fNjUOEiYaMZDbGCJcxmNMQUCVd2p2AqY/WNH76cX/rscF9K3/jhy/rLT58x0UkrmzHu8YUKENR8B/9MtjrY75lF0XDI80LkcF9KBzr7lHGPDvLlPmvN3bt136qP6LJtP/VcfvPae5/N5jnpwvkt+v7eNp01Z5r+/OPzCgYEv3HZWfraZz6sP/v2zwsakd2JtPpS3v9nf2rgTsimuqhOmVHn+ZraaEj/8NhL+vIFpxUM6m2+vFW7Xjs0pD5b1jpH9+8+oHX3PqvtV52t+3cfyB/TYh3oYccZ8WBrtS4LmnZVdNkaPxkjNdVHC8r5pvqojI+HeGospKXHDFpuXtGqqTEG+iarhhpHX7rgtCFtv4aasXfS9CZc3fbYSwVl0G2PvaSv/sGH1Fg79rT2JTP5gW5pIK9d98Ae7Vi92NfZswBQaqmM1W3H9D3cVmV9DxiYWfSpD88qaHdtCsDMorTrHb9/dRHxW01a6mNDbi7dsqJVLfWVffNVUPMd/OVnPTxpe8tLsfQLgqcv6Wrjoy9qWesc1SqkZGbg96/+wYcq+iI8mXH1/b1tBXs1SdL/+lT1xO+h3mS+o0fKdmTfs1s7Vi+u6DXiEdx8B/9Mtjq42MDcWGcWNdVFtW3looKlQDcsW6C/+94+SdK02qjn8XOthl1+c0Z9VIe6k/qfn5qvz53zfkVCZsjsuy/e95xuveRM3fvHH5FrrV4/1Kv//dAv1N6d0DcvW5gfJBz8f+auMxzHqDZWfJByWeucIYNM1967W+uXzs8v5ZlzzX/7QP41udk6uWNarAP97s+fM+R/9hps9Xup1SAp17I1qYzVzl37h8y28/Pu0/aeYdoBUdoBk1FXv1v0nE8Z4/Yqadd6tqn/4lPzx5VW19qi5TQABAl9D5AG2na5FaWkgTpt3b3PDtzAUsESaeIXUjjs6PSZDbp/zblKZ1yFQ45a6mMKV/jMzqDmO/jLz3p40l4ll2PvEFS+Ul3clxrxO3DuPDsr6UGpeEHNd/DPZCvDvAbmtq1clN83brQcx2jezAY9uG6J+pJpvdreo7/73j49t/+wpOLHL5SdLnW4LzXk+Qvnt8i1KpgFd/c153iWoy1TYjJGuuIff1bw/Bfue1b3XPMR7X2nq2AmXTx6dEBzRl3Mc5Dyoefe0urfPtXz8449TrmBwdzPGdcWHNMDh3uP24F+1pxp+vIFc5WxVu1diYIZe34vtRok5cp7jiN9dN7MgrtPNyxbIMfHa2naAdWnFOc8XCRPhMeZJ0KO47m07HiWGQWAiTDZ2u0Ym6C2u4hf5ITDjk6cNsY7wyZIUPMd/OVnOVbZQ9vjkNs7ZHbjQCZnj6jqlLu4H8yPi/tSI36De+7AucPkK8MGD8w9cf3v6MF1S8Y9O8xxjJobYqqvCasm4qi9OyEpt5eA9/GzGmjwb3n8VW1YVvj8DZ/8oL5wX+HdgK8f6vXMi5GQI5N9zWAHOvvUlUhr/dL52rF6se6+5hzNnFKjafGjA3W5Y/GddefpP687X3dcfY6a6iJae/6pOtSd8Py8mVNqtP2qs3XWnGm6cH6L7vr8OWppiGn7VWdr+9Vnq7E2UnBMayIhz/eJhgf2XjhrzjR95RPztP6hF/TRWx7XxZue0L6DXXKzF0R+L7UaJOXKe66rIbNFr9+5R66PN09Tl1SfUpzzeNTR5hWtBXli84pWxaPjuwyuiZj80rIf+9p/6uo7ntHSM2erJkJ8AgiWydZux9gEtd1F/CLIgprv4C8/y7FJO6PPlVU8GirYOyQeDckVo+LVJBIynhtaVvpeYa4tEr+2euK3Ntsxc+w+LbXj7JgJmmQyrfaepNKuVdgxaq6LKlrhS5YVzXesM141JmMdnBuY80tuDzkjq9mNtbrj6nMUMtKh7qQkq2aPfS57k2nNbozruf2HdeeTr+muz5+jkGP0q/YeHcnu7zfY1x97ueheBZ0eswJnN8b19uGB/QBnN8a1Y/VizWyoKRjQzKU7lXZlJN3y6C/1/b1t+f3//v7SM/UnO54vyPu3PfaynvxVh+64+mwl0q5WDtozcNsVizS9LlbwGV6zBretXKQTptTowXVLlExndOntTxedsef3UqtBYow045i982b4vHeeJGWKLFuY8bGd0lIf82wHVPpeGxi7SNho0+UL80sY5fcpGUe7vT9l9cjzB7T9qrMLZt5dueQD40prX7L4MqMsUw4gSIr2PQS43Y7Ri4SD2XdmnCL7RldXtxECKqj5Dv7ysx6u7N7icci40r/veXvIcipX+Lh3CCqftVJjXUT/snqxMq7NLqdjZSt8uW5rpeffeFcfm3+CXGvlGKMf7n1HF37ohIlOWtm815/R4788qPtWLZa1VsYYPfTsAV101iw1VkkHSjKZ1r72niGdnPOa6yp6sM9aqb4mpH9etTgfv2k3oyoap656Gde7DPvdKirDhpPbQ+7WH+zTleedkp8ZNbsxrq0rWuXIaEZ9VPFISIm0q4xr1ZtI65s/eqVgg+ab//2X+quLztD7m2oVcoy2X3W2vv7Yy/klQNu7E5oSD+fzoiSFHOlI/8DNA3dfc44OdSWVSGdUEwmpuSGmh597K78M41/+6y+0ful8hRxHqYyreCSkju6kVt1duGxne1dSktTZk9IHmut0x9XnqC+Z1qHupFxrddnik7L/udGauws7xlfdPXRJzcEzKJPpjKLhUH5pzuaGmN7q9F7aMzdjz++lVoPEtQMXCnOm18oxA79n3Izv+4aFjPFettDHEcVIJKR5zXXasXpxwc0ukcjkH7CtVqm01RuHugra7c+90TGuvGtkPZeZNePswC621FKGpZYABIxVkb6HM2i3VxPrStGw0R1Xn1PQhqz0vrN02mrfO0d01vubCtoOre9vmuikAccV1HwHf/lZD1duT/E4NcSc/HIqgzvIG2Lc1lFNQo7Rkf6M3ursz4+Kz2qs0fTayu4kqo05WnJai15t686ne8lpLaqtoviNhBzt2H1AX/uPl/OPzW6Ma9miOROYqvJq70nq9fb3hnR4Ta+NaFYFD/RJ0nt9ab3b05eP3+l1EdVGKjvN8M/UuKPWU2bosm1PF9TBU+PBKcNyM9eOHWga6d/EoyGlXatU2s3/vTSwf1xfKq3u/rSu/+QH1dmT1Pql87Xl8Vf13P7DWnPPbq1fOl83PbJ3YAZcKqO2rqSm1UZ0wyc/qJv//egMum9edpbauxIFdwBuXL5Atzy6T+3dCW2+vFV//cje/Os3Ll+gB599SxcvnKXrHtij5vqYvvKJebrhO/9VcJ4+Oq9ZNz68V3Nb6tXZm9K6e59Vc31MGz/z4fwgn3R0ucab/+g3ZYwpGLDcsGyBvv7Yy2rvTujWS87U5Yvfr8O9yREvqTncDMrjzdgbPFDouq4yVrJ24NyM5DwGmWulr33/JS1rnZMfEN65e79u/PQZvn5ObdS7ne1nO8V1rV7t6B0yYDveZXOrQSKR1qHeo6sBzKiNKhar/Dq4vsbRyc1T9NnbC+uO+pqxx5VrvZeZ3bF68bjSGgk5uu2zC4Z0LIZDwannKtVY6l8AYzelxrvdPmUcZS+CJ+1azzbkV//gQxOdtGHVxhydfuI0vXzwaN/Z6SdOq6q+MwRXUPMd/OVnPVz5V3xj1JO0nsup3L/mXE2tneDEoWwyrlVHd1LrH3qhoBN0ak1kopM2rETK6lBXYki6pwSgk8YvLfWxokvOVYvaqHeHV6UvX2ol9SYzQ+KXe9yrx3t9xZc0a6iZ4MSNQG7G3WgGGAb/TW4A7boH9hT8fSzsaOW3fpZ//spBS1huWLZAf/e9fXpu/2FNi0d0oLNPtzz6or74sbkFeWnT5Qu1ful8vdLWo2m1UV3+jz8tOM7XPbBHd3/+HKVdq43fe1Hf39tW8Nz2q87OD86sXzo/n8bca669Z7e2X3W2ntt/WLcsX6Cr73hGzfUx/fnH5xUdqHvf1Bpdtf2ZIR3p65fO15q7d2tGfVRXfOtnWr90vucAnRnlLLCRzNhzHKOmuuioz2Pw2SGzRP2YvXSs3mGWLWz0qZ3d0ZPMn7vcZwxeohXeEom0Xjo0dDWA02bUVfxgX3d/8biaGj/OHxdRqpl302sinm206RV+jVHpxlL/AhifI8O02+sD0G6HP4yRZxuy0ote+s4QZEHNd/CXn/VwZfcWj0Mq46q5PqatV7Rqx+rF2npFq5rrY0pnmP9aTVKuHdKJed0De5Sq8GV1gppuP4XDjubOGFiy6z+vO187Vi/W3Bl1CocnbbE1RLGO1N5kZZdj6SLxm66i+K12add618EBiYHcAEPuf7jtc2cpEnJ0sKtP7V0Judn/w3Wt2rsSequzV79+r1+3/mCfDnT2ae35pw7JA6vu2qU3Onrzz29/4jWtXzpfO1Yv1vql83Xnk69p7fmnanZjXIf7UpKkZa1z8ntV5d5n3b3PyjFGc6bXynWt1i+dr7PmTMun/UBnn9q6EjrSl8oP8g1+LuSY/Pu1NMQ8O8BD2SuLcPa1a88/Vdfv3KOOnqTnZuEhYzzfZ1o8otmNcWXswO9bHn9VG5YVbjK9YdkCjXb7zsEz9p64/nf04Lolnp3AxQaKOnqSo/vAALFFZi/5nfXKkceT6cyIZ4AGRSKR1ludvXqjo0dvdfYqkUj7/hmHepOebYdDvZUf98UG5cYTV2HHeJdb4+xB6ejzPs4dfZV/nP00uB4cXD+OVTWW28BEK0XZi+ApVxvSb/SdIciCmu/gLz/r4Ul7i0NN2BlyN/3G5QsUq6JBAgzM6PPKLOO9CC21oKbbT6lUxvOO9NNb6qtmf56gXnQRvyhWB9cEpA5OpjO6tHW2fueDMwtmFW9YtkB3Pvma/vvvnqbTmuv1yqGe/IDgly+Yq+s/+UGtPPdk1cXCnnmgNjpQdp04tcbzzr1ptZH8zD5pYOaa90CHq6u2e88GnN0YV1N9TLGw8Zw9l3GtZjfG1Vwf09TsQNzQGXbSPdeco0jY0ezGeH6GYW6g7tg9BSNFPqs3mcm3vWY3xvXc/sP6u+/t0/ql89VUF9X0uqg6upNKZFy1dyUKlmc73tJtwy3tOfg8TraBouNxbZHy1+dNUsuRx4+3RGvQlGumXVDbDtLRQbljz3l4HINyU+OONq9oHXLcx7uUNHv0lWb2XTWW28BEK0XZi+ApVxvSby59DwiwoOY7+MvPejgYPW5jkClyV0c1XXxBioYcz7t4IxW+f0ZQ0+2ntu6E553Sbd2JCU5Z+RS7C73SL7oiAU03/ONaedbBQamCayKOPn3WrPwgn3T07rplrXO05u7dOtidyA/y/fnH52n9Qy/ogq/9p274zn9pel3UMw/0JjPZ9w953rk3LR7RnU++puf2H9aF81v0vqk1nu/zenZm4OC/zc0G3LBsgW559Jd6ry+tO64+u2D23MblC7Ttx7/SxuUL9OUL5urmf//lkBl2W1e0KuNa3fCd/9KX7ntOG5cvUG8yM2Sg7oG152r7VWfrmdcOqT/l6puXnaXtV52tHasXa/tVZ+uOq8/WB09o0Lz3NeiEKTXatnJR/j1uemSvwiGjju6E/vT+5/XRWx7XxZue0L6DXXJdm+88/osH9+iFt9/TGx092t/Zq1RqdB29uYGiY49fUAeKRsIx3uWvM8rlUY/HVZE87uNnTKsJa8uK1oL43LKiVdNqgnmfYrlm2gW17SBJ8aijTZcvLDjnmy5fqPg4liw/0ufqtsdeKphBfdtjL+lI3/iitVQzBdNpV28f7tMbHT16+3Cf0unKXcWhFLPvqrHclqSTb/juqL8Av8SjAzdEDC57N69oHVfZi+ApVxvSb6ES1cdAOQQ138FfftbDwbxSHoFUkbs6mL5dXaysvnnZQn3hvmfzd5p+87KFUoXvFmZl9feXnqk/2fF8Pt1/f+mZqvR0+4k7paX6mKPvrDtXybRVxrUKOUbRsFFstOvclZlxpE2XL8wvOZjrqDNcK1aNZMb1viM/IMtnpzNWqczA0oTrl87Pb4y95fFX9cETGnTzH/2mMq7VXZ8/R/FoSD2JjG7+o9/U177/kiSp7b2E7rj6bO1/ty+/Kfyc6XGlXVfbrzpbYcdo/dL52vL4q3pu/2FJR2fcXPfx07V+6Xwd6Uvrr/71F0Nm0G2+fKH+90O/KEjvgc4+zW2p1/ql8/OzAdu6Ejp5Rq3uW7VYjrGSjIykdR/7DXX2JDRzSo2+v7dN7V3Jgv9xen1EPQlXd33+HGVcq0f/6x0tPnVGfs/U3EDd1z7zYR3qTui35s1UTdhRX8ot2Btj64pW1Uatcqd85pSYdqxerIy1ijiOrKRLtj5V0EF86w/26cZPn6G066q7P61r/tsH9Gff/nn+PbesaNXpMxtGvIRzYzziuddrY/z4e2gdb0ZhpTJGQ2KmFPs8pNLeeTzlYx5/L5FSNGx000Vn5PNRNGz0XiKlJp9n9ieTabX3/P/s3XucXFWd7/3vb9etq7sT0nQ6gEmAAAHMMMGkI3I5MyCog4JwziTKLQjRw1V0Bhm8PDOox8xzXgLDeHSEBHAEEVAiPI5OZNB5UPA8Ik4SEEajkTtp8JBOpwPpW932ev6orkpVd1WnL7sqtbs/79erXknt2pdVq3/7Uvu311ppZX2nqGfqaIkrHg/2Z1LWd/rku4/UyYs7iuf0J57rDryl3dzmeMUWbHOb4/teeD/rG8rp3l++orsufacininnO9358xd1zelH6cCWya0z6zv9ZOuOUV0Z/91ZS6ZUVs+rsq9N4Vonm/X1+zf2jDpmTeS4N9a6d/SllMn5ikU8zWtNTHmdhdbv5y5fIN85eWb6wVNdU2p9N54xWAEEK5X11dYc1XcuO7G4L5s5pRr4QQMEr9o1ZKPnG8JabgQvlcpq58De6/m5zfGGH5+a+IWUPw/PSZafh6XJnYcbO+KnIOaZ3rdknlZ2LizevHpoy3bFQnCTBsFpink6tL1JD1x+YvFg35zw1OitoJ2T/ri7X9+9/MTizaCnX+nRIQfMnNGwY57pij87XKtWHFq82fPg5ldD8UR6kN4czKqrJFmw4MCk5rXu+yb1/tQc99QxK152kopG8tMxM0Q90xfPPlanLzmkGAM/3frHUOy/vu/U3ZeW75z+rw8cq2s3PFPsmvMfzztezkn3/PJl/WTrjmIrue8/9Zre/6eH6JYPH6+IZ7r/yZd1+tsPLkt83XFxp5xzZdNGdrnpDY91t/DApLr3pNS9J13W1eXc1oQSMU+3fPj44g3wDVu68t1tSmqJR/TFc5bIM9NVJYn29as75Tun/lRWsYing2bnx1KrdJ309kP+RNt3DRSPOWf+6cG68+cv6a/fu1jfvfxERTzJkynjO2VzTjv7UnJOunZD/sGUZQvn6MrTjtRgJqcDXEypbFbbd2f08s4Bfe3R53TC4XN08cmLRnXxu2zhHF1y8qJi8u+uS99ZrCspn0S68t4teujKk2RmyuR8mZkiJnmeVzER1zuY0deGW/IUvuPXHv2D/u//tnTMbj9r0R1dvTgn5XLZsuueF3a8FXhr2mjEqxg/0alkOUYYyvh6aPP2UdcBHzl5UWDbkPJJvm3do7vUPKajJdBk35xkRG+fP0fn3/Fk2XbmJINNWiYSUR09PMZxmG50SPmn8s9/53w1xyPK+k6JqKfz3zl/Sk/lR6tcT075SX9n+tYTL5UdX771xEv6H+ccN+lV7uhLjWpJfuW9W7ThipP0tjnJfSxdXa0SiK1Nnk57+0G68M7ymG5tmvw6S8dgDduDFkBYRcxTNOqUzjr5TvJMikc9Ocd+N5M4p4rntS988E/2d9HGVuXadwY9Iw/Vr4v8oIV2v0OgIuYpGgvmPNy40T5FiZina05fPKpFSSLGjeaZJOJJr+5KjTrYH3rg2OP67G/NcU+Hd8wedTNoJiVKZic9nf2OBVpz96ayOpg9xTFVwmQo47RzT6osMXDzqqWanYjqgP1duDFks1L3nvSo4+/8GZSonulmJz11Lpo76uZfGPbfnv60rrx3i+792Lu0+r6nil1zlj5ld+uFyzUnGdeGLV26/sFn9Z3L3qXnd/Sre09KA+mcLnjX4Vr9z78qu1l7+be3aO25x43qcvOGs5do7catun11p4YyuYqJwCu+vUUf7lygi08+XKv/eUvZftXWHNVpxx6kT214Rt19Ka1f3SkzlbUYvPLe/LZbExFt2LRdF514qKKPqxXwAAAgAElEQVSep0+ccfSo8+NgOjPqmPOp9x2tPUNZ/f6Pu3Xs2+Zo555U2dhs61d3qqM1UbGubl61VDc9sk3dfSl945JOZbJO59/xpG44e0lZP/RXnnZkWXemzfHIqBZjHa0J7diTKktiFsZNvPa9x4xKxKWzuYoteb7wwbFbm1Trju77V5+yz3EB97fWhKe21qTOG3H90JoIdt9rjlvF+GmOB3dTMBYxnXX8/LLrgNsuWq5YwK3au/vTxa4dCz+w/+nRP+gLH/wTzQ8w0deX8it23fnA5SfqgObANiMpn+yb38A3NaqZnfS0ayA2Kn6ncu5ojle+npzqNbVn0ppTFo0ap3Iq+ahMldbw2Sm2lN3Rl6r40MMXzzluSgnEvqExYnryqx3XGKwAgtMUk17ZlR51Tj+swe+ZIFiJaJV7qA0+xvqsZOVr31kh+N2J4FTrIv+By09s6GvisO53CFaQ5+FpGzmDGb+4o0j5nfzq+57SYIbuB2aSaj9A+4YaOw4G0pXLPZBu7HIH6c3BynUw1TFVwiRTZazRRu+CeChb+fg7RPcvM8ZbVfbft0Kw/6azOXX1DspsuDXFiARUV++gPn7/U7rqtCO1bOEcdbQmtHsgnxw7744ndcMPfqNclUG1D2tv1u0Xd2rZwjnFaUfPa9U9Hz1BA+lc8UZ04bPC2HuSdPmpR46q06vve0oXn7xI33/qNT29fXex9UdX76DWbtyqv/mLY7Rs4Rx19eZbBV+74Rl94oyjNJDOaceeVMVxq17fnRp9zMk5rbl7k5Yd2q6uXYOjjktX3rtFnzxjccW6uv7B/Hfo6h1U1IsUk3TrH3uhbHzA9pZ4WZ3tHsyMGq/gk2csLi5fWkcrOxdWHBdqsmM9FWJg5N9vKt3R1Uu1ZFJfKth9r7/KdvoD3E4m5yqeSzK5YM+BZtIlJy/S2o1bdd4dT2rtxq265ORFgXeZU61L8qC77gyzWpw7anVNPZT1ddMj28qOoTc9sm1K1zrx4Zayt1/cqQcuP1G3X9yp9y2ZF8AY3a5yjE+xuQMxDUwPe6rcM9nT4PdMEKz+9N7us3963am669J36t5fvqL+dGNf/4b5dyeCE9ZrkrDudwhWkOfhxk1rT5Hvu4pjBvgNvpMjWGE92Ie13EGiDvLHsZOPaNdlf35E2Vg1jX4cy/qVxzabSX+7mS7M+28s4hW70VzQltScZKzid+npT+u69x2tTM6NSj692jNQ1lpNyieYntvRp7UbtxZb6nX3pZT180m0Wz50fNn8hS4wF89r1V2XvlOxiFVJPvk6d9l8Pbejr5jsK5S5tMWg75xuXrVUZqaOWU2KR01Xv/soXXP/02Wt42Y3RUdtwx9OXKZzfsWWdl29gzqio0U531Uce3DO8Jh43nDyVJKe3r672C3psQfPUibnyups/WMv6OZVS8tayxza3lxx24XvOzIRN9mxngoJwpF/v30lCBtBvfa9XJXjfC7AvtHr9V3q1WVO1LOKcRWGLo3rpRZ/81rFUdQzdfeldMW3txSnTfXv2RSzik91N8WmFiPOadRDGJ956Fk9cPmJU1ovMQ1MD2G+bkdwop7piRd7tGFLV3Hagrak/uo9i/djqfaN+IUU3muSsO53CFaQx7Fp26KvtSlSHDPgtJsfy//79oPU2tT4N2kQnMLBvlRYDvZhLHeQqIN813UXn3SY1ty9Saff8rjW3L1JF590mJrjjX0cS0Y9ffrMY8qeHP/0mccoSfcDM0aY91/nnNZdtFy9A2ndvGppfmzMCt+lpz+tQw5I6vC5o5NPX3v0Oa1f3VlcrpBEW//YC8UbrJ88Y7FuvXC5mmKeOloTOrAlrgevPEm3X9ypOy9erq9fuEzHHjxLsYino+a1KFalTiWVtfxb0JbU7sGMpPzFYXtLXF+/cJlmDyfbzr/jSb3nHx/XhXf+Spmsr47WRHHezzz0rJIjuiosJD2XLZyjnO+q1seL3f06/ZbHy1oSjiyP71S27NPbd2vtxq2KRz0lolZWZ919KTXHI/rOZSfqBx8/RTecvUR/3D1YcduF1n9mVnwQwvedevrTmt0U1YYrTtKvPne6vn/1KeMaZ6+QICz9+40nQdgIConqUgvakgG0CCqXqHKcTwS4nXodR7wqLfoCHG5QktSejGvdiOPCutWdak82flzVSzxaOX7jU7h+qFUcxaOebrtoednf87aLlk+prP3pyj0i9E+x9aGTKt48mKqOlsox3RGCYyWAvapdY8ZCcN2O4LQmvIrH9KC7fw8a8QspvNfZs5oq73ezpjDeMcInyOPYtI2cwSrdtAzOoK4PkR+Xo9JBs9HHugvrSSpITVVuoDTNoGRROuuPail01X1PKd3gXWBmXeUuR7MBtvRAY2uKVdl/QzBO7lDWV18qqznNMc2dlVBrIqrbKyTtHtqyXYmYV2z5V6q7L6VMLqe15x6nx68/TTecvUT/8ONtZa3cjuho0a0/e05dvYP6+//6J+rqHVTOdzqwOa5D5jTrvDue1Kk3P6a1G38r3+W7FxxZpzevWqo3B9Pq6h3UsQfP0v/7qVP1jUs6tf6xFyRJ71syT4cc0KS25rgS0Yju+sVLZfvltRue0U2rlmrZwjlatnCObjh7iWIR012XvlPLFs7Rgrakvnr+O7SrP6V1q5cpHvW0+KCWiuX42qPPFddbSDwWPlv/2AvDN+tt1Lntm5eu0K6+tC6481e64V9+o7XnHqdHrztVX/7LP9UXf7hVF9z5pOY0x7R241bd8pM/6OZVS8vPjRct14HN+WTmF3/4G217Y4+yWV/b3tijv/3+s/rN62/p9d2DSmV9tSVj8rx8MrB7T0qv9Q6oe09qVCtpzzMdc9Asff/qU/SLz7x73AnCRhD1TF/58PFldfSVDx8feHIsW6Vr6SCfno5FTOtGxNq6GozR51dp7eQHfKptaopqcXuLHrj8RD1+/Wl64PITtbi9RU1N07aDlQnzpFH7+M2rlk7pB2ssYhXPR1ONI5M0OxnV3WtO0E+vO1V3rzlBs5NRTWWtuSpP8061J4fEJLsx3pd4PKpjOspj+piOFsUDHNsSQO1FI17FY2804IeE0Nj6Ur42/rqrrAvBjb/uCrz796ARv5Ck3qGMtry0U/dfdqIeu/403X/Zidry0k71DmX2d9HGtGfIL5b78ZJy03XyzBLkcWzaXoXTfBtSfqzG1oSn71x2YrEL16yf01CDj9VYejMo6ztFPVN7Mj6jbgb1lfRVXdpt5cdPP0rt+7twdZKtMs5XoyfMsrkq5Q54XCU0rv5U5f33mtOPUnvr/i7d2GKe6a5fvKSr332UTNLcWXElY57u+egJ2tWfVk9/Wt964iWtOWWRop5pV39at6/u1BUlAyff8qHj9aV//Z2e3r5bv/jMu7V249ZR3Yi82N2vn2zdob96z9F6azA/xl9X76DuuvSdunbDr9XVO6hlC+fokpMX6aJv/EpdvYN635J5uuejJ+jNwYx27Enppke26fMfXKIFbUn9/v/s0dqNW7XuouVaPK9VHbPi+uQZR5cNTH/jyqXq3pMuSzi+OZjRF85ZolTG13Xfe6Y477qLlqsvldXfb/xdcV2F8f/et2Se7vvv71LOd4pFPH3yO08X11lY77EHz9L9l71Lb7yZ0mfff6wG0jmls75mN0V095oT5Fk+wZKIevro3fkydvUOas3dm7SgLakbzl5SXOeu/rTWnnucDp/brKhnun11p5oTEb28c0Cf/8Fv1d2X0i0fOl7de9K67J7N2nDFSfrKv2/TJScvKiZwFrQldfvFnTpm3iw91903qjvPkYk8zzN1zJr44Nf7W873FYt6WnvucWqORzSQzikW9ZQLOGtV7To7F+B1tnNSPGpl8ZLzcwr6FOhc5dZOQZ+xfN/ppd6BfcbeTJYqGfeu0I3qTY9s01fPf8ek15n1pR8981rZ+ejBza/qklOOmHJZ/+ePfqeVnQuLZX1oy/YpdfkaH26RO/J8MdUWuZPtxng84vGo5pPYA0JtKJML/NiL8Mn6Trf/75d1+/9+uWz6hScevl/KM17ELyQpEpGWH96uC+/c+9v3touWK9LYnWHJTFp88AFl5b5x5VLx02BmCfI41lBX5WZ2pqSvSopI+oZz7suTXVdY++dFsJyTLv7nTaPiYKpjUtSa7zt1D6SUyjp5JmVyTt0upfnxyIy5GURf1VLEKh/HItbYMRCpcvyNzJDYRT4Gwrr/RiOePnH6Yv3TT5/TmlMW6cCWhD7/g9/q6ncfpT1DWc1JxnTBCYfpwJa4uvekdM7Xf6FH/uq/6P7LTtSOt4bU05/Wl//t93p6++58t5eejRprbt1Fy/X5H/xWkjS7KaYrvr23B4LSMfCuPO3IspZGP9m6Q1v/uEc3nL1EV3x7ixa0JTWrKVYc86/Q6vc7l52ooUxOa+7ee/4rHbOvMKZUoQvSeMQrJhoL815131O669J3SpJWdi4sJvlGliMe8dTdlyqrw0IiU5LW3L2pbPq3P3aCnHPaNZBRT39a82Ylqo67V1hmx55U8fvecPYSSRqVPL3ue88Uv1sm52tl58JRrbSu+PYWbbjipOLN7sL0y+7ZrO9ffUooE3sj+U7FcRcLanHdE6uSkAjy6ems7/Sxb22p+XdJxCqPyZgIeEzGnv70tI69IESqjHs3leuHeMT0wXcsKB4PF7QltX51p+JTbNGX9Z1+snWHfrJ1R9n0vztryaTX2dGa0PrVncXjbaGshS6WJ6u0lXI6m1M8GlF7S3zG/KYAMLZaHHsRPmG9h0r8QpIyWVex+/NGv/dbq3GUES5BHscapi2zmUUk3Srp/ZKWSLrAzCb9S6lW3bQgXKp1gRPkE+e18OZQSr0DGV1613/o9Fse16V3/Yd6BzJ6cyi174WniUSVrjsTM6jrTjPpxpXlzbdvXLlUDZ7nq1purrVnjjDHQCaX77rzghMOk2emrt4BfeL0xbrtZ88rnfMV8UyHtTfr/9myXa+/OaQFbUm9smtQ9/3yJSViEa3duLWY5Fu3ulOpzN4WKj+97lTdcPYS9aWyxeSYP6LlbmG8OUmak4xVTYIV6jQetVHdgmZyvnb1pysuW2jBUWh5uP6xF8qSi6XzDqRz+pu/OEYHz26qWo6vPfrcqO4Vb1yZ78pz5HiiXb2D8sx0849/r1TW19qNW7VjT6pil3KFeiiMbVi6zfaW+Jj1Eot4VefJ5vyK09PZnKaDerS0k6SmmI0ah3L96k41xYLbyevVO0e9xmRMZ3PTOvaCUItzR8b3FY2orIvNaCQ/fSqqjScYm8p4glFPxx40SxuuOEk/v/40bbjiJB170CxFA7j2LbRSnt/WrI5ZCZJ8AIrCfN2O4NSry/SgEb+QwturX71+u6GxBXkca6QWfSdIet4596Ikmdl3JZ0raetkVpbNucrdtJy8KMAio9GF9amkgZRf9WmUtub9XLg6aY1FdUBzrKzLrljU1BprpMNWbTknfeuJl8qab3/riZem1C1UPYS13AhQlRj4YghiID48lt0lJy8qdmV5xZ8drr89a4m696TU05/WHT9/QZecvEjfeuIl3bhyqb71xEu69r3H6Ki5LdpwxUnK5nxFI546WuL641tDxaezbr+4U2s3blVHa0I3rlyqzzz0rHK+KztPrX/shWILwEKya+Q57G1z8i3bvvXES7rghMPKus1c0JZUzndVlz34gCY9+qlT9cc3B4stDwfSuYrzNscj+vj9+RaClT7fPZhRd19KQxlfd685QT19Ke0ezOgffrxN3X0pDaTLExiF8+//OOc47RpI51sMmiq2YDkgGdXac48rS2IuaEtqIJ3TYe3NFcszkM7pzo+s0LzWhLI5v2qLs0rTpzpWVaOodt0T9FPNpeOTFc7R8ahNaXyykaq1GpxqN4Yj1au1UzxaueXgdIm9INTi+sFk+sef/GFUF5tfOve4KZU1EfV064XL9fH7nyoeu269cOoPpEWjnt42J7nvGQEgIPx2g5TvRWpjhXuoH2nwe6jEL6Tw3vut1283NLYgj2PmGmSsJzNbJelM59x/H35/saR3OeeuGTHf5ZIul6RDDz2085VXXqm4vv6hIb3Ukxp142hRe0ItTU21/TJoGJOIg5oeTccbv6/09OvUmx8bNf3x60/TYe0ttSxiQxkayqpnMD1jxynsHxrSiz0pXVUSv+tWd+qIBo/foaGsnuvpH1Xuxe0tM+rvN5NN8hzcEPHr+07b3tijr/z7Nq3sXKj2lrg6ZiU0uymiTC7f4s/MFDEp56SISZ7nVU0MZLO+tu3Yoyu+vUUdrQl9+sxjdP2Dz6qjNaFPnrFYSw6Zpe6+dFld3b3mnUpEIxpIZ5XJubLPbrtoue795St64sUeffX8d2hWU1QfvXvzqM93D6b1iTOOLtsPb161VAe2xLRnKKe/fuDXxel3XbpCu/ozZWP03bhyqWY3RfXBr/9CP/7rP9POvnTZeHeFBOeaUxapY1ZCzjmtKSnHutWdSsY8XXrXprJpx85rVSTiadsbe4rdGF7xZ4fr4pMXyfedPM/07Sde0n+8vLtYV8Ux9lZ35luimPTGcHeepZ8dMqdJc5L5v0NpvZeOSbW4o3VcY/RNJsSmsvA+Vz6B698JnjcmZWgoq95UWtmcimMgRyNSWyK483Qmk9O2HX2jjiPHzGtVLBa+5Fjh2NKgY/RN2/hNpbJ6vqe//HhxcaeOam9RIjH5WPV9p5d7+vVKz0BxPMzD2pt1eHtLI/w9Z5qGiN+Cwz/7o1oWp+jlL59Vl+2g5vZ7/Nbr2gGNbVf/kLp6h4oPnBd+Vyxoa9KBLY37+434hTTpe1A1i9/xXju8NTSkVyrE72HtCc0mfmeMSR7HKsZvIyX6PiTpL0Yk+k5wzn2i2jIrVqxwmzdvrvjZa70D8l1OnkWKSYLC+/kzpUkU1L0npeaY0+5BvxgHc5KeBjJWbTyUuv0y31f8nnfHkxXHpSF+Z4433hxUa8JGxW9fyumgAyo+7d0Q8SuRpJ3pJnkObpj49X2nnv50YK17StcXi3rKZn3lhpMjZlJLwlPfUH4/j3gm3znd98uXtWrFoZrVFFXOd8V6bIp6GsjkFDGT51lxHNfC5/Gop8F0TlHPFI2asjkpm/Pleaa4Z3LKJ2Z8l+/e2swUjZi+8IPfjGrxsrJzodZu3KoHLj9Rnif5/t6kjjec6Ix5pmTCUybnNJT29cc3hzSUySnimea2xtUUi8p3+bLNa00UEzTV6rh0ejKej59M1h/1dxjP32g82wiw9VZDxO9rvQOKRaVMVsWYKLwP+vqhHsf5TCanHX2p4jZKYyiMahR7QZjW8ZtKZbVzYG+szm2OTynJV9DAf8+ZpiHit4BEHyZov8dvPa8d0Lh83+m13QNKZV2xt4ZE1DR/TvNY5zbiFw1jEr9N6hK/Y107+L5T72BKQ+m99/ya4p7aknSzPpNM8jhWMUAa6a5rl6SFJe8XSHp9sitrT8aHs/mbRmTz6QplJmlviVd9erqRzWtNaN3qzlHZ/HmtFZOTmKbakjG9sntQXbsGi0+LLzgwqcNC0KVTU1NU80nszVhhPwcXxjKq5/pmJfa29im09otFPHmead6sprIE145JtAoq3JDODN+QbkvGii3bSlsajmyxd/OqpUpl/XG1UvGTTm8NZctaC1YrW7U6GW/dj2e+qW4jjPbue6OfZg1aPY7zsdj0ekBvOsdeEGoVv4lEVPMDSOyNxN8TwHRQz2sHNC7Pyyf1wvYAC/GLgjDeg/I8U1syoR4/LRV/pzf+fodgBXkca6QWfVFJf5B0hqTXJG2SdKFz7rfVlqFFCcZjgk/b7vcnkgqm21PsmJx0Oqvu/r3HsY6WuOLx+nc9MNJ4nmjGzNaoT9RJjRu/4z1fBdWKpFrrORtuaej7Tsl4pNgdZpDfYRpqmPjl+heTQPwizBomfqX6teibDFoBNqSGiF+OvZgk4hdhtt9b9AEFQd0/a5gjn3Mua2bXSPqxpIikb46V5BuPMGbzEbywPm073Z5ix+TE41HNr57YAxoW5+CJC7I1W5Db29/rxMSw7yHMiF8AqD+OvQgz4hdA2AV1HGuoI6Fz7mFJD+/vcgAAAAAAAAAAAACNztvfBQAAAAAAAAAAAAAwcST6AAAAAAAAAAAAgBBqqK47AQAAAAAAsP8d/tkf1WU7L3/5rLpsBwAAYLoi0QcAAAAAADCN1StpN91Mpt5IXAIAgHoz59z+LsOkmVm3pFfGMetcSTtrXJxGRx2Mrw52OufOrEdhiN8JoQ6I3zCb6XUw3u/faPEbhr9bo5ex0csnBVfGRotfKRz1P17T6btIjfd9iN+poay1wfXD/sd3qT3it/FQBxx/w4w6aLD4nabXvrVCHUwxfkOd6BsvM9vsnFuxv8uxP1EH4a2DsJY7SNRBeOsgrOUO0kyvg7B+/zCUu9HL2Ojlk8JRxsmaTt9tOn0Xafp9n1oIUx1R1toIU1lLhbXclfBdZh7qiTqQwlsHYS13kKiD8NZBWMsdJOpg6nXgBVkYAAAAAAAAAAAAAPVBog8AAAAAAAAAAAAIoZmS6LtjfxegAVAH4a2DsJY7SNRBeOsgrOUO0kyvg7B+/zCUu9HL2Ojlk8JRxsmaTt9tOn0Xafp9n1oIUx1R1toIU1lLhbXclfBdZh7qiTqQwlsHYS13kKiD8NZBWMsdJOpginUwI8boAwAAAAAAAAAAAKabmdKiDwAAAAAAAAAAAJhWSPQBAAAAAAAAAAAAIUSiDwAAAAAAAAAAAAghEn0AAAAAAAAAAABACJHoAwAAAAAAAAAAAEKIRB8AAAAAAAAAAAAQQiT6AAAAAAAAAAAAgBAi0QcAAAAAAAAAAACEEIk+AAAAAAAAAAAAIIRI9AEAAAAAAAAAAAAhRKIPAAAAAAAAAAAACCESfQAAAAAAAAAAAEAIkegDAAAAAAAAAAAAQohEHwAAAAAAAAAAABBCJPoAAAAAAAAAAACAEAp1ou/MM890knjxCvJVN8Qvrxq86ob45VWDV90Qv7xq8Kob4pdXDV51Q/zyqsGrbohfXjV41Q3xy6sGr7ohfnnV4FUXxC6vGr0qCnWib+fOnfu7CMCkEb8IM+IXYUb8IsyIX4QZ8YswI34RZsQvwoz4RVgRu6inUCf6AAAAAAAAAAAAgJmKRB8AAAAAAAAAAAAQQiT6AAAAAAAAAAAAgBAi0QcAAAAAAAAAAACEEIk+AAAAAAAAAAAAIISi9diImX1T0tmSdjjnjqvwuUn6qqQPSBqQdKlz7qmpbndoKKuewbSyvlPUM7Un42pqqstXRgMJaxyEtdxBog7CWwdhLTeCE+YYyGRy6h3MKJ3zlfOd4hFP8aipP51TxEyxiCfnnDzPU3tLXJ5nkiTfd+rpTyudzSkW9RT1TIPpnGIRT9mcL/Mk31exThJRT00xKZOTBtJ+cfqspKc9g/n3yVhE2ZyvjO8Uj3ryJOWck3OS75winqeoSYNZX7GIp4jt/TznOyWinnwnpXO+Yp5JJkXMlHNS1JMyOVfcblPMK35HG57PzJTJ+co5p6ZYRG1NMXX3p5XJ+cXvkPF9RSz/7FjEk5yTUtm93yce9ZTJOUVMikZN6YxTOucXv89Q1i/GSGnMNMU89aXy9TevNaFodO/zaaV1HY9Gyv4OpbJZXzv6Usrk/IrrmY7qte/VYzupVFY7B/ZuY25zXIlEOI4jmJxaxFWtYrUW6x3vsQ2Nyfeddg+mNJj2FYtY2Tm2NeGpL5U/N7bEI0plfZmpeD73hs+9clIi5hWXzQ0vH4t4kpzSOaeWuKfBjBt1Hm9PxhWNeuruTymd9RXxTMmYp5wvZXL+pGJqZEwekIiou39v3M9rTcjMys61c5tj2jmQmVHn3oJMJqcdfamy+onFIvu7WON2+Gd/NOFlXv7yWTUoCTB+Yf7dCRC/CFK9IuduSV+XdE+Vz98vafHw612S1g3/O2lDQ1k919Ovq+7doq7eQS1oS2rd6k4tbm9hh5lBwhoHYS13kKiD8NZBWMuN4IQ5BjKZnF7pHVD3npSuf/DZYvlvXrVUNz2yTd19Kd28aqmS8Yhu+9nzuva9x+iYg2ZJkra9sUeX3bO5bJnvP/Wa/tvy+brrFy/pkpMX6TMP7V3nuouW66ADEnrjrfSouvqnR/+g7j1pffrMY8rK8ZUPH69Y1NM19z9dsWy3XrhMQxlf133vGXW0JkYt/7/Oe4diEdPDz76us46fr6vve6r42W0XLde9v3xFT7zYo69fuEwxz/TWULZs+ULZfrJ1R3GZeNT0jz/5gz595jGKRSN6cyBTtt51Fy3Xxmde0/uXvk2ZrK9rNzwzquznHn+wOhfNLauH0vKsX92pYw+apWjUk++7UXV950dW6JiDZpXdvMxmff3+jT26smSdpeuZjuq179VjO6lUVn/YOXobR89tIdk3TdUirmoVq7VY73iPbWhMvu/02u4B9Q5k9KNnXtPZx8/XVcPnwiv+7HCd/Y4FuureLTr5iHatPukwff2nz426Lrhx5VL9fNsbWrlioXr60qPO/20tcf3u9d06vGO2Nv66a9R5fP3qTiVjni65a5O6egf1viXzdM3pi8vmmUhMjYzJL5597Khz9frVnWqKebp0eJuVrhWm+7m3IJPJ6fc7+kYdF46d1xqqZB8QJmH+3QkQvwhaXa60nHM/l7RrjFnOlXSPy3tS0hwzO2Qq2+wZ3HvTTJK6egd11b1b1DOYnspqETJhjYOwljtI1EF46yCs5UZwwhwDO/pS2r5rsHhzTcqX//oHn9WVpx1Z/H9vf0YrOxfqsns2q6c/rZ7+dPFGWOkyl/35Ebr+wWe1snNh8WZe4fOr7ntK6ayrWFcrOxfqytOOHFWOazc8o97+TNWy7erP6Lrv5RNplZb/6wd+rV39Ga1acWjxxl/hs6vve0qX/fkR6uodVG9/Rjv2pEctXyhb6TJRL4q3iUIAACAASURBVDI8zVMm60at96r7ntKqFYeqtz9TTPKNLPvpSw4ZVQ+l5bny3i3a0ZeSpIp1Xfg7jPxbXjlinaXrmY7qte/VYzs7BypvY+dA4x9HMDm1iKtaxWpNyjrOYxsaU09/Wqnhc+CqFYcWk3yS8u+H4+WyPz9CV9/3VMXrgs889KxWrThUr/UOVTz/b981qGWHteuqe7dUPI9fee8WvbprsDhtZefCUfNMJKZGxmSlc/WV927R9pJtVrpWmO7n3oIdfamKx4WZ8N2B/SXMvzsB4hdBa5RHquZL2l7yvmt42ihmdrmZbTazzd3d3VVXmPVdcUcprrR3UFnfBVBchEWjxQHxO37UQePVAfGL8WrEGJhI/DbHIxXLPycZK/6/OR7RnGRMXb2DSmdzSmdzFZeJeFZcttLnuSp1NScZq7pMczxScX5JZWUfa/lCuSqVt7CefdVD4b1n+W15Jnmmqusda32+q1wPhfJ09Q4qm/MlqWpdp7O5smmZnF85DofXEyaNdvytx3Ya8TiCydmf8VurOKrFesd7bEN9jTd+09lc8Rw48hxb+n5f1wVjnS+b45HidUO183jpNUK1bYw3pkbGZLVz9VjXJYX3YTz3TlQjnrfGG79AIxpP/Dbifgc02m83zByNkuir1G9Exah2zt3hnFvhnFvR0dFRdYVRz7SgLVk2bUFbUlG6PZlRGi0OiN/xow4arw6IX4xXI8bAROJ3IJ2rWP7dg5ni/wfSOe0ezGhBW1LxaETxaKTiMjnfFZet9HmkSl3tHsxUXWYgnas4v6Syso+1fKFclcpbWM++6qHw3nf5bflO8p2qrnes9XlWuR4K5VnQllQ0kr9srVbX8Wj5jcZYxKsch5FGufwdv0Y7/tZjO414HMHk7M/4rVUc1WK94z22ob7GG7/xaKR4Dhx5ji19v6/rgrHOlwPpXPG6odp5vPQaodo2xhtTI2Oy2rl6rOuSwvswnnsnqhHPW+ONX6ARjSd+G3G/Axrttxtmjka52uqStLDk/QJJr09lhe3JuNat7izuMIV+btuT8amsFiET1jgIa7mDRB2Etw7CWm4EJ8wxMK81oYUH5seOKy3/zauWav1jLxT/39YS00NbtuvOj6xQe0tc7S1x3fmRFaOWufPnL+rmVUv10JbtunFl+TrXDY9vV6muHtqyXesfe2FUOfJj9MSqlu3Alphu+dDx+TFxKiz/v857hw5sienBza/qtouWl31220XLdefPX9SCtqTaWmKaNys+avlC2UqXyfq54Wm+YlEbtd51Fy3Xg5tfVVtLTF/58PEVy/7TrX8cVQ+l5Vm/ulPzWhOSVLGuC3+HkX/L9SPWWbqe6ahe+149tjO3ufI25jY3/nEEk1OLuKpVrNakrOM8tqExtbfElRg+Bz64+VWtKzkXPrj51WK83PnzF3XbRcsrXhfcuHKpHtz8qua3NVU8/y88MKmnX+nRutWdFc/j61d36tADk8VpD23ZPmqeicTUyJisdK5ev7pTC0u2WelaYbqfewvmtSYqHhdmwncH9pcw/+4EiF8EzZyrT3NQMztc0kbn3HEVPjtL0jWSPiDpXZK+5pw7YV/rXLFihdu8eXPVz4eGsuoZTCvrO0U9U3syzmCWM9AE46Buj00Qv/tGHRC/CK9JxEDDxG8mk1PvYEbpnK+c7xSPeIpHTf1pXxHLtxRzzsnzPLW3xOUNP3Hn+049/WmlsznFop6inmkwnVMs4imb82Weyfedcr5TxDMlop6aYlImJw2k/WJdzUp62jOYf5+MRZTN+cr4TvGoJ09Szjk5l+9CK+J5ipo0mPUVi3iK2N7Pc84pEfHku3w3llHPJJMiZso5KepJmZwrbrcp5qk/nVPETDY8n5kpk/OVc1JTzFNbU0zd/eni+hJRTxnfKWL5Ooh4knNSKrv3+ySintI5p4hJ0agpnXHK5HzFhr/PUNYvxkhpzDTFPPWncopGPM1rTSga3ft8Wmldx6ORsr9DqWzW146+lLI5v+J6AtQw8Vuv4289tpNKZbVzYO825jbHlUhwLqmBaR2/tYrVWqx3vMc2lGmY+PV9p92DKQ2mfcUiVnaObU146kvlz40t8YhSWV9mKp7PveFzr5yUiHnFZf3ha4ZYxJPMKZ11aol7Gsy4Uefx9mRc0ain7v6UMllfnmdKxjzl/Px1wGRiamRMHpCIqLt/b9zPa03IzMrOtXObY9o5kKnHubfhZDK5fF2U1E8sNmYLyoaJX0k6/LM/mvB6X/7yWZMtEsKvIeKXew+YpLrEb6P8dsO0UzF+6xI5ZvYdSadJmmtmXZK+ICkmSc659ZIeVj7J97ykAUlrgthuU1NU89k5ZrywxkFYyx0k6iC8dRDWciM4YY6BWCyieRVuyrTvYznPM3XMGvHUdsv4ttk2Yr7ZTeNbLmj7+o6S9LY5yX3PNEkjY6a9tfJ8Feu6gmjUq2l5G1G99r16bCeRiGo+ib0ZpRZxVatYrcV6x3tsQ2PyPNOBLU1Vz/0HNAe3rbYxPjvkgODOe5Vicn58dNyPPNe+rcI8M0EsFtH8tgD/0AD2Kcy/OwHiF0GqSyQ55y7Yx+dO0sfrURYAAAAAAAAAAABgOpgZ/ScAAAAAAAAAAAAA0wyJPgAAAAAAAAAAACCESPQBAAAAAAAAAAAAIUSiDwAAAAAAAAAAAAghEn0AAAAAAAAAAABACJHoAwAAAAAAAAAAAEKIRB8AAAAAAAAAAAAQQiT6AAAAAAAAAAAAgBAi0QcAAAAAAAAAAACEEIk+AAAAAAAAAAAAIIRI9AEAAAAAAAAAAAAhRKIPAAAAAAAAAAAACCESfQAAAAAAAAAAAEAIkegDAAAAAAAAAAAAQohEHwAAAAAAAAAAABBCJPoAAAAAAAAAAACAECLRBwAAAAAAAAAAAIQQiT4AAAAAAAAAAAAghOqW6DOzM81sm5k9b2afrfD5oWb2MzN72syeNbMP1KtsAAAAAAAAAAAAQNjUJdFnZhFJt0p6v6Qlki4wsyUjZvs7SRucc8sknS/ptnqUDQAAAAAAAAAAAAijerXoO0HS8865F51zaUnflXTuiHmcpNnD/z9A0ut1KhsAAAAAAAAAAAAQOvVK9M2XtL3kfdfwtFJflLTazLokPSzpE5VWZGaXm9lmM9vc3d1di7ICNUP8IsyIX4QZ8YswI34RZsQvwoz4RZgRvwgz4hdhRexif6lXos8qTHMj3l8g6W7n3AJJH5D0bTMbVT7n3B3OuRXOuRUdHR01KCpQO8Qvwoz4RZgRvwgz4hdhRvwizIhfhBnxizAjfhFWxC72l3ol+rokLSx5v0Cju+b8mKQNkuSc+6WkJklz61I6AAAAAAAAAAAAIGTqlejbJGmxmS0ys7ik8yX9cMQ8r0o6Q5LM7O3KJ/po3woAAAAAAAAAAABUUJdEn3MuK+kaST+W9DtJG5xzvzWzL5nZOcOzXSfpMjN7RtJ3JF3qnBvZvScAAAAAAAAAAAAASdF6bcg597Ckh0dM+3zJ/7dKOqVe5QEAAAAAAAAAAADCrF5ddwIAAAAAAAAAAAAIEIk+AAAAAAAAAAAAIIRI9AEAAAAAAAAAAAAhRKIPAAAAAAAAAAAACCESfQAAAAAAAAAAAEAIkegDAAAAAAAAAAAAQohEHwAAAAAAAAAAABBC4070mdlNZnZlhenXmtmNwRYLAAAAAAAAAAAAwFgm0qLvbEl3VJj+VUlnBVMcAAAAAAAAAAAAAOMxkUSfc875FSb6kiy4IgEAAAAAAAAAAADYl4kk+gbMbPHIicPTBoMrEgAAAAAAAAAAAIB9iU5g3s9L+jcz+3tJW4anrZD0OUl/HXTBAAAAAAAAAAAAAFQ37kSfc+7fzOy/Srpe0ieGJ/9G0krn3H/WonAAAAAAAAAAAAAAKptIiz45534j6ZKx5jGzf3LOfWKseQAAAAAAAAAAAABMzUTG6BuvU2qwTgAAAAAAAAAAAAAlapHoAwAAAAAAAAAAAFBjJPoAAAAAAAAAAACAEKpFos9qsE4AAAAAAAAAAAAAJWqR6PtqpYlmdqaZbTOz583ss1Xm+bCZbTWz35rZ/TUoGwAAAAAAAAAAADAtRMc7o5n9qyRX7XPn3DnD/95dYdmIpFslvVdSl6RNZvZD59zWknkWS/qcpFOcc71mNm+8ZQMAAAAAAAAAAABmmnEn+iT9w/C/fynpYEn3Dr+/QNLL+1j2BEnPO+delCQz+66kcyVtLZnnMkm3Oud6Jck5t2MCZQMAAAAAAAAAAABmlHF33emce9w597ikZc6585xz/zr8ulDSf9nH4vMlbS953zU8rdTRko42s1+Y2ZNmdmalFZnZ5Wa22cw2d3d3j7f4QEMgfhFmxC/CjPhFmBG/CDPiF2FG/CLMiF+EGfGLsCJ2sb9MZoy+DjM7ovDGzBZJ6tjHMlZh2shuQKOSFks6TflWgt8wszmjFnLuDufcCufcio6OfW0WaCzEL8KM+EWYEb8IM+IXYUb8IsyIX4QZ8YswI34RVsQu9peJdN1ZcK2kx8zsxeH3h0u6Yh/LdElaWPJ+gaTXK8zzpHMuI+klM9umfOJv0yTKCAAAAAAAAAAAAExrE070OeceMbPFko4dnvR751xqH4ttkrR4uPXfa5LOl3ThiHn+RfmWfHeb2Vzlu/J8UQAAAAAAAAAAAABGmXDXnWbWLOl6Sdc4556RdKiZnT3WMs65rKRrJP1Y0u8kbXDO/dbMvmRm5wzP9mNJPWa2VdLPJF3vnOuZaPkAAAAAAAAAAACAmWAyXXfeJWmLpJOG33dJ+p6kjWMt5Jx7WNLDI6Z9vuT/TtKnhl8AAAAAAAAAAAAAxjDhFn2SjnTO3SQpI0nOuUFJFmipAAAAAAAAAAAAAIxpMom+tJklJTlJMrMjJe1rjD4AAAAAAAAAAAAAAZpM151fkPSIpIVmdp+kUyRdGmShAAAAAAAAAAAAAIxtwok+59y/m9lTkk5UvsvOv3LO7Qy8ZAAAAAAAAAAAAACqmnDXnWb2Jedcj3PuR865jZJ2DbfsAwAAAAAAAAAAAFAnkxmj71Az+5wkmVlC0r9Iei7QUgEAAAAAAAAAAAAY02QSfWsk/elwsu9fJf3MOffFQEsFAAAAAAAAAAAAYEzjHqPPzJaXvP2qpNsl/ULS42a23Dn3VNCFAwAAAAAAAAAAAFDZuBN9km4Z8b5X0pLh6U7S6UEVCgAAAAAAAAAAAMDYxp3oc86928w8SR9yzj1QwzIBAAAAAAAAAAAA2IcJjdHnnPMlfbxGZQEAAAAAAAAAAAAwThNK9A37dzP7GzNbaGYHFl6BlwwAAAAAAAAAAABAVRMZo6/go8P/lrbsc5KOmHpxAAAAAAAAAAAAAIzHhBN9zrlFtSgIAAAAAAAAAAAAgPGbTIs+mdlxkpZIaipMc87dE1ShAAAAAAAAAAAAAIxtwok+M/uCpNOUT/Q9LOn9kv4/SST6AAAAAAAAAAAAgDrxJrHMKklnSPo/zrk1ko6XlAi0VAAAAAAAAAAAAADGNJlE36BzzpeUNbPZknZIOiLYYgEAAAAAAAAAAAAYy2QSfZvNbI6kOyVtkfSUpP/Y10JmdqaZbTOz583ss2PMt8rMnJmtmETZAAAAAAAAAAAAgBlhwmP0OeeuHv7vejN7RNJs59yzYy1jZhFJt0p6r6QuSZvM7IfOua0j5psl6ZOSfjXRcgEAAAAAAAAAAAAzyWRa9MnM/tLM/lHSJyQdOY5FTpD0vHPuRedcWtJ3JZ1bYb61km6SNDSZcgEAAAAAAAAAAAAzxYQTfWZ2m6QrJf2npN9IusLMbt3HYvMlbS953zU8rXS9yyQtdM5t3Mf2LzezzWa2ubu7e6LFB/Yr4hdhRvwizIhfhBnxizAjfhFmxC/CjPhFmBG/CCtiF/vLZFr0nSrpL5xzdznn7pL0AUmn7WMZqzDNFT808yR9RdJ1+9q4c+4O59wK59yKjo6O8ZcaaADEL8KM+EWYEb8IM+IXYUb8IsyIX4QZ8YswI34RVsQu9pfJJPq2STq05P1CSWOO0ad8C76FJe8XSHq95P0sScdJeszMXpZ0oqQfmtmKSZQPAAAAAAAAAAAAmPai453RzP5V+VZ4B0j6nZn9x/D7d0l6Yh+Lb5K02MwWSXpN0vmSLix86Jx7U9Lckm09JulvnHObx1s+AAAAAAAAAAAAYCYZd6JP0j9MdiPOuayZXSPpx5Iikr7pnPutmX1J0mbn3A8nu24AAAAAAAAAAABgJhp3os8593jpezObPcHlH5b08Ihpn68y72njXS8AAAAAAAAAAAAwE02kRZ8kycwul7RW0qAkX5Ip34XnEcEWDQAAAAAAAAAAAEA1E070Sbpe0p8453YGXRgAAAAAAAAAAAAA4+NNYpkXJA0EXRAAAAAAAAAAAAAA4zeZFn2fk/SEmf1KUqow0Tn3ycBKBQAAAAAAAAAAAGBMk0n03S7pp5L+U/kx+gAAAAAAAAAAAADU2WQSfVnn3KcCLwkAAAAAAAAAAACAcZvMGH0/M7PLzewQMzuw8Aq8ZAAAAAAAAAAAAACqmkyLvguH//1cyTQn6YipFwcAAAAAAAAAAADAeEw40eecW1SLggAAAAAAAAAAAAAYv3F33Wlmny75/4dGfPY/gywUAAAAAAAAAAAAgLFNZIy+80v+/7kRn50ZQFkAAAAAAAAAAAAAjNNEEn1W5f+V3gMAAAAAAAAAAACooYkk+lyV/1d6DwAAAAAAAAAAAKCGohOY93gze0v51nvJ4f9r+H1T4CUDAAAAAAAAAAAAUNW4E33OuUgtCwIAAAAAAAAAAABg/CbSdScAAAAAAAAAAACABkGiDwAAAAAAAAAAAAghEn0AAAAAAAAAAABACNUt0WdmZ5rZNjN73sw+W+HzT5nZVjN71sweNbPD6lU2AAAAAAAAAAAAIGzqkugzs4ikWyW9X9ISSReY2ZIRsz0taYVzbqmkByXdVI+yAQAAAAAAAAAAAGFUrxZ9J0h63jn3onMuLem7ks4tncE59zPn3MDw2yclLahT2QAAAAAAAAAAAIDQqVeib76k7SXvu4anVfMxSf9W6QMzu9zMNpvZ5u7u7gCLCNQe8YswI34RZsQvwoz4RZgRvwgz4hdhRvwizIhfhBWxi/2lXok+qzDNVZzRbLWkFZJurvS5c+4O59wK59yKjo6OAIsI1B7xizAjfhFmxC/CjPhFmBG/CDPiF2FG/CLMiF+EFbGL/SVap+10SVpY8n6BpNdHzmRm75H0t5JOdc6l6lQ2AAAAAAAAAAAAIHTq1aJvk6TFZrbIzOKSzpf0w9IZzGyZpNslneOc21GncgEAAAAAAAAAAAChVJcWfc65rJldI+nHkiKSvumc+62ZfUnSZufcD5XvqrNV0vfMTJJedc6dU4/yAQAAAAAAAMB0cfhnfzThZV7+8lk1KAkAoNbq1XWnnHMPS3p4xLTPl/z/PfUqCwAAAAAAAAAAABB29eq6EwAAAAAAAAAAAECASPQBAAAAAAAAAAAAIUSiDwAAAAAAAAAAAAghEn0AAAAAAAAAAABACJHoAwAAAAAAAAAAAEKIRB8AAAAAAAAAAAAQQiT6AAAAAAAAAAAAgBAi0QcAAAAAAAAAAACEEIk+AAAAAAAAAAAAIIRI9AEAAAAAAAAAAAAhRKIPAAAAAAAAAAAACCESfQAAAAAAAAAAAEAIkegDAAAAAAAAAAAAQohEHwAAAAAAAAAAABBCJPoAAAAAAAAAAACAECLRBwAAAAAAAAAAAIQQiT4AAAAAAAAAAAAghEj0AQAAAAAAAAAAACEUrdeGzOxMSV+VFJH0Defcl0d8npB0j6ROST2SznPOvTyVbQ4NZdUzmFbWd4p6pvZkXE1NdfvKaBBhjYOwljtI1EF46yCs5UZwwhwD6XRWOwcyaomb+lK+zCTnpJzv5HmmmGcyk7K+FDHJTMrkXPG7xiJWnD/jO8UinhJR02Dal2eS55kSMdNAylfWd5rVFNFg2i8uP6vJ01BGSud8+b5TZHh7Uc9TNucrMzxfc9xTJqeyaS0JT28O5oqfD2acMjlfUc+UiHoayubXGfVMMilippzLvy/9Dk0xT0MZXy0JT9mc5DunTM4p55wSEU9ZPz9vLOKpOW4ayjilsvntRD1TOucr4pkiZvKdk3NSZvi7xLz8Np2TmmKeMjkn30lmTr6/t56TMU+pTL5eCnUQj3hKZf2yus7knBJRT06m9pa4PM+UzfraNZBWOucr5zslYxHNbU3I80y+79TTn1Y6m1M8GikuU2o88zSqeu179djOdPou9dxOKpXVzoG925nbHFciEY7jby3qqFb1Xov1ptNZdffvXWdHS1zx+NTLWqv11iLWwnr8LcTDAUlPA2nJufw1QOEcVHquLpznnXOKR00DJdcATTFP6ZwvT1ZcPh7xFItY8Rwej3qa25JQNJp/bnvk37c14akv5SvnnJpiEc2ORctitTnu6a2hnGIRT/Na966nkkwmpx19qeKy81oTisUiY9ZFreINACoJ8+9OBCescRDWciNYQcVBXSLHzCKSbpX0XkldkjaZ2Q+dc1tLZvuYpF7n3FFmdr6kGyWdN9ltDg1l9VxPv666d4u6ege1oC2pdas7tbi9hR1mBglrHIS13EGiDsJbB2EtN4IT5hhIp7P6Q3e/dvUNqq01qX969A+65ORF+sxDzxa/y82rlqq9Na6HNm/XOe+YL99JV933VPHzb166QnuGsvqr7/66bJmbHtmm7r6Ubr1wuZoTEa25a5PO61yg095+ULGu3rdknq4/81jt3JPS/8/evQfJedd3vv98n77NTbbl0YxjJBkbR7bjJb5o5pBdcs5Zrx04AqdwKhLY2IqBpExkYxMIazBnT5EUW7UFeFngGEvCznIxMmCQqhIn9kI2gLN1wrJ4RsbaxXewjUYOzGgkX+bWt+d3/ujpVl9HPTPdj/rX835Vdc30c/n9vr9vf/t5np7fdPft+0/0+dl3XqqeRKAPfOOxipye3hvT9ff+pGLZk0de1j88NakPXnWBdpU9Brtv2Kov/uBZ/f0Tk9q0vlefv/YyJWKmhw+9pKsv3ahbysawZ+eIxp8/qpHzNuh1pyc1+WpGu/aNa2ggpY9uu7Aitj07R3TX958ptVs51suVzTt96IHKXPQmY9r9w+d0+7aLNLOQ055HnqvI81svHtatV26piOmv3jOibM5V5Hr3DVv10ONHdPWlG/XQ40f0B1s36zc39OvF43OaqsrhvX80qi3DA3p2akY33Td2YvmNo7rwrHWlPySHodPTv35tyW06VVTPvSj66aaxRNlPOp3TM0dr+7lgQ3/HT/a1I0ftyns72s1kcnp6qrbNC4f6VzVJ0q5221Frvh5/i/XQn5B+lQ2Uzub16kJOt+8/1PC8+RunJZXJh5p8OVeZwxu2aqAnpl+9cuIc9taLh3XblVsqzn9f+qMRXTi8TmEY1n18i+flu667ROcOnVaz/pEnf60Hxie0d+eILjprXd3Jvmw2r6cmZ2r2vWh4oOFkX7vqDQDq8fl1J1rH1zrwNW60VivrIKqP7nyTpOecc79wzmUkfUvSNVXbXCPpa4u/75d0lZmt+Gp+ej5TSpAkTRyf1837xjU9n1lpk/CQr3Xga9ytRA78zYGvcaN1fK6BqdnChNb5w4U/im0f2VyafJIKY7l9/yEdOb6gHaPnaPK1TOkPb8X1R44vlCb5yvfZdcX5mjg+rw9846Amjs1r4vi8rtm6qSJX20c2a+LYfOmPe8X9P/Kdx3VsNluTU1NQs+zNW4a0fWRzaZKvuO6W+w9q+8jm0v0PPfBTHZvNasfoOaUJtfJ2rrz4bN28b1wLWVdqa9cV59fEVsxTvbEem82WJvnK1x+fzZbGets3H6vJ8/aRzTUxxYNYTa5vuf9gKf4do+fopvvGNDmT1uE6Obzp64V1xT8gl5bfN6bp2RO1OT2bOek2nSqq514U/XTTWKLs5+hc/X6Ozq3N+m1X3tvR7tRs/TanVnnsaVe77ag1X4+/xXpIJRI6fGxek69lSuegRufNTM7JOavN4f0HFQ9iFftsH9lcc/7706+Pa3Im3fDxLZ6XL3/9YN3112zdVIhvX6GdeiZn0nX3bbS91L56A4B6fH7didbxtQ58jRut1co6iGqib6Okw2X3JxaX1d3GOZeT9IqkweqGzOz9ZjZmZmNTU1MNO8yFrpSgUqfH55UL3YoGAD91Wh1Qv80jB52XA+oXzerEGlhu/RZ/ntGbqDuWvmRMscDUl4zVrK+3rNhW+f5S4SMxy7c9ozfRcP/iPuXLQudqluVD1zDuYgzV46i3rXOVuSjG10y7xftLjaV8rNXt1usnMNVtqxh/8WcudA37zeXDusszuXzpfiaXP+k2Ueu0428U/XTTWLqxn+U4lfXbrnys9Vjb1a6vx9/ya4e+ZKziHNTovJkLCx+HXW9d9fKGbeTDho9D8Tycb7DeLV4/FNtZalz1Ym+kE49Ba1mzx1+gEy3n+FuOY87a02l10Gmv3dDZWlkHUU301XtnXnW0zWwj59w9zrlR59zo0NBQww7jgWnT+t6KZZvW9xa+kwZrRqfVAfXbPHLQeTmgftGsTqyB5dZv8efL89m6Y5nL5JUPneYy+Zr19ZYV2yrfX5ICq8zVy/PZhvsX9ylfFlR9+MGm9b2KBdYw7mIM1eOot61ZZS6K8TXTbvH+UmMpH2t1u/X6CZ3qtlWMv/gzHljDfuOxoO7yZPzEJGoyHjvpNlHrtONvFP1001i6sZ/lOJX12658rPVY29Wur8ff8muHuUy+4hzU6LwZX/we23rrqpc3bCMWNHwciufhWIP1xQ9PKraz1Ljqxd5IJx6D1rJmj79AJ1rO8bccx5y1p9PqoNNeu6GztbIOoprom5C0uez+JkkvNdrGzOKSTpd0bKUdDvYmtWfnSClRxc83HexNrrRJ2DiRUwAAIABJREFUeMjXOvA17lYiB/7mwNe40To+18BQf1J7d47o55Ovas/OER0YP6xPb7+kYix37rhEG9f3aP/YLzW8Lqk9N2ytWL9xfY++cN1lNfvsfeTn2rS+V3dfv1WbzuzVpvW9+puDExW5OjB+WJvOLGxfvv9n33mpzuxP1OTUKaxZ9qNnp3Rg/LD2Vj0Gu2/YqgPjh0v3P3/tZTqzP6H9Y7/U7qox7Nk5oh888c/as3NEPQkrtbX3kZ/XxFbMU72xntmf0Oevrc3F+v5Eaax3vfvymjwfGD9cE1MuzNfkevcNW0vx7x/7pe69cVTDAyltrpPDe/+osO7eG0crl984qsH+E7U52J886TadKqrnXhT9dNNYouxnQ1/9fjb0rc36bVfe29HuUH/9NodWeexpV7vtqDVfj7/Fekhns9p8Zq+G1yVL56BG581k3GTmanN4w1blwnzFPgfGD9ec/770RyMaHkg1fHyL5+XHXpyuu/5vDk4U4ttZaKee4YFU3X0bbS+1r94AoB6fX3eidXytA1/jRmu1sg7Mufa/HXRx4u4ZSVdJOiLpUUnXO+d+VrbNByT9tnNul5ldJ+kPnXPvWqrd0dFRNzY21nD9wkJO0/MZ5UKneGAa7E3yZZZr0DLrILJ/m6B+T44cUL/w1wpqoGPqN5PJ6ehcVv1J00w6lJnkXOHjr4LAlAhMFki5vBQzyUzK5l1prImYlbbPhk6JWKBU3DSfDRVICgJTKmGaSxc+cmtdT0zzmbC0/7reQAsZKZMPFYZOscBkJsWDQLl8qOzidn3JQNm8Kpb1pwK9Mp8vrZ/POuXyoWKBKRUPtJArtBkPTDIpZqa8K9wvH0NPItBCNlR/KlAuX/iI0Wy+8DFjqVhQ+Mix0CkeC9SXNC1knTK5Qj/xwJRZ7DNmptA5OVf4SIpi/vKLy3oSgbJ5p9BJZk5heCLPvYlA6WwhL7HAFJiUiAVK58KKXGfzTql4ICfTYH9SQWDK5UIdm8uUctiTiGnDQEpBYApDp+nZjDK5vJLxWGmfcs1sU6Vj6jeq428U/XTTWKLsJ53O6ejciX429CWVSvlx/G1HjtqV93a0m8nkNDV7os2h/qSSydXH2q52V1BrJ+Xr8bdYD6f3BprLSM4VrgHyoVNvIlZxrl7XE2ghW9gmGTfNlV0D9CQCZfKhApmyoVO4eB2RiFnpHJ6MB9rQn1I8Xvi/7erHdyAVaCYdKr94nj0tEa+o1b5koNcW8orHAg0PnGinnmw2r8mZdGnf4YGUEoml32HZrnrrUh1Rv0Xn3vHQstt94VNXrzQkRKDNj2lH1C9/e4DUuX9/6JTXbuhsrarfSCrHOZczs1slfU9STNKXnXM/M7NPShpzzj0o6T9L+rqZPafCO/muW22/PT1xbeTJseb5Wge+xt1K5MDfHPgaN1rH5xpIJuN63eIfpE7va18/68vb7q9cd1rPyts9o6zd9StvxmvxeKDhBkkMAtPQusbvRmh2m04V1XMvin66aSxR9pNKxbVxlZMtp0o7ctSuvLej3WQyro1tmBBpV7vtqDVfj7/l9TDQxDn8tLJPaFrf33i7ZtR7fKuvX6prtdk+E4mYNq5f3sVQu+oNAOrx+XUnWsfXOvA1brRWq+ogskpyzj0s6eGqZZ8o+31B0jujigcAAAAAAAAAAADwWVTf0QcAAAAAAAAAAACghZjoAwAAAAAAAAAAADzERB8AAAAAAAAAAADgISb6AAAAAAAAAAAAAA+Zc+5Ux7BiZjYl6cUmNt0g6Wibw+l05KC5HBx1zm2LIhjqd1nIAfXrs7Weg2bH32n168Pj1ukxdnp8Uuti7LT6lfzIf7O6aSxS542H+l0dYm0Prh9OPcbSftRv5yEHHH99Rg46rH679Nq3XcjBKuvX64m+ZpnZmHNu9FTHcSqRA39z4GvcrUQO/M2Br3G30lrPga/j9yHuTo+x0+OT/IhxpbppbN00Fqn7xtMOPuWIWNvDp1jL+Rp3PYxl7SFP5EDyNwe+xt1K5MDfHPgadyuRg9XngI/uBAAAAAAAAAAAADzERB8AAAAAAAAAAADgobUy0XfPqQ6gA5ADf3Pga9ytRA78zYGvcbfSWs+Br+P3Ie5Oj7HT45P8iHGlumls3TQWqfvG0w4+5YhY28OnWMv5Gnc9jGXtIU/kQPI3B77G3UrkwN8c+Bp3K5GDVeZgTXxHHwAAAAAAAAAAANBt1so7+gAAAAAAAAAAAICuwkQfAAAAAAAAAAAA4CEm+gAAAAAAAAAAAAAPMdEHAAAAAAAAAAAAeIiJPgAAAAAAAAAAAMBDTPQBAAAAAAAAAAAAHmKiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAh5joAwAAAAAAAAAAADzERB8AAAAAAAAAAADgISb6AAAAAAAAAAAAAA8x0QcAAAAAAAAAAAB4iIk+AAAAAAAAAAAAwENM9AEAAAAAAAAAAAAeYqIPAAAAAAAAAAAA8JDXE33btm1zkrhxa+UtMtQvtzbcIkP9cmvDLTLUL7c23CJD/XJrwy0y1C+3NtwiQ/1ya8MtMtQvtzbcIkP9cmvDLRLULrc23eryeqLv6NGjpzoEYMWoX/iM+oXPqF/4jPqFz6hf+Iz6hc+oX/iM+oWvqF1EyeuJPgAAAAAAAAAAAGCtYqIPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOCheBSdmNmXJf2+pEnn3BvrrDdJX5D0dklzkt7rnDu42n4XFnKans8oFzrFA9Ngb1I9PZEMGR3E1zrI5UJNzqSVzYdKxAIND6QUj6+tuXly4G/9+ho3WqcbaiAMnaZnM8rk8upNxpQLnbK5UMl4TIP9SUkqrTczxUwKgkCD/UkFgS3ZZjqXl0kyk+JB4biWyYelZal4oHTOKZsPFQ9MqXggJ6vptxhLEFhFvGYmM6cwLLTnnBQPTLHASu3UizEMnY7OprWQzSswUyIwJeKBzuit7aO871bludk2m9mnHbH6IKrnXjab1+RMutTP8EBKiUSspX1022OYyeQ0NXvisRnqTyqZ9Ou42G7tqN9uOB91qm57jrZCdb0lYqZs3mmwN1mxfENfUqlUc3XIayK0G8dJdINz73ho2fu88Kmr2xAJ0DyOv2ilqCrnq5K+KOm+BuvfJmnL4u13JO1Z/LliCws5PTs9q5v3jWvi+Lw2re/Vnp0j2jLYzxNmDfG1DnK5UE/9+jXtKot7784RXXTWujXzoo4c+Fu/vsaN1umGGghDp6d//Zpuum9MQwMpfXTbhbp9/6HSeO774zcpnQt1031jpWWf3n6Jvvaj5/Xht1yoC89aV3fiqdhmcZ/PvvNS9SQCfeAbj5WWff7ay3Rab1x//NUT2+2+YaseevyI/mDrZqXigW788k9K6+69cVRbhgb07NRM3Xje8+bz9LUfPa+b/o836PS+hA6MHdYfbN1cE2O9+O7ccYn6kjG93JPVOev7avq498bRumNdSZ6bbbOZfVbSbjeI6rmXzeb11ORMTT8XDQ+0bLKv2x7DTCanp6dqH5sLh/qZ7FvUjvrthvNRp+q252gr1Ku33Tds1cEXpjVy3oaaOrxgQ/9JJ/t4TYR24zgJAKcGx1+0WiRXhs65/ybp2BKbXCPpPlfwY0lnmNnZq+lzej5TeqJI0sTxed28b1zT85nVNAvP+FoHkzPp0os5qRD3rn3jmpxJn+LIokMO/K1fX+NG63RDDUzPZkp/vNx1xfmlST6pMJ4Xp+dK64vLPnbgkLaPbNZN941perZ2rOVtFvf5yHce17HZbMWyDz3wUx05vlCx7Jb7D2rH6Dm66b4xvTg9V7HupvvGNDmTbhhP8eeHv/24jhxfKLVTHWO9+G7ff0jHZrN6cXqubh+NxrqSPDfbZjP7rKTdbhDVc29yJl23n1aeo7vtMZyarf/YTHk6nnZoR/12w/moU3Xbc7QV6tXbLfcf1JUXn123Do/OnTxXvCZCu3GcBIBTg+MvWq1T/gVso6TDZfcnFpfVMLP3m9mYmY1NTU01bDAXutITpdTo8XnlQteCcOGLTquDZus3mw/rx50P2x1ixyAH/tZvp8WN6HViDTRbv0WZXL40hjN6EzXj6UvG6o6xuG0ml1+yzfJ9+pKxppbFAmu4rtExsxhP8WdfMlZqpzrGpeLrS8Ya9lFvrM1q1OdSbTazz0ra7WSddvyNop9ueww78bgYlVNZv2s57+3Wbc/RRpZz/dCo3pxbeR3ymgir0Uz9cpxEp1ru6zegU3TaazesHZ0y0Vfvsz3qVrVz7h7n3KhzbnRoaKhhg/HAtGl9b8WyTet7FV+jHyOyVnVaHTRbv4lYUD/uWKc8ZduPHPhbv50WN6LXiTXQbP0WJeOx0hhens/WjGcuk687xuK2yXjtxxeWt1m+z1wm39SyfOgarmt0zCzGU/w5l8mX2qmOcan45jL5hn3UG2uzGvW5VJvN7LOSdjtZpx1/o+in2x7DTjwuRuVU1u9aznu7ddtztJHlXD80qjezldchr4mwGs3UL8dJdKrlvn4DOkWnvXbD2tEpV4cTkjaX3d8k6aXVNDjYm9SenSOlJ0zxc24He5OraRae8bUOhgdS2lsV996dIxoeSJ3iyKJDDvytX1/jRut0Qw0M9id1742jhWPPIz/XnTsuqRjP6wf7SuuLyz69/RIdGD+se28c1WB/7VjL2yzu89l3Xqoz+xMVyz5/7WXauL6nYtnuG7Zq/9gvde+No3r9YF/FuntvHNXwQKphPMWfn3vXpdq4vqfUTnWM9eK7c8clOrM/odcP9tXto9FYV5LnZttsZp+VtNsNonruDQ+k6vbTynN0tz2GQ/31H5shT8fTDu2o3244H3WqbnuOtkK9ett9w1b94Il/rluHG/pOniteE6HdOE4CwKnB8RetZs5F83ZQMztX0t85595YZ93Vkm6V9HZJvyPp/3XOvelkbY6OjrqxsbGG6xcWcpqezygXOsUD02Bvki+zXIOWWQeR/dvEyeo3lws1OZNWLh8qHgs0PJBac1+4Tg78rV+Ov1hBDXRM/RaFodP0bEaZXF69yZhyoVM2FyoZj5X+kFlcb2aKmRQEgQb7kwoa/BdeeZuSFJgUCwrHtezix3AFJiXjgdI5p1w+VCwwpeKBnKym32IsQWAVbZuZzAr9BSaFrvAfg7HASu3UizEMnY7OprWQzSswUyIwJeKBzuit7aO879VYSZvN7NOOWJfQMfUb1fE3m80XztGL/QwPpJRItPadPBE/hm2XyeQ0NXvisRnqTyqZ7IhzY1fXL9ck7dMhz9GOqV+ptt4SMVM27zTYm6xYvqEvqVSquTrkNVFX64j65TiJFeqI+i06946Hlt3uC5+6eqUhwX+R1G+nvHZD16lbv5FUjpl9U9IVkjaY2YSkv5CUkCTn3F5JD6swyfecpDlJ72tFvz09cW3kybHm+VoH8Xig153Re/INuxg58Ld+fY0brdMNNRAEpqF1S//H/MnWr6TNZtRroxVtB4FpeF3PkutbEf9q22xmn3bE6oOonnuJREwb1/e1tY9uewyTybg2dsbEXsdqR/12w/moU3Xbc7QVlqq3ldYhr4nQbhwnAeDU4PiLVoqkkpxz7z7JeifpA1HEAgAAAAAAAAAAAHQDPu8BAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAADzHRBwAAAAAAAAAAAHiIiT4AAAAAAAAAAADAQ0z0AQAAAAAAAAAAAB5iog8AAAAAAAAAAADwEBN9AAAAAAAAAAAAgIeY6AMAAAAAAAAAAAA8xEQfAAAAAAAAAAAA4CEm+gAAAAAAAAAAAAAPMdEHAAAAAAAAAAAAeIiJPgAAAAAAAAAAAMBDTPQBAAAAAAAAAAAAHmKiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAhyKb6DOzbWb2tJk9Z2Z31Fl/jpn90MweM7NDZvb2qGIDAAAAAAAAAAAAfBPJRJ+ZxSTdLeltki6W9G4zu7hqs/9H0redc5dLuk7S7ihiAwAAAAAAAAAAAHwU1Tv63iTpOefcL5xzGUnfknRN1TZO0mmLv58u6aWIYgMAAAAAAAAAAAC8E9VE30ZJh8vuTywuK/eXknaa2YSkhyXdVq8hM3u/mY2Z2djU1FQ7YgXahvqFz6hf+Iz6hc+oX/iM+oXPqF/4jPqFz6hf+IraxakS1USf1Vnmqu6/W9JXnXObJL1d0tfNrCY+59w9zrlR59zo0NBQG0IF2of6hc+oX/iM+oXPqF/4jPqFz6hf+Iz6hc+oX/iK2sWpEtVE34SkzWX3N6n2ozn/RNK3Jck5998l9UjaEEl0AAAAAAAAAAAAgGeimuh7VNIWMzvPzJKSrpP0YNU2v5R0lSSZ2W+pMNHH+1sBAAAAAAAAAACAOiKZ6HPO5STdKul7kp6U9G3n3M/M7JNm9o7FzT4i6SYze1zSNyW91zlX/fGeAAAAAAAAAAAAACTFo+rIOfewpIerln2i7PcnJP1uVPEAAAAAAAAAAAAAPovqozsBAAAAAAAAAAAAtBATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAADzHRBwAAAAAAAAAAAHhoWRN9Zna5md1vZgcXb/eY2ZbFdfH2hAgAAAAAAAAAAACgWtMTfWa2XdJ3JH1f0nslvU/Sf5f0HTP7V5K+144AAQAAAAAAAAAAANRazrvw/kLS7znnXihb9riZ/VDSU5L+UysDAwAAAAAAAAAAANDYcj66M141ySdJWlz2onPu/25VUAAAAAAAAAAAAACWtpyJvqyZnVO90MxeLyndupAAAAAAAAAAAAAAnMxyP7rzH8zsP0gal+Qk/W+S7pD0sTbEBgAAAAAAAAAAAKCBpif6nHN/bWbPS/qIpNskmaT/JeldzrnH2xQfAAAAAAAAAAAAgDqW844+LU7o3bjUNmZ2l3PutlVFBQAAAAAAAAAAAGBJy/mOvmb9bhvaBAAAAAAAAAAAAFCmHRN9AAAAAAAAAAAAANqMiT4AAAAAAAAAAADAQ+2Y6LM2tAkAAAAAAAAAAACgTDsm+r5Qb6GZbTOzp83sOTO7o8E27zKzJ8zsZ2b2jTbEBgAAAAAAAAAAAHSFeLMbmtnfSnKN1jvn3rH486t19o1JulvSWyRNSHrUzB50zj1Rts0WSR+X9LvOueNmNtxsbAAAAAAAAAAAAMBa0/REn6T/uPjzDyX9hqR9i/ffLemFk+z7JknPOed+IUlm9i1J10h6omybmyTd7Zw7LknOucllxAYAAAAAAAAAAACsKU1/dKdz7h+dc/8o6XLn3LXOub9dvF0v6X8/ye4bJR0uuz+xuKzcBZIuMLN/MrMfm9m2eg2Z2fvNbMzMxqamppoNH+gI1C98Rv3CZ9QvfEb9wmfUL3xG/cJn1C98Rv3CV9QuTpWVfEffkJm9oXjHzM6TNHSSfazOsuqPAY1L2iLpChXeJfhXZnZGzU7O3eOcG3XOjQ4NnaxboLNQv/AZ9QufUb/wGfULn1G/8Bn1C59Rv/AZ9QtfUbs4VZbz0Z1FH5b0iJn9YvH+uZL+9CT7TEjaXHZ/k6SX6mzzY+dcVtLzZva0ChN/j64gRgAAAAAAAAAAAKCrLXuizzn3XTPbIumixUVPOefSJ9ntUUlbFt/9d0TSdZKur9rmr1V4J99XzWyDCh/l+QsBAAAAAAAAAAAAqLHsj+40sz5Jt0u61Tn3uKRzzOz3l9rHOZeTdKuk70l6UtK3nXM/M7NPmtk7Fjf7nqRpM3tC0g8l3e6cm15ufAAAAAAAAAAAAMBasJKP7vyKpHFJ/2rx/oSk70j6u6V2cs49LOnhqmWfKPvdSfrzxRsAAAAAAAAAAACAJSz7HX2SznfOfUZSVpKcc/OSrKVRAQAAAAAAAAAAAFjSSib6MmbWK8lJkpmdL+lk39EHAAAAAAAAAAAAoIVW8tGdfyHpu5I2m9n9kn5X0ntbGRQAAAAAAAAAAACApS17os8591/N7KCkf6nCR3b+mXPuaMsjAwAAAAAAAAAAANDQsj+608w+6Zybds495Jz7O0nHFt/ZBwAAAAAAAAAAACAiK/mOvnPM7OOSZGYpSX8t6dmWRgUAAAAAAAAAAABgSSuZ6HufpN9enOz7W0k/dM79ZUujAgAAAAAAAAAAALCkpr+jz8y2lt39gqQvSfonSf9oZludcwdbHRwAAAAAAAAAAACA+pqe6JP02ar7xyVdvLjcSbqyVUEBAAAAAAAAAAAAWFrTE33OuX9jZoGkdzrnHmhjTAAAAAAAAAAAAABOYlnf0eecCyV9oE2xAAAAAAAAAAAAAGjSsib6Fv1XM/u3ZrbZzM4s3loeGQAAAAAAAAAAAICGlvMdfUV/vPiz/J19TtIbVh8OAAAAAAAAAAAAgGYse6LPOXdeOwIBAAAAAAAAAAAA0LyVvKNPZvZGSRdL6ikuc87d16qgAAAAAAAAAAAAACxt2RN9ZvYXkq5QYaLvYUlvk/T/SWKiDwAAAAAAAAAAAIhIsIJ9dki6StKvnHPvk3SppFRLowIAAAAAAAAAAACwpJVM9M0750JJOTM7TdKkpDe0NiwAAAAAAAAAAAAAS1nJRN+YmZ0h6V5J45IOSvrJyXYys21m9rSZPWdmdyyx3Q4zc2Y2uoLYAAAAAAAAAAAAgDVh2d/R55y7ZfHXvWb2XUmnOecOLbWPmcUk3S3pLZImJD1qZg86556o2m6dpA9K+h/LjQsAAAAAAAAAAABYS1byjj6Z2R+a2X+SdJuk85vY5U2SnnPO/cI5l5H0LUnX1Nnu30v6jKSFlcQFAAAAAAAAAAAArBXLnugzs92Sdkn6n5L+l6Q/NbO7T7LbRkmHy+5PLC4rb/dySZudc393kv7fb2ZjZjY2NTW13PCBU4r6hc+oX/iM+oXPqF/4jPqFz6hf+Iz6hc+oX/iK2sWpspJ39P1rSf+Xc+4rzrmvSHq7pCtOso/VWeZKK80CSZ+T9JGTde6cu8c5N+qcGx0aGmo+aqADUL/wGfULn1G/8Bn1C59Rv/AZ9QufUb/wGfULX1G7OFVWMtH3tKRzyu5vlrTkd/Sp8A6+zWX3N0l6qez+OklvlPSImb0g6V9KetDMRlcQHwAAAAAAAAAAAND14s1uaGZ/q8K78E6X9KSZ/WTx/u9I+tFJdn9U0hYzO0/SEUnXSbq+uNI594qkDWV9PSLp3zrnxpqNDwAAAAAAAAAAAFhLmp7ok/QfV9qJcy5nZrdK+p6kmKQvO+d+ZmaflDTmnHtwpW0DAAAAAAAAAAAAa1HTE33OuX8sv29mpy1z/4clPVy17BMNtr2i2XYBAAAAAAAAAACAtWg57+iTJJnZ+yX9e0nzkkJJpsJHeL6htaEBAAAAAAAAAAAAaGTZE32Sbpf0L5xzR1sdDAAAAAAAAAAAAIDmBCvY5+eS5lodCAAAAAAAAAAAAIDmreQdfR+X9CMz+x+S0sWFzrkPtiwqAAAAAAAAAAAAAEtayUTflyT9QNL/VOE7+gAAAAAAAAAAAABEbCUTfTnn3J+3PBIAAAAAAAAAAAAATVvJd/T90Mzeb2Znm9mZxVvLIwMAAAAAAAAAAADQ0Ere0Xf94s+Ply1zkt6w+nAAAAAAAAAAAAAANGPZE33OufPaEQgAAAAAAAAAAACA5jX90Z1m9tGy399Zte4/tDIoAAAAAAAAAAAAAEtbznf0XVf2+8er1m1rQSwAAAAAAAAAAAAAmrSciT5r8Hu9+wAAAAAAAAAAAADaaDkTfa7B7/XuAwAAAAAAAAAAAGij+DK2vdTMXlXh3Xu9i79r8X5PyyMDAAAAAAAAAAAA0FDTE33OuVg7AwEAAAAAAAAAAADQvOV8dCcAAAAAAAAAAACADsFEHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAAD0U20Wdm28zsaTN7zszuqLP+z83sCTM7ZGbfN7PXRxUbAAAAAAAAAAAA4JtIJvrMLCbpbklvk3SxpHeb2cVVmz0madQ5d4mk/ZI+E0VsAAAAAAAAAAAAgI+iekffmyQ955z7hXMuI+lbkq4p38A590Pn3Nzi3R9L2hRRbAAAAAAAAAAAAIB3opro2yjpcNn9icVljfyJpP9Sb4WZvd/MxsxsbGpqqoUhAu1H/cJn1C98Rv3CZ9QvfEb9wmfUL3xG/cJn1C98Re3iVIlqos/qLHN1NzTbKWlU0p311jvn7nHOjTrnRoeGhloYItB+1C98Rv3CZ9QvfEb9wmfUL3xG/cJn1C98Rv3CV9QuTpV4RP1MSNpcdn+TpJeqNzKz35P07yT9a+dcOqLYAAAAAAAAAAAAAO9E9Y6+RyVtMbPzzCwp6TpJD5ZvYGaXS/qSpHc45yYjigsAAAAAAAAAAADwUiQTfc65nKRbJX1P0pOSvu2c+5mZfdLM3rG42Z2SBiR9x8x+amYPNmgOAAAAAAAAAAAAWPOi+uhOOecelvRw1bJPlP3+e1HFAgAAAAAAAAAAAPguqo/uBAAAAAAAAAAAANBCTPQBAAAAAAAAAAAAHmKiDwAAAAAAAAAAAPAQE30AAAAAAAAAAACAh5joAwAAAAAAAAAAADzERB8AAAAAAAAAAADgISb6AAAAAAAAAAAAAA8x0QcAAAAAAAAAAAB4iIk+AAAAAAAAAAAAwENM9AEAAAAAAAAAAAAeYqIPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAADzHRBwAAAAAAAAAAAHiIiT4AAAAAAAAAAADAQ0z0AQAAAAAAAAAAAB6KR9WRmW2T9AVJMUl/5Zz7VNX6lKT7JI1ImpZ0rXMhkYKAAAAgAElEQVTuhdX0ubCQ0/R8RrnQKR6YBnuT6umJbMjoEL7Wga9xtxI58DcHvsaN1vG5BhYWcjq+kFVf0jSTDmUmOSflQ6cgMCUCk5mUyxfuByZl86401kTMSttnQ6dELFDMpHQuVGxx38BMJmkhF2pdT0zzmbC0/7reQAsZKZMPFTMpdFJusZ24SfO5UPHA1JMIlM65imX9qUCvzOdL6zM5p0y+sC4VD7SQCxUu9iOTYmbKu8L98jH0JAItZEP1pwLNpAv7xAJTEEiBTLmwsG0yHqg3YVrIOqUXY4gHpnQ+VGCLuQqkXK6Qi+L6hVyoRGCKxwLlwlDOSf2pQLPpE3noSwaazYTKh06JxXiTsUDpXFiR62zeKRUP5GQa7E8qCEy5XKjJmbRyYaiYFXIumYYHUgoC0/RsRplcXsl4rLRPuTB0J92mU0X13Iuin24aS5T9pNM5HZ070c+GvqRSKX+Ov63OUbvy3o522/XYZTI5Tc2eaHeoP6lksjPb9fX4W6yH03sDzWUk5wrnvXzo1JuIKZcPS+fB03oDvTYfSnWuL+KBKROGismUWdw/GQuUjJvms4XzcTIeKJBJ5irO3X3Jwnnf6cQ1R088UBCo4jqjLxno1YW8ErFAwwMpxeON//+7+jEe7E1W1P3wQEpmpsmZtLL5UIlYoDN7Et5eA65WNptfvP44kZ9EInaqwzopn6/b0TrUARA9nneQWlcHkVSOmcUk3S3pLZImJD1qZg86554o2+xPJB13zv2mmV0n6dOSrl1pnwsLOT07Paub941r4vi8Nq3v1Z6dI9oy2M8TZg3xtQ58jbuVyIG/OfA1brSOzzWwsJDTc9OzOjYzr/UDvbrr+8/oPW8+Tx87cKg0ljt3XKLBgaQOjB3WOy7bqNBJN99/sLT+y+8d1WsLOf3Zt35asc9nvvu0pmbS+vT2S/S1Hz2v9/3ueTo8Pavf2nhGKVdvvXhYt2+7SEdfS+sr//R83b6L7ezZOaLx54/qwrNPr1j25JGX9Q9PTeqDV12gXWWPwe4btuqLP3hWf//EpDat79Xnr71MiZjp4UMv6epLN+qWsjHs2TmiF6Ze1blDp1U8jl+8/nJlc6E+/O3HK7a96/vPlNotj/Hu6y9XLAgq4ihf/7l3XapEPNDh6Zmavqrb/cp7RzWdDStyvfuGrXro8SO6+tKNeujxI/qDrZv1mxv69fTkTEWfxZx/8KoL1JeM6cYv/6S07t4bR3XhWetKf0gOQ6enf/2abrpvrOE2nSqq514U/XTTWKLsJ53O6Zmjtf1csKG/4yf72pGjduW9He2267HLZHJ6eqq23QuH+lc1KdeOdn09/hbroT8h/SobKJ3N69WFnG7ff0hDAyl9dNuFun3/oYo8HZ+ZVywWrznHbxhIKhkP9NLLC6V93nrxsG67ckvF+e+ud1+u/lRMf/zVE7nau3NEiZjpT742Vtrv3139W3plPldzjn/kyV/rgfEJ7d05oovOWld3sq/6Mf7L379II+dtqHjM9+4cUU8i0Hu/8mjD87cv14Crlc3m9dTkTM1z4qLhgY6e7PP5uh2tQx0A0eN5B6m1dRDVR3e+SdJzzrlfOOcykr4l6Zqqba6R9LXF3/dLusrMVnw1Pz2fKSVIkiaOz+vmfeOans+stEl4yNc68DXuViIH/ubA17jROj7XwPR8Rrv2jev84cKk0/aRzaU/wkmFsdy+/5COHF/QjtFzNPlapvSHt+L6I8cXSpN85fvsuuJ8TRyf18cOHNL2kc26ff8hvXnLUEWuto9s1sSxwvaN+i62c/O+cV158dk1y968ZUjbRzaXJrqK+95y/0FtH9lcuv+hB36qY7NZ7Rg9p/QHwOK6m/eN6/LXD9Y8jsdns6VJvvJty9stj+fYbLYmjvL1H/724zo+m63bV3W7ZkFNrm+5/2Ap/h2j5+im+8Y0OZOu6bOY8137xvXi9FzFupvuG9P07InanJ7NlP7I3GibThXVcy+KfrppLFH2c3Sufj9H59Zm/bYr7+1ot12P3dRs/XanVnlMa0e7vh5/i/WQSiR0+Ni8Jl/LlCbpdl1xful36USezh8+re45fuL4ggILKvbZPrK55vx32zcf05HjCxXLdu0b10svL1Tsl82r7jn+mq2bSvtMzqTrjqv6Mb7y4rNrHvNd+8Z1+Nj8kudvX64BV2tyJl33OdEov53C5+t2tA51AESP5x2k1tZBVBN9GyUdLrs/sbis7jbOuZykVyQNVjdkZu83szEzG5uammrYYS50pQSVOj0+r1zoVjQA+KnT6oD6bR456LwcUL9oVifWwHLrt/jzjN5E3bH0JWOKBaa+ZKxmfb1lxbbKf584Pq98Va7O6E2U9m/Ud3k7zrmaZfnQnXTf6nHU27Y6tmbGVn2/mVz0JWN1+6puNzDV3aYYf/Fno/or5qQvGatZl8nlS/czuXzd/cu3iVqnHX+j6KebxtKN/SzHqazfduVjrcfarnZ9Pf6WXzv0JWMV575G5+NG+etLxpR3tdcGjbZdatkZvYmG507n3IlY8uGS4yoKXeOYq5dVXxeshdcBvh5/OzFuRK8T66DZ6weg03Taazd0tlbWQVQTffXemVcdbTPbyDl3j3Nu1Dk3OjQ01LDDeGDatL63Ytmm9b2F76TBmtFpdUD9No8cdF4OqF80qxNrYLn1W/z58ny27ljmMnnlQ6e5TL5mfb1lxbbKf9+0vlexqly9PJ8t7d+o7/J2zKxmWSywk+5bPY5621bH1szYqu83k4u5TL5uX9Xthk51tynGX/zZqP6KOZnL5GvWJeMn/kiZjMfq7l++TdQ67fgbRT/dNJZu7Gc5TmX9tisfaz3WdrXr6/G3/NphLpOvOPc1Oh83yt9cJq+Y1V4bNNp2qWUvz2cbnjuLH560aX2v4rH6fxaqjjGwxjFXL6u+LlgLrwN8Pf52YtyIXifWQbPXD0Cn6bTXbuhsrayDqCb6JiRtLru/SdJLjbYxs7ik0yUdW2mHg71J7dk5UkpU8fNNB3uTK20SHvK1DnyNu5XIgb858DVutI7PNTDYm9TenSP6+eSr2rNzRAfGD+vT2y+pGMudOy7RxvU92j/2Sw2vS2rPDVsr1m9c36MvXHdZzT57H/m5Nq0vfF/cgfHDunPHJfrRs1MVuTowflibzixs36jvYjt7do7oB0/8c82yHz07pQPjh7W36jHYfcNWHRg/XLr/+Wsv05n9Ce0f+6V2V41hz84RPfbidM3juL4/oc+969KabcvbLY/nzP5ETRzl6z/3rku1vj9Rt6/qdp0La3K9+4atpfj3j/1S9944quGBVE2fxZzv3Tmi1w/2Vay798ZRDfafqM3B/qTuvXF0yW06VVTPvSj66aaxRNnPhr76/WzoW5v12668t6Pddj12Q/312x1a5TGtHe36evwt1kM6m9XmM3s1vC6pO3cUzt97H/l56XfpRJ5+Pvlq3XP8pvU9Cl1Ysc+B8cM157+73n25Nq7vqVi2d+eIXndGT8V+iZjqnuP/5uBEaZ/hgVTdcVU/xj944p9rHvO9O0e0+czeJc/fvlwDrtbwQKruc6JRfjuFz9ftaB3qAIgezztIra0DK35kQzstTtw9I+kqSUckPSrpeufcz8q2+YCk33bO7TKz6yT9oXPuXUu1Ozo66sbGxhquX1jIaXo+o1zoFA9Mg71JvsxyDVpmHUT2bxPU78mRA+oX/lpBDXRU/R5fyKovaZpJhzKTnJPyoVMQmBKByQIplyvcD0zK5l1prImYlbbPhk6JWKCYSelcqNji9mYmk7SQC7WuJ6b5TFjaf11voIWMlMmHilnhnWz50CkeCxQ3aT4XKh6YepKB0llXsaw/FeiV+XxhfSJQJueUzRf6TcUDLeRChYv9yKSYmfKucL98DD2JQAvZUP2pQDPpwj6xwBQEUiBTLnTKh06JeKDehGkh65RZHF88MGXyocwqc5VbbCMemNKL8cZjgXJhKOek/lSg2fSJPPQlA81mTsRrJiVigdK5sCLX2bxTKh7IyTTYn1QQmHK5UJMzaeXCUDEr5NzJNDyQUhCYpmczyuTySsZjpX3KhaE76TZVOqp+ozj+RtFPN40lyn7S6ZyOzp3oZ0NfUqmUP8ffVueoXXlvR7sreOyaksnkNDV7ot2h/qSSyc5s19fjb7EeTu8NNJeRnCtcA+RDp95ETLl8qOxink7rDfTafCjVub6IB6ZMGComU2Zx/2QsUDJums8WzonJeKBAJpmrOHf3JQvnfScpkwsVBKaeeKAgUMV1Rl8y0GsLecVjgYYHUorHG///d/VjPNibrKj74YGUzKxwzs2HiscCndmTWLOvA7LZ/OL1x4n8JBJLviO1o+p3LT5mOMHn12+SdO4dDy273Rc+dfVKQ4L/IqnfTnnths7WquNvJJXjnMuZ2a2SvicpJunLzrmfmdknJY055x6U9J8lfd3MnlPhnXzXrbbfnp64NvLkWPN8rQNf424lcuBvDnyNG63jcw309MR19mLsp/dF1Gl/5d3Telbe1BktjrnV7S2nr/X99bc7mXg80OvO6G24fmjd0v9dHwR20m06VVTPvSj66aaxRNlPKhXXxhZMDp0K7chRu/Lejnbb9dglk3FtbMHEXhTt+nr8La+HgSbO4etWcZ5fkarzabPn13qPcb26rz7n+noNuFqJREwb10d44dQiPl+3o3WoAyB6PO8gta4OIqsk59zDkh6uWvaJst8XJL0zqngAAAAAAAAAAEBn4V2awPJE9R19AAAAAAAAAAAAAFqIiT4AAAAAAAAAAADAQ0z0AQAAAAAAAAAAAB5iog8AAAAAAAAAAADwEBN9AAAAAAAAAAAAgIfMOXeqY1gxM5uS9GITm26QdLTN4XQ6ctBcDo4657ZFEQz1uyzkgPr12VrPQbPj77T69eFx6/QYOz0+qXUxdlr9Sn7kv1ndNBap88ZD/a4OsbYH1w+nHmNpP+q385ADjr8+IwcdVr9deu3bLuRglfXr9URfs8xszDk3eqrjOJXIgb858DXuViIH/ubA17hbaa3nwNfx+xB3p8fY6fFJfsS4Ut00tm4ai9R942kHn3JErO3hU6zlfI27Hsay9pAnciD5mwNf424lcuBvDnyNu5XIwepzwEd3AgAAAAAAAAAAAB5iog8AAAAAAAAAAADw0FqZ6LvnVAfQAciBvznwNe5WIgf+5sDXuFtprefA1/H7EHenx9jp8Ul+xLhS3TS2bhqL1H3jaQefckSs7eFTrOV8jbsexrL2kCdyIPmbA1/jbiVy4G8OfI27lcjBKnOwJr6jDwAAAAAAAAAAAOg2a+UdfQAAAAAAAAAAAEBXYaIPAAAAAAAAAAAA8BATfQAAAAAAAAAAAICHmOgDAAAAAAAAAAAAPMREHwAAAAAAAAAAAOAhJvoAAAAAAAAAAAAADzHRBwAAAAAAAAAAAHiIiT4AAAAAAAAAAADAQ0z0AQAAAAAAAAAAAB5iog8AAAAAAAAAAADwEBN9AAAAAAAAAAAAgIeY6AMAAAAAAAAAAAA8xEQfAAAAAAAAAAAA4CEm+gAAAAAAAAAAAAAPeT3Rt23bNieJG7dW3iJD/XJrwy0y1C+3NtwiQ/1ya8MtMtQvtzbcIkP9cmvDLTLUL7c23CJD/XJrwy0y1C+3NtwiQe1ya9OtLq8n+o4ePXqqQwBWjPqFz6hf+Iz6hc+oX/iM+oXPqF/4jPqFz6hf+IraRZS8nugDAAAAAAAAAAAA1iom+gAAAAAAAAAAAAAPMdEHAAAAAAAAAAAAeIiJPgAAAAAAAAAAAMBDTPQBAAAAAAAAAAAAHopH0YmZfVnS70uadM69sc56k/QFSW+XNCfpvc65g6vtd2Ehp+n5jHKhUzwwDfYm1dMTyZDRQXytA1/jbiVy4G8OfI0breNzDWQyOR2dy6o/aZpJhzKTnJPyoVMQmBKByUzK5Qv3A5OyeVcaayJmpe2zoVMiFigVN81nCm2ZSQOpQK8thMqFTut6YprPhKX91/UEWshKmXyoMHSKLfYXDwLl8qGyi9v1JQNl86pY1p8K9Mp8XvHA1JMMlM46ZfOh4oEpFQ+0kCu0GQ9MMilmprwr3C8fQ08i0EI2VF8yUCbnZItjzDunVCxQLixsm4gF6kuaFrJO6Vyhn3hgyuRDxQJTzEyhc3JOyi6OJREU+nRO6kkEyuYLv5sVclbMc28iUDpbyEsxB8lYoHTuRK5S8UCZvFPMpCAINNifVBCYcrlQkzNpZfOhErFAwwMpxeOF/28LQ6fp2YwyubyS8Vhpn3LNbNOponruRdFPN40lyn7S6ZyOzp3oZ0NfUqmUH8ffduSoXXlvR7uZTE5TsyfaHOpPKplcfaw+tevr8bdYD6f3BprLSM4VrgHyoVNvIlZxri6c5wvnvmTcNFd2DdCTCJTJhwpkpf2TsUCJmJXO4Yl4oKH+E+e16sdhIBVoJh0q75x6EjGdlohX1GpfMtCrC/ma82M92WxekzPp0r7DAyklErElc9GuekP7+HzdDlC/AFAQ1ZHvq5K+KOm+BuvfJmnL4u13JO1Z/LliCws5PTs9q5v3jWvi+Lw2re/Vnp0j2jLYzwF/DfG1DnyNu5XIgb858DVutI7PNZDJ5PTM1KyOzcxr/UCv7vr+M3rPm8/Txw4cKo3lzh2XaHAgqQNjh/WOyzYqdNLN9x8srf/ye0f12kJOf/atn1bs85nvPq2pmbTuvn6rZjMxve8rj+rakU264rfOKuXqrRcP6/ZtF+noa2ndvv9En59956XqSQT6wDceq8jp6b0xXX/vTyqWPXnkZf3DU5P64FUXaFfZY7D7hq364g+e1d8/MalN63v1+WsvUyJmevjQS7r60o26pWwMe3aOaPz5oxo5b4M2DMR19NWsbr7/oIYGUvrotgsrYtuzc0R3ff+ZUruVY71c2bzThx6ozEVvMqbdP3xOt2+7SDMLOe155LmKPL/14mHdeuWWipj+6j0jyuZcRa5337BVDz1+RP/nhWfpaz96Xh9+y4X6zQ39enpypmLse3eO6KKz1ikITE//+jXddN9Yad29N47qwsV1UuGPzCfbplNF9dyLop9uGkuU/aTTOT1ztLafCzb0d/xkXzty1K68t6PdTCanp6dq27xwqH9VkyQ+tevr8bdYD/0J6VfZQOlsXq8u5HT7/kMNz5u/cVpSmVyoyZlcZQ5v2KqBnph+9cqJ64C3Xjys267cUnH++9LOEV141jqFYVj3cSiel++67hKdO3RazfpHnvy1HhifKJ0f6032ZbN5PTU5U7PvRcMDDSf72lVvaB+fr9sB6hdApzr3joeWvc8Ln7p6VX1G8tGdzrn/JunYEptcI+k+V/BjSWeY2dmr6XN6PlM60EvSxPF53bxvXNPzmdU0C8/4Wge+xt1K5MDfHPgaN1rH5xqYms1o175xnT9c+KPY9pHNpcknqTCW2/cf0pHjC9oxeo4mX8uU/vBWXH/k+EJpkq98n11XnK+J4/P6wDcOauLYvCaOz+uarZsqcrV9ZLMmjs2X/rhX3P8j33lcx2azNTk1BTXL3rxlSNtHNpcmuorrbrn/oLaPbC7d/9ADP9Wx2ax2jJ5TmlArb+fKi8/WzfvGlQ+tNMZdV5xfE1sxT/XGemw2W5rkK19/fDZbGutt33ysJs/bRzbXxBQPYjW5vuX+g/r/27v3OEnK8uD7v6u7Z/bIcVmMsquAMRokoLsb1Pi8hnjIg4dIIiggxEMMhCjGxGiiT/Iao08Sj0lMFFDiGU8IjwkiD+qroFETZRcBBUUR0V1RWRCRXXZ3Dn29f1TNbM9Mz2zP7HRP18zv+/nMZ7qr7qq66u6r76rquw6nbnowf3nZjZyycT1nf2Azd+7YM2Xdz714C3fu2MPdO4fGf0AeG3f2BzZz9869udlJmX7Vq+9eL5azmNall8u56/72y7nr/qWZv92q927Md/vO9vPcvp9tT5XmW9X2dywflg0MsPVnu7jzvqHxbeV0282hkSSJqXX4oeto1OoTpjll4/op278/Krdr030OY9vlRz9kTdvxJ29YN2H72M6dO/a0nXa68tC9fFP3VHm/XTJ/JWmvfnlG3xHA1pb328phU0TEORGxOSI2b9++fdoZjjRzvKEfn+k9uxhp5jyEq6rotzwwfztnHfRfHZi/6lQ/5sBs83fs/8ErBtquy8rBOvVasHKwPmV8u2Fj82qdHqCZE+vq4BUD004/Nk3rsGbmlGGjzZw27rEYJq9Hu7JZxjba8nl2Ot+x9zOtS+u6Tp5vu+XUgrbzGot/bJpp82+0ydDIaNtxQyOj4+87KdNr/db+9mI5i2ldFuNyZmMh87db9bHUY+3WfKva/rbuO6wcrE/Y9k233RxpFrfDbjdu8vBp5zHanPZzGNsOj04zPsv9h7H5zLRe7WKfTj+2QUvZbPK3lZ+Z+oH5q6rqdN9Xmm/90tHX7j4cbVvlzHxXZm7KzE1r166ddoaNWrDukBUThq07ZEXxTBotGf2WB+Zv56yD/qsD81ed6sccmG3+jv3/+a7htuty/9Aoo83k/qHRKePbDRubV+v0ALWYWFc/3zU87fRj07QOq0VMGVavxbRxj8UweT3alY0ytnrL59npfMfez7Qures6eb7tltNM2s5rLP6xaabNv3qNwUa97bjBxt5O1E7K9Fq/tb+9WM5iWpfFuJzZWMj87VZ9LPVYuzXfqra/rfsO9w+NTtj2TbfdbJTPsW03bvLwaedRr037OYxth+vTjI9y/2FsPjOtV7vYp9OPbdBSNpv8beVnpn5g/qqqOt33leZbv3T0bQPWt7xfB9yxPzNcs2KQC87aON7gj92nec2Kwf2ZrSqmqnlQ1bjnk3VQ3TqoatyaP1XOgbWrBrnwrI18785fcMFZG7lsy1beeMpxE9blzacexxGHLOfSzT/k8AMGueDMDRPGH3HIct52+qOmTHPhNd9j3SEreMdzN7Du0BWsO2QF/3Hdtgl1ddmWraw7tCjfOv1bn308h64amFKnSXPKsK98dzuXbdnKhZM+g/PP3MBlW7aOv//n0x7FoasGuHTzDzl/0jpccNZGPn/zj7ngrI3Uazm+jhde870psY3VU7t1PXTVAP982tS6OGTVwPi6/usZj55Sz5dt2TolppHm6JS6Pv/MDVy6+Ye88ZTjuGzLVi563iYOX71syrpfeNZGDl+9jDWrBrnoeZsmjLvoeZtYs2pvbnZSpl/16rvXi+UspnXp5XIOW9l+OYetXJr5261678Z8165qP8+1+9n2VGm+VW1/x/Jhz/Aw6w9dweEHDI5vK6fbbg42giCn1uGZGxhpjk6Y5rItW6ds/95Zbtem+xzGtstf/8Hdbcf/x3XbJmwf2zl89bK2005XHrqXb+qeKu+3S+avJO0Vmb25nDkijgSuyMxj24x7OnAe8DTgMcC/ZOYJ+5rnpk2bcvPmzdOO3717hLt3DTHSTBq1YM2KQR/GugTNMg96dtqP+btv1oH5q+qaQw70Tf4ODY1w1/3DrBoMduxpEgGZxe2varVgoBZEDUZGive1gOHRHF/XgXqMlx9uJgP1Gssawa7hJgFEwOplNe7bXdxy64DldXYNNcenP2BFjd1DMDTapNlM6rUgAhq1GiOjTYbLcisHawyPMmHYqmU17t01SqMWLB+ssWc4GRltUq8Fyxo1do8U82zUAgLqEYxm8b51HZYP1Ng93GTlYI2hkSTKdRzNZFm9VtxyrJk06jVWDga7h5OhkWI5jVowVC6zHkEzk8zi1jpj9TdaDls+UGN4tHhdC8bnW6sFKwZq7Bku6qVe1vNAvcaekb11taxRY2g0qQfUajXWrBqkVgtGRprcuWMPI6NNGvUah69eRqNRnN/WbCZ37xxiaGSUwUZ9fJpWnZSZpG/yt1ftby+Ws5jWpZfL2bNnhLvu37ucw1YOsmxZNdrfbtRRt+q9G/MdGhph+86981y7apDBwf2PtUrzrWr7O5YPB62ocf8QZBb7AKPNZMVAfcK2+oDlNXYPF9u+wUZwf8s+wPKBGkOjTWoEw82kWe5HDNRjfBs+0KixdtXe7drkz2H1sho79jQZLbezBw40JuTqysEa9+0enbJ9bGd4eLTYnpbTHr56GQMDM19h2a18W6T6Kn89dtMsmb+qsp7k7772fbV4HfmqT816mtvf8PROi7bN3560fBHxEeBE4LCI2Ab8DTAAkJkXAldSdPLdCtwPvHA+lrt8eYMjbNyXvKrmQVXjnk/WQXXroKpxa/5UOQcGBxs8qPxB6qCV3VvOga13mVk1adzyuc/34C7GXBWNRo0HHbyi7bhaLVh7wPRXI3Rapl/16rvXi+UspnXp5XKWLWtwxMwde32rG3XUrXrvxnwHBxsc0YUOkSrNt6rtb2s+rO5gG966D3DIqunLdaLd5zB5/2Vyrna6zIGBOkccMrsdi27lm7qnyvvtkvkrSYWetISZecY+xifwkl7EIkmSJEmSJEmSJC0G/fKMPkmSJEmSJEmSJEmzYEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEF29EmSJEmSJEmSJEkVZEefJEmSJEmSJEmSVEE96+iLiJMi4paIuDUiXtVm/IMj4uqI+HpE3BgRT+tVbJIkSZIkSZIkSVLV9KSjLyLqwDuApwLHAGdExDGTiv01cElmPho4HTi/F7FJkiRJkiRJkiRJVdSrK/pOAG7NzNsycwj4KHDypDIJHFi+PopEekAAACAASURBVAi4o0exSZIkSZIkSZIkSZXT6NFyjgC2trzfBjxmUpnXAp+JiJcCq4An9yY0SZIkSZIkSZIkqXp6dUVftBmWk96fAbwvM9cBTwM+GBFT4ouIcyJic0Rs3r59exdClbrH/FWVmb+qMvNXVWb+qsrMX1WZ+asqM39VZeavqsrc1ULpVUffNmB9y/t1TL0154uASwAy87+A5cBhk2eUme/KzE2ZuWnt2rVdClfqDvNXVWb+qsrMX1WZ+asqM39VZeavqsz8VZWZv6oqc1cLpVcdfdcCD4uIoyJiEDgduHxSmR8CTwKIiF+l6Oiz21uSJEmSJEmSJElqoycdfZk5ApwHfBr4FnBJZt4UEa+LiGeWxf4cODsibgA+ArwgMyff3lOSJEmSJEmSJEkS0OjVgjLzSuDKScNe0/L6ZuDxvYpHkiRJkiRJkiRJqrJe3bpTkiRJkiRJkiRJ0jyyo0+SJEmSJEmSJEmqIDv6JEmSJEmSJEmSpAqyo0+SJEmSJEmSJEmqIDv6JEmSJEmSJEmSpAqyo0+SJEmSJEmSJEmqIDv6JEmSJEmSJEmSpAqac0dfRKyJiN+LiI3zGZAkSZIkSZIkSZKkfeu4oy8iroiIY8vXDwS+CfwB8MGI+NMuxSdJkiRJkiRJkiSpjdlc0XdUZn6zfP1C4LOZ+TvAYyg6/CRJkiRJkiRJkiT1yGw6+oZbXj8JuBIgM+8DmvMZlCRJkiRJkiRJkqSZNWZRdmtEvBTYBmwArgKIiBXAQBdikyRJkiRJkiRJkjSN2VzR9yLgkcALgNMy8+fl8McC753nuCRJkiRJkiRJkiTNoOMr+jLzTuDcNsOvBq4eex8R/5qZL52f8CRJkiRJkiRJkiS1M5sr+jr1+C7MU5IkSZIkSZIkSVKLbnT0SZIkSZIkSZIkSeoyO/okSZIkSZIkSZKkCupGR190YZ6SJEmSJEmSJEmSWnSjo+9t7QZGxEkRcUtE3BoRr5qmzHMi4uaIuCkiPtyF2CRJkiRJkiRJkqRFodFpwYj4JJDTjc/MZ5b/39dm2jrwDuApwDbg2oi4PDNvbinzMODVwOMz856IOLzT2CRJkiRJkiRJkqSlpuOOPuAt5f9nAb8EXFy+PwO4fR/TngDcmpm3AUTER4GTgZtbypwNvCMz7wHIzDtnEZskSZIkSZIkSZK0pHTc0ZeZXwCIiNdn5hNaRn0yIr64j8mPALa2vN8GPGZSmV8p5/9loA68NjOv6jQ+SZIkSZIkSZIkaSmZyzP61kbE0WNvIuIoYO0+pok2wybfBrQBPAw4keIqwX+LiIOnzCjinIjYHBGbt2/fPqvApYVm/qrKzF9VmfmrKjN/VWXmr6rM/FWVmb+qMvNXVWXuaqHMpaPvz4BrIuKaiLgGuBr4031Msw1Y3/J+HXBHmzL/kZnDmfl94BaKjr8JMvNdmbkpMzetXbuv/kWpv5i/qjLzV1Vm/qrKzF9VmfmrKjN/VWXmr6rM/FVVmbtaKLN5Rh8AmXlVRDwMeEQ56NuZuWcfk10LPKy8+u9HwOnAcyeV+XeKK/neFxGHUdzK87bZxidJkiRJkiRJkiQtBbO+oi8iVgKvBM7LzBuAB0fEM2aaJjNHgPOATwPfAi7JzJsi4nUR8cyy2KeBuyPiZoqrBF+ZmXfPNj5JkiRJkiRJkiRpKZj1FX3Ae4EtwOPK99uAjwNXzDRRZl4JXDlp2GtaXifw8vJPkiRJkiRJkiRJ0gzm8oy+h2bmm4BhgMzcBcS8RiVJkiRJkiRJkiRpRnPp6BuKiBVAAkTEQ4F9PaNPkiRJkiRJkiRJ0jyay607/wa4ClgfER8CHg+8YD6DkiRJkiRJkiRJkjSzWXf0ZeZnI+I64LEUt+x8WWbeNe+RSZIkSZIkSZIkSZrWrG/dGRGvy8y7M/NTmXkF8LPyyj5JkiRJkiRJkiRJPTKXZ/Q9OCJeDRARy4B/B747r1FJkiRJkiRJkiRJmtFcOvpeCPxa2dn3SeDqzHztvEYlSZIkSZIkSZIkaUYdP6MvIja0vH0b8E7gy8AXImJDZl4338FJkiRJkiRJkiRJaq/jjj7grZPe3wMcUw5P4InzFZQkSZIkSZIkSZKkmXXc0ZeZvxURNeDZmfmxLsYkSZIkSZIkSZIkaR9m9Yy+zGwCL+lSLJIkSZIkSZIkSZI6NKuOvtJnI+IVEbE+Ig4d+5v3yCRJkiRJkiRJkiRNazbP6BvzB+X/1iv7Ejh6/8ORJEmSJEmSJEmS1IlZd/Rl5lHdCESSJEmSJEmSJElS5+ZyRR8RcSxwDLB8bFhmfmC+gpIkSZIkSZIkSZI0s1l39EXE3wAnUnT0XQk8FfgSYEefJEmSJEmSJEmS1CO1OUxzKvAk4CeZ+ULgeGDZvEYlSZIkSZIkSZIkaUZz6ejblZlNYCQiDgTuBI6e37AkSZIkSZIkSZIkzWQuHX2bI+Jg4CJgC3Ad8LV9TRQRJ0XELRFxa0S8aoZyp0ZERsSmOcQmSZIkSZIkSZIkLQmzfkZfZr64fHlhRFwFHJiZN840TUTUgXcATwG2AddGxOWZefOkcgcAfwJ8dbZxSZIkSZIkSZIkSUvJXK7oIyKeFRH/CLwUeGgHk5wA3JqZt2XmEPBR4OQ25V4PvAnYPZe4JEmSJEmSJEmSpKVi1h19EXE+cC7wDeCbwB9FxDv2MdkRwNaW99vKYa3zfTSwPjOvmG1MkiRJkiRJkiRJ0lIz61t3Ar8JHJuZCRAR76fo9JtJtBmW4yMjasA/AS/Y18Ij4hzgHIAHP/jBnUUs9QnzV1Vm/qrKzF9VmfmrKjN/VWXmr6rM/FWVmb+qKnNXC2Uut+68BWjN0vXAjM/oo7iCb33L+3XAHS3vDwCOBa6JiNuBxwKXR8SmyTPKzHdl5qbM3LR27do5hC8tHPNXVWb+qsrMX1WZ+asqM39VZeavqsz8VZWZv6oqc1cLpeMr+iLikxRX4R0EfCsivla+fwzwlX1Mfi3wsIg4CvgRcDrw3LGRmXkvcFjLsq4BXpGZmzuNT5IkSZIkSZIkSVpKZnPrzrfMdSGZORIR5wGfBurAezLzpoh4HbA5My+f67wlSZIkSZIkSZKkpajjjr7M/ELr+4g4cJbTXwlcOWnYa6Ype2Kn85UkSZIkSZIkSZKWotlc0QeMP1Dy9cAuoAkExS08j57f0CRJkiRJkiRJkiRNZ9YdfcArgUdm5l3zHYwkSZIkSZIkSZKkztTmMM33gPvnOxBJkiRJkiRJkiRJnZvLFX2vBr4SEV8F9owNzMw/mbeoJEmSJEmSJEmSJM1oLh197wQ+D3yD4hl9kiRJkiRJkiRJknpsLh19I5n58nmPRJIkSZIkSZIkSVLH5vKMvqsj4pyIeGBEHDr2N++RSZIkSZIkSZIkSZrWXK7oe275/9UtwxI4ev/DkSRJkiRJkiRJktSJWXf0ZeZR3QhEkiRJkiRJkiRJUuc6vnVnRPxFy+tnTxr39/MZlCRJkiRJkiRJkqSZzeYZfae3vH71pHEnzUMskiRJkiRJkiRJkjo0m46+mOZ1u/eSJEmSJEmSJEmSumg2HX05zet27yVJkiRJkiRJkiR1UWMWZY+PiF9QXL23onxN+X75vEcmSZIkSZIkSZIkaVodd/RlZr2bgUiSJEmSJEmSJEnq3Gxu3SlJkiRJkiRJkiSpT9jRJ0mSJEmSJEmSJFXQbJ7RJ0mSJEmSJEmSpHlw5Ks+Netpbn/D07sQiarMK/okSZIkSZIkSZKkCupZR19EnBQRt0TErRHxqjbjXx4RN0fEjRHxuYh4SK9ikyRJkiRJkiRJkqqmJx19EVEH3gE8FTgGOCMijplU7OvApsw8DrgUeFMvYpMkSZIkSZIkSZKqqFdX9J0A3JqZt2XmEPBR4OTWApl5dWbeX779b2Bdj2KTJEmSJEmSJEmSKqdXHX1HAFtb3m8rh03nRcD/bTciIs6JiM0RsXn79u3zGKLUfeavqsz8VZWZv6oy81dVZv6qysxfVZn5qyozf1VV5q4WSq86+qLNsGxbMOIsYBPw5nbjM/NdmbkpMzetXbt2HkOUus/8VZWZv6oy81dVZv6qysxfVZn5qyozf1Vl5q+qytzVQmn0aDnbgPUt79cBd0wuFBFPBv4K+M3M3NOj2CRJkiRJkiRJkqTK6dUVfdcCD4uIoyJiEDgduLy1QEQ8Gngn8MzMvLNHcUmSJEmSJEmSJEmV1JOOvswcAc4DPg18C7gkM2+KiNdFxDPLYm8GVgMfj4jrI+LyaWYnSZIkSZIkSZIkLXm9unUnmXklcOWkYa9pef3kXsUiSZIkSZIkSZIkVV2vbt0pSZIkSZIkSZIkaR7Z0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVUKNXC4qIk4C3AXXg3zLzDZPGLwM+AGwE7gZOy8zb92eZu3ePcPeuIUaaSaMWrFkxyPLlPVtl9Ymq5kFV455P1kF166CqcWv+VDkHhodHuWfXMEOjTUabyWC9RqMe3D80Sr0WDNQCgIFGjZFmMjzSJCIYrAdDo0lmMtCo0agFu4ZGGajXGBltEjXIZjDcbI7Pp0myvFHj/qHmeF0duKLGL3YV71cM1BkZbTLcTAYbNWrAaCaZ0MykXqvRCNg10hwvS0BmUW5ZvcZoJsOjyUAtaNSjHAeNGgyP5vhylw/UGBpt0mzCaDOp14JljRq7R5o0M1k+UOfgZQ2279z7ua4YrLFnpEk9inPH6rVi2XtG9q7PYKPG8GhSD2g0gqHhZGi0Ob4+u0eaNGrBqmU1du5pTohnx56i/g5fvYxGY+/5aSMjTe7csYfh0Wbb8QDNZnLXjj3sGi4+t8F6jUNXDk4pt9j06rvXi+VUuR1pZ2hoZML3Z+2qQQYHq7s+3dCNz7xbedSN+Tabyd07hxgaGWWwUWfNqkFq5TanH1Ut3m5rNpMdQ3u4b9febVmU2+QDlte4b3cxfNVgnaGRvdvrZia1KMqSsGygNr59Hi3nM1CvAcX2fPWyGjuHcsp2fM2KYhu3fecehkaKfY0VAzVGmzA82pzTZzT5Mz5oWX1CO3b46mVExIRt8mErB7jr/uEZt9GL1fDwKHfu2DOhfgYG6gsd1j4ttu2t5qaqeVDVuDW/zAOpRx19EVEH3gE8BdgGXBsRl2fmzS3FXgTck5m/HBGnA28ETpvrMnfvHuG7d+/kjy/ewrZ7drHukBVccNZGHrZmlV/0JaSqeVDVuOeTdVDdOqhq3Jo/Vc6B4eFRfnDP/Wy/bw+vvPTG8fjffOpxvOmqW9i+Yw9vPvU4Dlk1wH27RvizS24YL3P+mRt4++e/y2duvnN8mk9c9yN+b8MRvPfL3+dF/+No/vzjN0yY58ErB2jUa7zwvddOqKt//dx32H7fEH9x0sMnxPFPzzmegUaN8z789QnzaV3O83/jKP7yshtZu3rZlOkvPGsjQXL59T/i6ccfwYs/dN3e5Z65AYA/Lof99jGHc94THzaxTBnb2DpecNZGVi+r8Q9X3sRfnPRwBhp17r1/eMp8r7jhRzz1uAcxPNKcUGdj9Xry8b/ExqMOm5Az55+5gYv/6wd85ba7ufCsjTziAQfQaNQYGWny7Z/ex7ktZVvHQ/HD5C0/uY+zP7h5wrJ+ccAyjjx01aL9wbFX371eLKfK7Ug7Q0Mj3LJ96vo8fO0qO/tK3fjMu5VH3Zhvs5nc8tP7OPsDe9uti563iYc/4IC+7DyrWrzd1mwm9+zawx0/3zMhL954ynF89yf3jm/jfuPoNZz1uIfw9s9/d3x73Vr2i7f8lFM2refuHUNTtv8Hrxrk23f8nCPXHsgV12+bsh2/8KyNrBio8fxyn6Lddnw2n9Hkz/i1z3jElG31hWdtZPlAjRe02Y8Z21eYvI1erIaHR/n2nTumtAuPOHx1X3f2LbbtreamqnlQ1bg1v8wDqdCrPa0TgFsz87bMHAI+Cpw8qczJwPvL15cCT4qIOR8h3L1raPwLDrDtnl388cVbuHvX0FxnqQqqah5UNe75ZB1Utw6qGrfmT5Vz4M4de9j6s13jP65BEf8rL72Rc0986PjrRq0+3mE1VubFH7qOUzaunzDN2U84mldeeiOnbFw/3snXOv4n9+5h2892TamrUzau59wTHzoljj+75Abu2Tk8ZT6tyxn70bDd9OdevIU77xvi1E0PHv/hb3y5H7qOu3YMjQ87ZeP6qWXK2FrfN2r1cliN4ZFsO99TNz2Ye3YOT6mzsXp94jEPnJIzL/7QdZz9hKP3xr1jz/hndO6ksq3jAe7eOTTeyde6rK0/2zWh3GLTq+9eL5ZT5Xakne0726/P9p3VXJ9u6MZn3q086kqsO4fGO1TG5nn2BzZzd5/mSNXi7ba7dw6xe6g5JS/+8rIbJ2zjzn7C0eP7C2Pb69ayp256MD+6Z3fb7f+2n+3i0Q9Zwx9fvKXtdvzci7fww5Z9inbb8dl8RpM/43bb6nMv3sLWafZjWsss5m3vmDt37GnbLvT7ui+27a3mpqp5UNW4Nb/MA6nQq46+I4CtLe+3lcPalsnMEeBeYM3kGUXEORGxOSI2b9++fdoFjjRz/As+vtB7djHSzDmtgKqp3/LA/O2cddB/dWD+qlP9mAOzyd+Vg/W28R+8YmD8dS2YsczY+3otxoe3K79ysM7KwXrb+cw0zeRh7ZYz0/Rj5Wea93TTT17HZiYHrxigFkxbL/VazFivzWyfM/XyioNt9+wqbktKcfuxtvlVjgcYGhmddv1ay1VFv7W/vVhOP7Yj+2Oxrc9sLGT+dqveuzHf6dqtoZHROc+zm6oW71x1mr9DI6PT5kXrNm5f+wUzbS9XDtYZLZexP9vxTj+jyZ/xdNvq6fZjWt9Xcds7W/3YzneSv/0Yt3qvH/PA/FWn+i0POt13kOZbrzr62l2ZN/nb1kkZMvNdmbkpMzetXbt22gU2asG6Q1ZMGLbukBU0luBtRJayfssD87dz1kH/1YH5q071Yw7MJn/vHxptG//Pdw2Pv24mM5YZez/azPHh7crfPzTK/UOjbecz0zSTh7VbzkzTj5Wfad7TTT95HWsR/HzXMM1k2noZbeaM9VqL9jkzWh6crTtkBY16sds6UK+1z6/63t3awUZ92vVrLVcV/db+9mI5/diO7I/Ftj6zsZD5261678Z8p2u3Bhv9ecu/qsU7V53m72CjPm1etG7j9rVfMNP2cuxZwa3b/XZlxky3jE4/o8mf8XTb6un2Y1rfV3HbO1v92M53kr/9GLd6rx/zwPxVp/otDzrdd5DmW6/2trYB61verwPumK5MRDSAg4CfzXWBa1YMcsFZG8e/6GP3512zYnCus1QFVTUPqhr3fLIOqlsHVY1b86fKOXD46mWsP7R4nltr/G8+9TguvOZ7469HmqP803OOn1Dm/DM3cNmWrROmueiLt/HmU4/jsi1beeuzj58yz186aBnrDl0xpa4u27KVC6/53pQ4/uk5x3PIqoEp82ldzhtPKaZpN/2FZ23k8AMGuXTzDzn/zA0Tl3vmBg5bPTg+7LItW6eWKWNrfT/SHC2HNRloRNv5Xrr5hxyyamBKnY3V6+dv/vGUnDn/zA1c9MXb9sa9etn4Z3ThpLKt4wHWrBrkot/fNGVZ6w9dMaHcYtOr714vllPldqSdtavar8/aVdVcn27oxmferTzqSqyrBrnoeRPbrYuet4k1fZojVYu329asGmT5YG1KXrzxlOMmbOMu+uJt4/sLY9vr1rKXbv4hRxyyvO32f92hK/j6D+7mgrM2tt2OX3jWRh7csk/Rbjs+m89o8mfcblt94VkbWT/NfkxrmcW87R1z+OplbduFfl/3xba91dxUNQ+qGrfml3kgFSKz+5exlh133wGeBPwIuBZ4bmbe1FLmJcCvZea5EXE68KzMfM5M8920aVNu3rx52vG7d49w964hRppJoxasWTHoQziXoFnmQc9O9zB/9806MH9VXXPIgb7J3+HhUe7ZNczQaJPRZjJYr9GoB7uGRqnVgoHyzMCBRo2RZjI80iQiGKwHQ6NJZjLQqNGoFdMM1GuMjDaJWpBNGGk2x+fTJFneqHH/UHO8rg5cUeMXu4r3KwaKW00ON5PBRo0aMJpJZnELrXqtRiNg10hzvCzB+PjBeo3RTEZGi3k36kEmjCY0ajA8muPLXT5QY2i0SbNZXHFQrwXLGjV2jzRpZrJ8oM7Byxps37n3c10xWGPPSFIvH6tcrxXL3jOyd32WNWoMjSb1gEYjGBpOhkebDJTrs2ekSb0WrFpWY+ee5oR4du4prsA7fPUyGo2956eNjDS5c8ceRkabbccDNJvJXTv2sHu4+NwG6zUOXTk4pdw86Zv87VX724vlLLZtydDQyITvz9pVgwwO9sX6LOr87VYedWO+zWZy984hhkZGGWzUWbNqkFofX5XQJ/H2Tf42m8mOoT3ct6s5vh2tRXG1+wHLa9y3u9jGrRqsMzQycXtdiyACSFg2UBvfPjfL+QzUa0AyPJqsXlZj51BO2Y6vWVFs47bv3MPwSLGvsWKgxmizuO31XD6jyZ/xQcvqE9qxw1cvIyImbJMPWznAXfcPz7iNXqyGh0eLumipn4GBGa+g7Iv8XWzbW81NVY/fzF9B/+bvvvYdxhz5qk/Net63v+HpcwlJPdLlz7Rt/vak5cvMkYg4D/g0UAfek5k3RcTrgM2ZeTnwbuCDEXErxZV8p+/vcpcvb3CEjfuSV9U8qGrc88k6qG4dVDVuzZ8q58DAQJ3DZ/5RpnOrOit2yKRyByyfn8V3wxFd7Jg4eOXE92tWty/XaNR40MEr2o8s1WrB4Qf2cUV2Sa++e71YTpXbkXYGBxtd/f4sBt34zLuVR92Yb60WrD2gv6/+aVW1eLutVgsOXL6c6TY9B8682ZqVg1ZOP+6BB83fgtp9xu3ascnb5Act0bZuYKDOEYfM8OH0qcW2vdXcVDUPqhq35pd5IPWoow8gM68Erpw07DUtr3cDz+5VPJIkSZIkSZIkSVKVLY37J0iSJEmSJEmSJEmLjB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgVFZi50DHMWEduBH3RQ9DDgri6H0++sg87q4K7MPKkXwZi/s2IdmL9VttTroNP177f8rcLn1u8x9nt8MH8x9lv+QjXqv1OLaV2g/9bH/N0/xtod7j8sPNel+8zf/mMd2P5WmXXQZ/m7SPd9u8U62M/8rXRHX6ciYnNmblroOBaSdVDdOqhq3PPJOqhuHVQ17vm01Ougqutfhbj7PcZ+jw+qEeNcLaZ1W0zrAotvfbqhSnVkrN1RpVhbVTXudlyXpcd6sg6gunVQ1bjnk3VQ3TqoatzzyTrY/zrw1p2SJEmSJEmSJElSBdnRJ0mSJEmSJEmSJFXQUunoe9dCB9AHrIPq1kFV455P1kF166Cqcc+npV4HVV3/KsTd7zH2e3xQjRjnajGt22JaF1h869MNVaojY+2OKsXaqqpxt+O6LD3Wk3UA1a2DqsY9n6yD6tZBVeOeT9bBftbBknhGnyRJkiRJkiRJkrTYLJUr+iRJkiRJkiRJkqRFxY4+SZIkSZIkSZIkqYIWVUdfRJwUEbdExK0R8ao245dFxMfK8V+NiCN7H2X3dLD+L4iI7RFxffn3hwsRZzdFxHsi4s6I+OY04yMi/qWsoxsjYkOvY+zUvtZlsYuI9RFxdUR8KyJuioiXLXRMvRYRyyPiaxFxQ1kHf7vQMbWz1NtesP2FxdP+7uuzXAjTtYcR8dqI+FFLXj1tgeO8PSK+UcayuRx2aER8NiK+W/4/ZAHje3hLXV0fEb+IiD/tt3rcX/2Yw3O1WPcFIqIeEV+PiCsWOpZ+U5X8rWJuVinvIuLgiLg0Ir5d1vHjFjqmTlQlf2HGfYu22+0q7MdNzvGIOKo89vhueSwyWA5f9Mcm++Lxm8dvVT52M3/NX/O32szfLuZvZi6KP6AOfA84GhgEbgCOmVTmxcCF5evTgY8tdNw9Xv8XAG9f6Fi7XA9PADYA35xm/NOA/wsE8Fjgqwsd81zXZbH/AQ8ENpSvDwC+MzmnF/tfmaery9cDwFeBxy50XJNiXNJt7yzqwPa3Au1vJ5/lAsXVtj0EXgu8YqHja4nzduCwScPeBLyqfP0q4I0LHWfLZ/0T4CH9Vo/zsF59l8P7sT6Lcl8AeDnwYeCKhY6ln/6qlL9VzM0q5R3wfuAPy9eDwMELHVMHMVcmf8t4p9u3aLvdrsh+3IQcBy4BTi9fXwj8cfl6UR+bdFBPHr95/AYVPXYzf83fcv3M34r+mb/dzd/FdEXfCcCtmXlbZg4BHwVOnlTmZIqDBoBLgSdFRPQwxm7qZP0Xvcz8IvCzGYqcDHwgC/8NHBwRD+xNdLPTwbosapn548y8rnx9H/At4IiFjaq3yjzdUb4dKP9yAUNqZ6m3vWD7Cyya9rcvP8uKt4et3//3A7+7gLG0ehLwvcz8wUIHMs/6MofnquK531ZErAOeDvzbQsfShyqTv1XLzSrlXUQcSPEDyLsBMnMoM3++sFF1pDL5CzPm8HTb7b7ej5uc4+WxxhMpjj1g6ros5mOTffH4rWLf126o8LGb+Wv+mr/VZv52MX8XU0ffEcDWlvfbmHqwNV4mM0eAe4E1PYmu+zpZf4BTyss+L42I9b0Jra90Wk/qI+Wl6o+m+TWaiwAAEWZJREFUuKJtSYniFjTXA3cCn83MfquDpd72gu1vp6rQ/vZ9jG3aw/PKvHpPLOBtMUsJfCYitkTEOeWwB2Tmj6H4URE4fMGim+h04CMt7/upHvdH3+fwXC2ifYF/Bv4CaC50IH2okvlbkdysUt4dDWwH3hvFbRj/LSJWLXRQHahk/sKUHJ5uu93v6zc5x9cAPy+PPWBivIv92GRfPH7z+K0T/fqdN3/N306Yv/3L/N23OefvYuroa9e7PfnKl07KVFUn6/ZJ4MjMPA74/9h7hsBSsphzYFGKiNXAZcCfZuYvFjqeXsvM0cx8FLAOOCEijl3omCZZ6m0v2P52qgp50NcxtmkPLwAeCjwK+DHw1gUMD+DxmbkBeCrwkoh4wgLH01YUz+h5JvDxclC/1eP+6OscnqvFsi8QEc8A7szMLQsdS5+qXP5WITcrmHcNitsZXZCZjwZ2UtxCst9VLn9hVjnct+s3TY7PFG/frkuPePzm8Vsn+jUHzF/ztxP9mgPmr/nbiTnnwGLq6NsGtPbwrgPumK5MRDSAg1g8t0bc5/pn5t2Zuad8exGwsUex9ZNO8kR9IiIGKA48P5SZ/2eh41lI5S2LrgFOWuBQJlvqbS/Y/naqCu1v38bYrj3MzJ+WJwM0KfLqhIWMMTPvKP/fCXyijOenY7eZKP/fuXARjnsqcF1m/hT6rx73U9/m8Fwtsn2BxwPPjIjbKW5T88SIuHhhQ+orlcrfCuVm1fJuG7Ct5S4Wl1J0/PW7SuUvTJvD0223+3n9puQ4xRV+B5fHHjAx3sV+bLIvHr95/NaJfv3Om7/mbyfM3/5l/u7bnPN3MXX0XQs8LCKOKs/UPh24fFKZy4Hnl69PBT6fmYulV3yf6z/pfq7PpLgP/1JzOfC8KDwWuHfs1iTqL+U9qN8NfCsz/3Gh41kIEbE2Ig4uX68Angx8e2GjmmKpt71g+9upKrS/neRzz03XHk7Kq98Dvtnr2FpiWRURB4y9Bn67jKf1+/984D8WJsIJzqDltp39VI/zoC9zeK4W275AZr46M9dl5pEUn83nM/OsBQ6rn1Qmf6uUm1XLu8z8CbA1Ih5eDnoScPMChtSpyuQvzJjD0223+3Y/bpocPxO4muLYA6auy2I+NtkXj988futEv37nzV/ztxPmb/8yf/dtzvnb2HeRasjMkYg4D/g0UAfek5k3RcTrgM2ZeTnFjuwHI+JWit7w0xcu4vnV4fr/SUQ8ExihWP8XLFjAXRIRHwFOBA6LiG3A3wADAJl5IXAl8DTgVuB+4IULE+m+tVuXzHz3wkbVU48Hfh/4RhTPqAP4X5l55QLG1GsPBN4fEXWKEzMuycwrFjimCZZ62wu2v2MWQ/s73We5wGHBNO0hcEZEPIriNg63A3+0MOEB8ADgE8XvhjSAD2fmVRFxLXBJRLwI+CHw7AWMkYhYCTyFiXX1pj6qx/3Sxzk8V+4LLCEVy19zs7teCnyo/AHoNvpwn2GyiuUvTL9v8Qbab7f7fj+ujb8EPhoR/xv4OsUxCSzyY5N98fjN4zeo7rGb+Wv+gvlbZeZvd/M3FlensCRJkiRJkiRJkrQ0LKZbd0qSJEmSJEmSJElLhh19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19kiRJkiRJkiRJUgXZ0SdJkiRJkiRJkiRVkB19fSgi/ioiboqIGyPi+oh4zDzM85kR8ap5im/HfMxHS0tEjJb5/M2I+HhErJyh7Gsj4hW9jE+aq2602VKvRMTvRURGxCMWOhZpJu3a2oj4t4g4phzfdv80Ih4bEV8tp/lWRLy2p4FryZvNPvAs5vmCiHj7fMQndaoll8f+jlzomCRJklSwo6/PRMTjgGcAGzLzOODJwNYOp21MNy4zL8/MN8xPlNKc7MrMR2XmscAQcO5CByTtr/1ps6U+cQbwJeD0hQ5Ems50bW1m/mFm3ryPyd8PnJOZjwKOBS7pbrTSFHPeB46IevfCkmZtLJfH/m7vZCLzWHPRpmO54xPXI+LEiLhiP5d/TURsmuO0+1x+RDw1IjaXJyF9OyLeMrdI1W+WQO7+bnni3bfLk5hOnVuk6leLOYcj4gERcUVE3BARN0fElXOPtP/Y0dd/HgjclZl7ADLzrsy8IyJuj4jDACJiU0RcU75+bUS8KyI+A3ygPGP5kWMzK78cG8fO+oyIg8p51crxKyNia0QMRMRDI+KqiNgSEf85dnZ/RBwVEf8VEddGxOt7XB9anP4T+GWAiHheuZNwQ0R8cHLBiDi7zL0bIuKysbOgI+LZ5U7FDRHxxXLYIyPia+WG6MaIeFhP10pL0XRt9saI+ELZnn46Ih4YEY0yl08EiIh/iIi/W8jgtbRFxGrg8cCLKDv6IqIWEedHceXUFRFx5djBW7u8XsDwtbRM19ZOOAiMiLdGxHUR8bmIWFsOPhz4cTnd6FjHYLkP/cGI+HxEfDcizu7xOmlpat0H/veyPb0pIs4ZKxAROyLidRHxVeBxEfHrEfGVcp/3axFxQFn0QeWx23cj4k0LsC4SEXFk+dvBdeXfb5TDT4yIqyPiw8A3ymFntRyrvTPsANTMJncs9+zE9W7nZkQcC7wdOCszf5XiRKTburlM9dRizt3jgbcAJ2fmI4DfAd4YERu7uVz13KLNYeB1wGcz8/jMPAaYl7sf9gs7+vrPZ4D1EfGd8oe23+xgmo0UjexzgY8CzwEof4B7UGZuGSuYmfcCNwBj8/0d4NOZOQy8C3hpZm4EXgGcX5Z5G3BBZv468JP9XkMtaVFcefpU4Btlp/RfAU/MzOOBl7WZ5P9k5q+X479F8YM0wGuA/1kOf2Y57FzgbeVZ+5uAbV1cFQnatNkRMQD8K3Bq2Z6+B/i7zBwBXgBcEBFPAU4C/nahApeA3wWuyszvAD+LiA3As4AjgV8D/hB4HMB0eb0QQWtJ6mT/eBVwXWZuAL4A/E05/J+AWyLiExHxRxGxvGWa44CnU+T5ayLiQV1cBy1xrfvA5aA/KNvTTcCfRMSacvgq4JuZ+Rjga8DHgJeV+7xPBnaV5R4FnEbRXp8WEet7syZawlbE3rP7P1EOuxN4Stn2ngb8S0v5E4C/ysxjIuJXy/GPL4/VRoEzexm8FocoTlz/+/Jk9M0RsaE8Ae17EdF6xfSB5bb/5oi4MPae7H5BOd1NEfG3k+b7moj4EvDsluG1iHh/RPzv8v1vl8u+LorbMa8uh58UxRVOX6LYn57JX1AcH34bIDNHMvP8fUyjilskufsK4O8z8/sA5f+/B/58HqpIfW6R5PADafmtODNv3O+K6SN29PWZzNxB0XF3DrAd+FhEvGAfk12emWMHfJew90vxHODjbcp/jGInG4oz+D9Wfjl+A/h4RFwPvJMi+aE42/8j5espV1xJHVpR5tZm4IfAu4EnApdm5l0AmfmzNtMdG8VZot+gOBgcu2L1y8D7ojgDf+yMj/8C/ldE/CXwkJbvhdQV7dps4I8ozsr8bJnzfw2sK8vfRNGOfpLiB76hhYhbKp1BcYIQ5f8zgP8BfDwzm5n5E+DqcvzDmSavpW7rcP+4SdEGA1xMkctk5usoOlI+AzwXuKplmv/IzF3lfsjVFD9KS/Ot3T4wFJ17NwD/DawHxu5EMQpcVr5+OPDjzLwWIDN/UZ44BPC5zLw3M3cDNwMP6f6qaIlrPcP/98phA8BF5bHax4FjWsp/bezHYOBJFO34teX34UnA0b0KXJXU2rF8fUSc1jJua2Y+juIq6fcBpwKPpbhSY8wJFJ0PvwY8lL0//v5VZm6iONnnNyPiuJZpdmfm/8jMsf3jBvAh4DuZ+ddR3GXrr4Enl53bm4GXlycRXURxIv3/A/zSPtbtWGDLPsqouhZz7j6Sqbm7mYltv6pvMefwO4B3R3HXgb+KRXai57TPdNPCycxR4BrgmnKH+fnACHs7ZpdPmmRny7Q/ioi7yy/LaRQ/OE92OfAPEXEoxc725ynOHP15eXZd27DmuDrSmF2T8ysign3n1vuA383MG8of9U4EyMxzI+IxFGfiXx8Rj8rMD0dxm6OnA5+OiD/MzM/P83pIE7Rps18C3FTu/LTza8DPgQf0JkJpqiiuHHkixckUSXHCRAKfmG4SZs5rqaum2T+ecZKWab9HcTX1RcD22Hvl1OR9EPd31Q3t9oFPpLg673GZeX8Uj2UYO8bbXeY7FG3vdHm5p+X1KB7ba2H8GfBT4HiK3yt2t4zb2fI6gPdn5qt7GJuqbUrb2eLy8v83gNWZeR9wX0TsjoiDy3Ffy8zbACLiIxQnAF0KPCeK2yU3KE5uPwYYu6Jj7IShMe8ELsnMsbtYPLYs/+XipwwGKU42fgTw/cz8brm8iylOTtLStJhzt91+ScxQXtW0aHM4Mz8dEUdT3GHrqcDXI+LYzNw+Y41UhFf09ZmIeHhMfK7Yo4AfALdTdMoBnLKP2XyU4lYAB2XmNyaPLM+K/hrFLTmvKJ9X8gvg+xHx7DKOiOLey1BcOXV6+drba2g+fY6ioV8DUHY+T3YA8OMobhs3nn8R8dDM/Gpmvga4i+KWXkcDt2Xmv1BsfI5rMz9p3kzTZn8LWBsR47c8jPLZqRHxLGAN8ATgX1p2hKReOxX4QGY+JDOPzMz1wPcp2tNTyttkPIDy5ArgFqbJa6nbZtg/blWjyGsortz7Ujnt08sTi6C4YmqU4mQLgJMjYnm5H3IicG0XwpfaOQi4p+zkewTFjxftfJviWXy/DhARB0RxC1CpXxxEcdVpE/h99t5pZbLPAadGxOFQHPdFhFehaq7GTnRoMvGkhyZ7T3qYcjJPRBxFcevBJ2XmccCnmHgi/c5J03wF+K3Ye9vvoHi209iVrcdk5tijRWZzstBN7P19T0vLYsjdTZOGjV1dpaWh6jlMZv4sMz+cmb9Pcfz3hNlM38/s6Os/q4H3R3Ef2xspeqtfS/Ecp7dFxH9S/EAxk0spOuYumaHMx4CzmNhjfibwovIWMjcBJ5fDXwa8JCKupdiRl+ZFeRvDvwO+UObdP7Yp9v8CXwU+S/Fjx5g3R8Q3IuKbwBcpnj15GvDN8nYwjwA+0M34Jdq32a+h+LH5jWVeXw/8RnmrgTcALyqfifZ2ihMupIVwBlOv3rsMeBDFPeu/SXEW3VeBe8vbzE7J696FqyVuuv3jVjuBR0bEFoqrVcduH/P7FM/ou57i1slntlwt9TWKg8z/Bl6fmXd0dzWkcVcBjTKfX0+Rg1OUbe9pwL+Wbe9nmXp3F2khnQ88PyL+G/gVpv5QB0Bm3kxxy63PlHn/WfY+KkTqhhMi4qgong11GsUJQAdS5Oi95QltT93HPN4NXEnxiJsGRVv9+Ij4ZYCIWBkRv0LxO8VREfHQcroz9jHfN1M8cuRXyvnUIuLls19FLVL9nLtvAV4dEUeW8zkS+FOKnJbG9G0OR8QTI2Jl+foAiluL/nAuK9mPItM71EiSJKkQEaszc0d5ldPXgMeXz+uTFo2IeC2wIzPfstCxSJKkiSJilOLWcGOuysxXRcTtwKbMvCuKR3tsyszzymlup7ja6FiKky+3Uzw24YvAizOzGRHvAx4D3EZxNcrlmfm+1vmW87oGeEVmbo6Iv6XoyD6T4g4AbwSWlXH9dWZeHhEnAf9McXeMLwHHZuYzZli/Z1Cc0L+S4mqUT2XmK+dWW+onSyB3n0WRu8uAI4Hfyswvz6my1JcWcw5HxCuBF7L3EWnvzcy3zr22+osdfZIkSRpX7lgfTHHf+zdl5vsWNCCpC+zokyRJkuYuIt5A0XHzP8u7EEhaQHb0SZIkSZIkSZIkSRXkg7wlSZIkSZIkaZ5ExAuBl00a/OXMfMlCxCN1ytxV1S3VHPaKPkmSJEmSJEmSJKmCagsdgCRJkiRJkiRJkqTZs6NPkiRJkiRJkiRJqiA7+iRJkiRJkiRJkqQKsqNPkiRJkiRJkiRJqqD/HxipdZWgvs4FAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(titanic_df)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Machine Learning/03-MultipleClassificationModels_Titanic.ipynb b/Machine Learning/03-MultipleClassificationModels_Titanic.ipynb new file mode 100644 index 00000000..6f1b784a --- /dev/null +++ b/Machine Learning/03-MultipleClassificationModels_Titanic.ipynb @@ -0,0 +1,1171 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import recall_score\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", + "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", + "from sklearn.linear_model import SGDClassifier\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.neighbors import RadiusNeighborsClassifier\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_S
    003014.0007.8542001
    111128.00026.5500001
    211036.012120.0000001
    303117.0107.0542001
    40314.04231.2750001
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked_C \\\n", + "0 0 3 0 14.0 0 0 7.8542 0 \n", + "1 1 1 1 28.0 0 0 26.5500 0 \n", + "2 1 1 0 36.0 1 2 120.0000 0 \n", + "3 0 3 1 17.0 1 0 7.0542 0 \n", + "4 0 3 1 4.0 4 2 31.2750 0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = pd.read_csv('datasets/titanic_processed.csv')\n", + "\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Pclass',\n", + " 'Sex',\n", + " 'Age',\n", + " 'SibSp',\n", + " 'Parch',\n", + " 'Fare',\n", + " 'Embarked_C',\n", + " 'Embarked_Q',\n", + " 'Embarked_S']" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FEATURES = list(titanic_df.columns[1:])\n", + "\n", + "FEATURES" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "result_dict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_classification(y_test, y_pred):\n", + " \n", + " acc = accuracy_score(y_test, y_pred, normalize=True)\n", + " num_acc = accuracy_score(y_test, y_pred, normalize=False)\n", + "\n", + " prec = precision_score(y_test, y_pred)\n", + " recall = recall_score(y_test, y_pred)\n", + " \n", + " return {'accuracy': acc, \n", + " 'precision': prec,\n", + " 'recall':recall, \n", + " 'accuracy_count':num_acc}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(classifier_fn, \n", + " name_of_y_col, \n", + " names_of_x_cols, \n", + " dataset, \n", + " test_frac=0.2):\n", + " \n", + " X = dataset[names_of_x_cols]\n", + " Y = dataset[name_of_y_col]\n", + "\n", + " x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=test_frac)\n", + " \n", + " model = classifier_fn(x_train, y_train)\n", + " \n", + " y_pred = model.predict(x_test)\n", + "\n", + " y_pred_train = model.predict(x_train)\n", + " \n", + " train_summary = summarize_classification(y_train, y_pred_train)\n", + " test_summary = summarize_classification(y_test, y_pred)\n", + " \n", + " pred_results = pd.DataFrame({'y_test': y_test,\n", + " 'y_pred': y_pred})\n", + " \n", + " model_crosstab = pd.crosstab(pred_results.y_pred, pred_results.y_test)\n", + " \n", + " return {'training': train_summary, \n", + " 'test': test_summary,\n", + " 'confusion_matrix': model_crosstab}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def compare_results():\n", + " for key in result_dict:\n", + " print('Classification: ', key)\n", + "\n", + " print()\n", + " print('Training data')\n", + " for score in result_dict[key]['training']:\n", + " print(score, result_dict[key]['training'][score])\n", + "\n", + " print()\n", + " print('Test data')\n", + " for score in result_dict[key]['test']:\n", + " print(score, result_dict[key]['test'][score])\n", + " \n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def logistic_fn(x_train, y_train):\n", + " \n", + " model = LogisticRegression(solver='liblinear')\n", + " model.fit(x_train, y_train)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ logistic'] = build_model(logistic_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def linear_discriminant_fn(x_train, y_train, solver='svd'):\n", + " \n", + " model = LinearDiscriminantAnalysis(solver=solver)\n", + " model.fit(x_train, y_train)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7752293577981652\n", + "recall 0.7161016949152542\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7692307692307693\n", + "precision 0.7209302325581395\n", + "recall 0.5961538461538461\n", + "accuracy_count 110\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/anaconda3/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n", + " warnings.warn(\"Variables are collinear.\")\n" + ] + } + ], + "source": [ + "result_dict['survived ~ linear_discriminant_analysis'] = build_model(linear_discriminant_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ linear_discriminant_analysis'] = build_model(linear_discriminant_fn,\n", + " 'Survived',\n", + " FEATURES[0:-1],\n", + " titanic_df)\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def quadratic_discriminant_fn(x_train, y_train):\n", + " \n", + " model = QuadraticDiscriminantAnalysis()\n", + " model.fit(x_train, y_train)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ quadratic_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.81195079086116\n", + "precision 0.7741935483870968\n", + "recall 0.7433628318584071\n", + "accuracy_count 462\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.7719298245614035\n", + "recall 0.7096774193548387\n", + "accuracy_count 112\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ quadratic_discriminant_analysis'] = build_model(quadratic_discriminant_fn,\n", + " 'Survived',\n", + " FEATURES[0:-1],\n", + " titanic_df)\n", + "\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def sgd_fn(x_train, y_train, max_iter=1000, tol=1e-3):\n", + " \n", + " model = SGDClassifier(max_iter=max_iter, tol=tol)\n", + " model.fit(x_train, y_train)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ quadratic_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.81195079086116\n", + "precision 0.7741935483870968\n", + "recall 0.7433628318584071\n", + "accuracy_count 462\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.7719298245614035\n", + "recall 0.7096774193548387\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ sgd\n", + "\n", + "Training data\n", + "accuracy 0.7363796133567663\n", + "precision 0.7547169811320755\n", + "recall 0.5194805194805194\n", + "accuracy_count 419\n", + "\n", + "Test data\n", + "accuracy 0.7482517482517482\n", + "precision 0.7441860465116279\n", + "recall 0.5614035087719298\n", + "accuracy_count 107\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ sgd'] = build_model(sgd_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "\n", + "compare_results()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LinearSVC\n", + "\n", + "https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html\n", + "\n", + "* SVC with a linear kernel\n", + "* dual=False when number of samples > number of features" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def linear_svc_fn(x_train, y_train, C=1.0, max_iter=1000, tol=1e-3):\n", + " \n", + " model = LinearSVC(C=C, max_iter=max_iter, tol=tol, dual=False)\n", + " model.fit(x_train, y_train) \n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ quadratic_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.81195079086116\n", + "precision 0.7741935483870968\n", + "recall 0.7433628318584071\n", + "accuracy_count 462\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.7719298245614035\n", + "recall 0.7096774193548387\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ sgd\n", + "\n", + "Training data\n", + "accuracy 0.7363796133567663\n", + "precision 0.7547169811320755\n", + "recall 0.5194805194805194\n", + "accuracy_count 419\n", + "\n", + "Test data\n", + "accuracy 0.7482517482517482\n", + "precision 0.7441860465116279\n", + "recall 0.5614035087719298\n", + "accuracy_count 107\n", + "\n", + "Classification: survived ~ linear_svc\n", + "\n", + "Training data\n", + "accuracy 0.789103690685413\n", + "precision 0.7652582159624414\n", + "recall 0.6995708154506438\n", + "accuracy_count 449\n", + "\n", + "Test data\n", + "accuracy 0.8531468531468531\n", + "precision 0.84\n", + "recall 0.7636363636363637\n", + "accuracy_count 122\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ linear_svc'] = build_model(linear_svc_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def radius_neighbor_fn(x_train, y_train, radius=40.0):\n", + "\n", + " model = RadiusNeighborsClassifier(radius=radius)\n", + " model.fit(x_train, y_train) \n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ quadratic_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.81195079086116\n", + "precision 0.7741935483870968\n", + "recall 0.7433628318584071\n", + "accuracy_count 462\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.7719298245614035\n", + "recall 0.7096774193548387\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ sgd\n", + "\n", + "Training data\n", + "accuracy 0.7363796133567663\n", + "precision 0.7547169811320755\n", + "recall 0.5194805194805194\n", + "accuracy_count 419\n", + "\n", + "Test data\n", + "accuracy 0.7482517482517482\n", + "precision 0.7441860465116279\n", + "recall 0.5614035087719298\n", + "accuracy_count 107\n", + "\n", + "Classification: survived ~ linear_svc\n", + "\n", + "Training data\n", + "accuracy 0.789103690685413\n", + "precision 0.7652582159624414\n", + "recall 0.6995708154506438\n", + "accuracy_count 449\n", + "\n", + "Test data\n", + "accuracy 0.8531468531468531\n", + "precision 0.84\n", + "recall 0.7636363636363637\n", + "accuracy_count 122\n", + "\n", + "Classification: survived ~ radius_neighbors\n", + "\n", + "Training data\n", + "accuracy 0.6590509666080844\n", + "precision 0.6931818181818182\n", + "recall 0.2675438596491228\n", + "accuracy_count 375\n", + "\n", + "Test data\n", + "accuracy 0.6853146853146853\n", + "precision 0.8571428571428571\n", + "recall 0.3\n", + "accuracy_count 98\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ radius_neighbors'] = build_model(radius_neighbor_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "compare_results()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "max_depth = None [ If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples ]\n", + "\n", + "max_features = None [None -- max_features=n_features, \n", + " auto -- then max_features=sqrt(n_features), \n", + " sqrt -- then max_features=sqrt(n_features), \n", + " log2 -- then max_features=log2(n_features)]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def decision_tree_fn(x_train, y_train, max_depth=None, max_features=None): \n", + " \n", + " model = DecisionTreeClassifier(max_depth=max_depth, max_features=max_features)\n", + " model.fit(x_train, y_train)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ quadratic_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.81195079086116\n", + "precision 0.7741935483870968\n", + "recall 0.7433628318584071\n", + "accuracy_count 462\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.7719298245614035\n", + "recall 0.7096774193548387\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ sgd\n", + "\n", + "Training data\n", + "accuracy 0.7363796133567663\n", + "precision 0.7547169811320755\n", + "recall 0.5194805194805194\n", + "accuracy_count 419\n", + "\n", + "Test data\n", + "accuracy 0.7482517482517482\n", + "precision 0.7441860465116279\n", + "recall 0.5614035087719298\n", + "accuracy_count 107\n", + "\n", + "Classification: survived ~ linear_svc\n", + "\n", + "Training data\n", + "accuracy 0.789103690685413\n", + "precision 0.7652582159624414\n", + "recall 0.6995708154506438\n", + "accuracy_count 449\n", + "\n", + "Test data\n", + "accuracy 0.8531468531468531\n", + "precision 0.84\n", + "recall 0.7636363636363637\n", + "accuracy_count 122\n", + "\n", + "Classification: survived ~ radius_neighbors\n", + "\n", + "Training data\n", + "accuracy 0.6590509666080844\n", + "precision 0.6931818181818182\n", + "recall 0.2675438596491228\n", + "accuracy_count 375\n", + "\n", + "Test data\n", + "accuracy 0.6853146853146853\n", + "precision 0.8571428571428571\n", + "recall 0.3\n", + "accuracy_count 98\n", + "\n", + "Classification: survived ~ decision_tree\n", + "\n", + "Training data\n", + "accuracy 0.9859402460456942\n", + "precision 1.0\n", + "recall 0.9644444444444444\n", + "accuracy_count 561\n", + "\n", + "Test data\n", + "accuracy 0.7132867132867133\n", + "precision 0.6896551724137931\n", + "recall 0.6349206349206349\n", + "accuracy_count 102\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ decision_tree'] = build_model(decision_tree_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def naive_bayes_fn(x_train,y_train, priors=None):\n", + " \n", + " model = GaussianNB(priors=priors)\n", + " model.fit(x_train, y_train)\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Classification: survived ~ logistic\n", + "\n", + "Training data\n", + "accuracy 0.7908611599297012\n", + "precision 0.7729468599033816\n", + "recall 0.6896551724137931\n", + "accuracy_count 450\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7407407407407407\n", + "recall 0.7142857142857143\n", + "accuracy_count 113\n", + "\n", + "Classification: survived ~ linear_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.7961335676625659\n", + "precision 0.7560975609756098\n", + "recall 0.7013574660633484\n", + "accuracy_count 453\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.86\n", + "recall 0.6417910447761194\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ quadratic_discriminant_analysis\n", + "\n", + "Training data\n", + "accuracy 0.81195079086116\n", + "precision 0.7741935483870968\n", + "recall 0.7433628318584071\n", + "accuracy_count 462\n", + "\n", + "Test data\n", + "accuracy 0.7832167832167832\n", + "precision 0.7719298245614035\n", + "recall 0.7096774193548387\n", + "accuracy_count 112\n", + "\n", + "Classification: survived ~ sgd\n", + "\n", + "Training data\n", + "accuracy 0.7363796133567663\n", + "precision 0.7547169811320755\n", + "recall 0.5194805194805194\n", + "accuracy_count 419\n", + "\n", + "Test data\n", + "accuracy 0.7482517482517482\n", + "precision 0.7441860465116279\n", + "recall 0.5614035087719298\n", + "accuracy_count 107\n", + "\n", + "Classification: survived ~ linear_svc\n", + "\n", + "Training data\n", + "accuracy 0.789103690685413\n", + "precision 0.7652582159624414\n", + "recall 0.6995708154506438\n", + "accuracy_count 449\n", + "\n", + "Test data\n", + "accuracy 0.8531468531468531\n", + "precision 0.84\n", + "recall 0.7636363636363637\n", + "accuracy_count 122\n", + "\n", + "Classification: survived ~ radius_neighbors\n", + "\n", + "Training data\n", + "accuracy 0.6590509666080844\n", + "precision 0.6931818181818182\n", + "recall 0.2675438596491228\n", + "accuracy_count 375\n", + "\n", + "Test data\n", + "accuracy 0.6853146853146853\n", + "precision 0.8571428571428571\n", + "recall 0.3\n", + "accuracy_count 98\n", + "\n", + "Classification: survived ~ decision_tree\n", + "\n", + "Training data\n", + "accuracy 0.9859402460456942\n", + "precision 1.0\n", + "recall 0.9644444444444444\n", + "accuracy_count 561\n", + "\n", + "Test data\n", + "accuracy 0.7132867132867133\n", + "precision 0.6896551724137931\n", + "recall 0.6349206349206349\n", + "accuracy_count 102\n", + "\n", + "Classification: survived ~ naive_bayes\n", + "\n", + "Training data\n", + "accuracy 0.7680140597539543\n", + "precision 0.7021276595744681\n", + "recall 0.7268722466960352\n", + "accuracy_count 437\n", + "\n", + "Test data\n", + "accuracy 0.7902097902097902\n", + "precision 0.7540983606557377\n", + "recall 0.7540983606557377\n", + "accuracy_count 113\n", + "\n" + ] + } + ], + "source": [ + "result_dict['survived ~ naive_bayes'] = build_model(naive_bayes_fn,\n", + " 'Survived',\n", + " FEATURES,\n", + " titanic_df)\n", + "\n", + "compare_results()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Machine Learning/04-HyperparameterTuningWithGridSearch.ipynb b/Machine Learning/04-HyperparameterTuningWithGridSearch.ipynb new file mode 100644 index 00000000..7323b692 --- /dev/null +++ b/Machine Learning/04-HyperparameterTuningWithGridSearch.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import precision_score\n", + "from sklearn.metrics import recall_score\n", + "\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    SurvivedPclassSexAgeSibSpParchFareEmbarked_CEmbarked_QEmbarked_S
    003014.0007.8542001
    111128.00026.5500001
    211036.012120.0000001
    303117.0107.0542001
    40314.04231.2750001
    \n", + "
    " + ], + "text/plain": [ + " Survived Pclass Sex Age SibSp Parch Fare Embarked_C \\\n", + "0 0 3 0 14.0 0 0 7.8542 0 \n", + "1 1 1 1 28.0 0 0 26.5500 0 \n", + "2 1 1 0 36.0 1 2 120.0000 0 \n", + "3 0 3 1 17.0 1 0 7.0542 0 \n", + "4 0 3 1 4.0 4 2 31.2750 0 \n", + "\n", + " Embarked_Q Embarked_S \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic_df = pd.read_csv('datasets/titanic_processed.csv')\n", + "\n", + "titanic_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "X = titanic_df.drop('Survived', axis=1)\n", + "\n", + "Y = titanic_df['Survived']\n", + "\n", + "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_classification(y_test, y_pred):\n", + " \n", + " acc = accuracy_score(y_test, y_pred, normalize=True)\n", + " num_acc = accuracy_score(y_test, y_pred, normalize=False)\n", + "\n", + " prec = precision_score(y_test, y_pred)\n", + " recall = recall_score(y_test, y_pred)\n", + " \n", + " print(\"Test data count: \",len(y_test))\n", + " print(\"accuracy_count : \" , num_acc)\n", + " print(\"accuracy_score : \" , acc)\n", + " print(\"precision_score : \" , prec)\n", + " print(\"recall_score : \", recall)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'max_depth': 5}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "parameters = {'max_depth': [2, 4, 5, 7, 9, 10]}\n", + "\n", + "grid_search = GridSearchCV(DecisionTreeClassifier(), parameters, cv=3, return_train_score=True)\n", + "grid_search.fit(x_train, y_train)\n", + "\n", + "grid_search.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters: {'max_depth': 2}\n", + "Mean Test Score: 0.7609841827768014\n", + "Rank: 5\n", + "Parameters: {'max_depth': 4}\n", + "Mean Test Score: 0.7644991212653779\n", + "Rank: 4\n", + "Parameters: {'max_depth': 5}\n", + "Mean Test Score: 0.7855887521968365\n", + "Rank: 1\n", + "Parameters: {'max_depth': 7}\n", + "Mean Test Score: 0.7803163444639719\n", + "Rank: 2\n", + "Parameters: {'max_depth': 9}\n", + "Mean Test Score: 0.7662565905096661\n", + "Rank: 3\n", + "Parameters: {'max_depth': 10}\n", + "Mean Test Score: 0.7557117750439367\n", + "Rank: 6\n" + ] + } + ], + "source": [ + "for i in range(6):\n", + " print('Parameters: ', grid_search.cv_results_['params'][i])\n", + "\n", + " print('Mean Test Score: ', grid_search.cv_results_['mean_test_score'][i])\n", + " \n", + " print('Rank: ', grid_search.cv_results_['rank_test_score'][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "decision_tree_model = DecisionTreeClassifier( \\\n", + " max_depth = grid_search.best_params_['max_depth']).fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = decision_tree_model.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data count: 143\n", + "accuracy_count : 117\n", + "accuracy_score : 0.8181818181818182\n", + "precision_score : 0.7894736842105263\n", + "recall_score : 0.7627118644067796\n", + "\n" + ] + } + ], + "source": [ + "summarize_classification(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'C': 0.4, 'penalty': 'l1'}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "parameters = {'penalty': ['l1', 'l2'], \n", + " 'C': [0.1, 0.4, 0.8, 1, 2, 5]}\n", + "\n", + "grid_search = GridSearchCV(LogisticRegression(solver='liblinear'), parameters, cv=3, return_train_score=True)\n", + "grid_search.fit(x_train, y_train)\n", + "\n", + "grid_search.best_params_" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters: {'C': 0.1, 'penalty': 'l1'}\n", + "Mean Test Score: 0.7451669595782073\n", + "Rank: 12\n", + "Parameters: {'C': 0.1, 'penalty': 'l2'}\n", + "Mean Test Score: 0.7539543057996485\n", + "Rank: 11\n", + "Parameters: {'C': 0.4, 'penalty': 'l1'}\n", + "Mean Test Score: 0.7803163444639719\n", + "Rank: 1\n", + "Parameters: {'C': 0.4, 'penalty': 'l2'}\n", + "Mean Test Score: 0.7750439367311072\n", + "Rank: 8\n", + "Parameters: {'C': 0.8, 'penalty': 'l1'}\n", + "Mean Test Score: 0.7785588752196837\n", + "Rank: 4\n", + "Parameters: {'C': 0.8, 'penalty': 'l2'}\n", + "Mean Test Score: 0.7785588752196837\n", + "Rank: 4\n", + "Parameters: {'C': 1, 'penalty': 'l1'}\n", + "Mean Test Score: 0.7750439367311072\n", + "Rank: 8\n", + "Parameters: {'C': 1, 'penalty': 'l2'}\n", + "Mean Test Score: 0.7768014059753954\n", + "Rank: 7\n", + "Parameters: {'C': 2, 'penalty': 'l1'}\n", + "Mean Test Score: 0.7750439367311072\n", + "Rank: 8\n", + "Parameters: {'C': 2, 'penalty': 'l2'}\n", + "Mean Test Score: 0.7803163444639719\n", + "Rank: 1\n", + "Parameters: {'C': 5, 'penalty': 'l1'}\n", + "Mean Test Score: 0.7803163444639719\n", + "Rank: 1\n", + "Parameters: {'C': 5, 'penalty': 'l2'}\n", + "Mean Test Score: 0.7785588752196837\n", + "Rank: 4\n" + ] + } + ], + "source": [ + "for i in range(12):\n", + " print('Parameters: ', grid_search.cv_results_['params'][i])\n", + " print('Mean Test Score: ', grid_search.cv_results_['mean_test_score'][i])\n", + " print('Rank: ', grid_search.cv_results_['rank_test_score'][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "logistic_model = LogisticRegression(solver='liblinear', \\\n", + " penalty=grid_search.best_params_['penalty'], C=grid_search.best_params_['C']). \\\n", + " fit(x_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = logistic_model.predict(x_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test data count: 143\n", + "accuracy_count : 116\n", + "accuracy_score : 0.8111888111888111\n", + "precision_score : 0.7962962962962963\n", + "recall_score : 0.7288135593220338\n", + "\n" + ] + } + ], + "source": [ + "summarize_classification(y_test, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}