<-----------------------------------------------------------------------TRAINING WITH NORMAL DATA---------------------------------------------------> C:\Kunal>python test_data.py -------------------------------------------------------------------------Current Time: 2017-09-14 17:17:06 ------------------------------------------------------------ Loss after iteration 0: 0.844991 Loss after iteration 1000: 0.638522 Loss after iteration 2000: 0.637808 Loss after iteration 3000: 0.637781 Loss after iteration 4000: 0.637647 Loss after iteration 5000: 0.637522 Loss after iteration 6000: 0.633508 Loss after iteration 7000: 0.638857 Loss after iteration 8000: 0.519483 Loss after iteration 9000: 0.606397 Loss after iteration 10000: 0.558385 Loss after iteration 11000: 0.480035 Loss after iteration 12000: 0.527276 Loss after iteration 13000: 0.525962 Loss after iteration 14000: 0.525897 Loss after iteration 15000: 0.526438 Loss after iteration 16000: 0.480675 Loss after iteration 17000: 0.527169 Loss after iteration 18000: 0.493254 Loss after iteration 19000: 0.499121 -------------------------------------------------------------------------Current Time: 2017-09-14 17:17:10 ------------------------------------------------------------ Testing new data: [[ 3 0 0 0 7 1] [ 2 1 0 0 21 1] [ 3 0 0 0 7 1] [ 2 0 0 0 13 1] [ 3 0 1 0 7 3] [ 1 1 1 0 113 2] [ 3 1 0 0 7 1] [ 2 0 1 0 27 1] [ 1 1 0 0 76 2] [ 2 0 0 0 10 1] [ 3 0 0 0 8 1] [ 2 0 0 0 13 1] [ 3 0 0 0 8 1] [ 3 0 0 0 7 1] [ 1 0 1 0 90 1] [ 3 0 0 0 9 1] [ 2 0 0 0 10 1] [ 3 0 0 0 7 1] [ 2 0 0 0 13 1] [ 3 1 3 1 25 1]] Actual result: [0 1 0 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 0] Predicted result: [0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1] Prediction accuracy: 80.0 % <-----------------------------------------------------------------------TRAINING WITH WINSORIZED DATA---------------------------------------------------> C:\Kunal>python test_winsorized_data.py -------------------------------------------------------------------------Current Time: 2017-09-14 17:12:18 ------------------------------------------------------------ Loss after iteration 0: 0.834748 Loss after iteration 1000: 0.585702 Loss after iteration 2000: 0.497451 Loss after iteration 3000: 0.521956 Loss after iteration 4000: 0.673667 Loss after iteration 5000: 0.490575 Loss after iteration 6000: 0.453375 Loss after iteration 7000: 0.461687 Loss after iteration 8000: 0.438181 Loss after iteration 9000: 0.495037 Loss after iteration 10000: 0.465121 Loss after iteration 11000: 0.481831 Loss after iteration 12000: 0.419259 Loss after iteration 13000: 0.418817 Loss after iteration 14000: 0.418504 Loss after iteration 15000: 0.418253 Loss after iteration 16000: 0.418041 Loss after iteration 17000: 0.417856 Loss after iteration 18000: 0.417691 Loss after iteration 19000: 0.417542 -------------------------------------------------------------------------Current Time: 2017-09-14 17:12:42 ------------------------------------------------------------ Testing new data: [[ 3 0 0 0 7 1] [ 2 1 0 0 21 1] [ 3 0 0 0 7 1] [ 2 0 0 0 13 1] [ 3 0 1 0 7 2] [ 1 1 1 0 39 2] [ 3 1 0 0 7 1] [ 2 0 1 0 27 1] [ 1 1 0 0 39 2] [ 2 0 0 0 10 1] [ 3 0 0 0 8 1] [ 2 0 0 0 13 1] [ 3 0 0 0 8 1] [ 3 0 0 0 7 1] [ 1 0 1 0 39 1] [ 3 0 0 0 9 1] [ 2 0 0 0 10 1] [ 3 0 0 0 7 1] [ 2 0 0 0 13 1] [ 3 1 1 1 25 1]] Actual result sample: [0 1 0 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 0] Predicted result sample: [0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0] Prediction accuracy: 85.0 % <-----------------------------------------------------------------------------------END--------------------------------------------------------------------------------->