├── youtube_url.txt ├── .gitignore ├── diabetes_escalonated ├── error.png ├── Age_distribution.png ├── BMI_distribution.png ├── Glucose_distribution.png ├── Insulin_distribution.png ├── BloodPressure_distribution.png ├── Pregnancies_distribution.png ├── SkinThickness_distribution.png ├── DiabetesPedigreeFunction_distribution.png ├── 10_107.txt ├── 14_107.txt ├── 16_107.txt ├── 19_107.txt ├── 21_107.txt ├── 23_107.txt ├── 24_107.txt ├── 25_107.txt ├── 26_107.txt ├── 28_107.txt ├── 32_107.txt ├── 33_107.txt ├── 34_107.txt ├── 37_107.txt ├── 3_107.txt ├── 40_107.txt ├── 42_107.txt ├── 43_107.txt ├── 45_107.txt ├── 47_107.txt ├── 4_107.txt ├── 50_107.txt ├── 52_107.txt ├── 53_107.txt ├── 54_107.txt ├── 55_107.txt ├── 57_107.txt ├── 58_107.txt ├── 60_107.txt ├── 63_107.txt ├── 65_107.txt ├── 66_107.txt ├── 69_107.txt ├── 76_107.txt ├── 77_107.txt ├── 78_107.txt ├── 7_107.txt ├── 82_107.txt ├── 84_107.txt ├── 89_107.txt ├── 93_107.txt ├── 96_107.txt ├── 97_107.txt ├── 1_107.txt ├── 2_107.txt ├── 5_107.txt ├── 6_107.txt ├── 8_107.txt ├── 9_107.txt ├── 100_107.txt ├── 101_107.txt ├── 102_107.txt ├── 103_107.txt ├── 104_107.txt ├── 105_107.txt ├── 11_107.txt ├── 12_107.txt ├── 13_107.txt ├── 15_107.txt ├── 17_107.txt ├── 18_107.txt ├── 20_107.txt ├── 22_107.txt ├── 27_107.txt ├── 29_107.txt ├── 30_107.txt ├── 31_107.txt ├── 35_107.txt ├── 36_107.txt ├── 38_107.txt ├── 39_107.txt ├── 41_107.txt ├── 44_107.txt ├── 46_107.txt ├── 48_107.txt ├── 49_107.txt ├── 56_107.txt ├── 59_107.txt ├── 62_107.txt ├── 64_107.txt ├── 67_107.txt ├── 68_107.txt ├── 70_107.txt ├── 71_107.txt ├── 72_107.txt ├── 73_107.txt ├── 74_107.txt ├── 75_107.txt ├── 79_107.txt ├── 80_107.txt ├── 81_107.txt ├── 83_107.txt ├── 85_107.txt ├── 86_107.txt ├── 87_107.txt ├── 88_107.txt ├── 90_107.txt ├── 91_107.txt ├── 92_107.txt ├── 94_107.txt ├── 95_107.txt ├── 98_107.txt ├── 99_107.txt ├── 61_107.txt ├── 0_107.txt ├── 51_107.txt └── error_growth.txt ├── images └── diabetes_escalonated │ ├── error.png │ ├── Age_distribution.png │ ├── BMI_distribution.png │ ├── Glucose_distribution.png │ ├── Insulin_distribution.png │ ├── Pregnancies_distribution.png │ ├── BloodPressure_distribution.png │ ├── SkinThickness_distribution.png │ └── DiabetesPedigreeFunction_distribution.png ├── Deep_Learning_From_Scratch_Final_Report.pdf ├── results └── diabetes_escalonated │ ├── 3_107.txt │ ├── 4_107.txt │ ├── 7_107.txt │ ├── 10_107.txt │ ├── 14_107.txt │ ├── 16_107.txt │ ├── 19_107.txt │ ├── 21_107.txt │ ├── 23_107.txt │ ├── 24_107.txt │ ├── 25_107.txt │ ├── 26_107.txt │ ├── 28_107.txt │ ├── 32_107.txt │ ├── 33_107.txt │ ├── 34_107.txt │ ├── 37_107.txt │ ├── 40_107.txt │ ├── 42_107.txt │ ├── 43_107.txt │ ├── 45_107.txt │ ├── 47_107.txt │ ├── 50_107.txt │ ├── 52_107.txt │ ├── 53_107.txt │ ├── 54_107.txt │ ├── 55_107.txt │ ├── 57_107.txt │ ├── 58_107.txt │ ├── 60_107.txt │ ├── 63_107.txt │ ├── 65_107.txt │ ├── 66_107.txt │ ├── 69_107.txt │ ├── 76_107.txt │ ├── 77_107.txt │ ├── 78_107.txt │ ├── 82_107.txt │ ├── 84_107.txt │ ├── 89_107.txt │ ├── 93_107.txt │ ├── 96_107.txt │ ├── 97_107.txt │ ├── 100_107.txt │ ├── 101_107.txt │ ├── 102_107.txt │ ├── 103_107.txt │ ├── 104_107.txt │ ├── 105_107.txt │ ├── 11_107.txt │ ├── 12_107.txt │ ├── 13_107.txt │ ├── 15_107.txt │ ├── 17_107.txt │ ├── 18_107.txt │ ├── 1_107.txt │ ├── 20_107.txt │ ├── 22_107.txt │ ├── 27_107.txt │ ├── 29_107.txt │ ├── 2_107.txt │ ├── 30_107.txt │ ├── 31_107.txt │ ├── 35_107.txt │ ├── 36_107.txt │ ├── 38_107.txt │ ├── 39_107.txt │ ├── 41_107.txt │ ├── 44_107.txt │ ├── 46_107.txt │ ├── 48_107.txt │ ├── 49_107.txt │ ├── 56_107.txt │ ├── 59_107.txt │ ├── 5_107.txt │ ├── 62_107.txt │ ├── 64_107.txt │ ├── 67_107.txt │ ├── 68_107.txt │ ├── 6_107.txt │ ├── 70_107.txt │ ├── 71_107.txt │ ├── 72_107.txt │ ├── 73_107.txt │ ├── 74_107.txt │ ├── 75_107.txt │ ├── 79_107.txt │ ├── 80_107.txt │ ├── 81_107.txt │ ├── 83_107.txt │ ├── 85_107.txt │ ├── 86_107.txt │ ├── 87_107.txt │ ├── 88_107.txt │ ├── 8_107.txt │ ├── 90_107.txt │ ├── 91_107.txt │ ├── 92_107.txt │ ├── 94_107.txt │ ├── 95_107.txt │ ├── 98_107.txt │ ├── 99_107.txt │ ├── 9_107.txt │ ├── 61_107.txt │ ├── 0_107.txt │ ├── 51_107.txt │ └── error_growth.txt ├── README.md ├── utils.py ├── values_analysis ├── diabetes_analysis.txt ├── diabetes_escalonated_analysis.txt ├── creditcard_analysis.txt └── data_analysis.txt ├── discriminator.py ├── generator.py ├── compare_data.py ├── data_treatment.py ├── train_generator_discriminator.py ├── data_analysis.ipynb ├── create_fake_data.ipynb └── original_data └── diabetes.csv /youtube_url.txt: -------------------------------------------------------------------------------- 1 | https://youtu.be/cRlbOHfYKHs -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .idea/* 3 | 4 | __pycache__/ 5 | -------------------------------------------------------------------------------- /diabetes_escalonated/error.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/error.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/error.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/error.png -------------------------------------------------------------------------------- /diabetes_escalonated/Age_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/Age_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/BMI_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/BMI_distribution.png -------------------------------------------------------------------------------- /Deep_Learning_From_Scratch_Final_Report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/Deep_Learning_From_Scratch_Final_Report.pdf -------------------------------------------------------------------------------- /diabetes_escalonated/Glucose_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/Glucose_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/Insulin_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/Insulin_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/Age_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/Age_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/BMI_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/BMI_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/BloodPressure_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/BloodPressure_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/Pregnancies_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/Pregnancies_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/SkinThickness_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/SkinThickness_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/Glucose_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/Glucose_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/Insulin_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/Insulin_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/Pregnancies_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/Pregnancies_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/BloodPressure_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/BloodPressure_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/SkinThickness_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/SkinThickness_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/DiabetesPedigreeFunction_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/diabetes_escalonated/DiabetesPedigreeFunction_distribution.png -------------------------------------------------------------------------------- /images/diabetes_escalonated/DiabetesPedigreeFunction_distribution.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/kjschmidt913/GAN-generate-data/HEAD/images/diabetes_escalonated/DiabetesPedigreeFunction_distribution.png -------------------------------------------------------------------------------- /diabetes_escalonated/10_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3854, grad_fn=) 2 | Generator error: tensor(0.6746, grad_fn=) 3 | Points: tensor([[0.1179, 0.2203, 0.3620, 0.1836, 0.0849, 0.3215, 0.1569, 0.2626, 0.1254], 4 | [0.1160, 0.2265, 0.4171, 0.2133, 0.0723, 0.3783, 0.1101, 0.2783, 0.1455]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/14_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3430, grad_fn=) 2 | Generator error: tensor(0.6997, grad_fn=) 3 | Points: tensor([[0.1619, 0.4325, 0.4992, 0.2415, 0.3125, 0.3301, 0.1075, 0.1753, 0.3420], 4 | [0.1305, 0.4703, 0.4677, 0.2185, 0.2177, 0.2538, 0.1856, 0.0757, 0.3250]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/16_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5321, grad_fn=) 2 | Generator error: tensor(0.6723, grad_fn=) 3 | Points: tensor([[0.1859, 0.3285, 0.4595, 0.1438, 0.0823, 0.3678, 0.1550, 0.1865, 0.3879], 4 | [0.2013, 0.3950, 0.5537, 0.1199, 0.0775, 0.3453, 0.1832, 0.2885, 0.4098]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/19_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3347, grad_fn=) 2 | Generator error: tensor(0.6661, grad_fn=) 3 | Points: tensor([[0.1442, 0.8002, 0.7969, 0.3464, 0.1262, 0.5878, 0.0600, 0.1397, 0.3364], 4 | [0.2020, 0.8221, 0.7822, 0.2853, 0.1448, 0.5950, 0.0532, 0.2355, 0.3849]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/21_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5338, grad_fn=) 2 | Generator error: tensor(0.7044, grad_fn=) 3 | Points: tensor([[0.1884, 0.4417, 0.5032, 0.1300, 0.0641, 0.3736, 0.0956, 0.1906, 0.2844], 4 | [0.2387, 0.5085, 0.5692, 0.2002, 0.1308, 0.4566, 0.1248, 0.1931, 0.2864]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/23_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3422, grad_fn=) 2 | Generator error: tensor(1.5392, grad_fn=) 3 | Points: tensor([[1.2261, 0.3630, 0.8530, 0.3946, 0.2556, 0.4310, 1.5971, 1.1309, 1.2499], 4 | [1.0250, 0.3562, 0.8527, 0.3454, 0.1588, 0.4010, 1.3555, 1.0154, 1.0466]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/24_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3877, grad_fn=) 2 | Generator error: tensor(0.6531, grad_fn=) 3 | Points: tensor([[0.4492, 0.6527, 0.5941, 0.0854, 0.0434, 0.5871, 0.3301, 0.2657, 0.7470], 4 | [0.3059, 0.6291, 0.4844, 0.0236, 0.0654, 0.5200, 0.2567, 0.2147, 0.5321]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/25_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2924, grad_fn=) 2 | Generator error: tensor(0.6784, grad_fn=) 3 | Points: tensor([[0.3178, 0.5813, 0.5379, 0.4404, 0.0737, 0.5152, 0.0711, 0.4590, 0.0940], 4 | [0.2867, 0.5409, 0.5231, 0.3303, 0.1189, 0.4369, 0.1598, 0.4128, 0.1323]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/26_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3686, grad_fn=) 2 | Generator error: tensor(0.8698, grad_fn=) 3 | Points: tensor([[0.8383, 1.7335, 1.2614, 0.7732, 0.0701, 0.8916, 0.5468, 0.3909, 0.5851], 4 | [0.8060, 1.6933, 1.3716, 0.7818, 0.0056, 0.8504, 0.4815, 0.3265, 0.7077]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/28_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4217, grad_fn=) 2 | Generator error: tensor(0.7522, grad_fn=) 3 | Points: tensor([[0.1774, 0.5988, 0.5160, 0.1290, 0.1763, 0.4340, 0.2597, 0.3740, 0.6012], 4 | [0.2063, 0.6441, 0.4807, 0.1266, 0.1130, 0.4590, 0.2261, 0.3490, 0.8017]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/32_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3717, grad_fn=) 2 | Generator error: tensor(0.8018, grad_fn=) 3 | Points: tensor([[0.3605, 0.8516, 0.8632, 0.3985, 0.2146, 0.5931, 0.1287, 0.3310, 0.1337], 4 | [0.3766, 0.7064, 0.6798, 0.3754, 0.0827, 0.5182, 0.1410, 0.2565, 0.2505]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/33_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3926, grad_fn=) 2 | Generator error: tensor(0.7112, grad_fn=) 3 | Points: tensor([[0.1052, 0.5706, 0.5050, 0.1602, 0.0597, 0.5049, 0.2226, 0.1364, 0.5374], 4 | [0.0714, 0.5240, 0.4949, 0.1513, 0.0502, 0.3441, 0.2288, 0.1519, 0.5172]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/34_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3948, grad_fn=) 2 | Generator error: tensor(0.6439, grad_fn=) 3 | Points: tensor([[0.2816, 0.5882, 0.4722, 0.1495, 0.1728, 0.4671, 0.1168, 0.1179, 0.0896], 4 | [0.2948, 0.6204, 0.5676, 0.1193, 0.1704, 0.4234, 0.1903, 0.1153, 0.1000]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/37_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3417, grad_fn=) 2 | Generator error: tensor(0.6854, grad_fn=) 3 | Points: tensor([[0.3623, 0.6266, 0.6208, 0.0737, 0.2507, 0.5564, 0.1815, 0.2057, 0.2727], 4 | [0.3675, 0.6379, 0.6410, 0.1115, 0.2546, 0.5520, 0.1232, 0.2502, 0.2777]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/3_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3459, grad_fn=) 2 | Generator error: tensor(0.8369, grad_fn=) 3 | Points: tensor([[0.3280, 0.6853, 0.7178, 0.3342, 0.2892, 0.5374, 0.3275, 0.1016, 0.2987], 4 | [0.2299, 0.4967, 0.5600, 0.2213, 0.1990, 0.4091, 0.2162, 0.1134, 0.2662]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/40_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4145, grad_fn=) 2 | Generator error: tensor(0.6907, grad_fn=) 3 | Points: tensor([[0.2165, 0.5448, 0.6237, 0.3566, 0.1385, 0.6276, 0.3585, 0.0473, 0.4400], 4 | [0.2013, 0.5177, 0.6311, 0.2945, 0.2388, 0.5618, 0.2325, 0.1051, 0.4280]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/42_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3951, grad_fn=) 2 | Generator error: tensor(0.7034, grad_fn=) 3 | Points: tensor([[0.2366, 0.4810, 0.7933, 0.1725, 0.1685, 0.5648, 0.1141, 0.1405, 0.0769], 4 | [0.2835, 0.4622, 0.6402, 0.0991, 0.1662, 0.4913, 0.0839, 0.2009, 0.0505]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/43_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3914, grad_fn=) 2 | Generator error: tensor(0.7700, grad_fn=) 3 | Points: tensor([[0.3808, 0.7510, 0.3737, 0.3667, 0.4212, 0.3816, 0.2455, 0.4017, 1.2934], 4 | [0.6675, 1.0429, 0.3253, 0.6429, 0.8856, 0.4908, 0.4900, 0.7523, 2.7118]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/45_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4254, grad_fn=) 2 | Generator error: tensor(0.7436, grad_fn=) 3 | Points: tensor([[0.1774, 0.5754, 0.5996, 0.1234, 0.0070, 0.4162, 0.0687, 0.1269, 0.4649], 4 | [0.2578, 0.5311, 0.5544, 0.3687, 0.0224, 0.5068, 0.0547, 0.1047, 0.4025]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/47_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4202, grad_fn=) 2 | Generator error: tensor(0.6769, grad_fn=) 3 | Points: tensor([[0.3032, 0.5683, 0.5676, 0.2985, 0.0978, 0.3941, 0.0485, 0.1473, 0.1993], 4 | [0.3661, 0.6656, 0.5848, 0.2786, 0.1369, 0.3846, 0.0244, 0.1878, 0.2692]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/4_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5805, grad_fn=) 2 | Generator error: tensor(0.7174, grad_fn=) 3 | Points: tensor([[0.4280, 0.6797, 0.6442, 0.1798, 0.0731, 0.5210, 0.2429, 0.3860, 1.2002], 4 | [0.3855, 0.6602, 0.6327, 0.2003, 0.0939, 0.5126, 0.1940, 0.4182, 1.0794]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/50_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3821, grad_fn=) 2 | Generator error: tensor(0.7435, grad_fn=) 3 | Points: tensor([[0.2777, 0.6783, 0.4660, 0.2552, 0.1993, 0.4709, 0.2361, 0.1528, 0.5614], 4 | [0.3436, 0.7474, 0.4235, 0.1510, 0.1982, 0.5611, 0.3402, 0.1183, 0.9018]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/52_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4502, grad_fn=) 2 | Generator error: tensor(0.7720, grad_fn=) 3 | Points: tensor([[0.2881, 0.6018, 0.5165, 0.3298, 0.1601, 0.5072, 0.1574, 0.0977, 0.2050], 4 | [0.3511, 0.6532, 0.5094, 0.2987, 0.1925, 0.4741, 0.1723, 0.1076, 0.1789]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/53_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2733, grad_fn=) 2 | Generator error: tensor(0.7634, grad_fn=) 3 | Points: tensor([[0.3818, 0.6655, 0.4430, 0.2604, 0.1361, 0.3836, 0.1910, 0.1703, 0.3181], 4 | [0.3816, 0.7371, 0.3279, 0.3048, 0.1606, 0.4258, 0.2062, 0.1660, 0.3333]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/54_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2885, grad_fn=) 2 | Generator error: tensor(0.6820, grad_fn=) 3 | Points: tensor([[0.3670, 0.5480, 0.5835, 0.2021, 0.0993, 0.4403, 0.2068, 0.3226, 0.2284], 4 | [0.5098, 0.5780, 0.7192, 0.2943, 0.1756, 0.4884, 0.2211, 0.4407, 0.3011]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/55_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2891, grad_fn=) 2 | Generator error: tensor(0.6777, grad_fn=) 3 | Points: tensor([[0.1728, 0.5173, 0.5656, 0.3152, 0.2063, 0.4164, 0.1960, 0.1873, 0.2006], 4 | [0.1329, 0.6021, 0.6189, 0.2571, 0.1329, 0.5027, 0.2267, 0.1621, 0.3035]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/57_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4450, grad_fn=) 2 | Generator error: tensor(0.5904, grad_fn=) 3 | Points: tensor([[0.2701, 0.4624, 0.5479, 0.3565, 0.0245, 0.5014, 0.0811, 0.5628, 0.5745], 4 | [0.3432, 0.4420, 0.4631, 0.3231, 0.0708, 0.4931, 0.0226, 0.5165, 0.5239]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/58_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3807, grad_fn=) 2 | Generator error: tensor(0.7401, grad_fn=) 3 | Points: tensor([[0.1097, 0.4766, 0.5937, 0.3615, 0.0525, 0.4677, 0.0502, 0.3488, 0.1646], 4 | [0.1228, 0.5501, 0.6259, 0.4274, 0.0598, 0.5128, 0.1631, 0.3655, 0.2582]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/60_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3933, grad_fn=) 2 | Generator error: tensor(0.7798, grad_fn=) 3 | Points: tensor([[0.4757, 0.5353, 0.4552, 0.1387, 0.0092, 0.4501, 0.2150, 0.2660, 0.2308], 4 | [0.5485, 0.5774, 0.4555, 0.1562, 0.0412, 0.4773, 0.2282, 0.2753, 0.2272]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/63_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3668, grad_fn=) 2 | Generator error: tensor(0.5927, grad_fn=) 3 | Points: tensor([[0.0409, 0.4956, 0.5752, 0.2947, 0.0414, 0.2557, 0.2781, 0.1162, 0.2991], 4 | [0.0296, 0.4302, 0.6326, 0.2721, 0.0757, 0.2903, 0.2410, 0.1131, 0.3106]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/65_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5890, grad_fn=) 2 | Generator error: tensor(0.6170, grad_fn=) 3 | Points: tensor([[0.1194, 0.4739, 0.5975, 0.1651, 0.1383, 0.3878, 0.0605, 0.1636, 0.1117], 4 | [0.0905, 0.5582, 0.5276, 0.2136, 0.1826, 0.3635, 0.1097, 0.1388, 0.2164]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/66_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5525, grad_fn=) 2 | Generator error: tensor(0.7877, grad_fn=) 3 | Points: tensor([[0.4693, 0.5461, 0.4328, 0.1270, 0.1380, 0.3551, 0.1439, 0.2025, 0.1110], 4 | [0.3528, 0.5682, 0.4985, 0.1236, 0.1464, 0.3118, 0.1885, 0.2377, 0.1670]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/69_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4859, grad_fn=) 2 | Generator error: tensor(0.6239, grad_fn=) 3 | Points: tensor([[0.2466, 0.4628, 0.3320, 0.1510, 0.3759, 0.5564, 0.0511, 0.0684, 0.6337], 4 | [0.2668, 0.4778, 0.2921, 0.1768, 0.5228, 0.6003, 0.0603, 0.0169, 0.8976]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/76_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3822, grad_fn=) 2 | Generator error: tensor(0.7395, grad_fn=) 3 | Points: tensor([[0.1654, 0.5646, 0.6096, 0.0941, 0.2483, 0.4189, 0.1419, 0.1709, 0.1290], 4 | [0.1632, 0.5954, 0.6125, 0.1404, 0.2448, 0.4119, 0.0970, 0.2839, 0.2875]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/77_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1195, grad_fn=) 2 | Generator error: tensor(0.6893, grad_fn=) 3 | Points: tensor([[0.2707, 0.5044, 0.6231, 0.1663, 0.0155, 0.4986, 0.2502, 0.1798, 0.3405], 4 | [0.3599, 0.5402, 0.6628, 0.1184, 0.0271, 0.5725, 0.2257, 0.2076, 0.4527]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/78_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2318, grad_fn=) 2 | Generator error: tensor(0.7641, grad_fn=) 3 | Points: tensor([[0.2776, 0.6640, 0.6452, 0.2657, 0.0911, 0.6326, 0.2455, 0.1670, 0.4898], 4 | [0.1641, 0.5904, 0.5376, 0.2041, 0.1221, 0.5538, 0.2528, 0.1065, 0.3458]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/7_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3281, grad_fn=) 2 | Generator error: tensor(0.7110, grad_fn=) 3 | Points: tensor([[0.2425, 0.4759, 0.3506, 0.2983, 0.2256, 0.3481, 0.1995, 0.1317, 0.3155], 4 | [0.2425, 0.4557, 0.3425, 0.2590, 0.1943, 0.3238, 0.2297, 0.1021, 0.3471]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/82_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4415, grad_fn=) 2 | Generator error: tensor(0.6555, grad_fn=) 3 | Points: tensor([[0.2224, 0.4997, 0.1981, 0.4005, 0.4179, 0.5153, 0.4070, 0.2262, 0.5712], 4 | [0.2033, 0.4816, 0.2567, 0.4607, 0.4116, 0.5016, 0.3667, 0.1844, 0.5220]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/84_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.0176, grad_fn=) 2 | Generator error: tensor(0.8942, grad_fn=) 3 | Points: tensor([[0.1708, 0.5505, 0.6009, 0.1816, 0.0757, 0.5018, 0.0513, 0.1069, 0.0517], 4 | [0.1628, 0.5354, 0.6070, 0.1504, 0.0612, 0.4949, 0.0272, 0.1544, 0.0369]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/89_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4125, grad_fn=) 2 | Generator error: tensor(0.6860, grad_fn=) 3 | Points: tensor([[0.3542, 0.6829, 0.7199, 0.0396, 0.0726, 0.7438, 0.2063, 0.2467, 0.5155], 4 | [0.3395, 0.6416, 0.7177, 0.1581, 0.0754, 0.6409, 0.1708, 0.1958, 0.4163]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/93_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.7080, grad_fn=) 2 | Generator error: tensor(0.9132, grad_fn=) 3 | Points: tensor([[0.1503, 0.6008, 0.6023, 0.2204, 0.1002, 0.5014, 0.1542, 0.2256, 0.0180], 4 | [0.1969, 0.6112, 0.6673, 0.2512, 0.0551, 0.4906, 0.1609, 0.2162, 0.0645]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/96_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3452, grad_fn=) 2 | Generator error: tensor(0.8110, grad_fn=) 3 | Points: tensor([[0.5897, 1.2343, 0.4678, 0.8527, 0.3735, 0.6146, 0.2790, 0.1560, 0.9022], 4 | [0.3276, 0.7091, 0.4849, 0.4241, 0.1440, 0.4942, 0.2532, 0.2149, 0.3310]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/97_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2170, grad_fn=) 2 | Generator error: tensor(1.0021, grad_fn=) 3 | Points: tensor([[0.3640, 1.0714, 0.9909, 0.4433, 0.3089, 0.6853, 0.2410, 0.3615, 0.1695], 4 | [0.4063, 1.1384, 0.9313, 0.5276, 0.3568, 0.6790, 0.3213, 0.2552, 0.0902]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/3_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3459, grad_fn=) 2 | Generator error: tensor(0.8369, grad_fn=) 3 | Points: tensor([[0.3280, 0.6853, 0.7178, 0.3342, 0.2892, 0.5374, 0.3275, 0.1016, 0.2987], 4 | [0.2299, 0.4967, 0.5600, 0.2213, 0.1990, 0.4091, 0.2162, 0.1134, 0.2662]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/4_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5805, grad_fn=) 2 | Generator error: tensor(0.7174, grad_fn=) 3 | Points: tensor([[0.4280, 0.6797, 0.6442, 0.1798, 0.0731, 0.5210, 0.2429, 0.3860, 1.2002], 4 | [0.3855, 0.6602, 0.6327, 0.2003, 0.0939, 0.5126, 0.1940, 0.4182, 1.0794]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/7_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3281, grad_fn=) 2 | Generator error: tensor(0.7110, grad_fn=) 3 | Points: tensor([[0.2425, 0.4759, 0.3506, 0.2983, 0.2256, 0.3481, 0.1995, 0.1317, 0.3155], 4 | [0.2425, 0.4557, 0.3425, 0.2590, 0.1943, 0.3238, 0.2297, 0.1021, 0.3471]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/10_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3854, grad_fn=) 2 | Generator error: tensor(0.6746, grad_fn=) 3 | Points: tensor([[0.1179, 0.2203, 0.3620, 0.1836, 0.0849, 0.3215, 0.1569, 0.2626, 0.1254], 4 | [0.1160, 0.2265, 0.4171, 0.2133, 0.0723, 0.3783, 0.1101, 0.2783, 0.1455]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/14_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3430, grad_fn=) 2 | Generator error: tensor(0.6997, grad_fn=) 3 | Points: tensor([[0.1619, 0.4325, 0.4992, 0.2415, 0.3125, 0.3301, 0.1075, 0.1753, 0.3420], 4 | [0.1305, 0.4703, 0.4677, 0.2185, 0.2177, 0.2538, 0.1856, 0.0757, 0.3250]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/16_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5321, grad_fn=) 2 | Generator error: tensor(0.6723, grad_fn=) 3 | Points: tensor([[0.1859, 0.3285, 0.4595, 0.1438, 0.0823, 0.3678, 0.1550, 0.1865, 0.3879], 4 | [0.2013, 0.3950, 0.5537, 0.1199, 0.0775, 0.3453, 0.1832, 0.2885, 0.4098]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/19_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3347, grad_fn=) 2 | Generator error: tensor(0.6661, grad_fn=) 3 | Points: tensor([[0.1442, 0.8002, 0.7969, 0.3464, 0.1262, 0.5878, 0.0600, 0.1397, 0.3364], 4 | [0.2020, 0.8221, 0.7822, 0.2853, 0.1448, 0.5950, 0.0532, 0.2355, 0.3849]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/21_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5338, grad_fn=) 2 | Generator error: tensor(0.7044, grad_fn=) 3 | Points: tensor([[0.1884, 0.4417, 0.5032, 0.1300, 0.0641, 0.3736, 0.0956, 0.1906, 0.2844], 4 | [0.2387, 0.5085, 0.5692, 0.2002, 0.1308, 0.4566, 0.1248, 0.1931, 0.2864]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/23_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3422, grad_fn=) 2 | Generator error: tensor(1.5392, grad_fn=) 3 | Points: tensor([[1.2261, 0.3630, 0.8530, 0.3946, 0.2556, 0.4310, 1.5971, 1.1309, 1.2499], 4 | [1.0250, 0.3562, 0.8527, 0.3454, 0.1588, 0.4010, 1.3555, 1.0154, 1.0466]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/24_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3877, grad_fn=) 2 | Generator error: tensor(0.6531, grad_fn=) 3 | Points: tensor([[0.4492, 0.6527, 0.5941, 0.0854, 0.0434, 0.5871, 0.3301, 0.2657, 0.7470], 4 | [0.3059, 0.6291, 0.4844, 0.0236, 0.0654, 0.5200, 0.2567, 0.2147, 0.5321]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/25_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2924, grad_fn=) 2 | Generator error: tensor(0.6784, grad_fn=) 3 | Points: tensor([[0.3178, 0.5813, 0.5379, 0.4404, 0.0737, 0.5152, 0.0711, 0.4590, 0.0940], 4 | [0.2867, 0.5409, 0.5231, 0.3303, 0.1189, 0.4369, 0.1598, 0.4128, 0.1323]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/26_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3686, grad_fn=) 2 | Generator error: tensor(0.8698, grad_fn=) 3 | Points: tensor([[0.8383, 1.7335, 1.2614, 0.7732, 0.0701, 0.8916, 0.5468, 0.3909, 0.5851], 4 | [0.8060, 1.6933, 1.3716, 0.7818, 0.0056, 0.8504, 0.4815, 0.3265, 0.7077]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/28_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4217, grad_fn=) 2 | Generator error: tensor(0.7522, grad_fn=) 3 | Points: tensor([[0.1774, 0.5988, 0.5160, 0.1290, 0.1763, 0.4340, 0.2597, 0.3740, 0.6012], 4 | [0.2063, 0.6441, 0.4807, 0.1266, 0.1130, 0.4590, 0.2261, 0.3490, 0.8017]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/32_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3717, grad_fn=) 2 | Generator error: tensor(0.8018, grad_fn=) 3 | Points: tensor([[0.3605, 0.8516, 0.8632, 0.3985, 0.2146, 0.5931, 0.1287, 0.3310, 0.1337], 4 | [0.3766, 0.7064, 0.6798, 0.3754, 0.0827, 0.5182, 0.1410, 0.2565, 0.2505]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/33_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3926, grad_fn=) 2 | Generator error: tensor(0.7112, grad_fn=) 3 | Points: tensor([[0.1052, 0.5706, 0.5050, 0.1602, 0.0597, 0.5049, 0.2226, 0.1364, 0.5374], 4 | [0.0714, 0.5240, 0.4949, 0.1513, 0.0502, 0.3441, 0.2288, 0.1519, 0.5172]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/34_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3948, grad_fn=) 2 | Generator error: tensor(0.6439, grad_fn=) 3 | Points: tensor([[0.2816, 0.5882, 0.4722, 0.1495, 0.1728, 0.4671, 0.1168, 0.1179, 0.0896], 4 | [0.2948, 0.6204, 0.5676, 0.1193, 0.1704, 0.4234, 0.1903, 0.1153, 0.1000]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/37_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3417, grad_fn=) 2 | Generator error: tensor(0.6854, grad_fn=) 3 | Points: tensor([[0.3623, 0.6266, 0.6208, 0.0737, 0.2507, 0.5564, 0.1815, 0.2057, 0.2727], 4 | [0.3675, 0.6379, 0.6410, 0.1115, 0.2546, 0.5520, 0.1232, 0.2502, 0.2777]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/40_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4145, grad_fn=) 2 | Generator error: tensor(0.6907, grad_fn=) 3 | Points: tensor([[0.2165, 0.5448, 0.6237, 0.3566, 0.1385, 0.6276, 0.3585, 0.0473, 0.4400], 4 | [0.2013, 0.5177, 0.6311, 0.2945, 0.2388, 0.5618, 0.2325, 0.1051, 0.4280]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/42_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3951, grad_fn=) 2 | Generator error: tensor(0.7034, grad_fn=) 3 | Points: tensor([[0.2366, 0.4810, 0.7933, 0.1725, 0.1685, 0.5648, 0.1141, 0.1405, 0.0769], 4 | [0.2835, 0.4622, 0.6402, 0.0991, 0.1662, 0.4913, 0.0839, 0.2009, 0.0505]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/43_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3914, grad_fn=) 2 | Generator error: tensor(0.7700, grad_fn=) 3 | Points: tensor([[0.3808, 0.7510, 0.3737, 0.3667, 0.4212, 0.3816, 0.2455, 0.4017, 1.2934], 4 | [0.6675, 1.0429, 0.3253, 0.6429, 0.8856, 0.4908, 0.4900, 0.7523, 2.7118]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/45_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4254, grad_fn=) 2 | Generator error: tensor(0.7436, grad_fn=) 3 | Points: tensor([[0.1774, 0.5754, 0.5996, 0.1234, 0.0070, 0.4162, 0.0687, 0.1269, 0.4649], 4 | [0.2578, 0.5311, 0.5544, 0.3687, 0.0224, 0.5068, 0.0547, 0.1047, 0.4025]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/47_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4202, grad_fn=) 2 | Generator error: tensor(0.6769, grad_fn=) 3 | Points: tensor([[0.3032, 0.5683, 0.5676, 0.2985, 0.0978, 0.3941, 0.0485, 0.1473, 0.1993], 4 | [0.3661, 0.6656, 0.5848, 0.2786, 0.1369, 0.3846, 0.0244, 0.1878, 0.2692]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/50_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3821, grad_fn=) 2 | Generator error: tensor(0.7435, grad_fn=) 3 | Points: tensor([[0.2777, 0.6783, 0.4660, 0.2552, 0.1993, 0.4709, 0.2361, 0.1528, 0.5614], 4 | [0.3436, 0.7474, 0.4235, 0.1510, 0.1982, 0.5611, 0.3402, 0.1183, 0.9018]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/52_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4502, grad_fn=) 2 | Generator error: tensor(0.7720, grad_fn=) 3 | Points: tensor([[0.2881, 0.6018, 0.5165, 0.3298, 0.1601, 0.5072, 0.1574, 0.0977, 0.2050], 4 | [0.3511, 0.6532, 0.5094, 0.2987, 0.1925, 0.4741, 0.1723, 0.1076, 0.1789]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/53_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2733, grad_fn=) 2 | Generator error: tensor(0.7634, grad_fn=) 3 | Points: tensor([[0.3818, 0.6655, 0.4430, 0.2604, 0.1361, 0.3836, 0.1910, 0.1703, 0.3181], 4 | [0.3816, 0.7371, 0.3279, 0.3048, 0.1606, 0.4258, 0.2062, 0.1660, 0.3333]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/54_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2885, grad_fn=) 2 | Generator error: tensor(0.6820, grad_fn=) 3 | Points: tensor([[0.3670, 0.5480, 0.5835, 0.2021, 0.0993, 0.4403, 0.2068, 0.3226, 0.2284], 4 | [0.5098, 0.5780, 0.7192, 0.2943, 0.1756, 0.4884, 0.2211, 0.4407, 0.3011]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/55_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2891, grad_fn=) 2 | Generator error: tensor(0.6777, grad_fn=) 3 | Points: tensor([[0.1728, 0.5173, 0.5656, 0.3152, 0.2063, 0.4164, 0.1960, 0.1873, 0.2006], 4 | [0.1329, 0.6021, 0.6189, 0.2571, 0.1329, 0.5027, 0.2267, 0.1621, 0.3035]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/57_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4450, grad_fn=) 2 | Generator error: tensor(0.5904, grad_fn=) 3 | Points: tensor([[0.2701, 0.4624, 0.5479, 0.3565, 0.0245, 0.5014, 0.0811, 0.5628, 0.5745], 4 | [0.3432, 0.4420, 0.4631, 0.3231, 0.0708, 0.4931, 0.0226, 0.5165, 0.5239]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/58_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3807, grad_fn=) 2 | Generator error: tensor(0.7401, grad_fn=) 3 | Points: tensor([[0.1097, 0.4766, 0.5937, 0.3615, 0.0525, 0.4677, 0.0502, 0.3488, 0.1646], 4 | [0.1228, 0.5501, 0.6259, 0.4274, 0.0598, 0.5128, 0.1631, 0.3655, 0.2582]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/60_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3933, grad_fn=) 2 | Generator error: tensor(0.7798, grad_fn=) 3 | Points: tensor([[0.4757, 0.5353, 0.4552, 0.1387, 0.0092, 0.4501, 0.2150, 0.2660, 0.2308], 4 | [0.5485, 0.5774, 0.4555, 0.1562, 0.0412, 0.4773, 0.2282, 0.2753, 0.2272]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/63_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3668, grad_fn=) 2 | Generator error: tensor(0.5927, grad_fn=) 3 | Points: tensor([[0.0409, 0.4956, 0.5752, 0.2947, 0.0414, 0.2557, 0.2781, 0.1162, 0.2991], 4 | [0.0296, 0.4302, 0.6326, 0.2721, 0.0757, 0.2903, 0.2410, 0.1131, 0.3106]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/65_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5890, grad_fn=) 2 | Generator error: tensor(0.6170, grad_fn=) 3 | Points: tensor([[0.1194, 0.4739, 0.5975, 0.1651, 0.1383, 0.3878, 0.0605, 0.1636, 0.1117], 4 | [0.0905, 0.5582, 0.5276, 0.2136, 0.1826, 0.3635, 0.1097, 0.1388, 0.2164]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/66_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5525, grad_fn=) 2 | Generator error: tensor(0.7877, grad_fn=) 3 | Points: tensor([[0.4693, 0.5461, 0.4328, 0.1270, 0.1380, 0.3551, 0.1439, 0.2025, 0.1110], 4 | [0.3528, 0.5682, 0.4985, 0.1236, 0.1464, 0.3118, 0.1885, 0.2377, 0.1670]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/69_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4859, grad_fn=) 2 | Generator error: tensor(0.6239, grad_fn=) 3 | Points: tensor([[0.2466, 0.4628, 0.3320, 0.1510, 0.3759, 0.5564, 0.0511, 0.0684, 0.6337], 4 | [0.2668, 0.4778, 0.2921, 0.1768, 0.5228, 0.6003, 0.0603, 0.0169, 0.8976]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/76_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3822, grad_fn=) 2 | Generator error: tensor(0.7395, grad_fn=) 3 | Points: tensor([[0.1654, 0.5646, 0.6096, 0.0941, 0.2483, 0.4189, 0.1419, 0.1709, 0.1290], 4 | [0.1632, 0.5954, 0.6125, 0.1404, 0.2448, 0.4119, 0.0970, 0.2839, 0.2875]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/77_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1195, grad_fn=) 2 | Generator error: tensor(0.6893, grad_fn=) 3 | Points: tensor([[0.2707, 0.5044, 0.6231, 0.1663, 0.0155, 0.4986, 0.2502, 0.1798, 0.3405], 4 | [0.3599, 0.5402, 0.6628, 0.1184, 0.0271, 0.5725, 0.2257, 0.2076, 0.4527]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/78_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2318, grad_fn=) 2 | Generator error: tensor(0.7641, grad_fn=) 3 | Points: tensor([[0.2776, 0.6640, 0.6452, 0.2657, 0.0911, 0.6326, 0.2455, 0.1670, 0.4898], 4 | [0.1641, 0.5904, 0.5376, 0.2041, 0.1221, 0.5538, 0.2528, 0.1065, 0.3458]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/82_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4415, grad_fn=) 2 | Generator error: tensor(0.6555, grad_fn=) 3 | Points: tensor([[0.2224, 0.4997, 0.1981, 0.4005, 0.4179, 0.5153, 0.4070, 0.2262, 0.5712], 4 | [0.2033, 0.4816, 0.2567, 0.4607, 0.4116, 0.5016, 0.3667, 0.1844, 0.5220]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/84_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.0176, grad_fn=) 2 | Generator error: tensor(0.8942, grad_fn=) 3 | Points: tensor([[0.1708, 0.5505, 0.6009, 0.1816, 0.0757, 0.5018, 0.0513, 0.1069, 0.0517], 4 | [0.1628, 0.5354, 0.6070, 0.1504, 0.0612, 0.4949, 0.0272, 0.1544, 0.0369]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/89_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4125, grad_fn=) 2 | Generator error: tensor(0.6860, grad_fn=) 3 | Points: tensor([[0.3542, 0.6829, 0.7199, 0.0396, 0.0726, 0.7438, 0.2063, 0.2467, 0.5155], 4 | [0.3395, 0.6416, 0.7177, 0.1581, 0.0754, 0.6409, 0.1708, 0.1958, 0.4163]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/93_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.7080, grad_fn=) 2 | Generator error: tensor(0.9132, grad_fn=) 3 | Points: tensor([[0.1503, 0.6008, 0.6023, 0.2204, 0.1002, 0.5014, 0.1542, 0.2256, 0.0180], 4 | [0.1969, 0.6112, 0.6673, 0.2512, 0.0551, 0.4906, 0.1609, 0.2162, 0.0645]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/96_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3452, grad_fn=) 2 | Generator error: tensor(0.8110, grad_fn=) 3 | Points: tensor([[0.5897, 1.2343, 0.4678, 0.8527, 0.3735, 0.6146, 0.2790, 0.1560, 0.9022], 4 | [0.3276, 0.7091, 0.4849, 0.4241, 0.1440, 0.4942, 0.2532, 0.2149, 0.3310]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/97_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2170, grad_fn=) 2 | Generator error: tensor(1.0021, grad_fn=) 3 | Points: tensor([[0.3640, 1.0714, 0.9909, 0.4433, 0.3089, 0.6853, 0.2410, 0.3615, 0.1695], 4 | [0.4063, 1.1384, 0.9313, 0.5276, 0.3568, 0.6790, 0.3213, 0.2552, 0.0902]], 5 | grad_fn=) 6 | 7 | 8 | -------------------------------------------------------------------------------- /diabetes_escalonated/1_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1974, grad_fn=) 2 | Generator error: tensor(1.2049, grad_fn=) 3 | Points: tensor([[ 0.3813, 0.3765, -0.0838, 0.4082, 0.6939, -0.0652, 0.4916, 0.6328, 4 | 0.4631], 5 | [ 0.3344, 0.4698, -0.0112, 0.3778, 0.7474, 0.0366, 0.4725, 0.6401, 6 | 0.4642]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/2_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3745, grad_fn=) 2 | Generator error: tensor(0.6844, grad_fn=) 3 | Points: tensor([[ 0.0596, 0.1586, 0.2358, -0.0233, -0.0659, 0.1434, 0.0103, 0.1839, 4 | 0.0713], 5 | [ 0.0744, 0.2637, 0.2073, 0.0018, -0.0255, 0.2214, -0.0065, 0.2199, 6 | 0.0940]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/5_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2623, grad_fn=) 2 | Generator error: tensor(0.7913, grad_fn=) 3 | Points: tensor([[ 0.1985, 0.7167, 0.6130, 0.0435, -0.1024, 0.4693, 0.0013, 0.1588, 4 | 0.6513], 5 | [ 0.1868, 0.5501, 0.5555, 0.0935, -0.0400, 0.4428, 0.0526, 0.1717, 6 | 0.5023]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/6_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4062, grad_fn=) 2 | Generator error: tensor(0.6151, grad_fn=) 3 | Points: tensor([[ 0.0679, 0.3575, 0.4354, 0.0882, 0.1458, 0.3678, 0.0166, 0.3376, 4 | 0.3243], 5 | [ 0.0780, 0.4198, 0.3793, 0.0435, -0.0059, 0.3504, 0.0234, 0.1783, 6 | 0.2472]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/8_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1390, grad_fn=) 2 | Generator error: tensor(0.7722, grad_fn=) 3 | Points: tensor([[ 0.1034, 0.6577, 0.8363, 0.0904, -0.1354, 0.6196, -0.0182, 0.2977, 4 | -0.0264], 5 | [ 0.1037, 0.6024, 0.8193, 0.0729, -0.1180, 0.5779, -0.0158, 0.2412, 6 | -0.0027]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/9_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.6051, grad_fn=) 2 | Generator error: tensor(0.6125, grad_fn=) 3 | Points: tensor([[ 0.0697, 0.1853, 0.8212, 0.5049, -0.0649, 0.5449, -0.0111, 0.0386, 4 | -0.5204], 5 | [ 0.0247, 0.2246, 0.8630, 0.5235, 0.0331, 0.6347, -0.0953, 0.0856, 6 | -0.4123]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/100_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.9325, grad_fn=) 2 | Generator error: tensor(0.8124, grad_fn=) 3 | Points: tensor([[ 0.2000, 0.6714, 0.5417, 0.1442, 0.1062, 0.3207, 0.1050, 0.1412, 4 | 0.0490], 5 | [ 0.2341, 0.8002, 0.5673, 0.1070, 0.1146, 0.3928, 0.0398, 0.1485, 6 | -0.0627]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/101_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5180, grad_fn=) 2 | Generator error: tensor(0.8683, grad_fn=) 3 | Points: tensor([[ 0.2805, 0.4918, 0.7561, 0.2120, 0.1264, 0.7280, 0.2553, 0.2613, 4 | 0.0781], 5 | [ 0.4137, 0.4015, 0.6389, 0.2605, 0.1059, 0.6390, 0.1009, 0.2733, 6 | -0.0472]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/102_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.0549, grad_fn=) 2 | Generator error: tensor(0.8718, grad_fn=) 3 | Points: tensor([[-0.1216, 0.6135, 0.5511, 0.2528, 0.0239, 0.2375, 0.2928, 0.0288, 4 | 0.0077], 5 | [ 0.0050, 0.5549, 0.5397, 0.2176, 0.0243, 0.3975, 0.2704, 0.1307, 6 | -0.0117]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/103_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.8135, grad_fn=) 2 | Generator error: tensor(0.7377, grad_fn=) 3 | Points: tensor([[ 0.2652, 0.7573, 0.6526, 0.1367, 0.3027, 0.8540, 0.0341, 0.3881, 4 | 0.0117], 5 | [ 0.3710, 0.9688, 0.8550, 0.1018, 0.4658, 1.0245, -0.0331, 0.4974, 6 | 0.0976]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/104_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.7362, grad_fn=) 2 | Generator error: tensor(0.5830, grad_fn=) 3 | Points: tensor([[-0.0400, 0.9521, 0.4916, -0.0656, 0.1207, 0.5483, 0.3878, -0.1071, 4 | 0.5028], 5 | [-0.0217, 0.8662, 0.5050, 0.0280, 0.1861, 0.5752, 0.2895, -0.0804, 6 | 0.4181]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/105_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4033, grad_fn=) 2 | Generator error: tensor(0.9397, grad_fn=) 3 | Points: tensor([[ 0.2084, 0.6342, 0.7016, 0.1889, 0.1970, 0.5475, 0.1268, 0.0941, 4 | -0.2275], 5 | [ 0.3955, 0.8661, 0.8058, 0.1918, 0.1405, 0.6711, 0.3709, 0.0683, 6 | 0.3539]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/11_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4185, grad_fn=) 2 | Generator error: tensor(0.6878, grad_fn=) 3 | Points: tensor([[ 0.1961, 0.7192, 0.4434, 0.0267, 0.0614, 0.2452, 0.0980, -0.0488, 4 | 0.0954], 5 | [ 0.1745, 0.7819, 0.5065, 0.1272, 0.0855, 0.2826, 0.0564, -0.0180, 6 | 0.1389]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/12_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3825, grad_fn=) 2 | Generator error: tensor(0.8076, grad_fn=) 3 | Points: tensor([[ 0.6918, 0.3406, 0.5229, 0.3233, -0.2941, 0.9175, 0.5132, 0.3870, 4 | 1.0148], 5 | [ 0.8159, 0.4348, 0.6664, 0.4177, -0.3209, 1.1944, 0.6309, 0.5840, 6 | 1.2077]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/13_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4726, grad_fn=) 2 | Generator error: tensor(0.6932, grad_fn=) 3 | Points: tensor([[ 0.1387, 0.3474, 0.2079, 0.0733, -0.0293, 0.2730, 0.1969, 0.1381, 4 | 0.1170], 5 | [ 0.1828, 0.2945, 0.2372, 0.0762, -0.0273, 0.2517, 0.1995, 0.1452, 6 | 0.1311]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/15_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5421, grad_fn=) 2 | Generator error: tensor(0.6554, grad_fn=) 3 | Points: tensor([[ 0.1408, 0.6473, 0.5020, 0.1610, 0.0721, 0.4887, 0.0218, 0.2292, 4 | 0.3389], 5 | [ 0.0565, 0.5692, 0.3906, 0.2077, 0.0860, 0.4305, -0.0118, 0.1524, 6 | 0.2467]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/17_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3653, grad_fn=) 2 | Generator error: tensor(0.7816, grad_fn=) 3 | Points: tensor([[ 0.5829, 0.5451, 0.5422, -0.1635, 0.0656, 0.3758, 0.4004, 0.4916, 4 | 1.5153], 5 | [ 0.6819, 0.5533, 0.4519, -0.2610, -0.1902, 0.3764, 0.2257, 0.5318, 6 | 1.9321]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/18_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3895, grad_fn=) 2 | Generator error: tensor(0.6536, grad_fn=) 3 | Points: tensor([[ 0.1408, 0.5176, 0.5483, 0.1986, 0.0622, 0.4181, 0.2009, 0.1566, 4 | -0.0692], 5 | [ 0.2124, 0.6179, 0.5654, 0.2309, 0.1214, 0.4210, 0.3261, 0.1873, 6 | 0.0260]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/20_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3817, grad_fn=) 2 | Generator error: tensor(0.6569, grad_fn=) 3 | Points: tensor([[-0.0102, 0.4669, 0.5456, 0.1334, -0.0925, 0.1456, 0.0542, -0.1105, 4 | -0.2764], 5 | [ 0.0170, 0.4566, 0.5628, 0.1880, 0.0090, 0.1808, 0.1053, -0.0766, 6 | -0.2422]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/22_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3495, grad_fn=) 2 | Generator error: tensor(0.6513, grad_fn=) 3 | Points: tensor([[ 0.1876, 0.8316, 0.6357, 0.0813, 0.1001, 0.5861, 0.1494, 0.0926, 4 | -0.0287], 5 | [ 0.1108, 0.7576, 0.5222, 0.1696, 0.1610, 0.5431, 0.2287, 0.0028, 6 | 0.0286]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/27_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3616, grad_fn=) 2 | Generator error: tensor(0.6674, grad_fn=) 3 | Points: tensor([[ 0.2388, 0.5734, 0.5673, 0.1871, 0.0651, 0.5034, 0.1104, 0.1125, 4 | 0.2894], 5 | [ 0.2951, 0.8262, 0.6976, 0.2927, -0.0294, 0.4208, 0.1602, -0.0641, 6 | 0.3563]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/29_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3213, grad_fn=) 2 | Generator error: tensor(0.6731, grad_fn=) 3 | Points: tensor([[ 0.4092, 0.5608, 0.6434, 0.1001, -0.1409, 0.5046, -0.0837, 0.3744, 4 | 0.6330], 5 | [ 0.4260, 0.5845, 0.6433, 0.1355, -0.1041, 0.5845, -0.1113, 0.3677, 6 | 0.6351]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/30_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4229, grad_fn=) 2 | Generator error: tensor(0.6667, grad_fn=) 3 | Points: tensor([[ 0.4960, 0.6127, 0.9919, 0.1179, 0.0027, 0.7497, 0.1542, 0.4180, 4 | -1.0827], 5 | [ 0.4735, 0.6766, 0.8759, 0.1189, -0.0845, 0.5220, 0.1487, 0.3545, 6 | -0.8801]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/31_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3029, grad_fn=) 2 | Generator error: tensor(0.7607, grad_fn=) 3 | Points: tensor([[-0.1992, 0.5854, 0.3717, 0.2407, 0.2218, 0.5397, 0.2198, -0.2355, 4 | 0.8310], 5 | [-0.2300, 0.6572, 0.4172, 0.2487, 0.3337, 0.6444, 0.1335, -0.2597, 6 | 1.0066]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/35_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4991, grad_fn=) 2 | Generator error: tensor(0.7248, grad_fn=) 3 | Points: tensor([[ 0.3420, 0.6204, 0.6129, 0.4037, -0.0398, 0.5621, 0.1377, 0.3310, 4 | 0.5909], 5 | [ 0.3730, 0.6627, 0.7239, 0.3934, -0.0860, 0.4841, 0.1444, 0.3811, 6 | 0.8210]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/36_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4036, grad_fn=) 2 | Generator error: tensor(0.6133, grad_fn=) 3 | Points: tensor([[ 0.2528, 0.7015, 0.7243, 0.5868, -0.0337, 0.7735, 0.4549, 0.2351, 4 | 0.0526], 5 | [ 0.0542, 0.6773, 0.7136, 0.5379, -0.0517, 0.6719, 0.3316, 0.1880, 6 | -0.1182]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/38_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3896, grad_fn=) 2 | Generator error: tensor(0.6941, grad_fn=) 3 | Points: tensor([[ 0.2457, 0.7444, 0.5034, 0.8810, -0.3111, 0.3339, 0.4418, 0.4032, 4 | 1.0888], 5 | [ 0.2832, 0.8230, 0.5013, 1.2787, -0.6139, 0.2571, 0.5814, 0.6440, 6 | 1.5625]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/39_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3410, grad_fn=) 2 | Generator error: tensor(0.7495, grad_fn=) 3 | Points: tensor([[ 0.2050, 0.4993, 0.4168, -0.0312, 0.0560, 0.1112, 0.0497, 0.2669, 4 | -0.0591], 5 | [ 0.3356, 0.7673, 0.2808, 0.0630, -0.1385, -0.2780, 0.0645, 0.3091, 6 | -0.3287]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/41_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3279, grad_fn=) 2 | Generator error: tensor(0.6998, grad_fn=) 3 | Points: tensor([[ 0.1689, 0.4392, 0.2973, 0.0110, 0.0429, 0.4134, 0.0365, 0.1962, 4 | 0.3433], 5 | [ 0.1947, 0.5944, 0.3389, -0.0018, 0.0877, 0.5933, 0.0786, 0.2115, 6 | 0.4168]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/44_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3652, grad_fn=) 2 | Generator error: tensor(0.7918, grad_fn=) 3 | Points: tensor([[ 0.1199, 0.5782, 0.5414, 0.0354, 0.0553, 0.3859, 0.1919, 0.3090, 4 | 0.0313], 5 | [ 0.1093, 0.6820, 0.5717, 0.0189, 0.0681, 0.4056, 0.2308, 0.3606, 6 | -0.1804]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/46_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4301, grad_fn=) 2 | Generator error: tensor(0.7705, grad_fn=) 3 | Points: tensor([[ 0.3457, 0.5442, 0.4172, 0.2032, 0.2548, 0.3664, 0.2024, 0.5171, 4 | -0.3812], 5 | [ 0.3308, 0.5563, 0.3579, 0.1876, 0.2331, 0.2985, 0.1231, 0.4371, 6 | -0.2277]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/48_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4045, grad_fn=) 2 | Generator error: tensor(0.6532, grad_fn=) 3 | Points: tensor([[-0.0838, 0.7734, 0.5371, 0.2392, 0.0919, 0.9926, 0.6386, 0.2626, 4 | 0.9704], 5 | [-0.0927, 0.7884, 0.4633, 0.3362, 0.1467, 0.9784, 0.6000, 0.2386, 6 | 0.9340]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/49_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5066, grad_fn=) 2 | Generator error: tensor(0.6774, grad_fn=) 3 | Points: tensor([[ 0.1547, 0.5310, 0.3753, 0.3799, 0.3482, 0.6410, 0.4839, 0.1168, 4 | -0.1659], 5 | [ 0.1943, 0.5486, 0.4561, 0.3971, 0.3878, 0.6113, 0.3825, 0.1318, 6 | -0.1194]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/56_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3266, grad_fn=) 2 | Generator error: tensor(0.8273, grad_fn=) 3 | Points: tensor([[ 0.4079, 0.6566, 0.2798, 0.0291, -0.0760, 0.3019, 0.1016, 0.0726, 4 | 0.1014], 5 | [ 0.4165, 0.7094, 0.3098, 0.0778, -0.0363, 0.3336, 0.1502, 0.0598, 6 | 0.0293]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/59_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3810, grad_fn=) 2 | Generator error: tensor(0.6283, grad_fn=) 3 | Points: tensor([[-0.0371, 0.6032, 0.6380, 0.3414, 0.1029, 0.4522, 0.2210, 0.0894, 4 | 0.3963], 5 | [-0.1659, 0.4998, 0.6609, 0.4662, 0.1331, 0.4257, 0.1525, -0.0255, 6 | 0.4564]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/62_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3858, grad_fn=) 2 | Generator error: tensor(0.7818, grad_fn=) 3 | Points: tensor([[ 0.1633, 0.5877, 0.2447, 0.2688, -0.0742, 0.5093, 0.0831, 0.1787, 4 | 0.1219], 5 | [ 0.1751, 0.4720, 0.3367, 0.2550, -0.0189, 0.4719, 0.0797, 0.1554, 6 | 0.0894]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/64_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3824, grad_fn=) 2 | Generator error: tensor(0.7523, grad_fn=) 3 | Points: tensor([[ 0.3226, 0.7295, 0.8776, 0.1643, -0.0362, 0.4199, 0.3118, 0.3115, 4 | 0.5597], 5 | [ 0.4552, 0.6580, 0.8883, 0.2034, -0.0536, 0.3923, 0.3181, 0.2957, 6 | 0.5541]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/67_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4247, grad_fn=) 2 | Generator error: tensor(0.6511, grad_fn=) 3 | Points: tensor([[ 0.0661, 0.5456, 0.6932, 0.2247, -0.0382, 0.4563, 0.2092, 0.1077, 4 | 0.2728], 5 | [ 0.1253, 0.6763, 0.7284, 0.2050, 0.0024, 0.5438, 0.2816, 0.1917, 6 | 0.3795]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/68_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3939, grad_fn=) 2 | Generator error: tensor(0.8233, grad_fn=) 3 | Points: tensor([[ 0.4543, 0.6724, 0.3601, 0.1879, 0.0378, 0.4326, 0.2480, 0.5164, 4 | -0.1026], 5 | [ 0.3560, 0.6705, 0.2962, 0.1801, 0.0079, 0.3976, 0.2787, 0.5398, 6 | -0.1410]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/70_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4159, grad_fn=) 2 | Generator error: tensor(0.8089, grad_fn=) 3 | Points: tensor([[ 0.3627, 0.6754, 0.5664, 0.1295, 0.0660, 0.4787, -0.0111, 0.2684, 4 | -0.0183], 5 | [ 0.2802, 0.7434, 0.5862, 0.2848, 0.1143, 0.4770, 0.1003, 0.2836, 6 | 0.1460]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/71_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3314, grad_fn=) 2 | Generator error: tensor(0.6281, grad_fn=) 3 | Points: tensor([[ 0.2927, 0.5267, 0.5777, 0.2757, 0.0652, 0.4424, 0.0743, 0.0697, 4 | 0.4818], 5 | [ 0.4101, 0.6419, 0.6566, 0.3613, -0.0288, 0.5453, 0.0717, 0.1278, 6 | 0.6812]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/72_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4048, grad_fn=) 2 | Generator error: tensor(0.6827, grad_fn=) 3 | Points: tensor([[-0.0676, 0.7242, 0.4631, 0.2539, 0.2124, 0.5180, 0.3605, 0.1754, 4 | -0.1460], 5 | [-0.0298, 0.6345, 0.4933, 0.1578, 0.1725, 0.4652, 0.2955, 0.1471, 6 | -0.0183]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/73_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3218, grad_fn=) 2 | Generator error: tensor(0.6312, grad_fn=) 3 | Points: tensor([[-0.3166, 0.8344, 0.4052, 0.4898, 0.2959, 0.6848, 0.1342, -0.0018, 4 | 0.5577], 5 | [-0.1871, 0.7378, 0.2692, 0.2345, 0.3256, 0.6095, 0.0467, -0.1604, 6 | 0.5901]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/74_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3451, grad_fn=) 2 | Generator error: tensor(0.7928, grad_fn=) 3 | Points: tensor([[ 0.3252, 0.2771, 0.4285, 0.0207, 0.0898, 0.3789, 0.0191, 0.1056, 4 | -0.0978], 5 | [ 0.3230, 0.4338, 0.5376, 0.2585, 0.1251, 0.5077, 0.1222, 0.1053, 6 | 0.1026]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/75_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4353, grad_fn=) 2 | Generator error: tensor(0.8723, grad_fn=) 3 | Points: tensor([[ 0.1244, 0.9924, 0.6466, 0.2439, -0.0809, 0.2750, 0.4433, 0.5247, 4 | 0.5715], 5 | [ 0.1025, 1.1361, 0.6748, 0.2206, -0.0835, 0.3254, 0.5720, 0.6490, 6 | 0.7284]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/79_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(2.0625, grad_fn=) 2 | Generator error: tensor(0.4941, grad_fn=) 3 | Points: tensor([[ 0.0627, 0.9172, 0.7337, 0.2135, 0.1160, 0.6493, 0.2839, 0.1675, 4 | -0.4549], 5 | [ 0.1830, 0.5721, 0.5201, 0.2007, 0.0935, 0.4902, 0.1729, 0.1298, 6 | -0.0526]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/80_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3834, grad_fn=) 2 | Generator error: tensor(0.6189, grad_fn=) 3 | Points: tensor([[ 0.2357, 0.8527, 0.3998, 0.0401, -0.0393, 0.5988, 0.1856, 0.0684, 4 | 0.9108], 5 | [ 0.1756, 0.6393, 0.3786, -0.0077, -0.0404, 0.4693, 0.1065, 0.0779, 6 | 0.7639]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/81_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.9804, grad_fn=) 2 | Generator error: tensor(0.6425, grad_fn=) 3 | Points: tensor([[ 0.0717, 0.4361, 0.5016, 0.2268, 0.0062, 0.3869, 0.1577, 0.0446, 4 | -0.2964], 5 | [ 0.1121, 0.4329, 0.5341, 0.2611, 0.0204, 0.4046, 0.1672, 0.0711, 6 | -0.2133]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/83_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5684, grad_fn=) 2 | Generator error: tensor(0.7897, grad_fn=) 3 | Points: tensor([[ 0.2291, 0.5808, 0.5884, 0.2172, 0.1784, 0.4527, 0.1576, 0.2862, 4 | -0.0439], 5 | [ 0.2003, 0.5700, 0.6351, 0.1675, 0.1623, 0.4413, 0.1037, 0.3535, 6 | 0.0442]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/85_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3471, grad_fn=) 2 | Generator error: tensor(1.8976, grad_fn=) 3 | Points: tensor([[ 1.3131, 0.5018, 0.7604, 0.6624, -0.2431, 1.2420, -1.0533, 0.7792, 4 | 1.3805], 5 | [ 0.8753, 0.4417, 0.5371, 0.5084, -0.0977, 0.8149, -0.3601, 0.5767, 6 | 0.7220]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/86_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3618, grad_fn=) 2 | Generator error: tensor(0.8981, grad_fn=) 3 | Points: tensor([[ 0.2283, 0.4217, 0.4069, 0.2261, 0.0817, 0.4599, 0.2717, 0.2932, 4 | 0.0971], 5 | [ 0.4430, 0.3155, 0.5727, 0.3241, 0.0056, 0.5408, -0.0179, 0.3613, 6 | -0.1674]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/87_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4518, grad_fn=) 2 | Generator error: tensor(0.8603, grad_fn=) 3 | Points: tensor([[-0.0612, 0.7350, 0.7725, 0.3098, 0.2511, 0.4870, 0.2194, -0.0107, 4 | 0.1384], 5 | [-0.0087, 0.5946, 0.6162, 0.2352, 0.1449, 0.5147, 0.2936, 0.0233, 6 | 0.1555]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/88_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.9970, grad_fn=) 2 | Generator error: tensor(0.8727, grad_fn=) 3 | Points: tensor([[ 0.3583, 0.7587, 0.7234, -0.0488, -0.0151, 0.4879, -0.1930, 0.1752, 4 | 0.3134], 5 | [ 0.2893, 0.5202, 0.5186, 0.1552, 0.0026, 0.4135, -0.0761, 0.1938, 6 | 0.2385]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/90_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.0658, grad_fn=) 2 | Generator error: tensor(1.1643, grad_fn=) 3 | Points: tensor([[ 0.4228, 0.8672, 0.1818, 0.3371, 0.1788, 0.4853, -0.0359, 0.4547, 4 | 0.5936], 5 | [ 0.2321, 0.5651, 0.1710, 0.2772, 0.0854, 0.3390, 0.0541, 0.2681, 6 | 0.3851]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/91_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.7670, grad_fn=) 2 | Generator error: tensor(0.7796, grad_fn=) 3 | Points: tensor([[-0.0846, 0.5268, 0.4304, 0.1990, 0.1910, 0.5348, 0.3076, 0.2181, 4 | 0.0557], 5 | [-0.1110, 0.5785, 0.4781, 0.2680, 0.2037, 0.4931, 0.3777, 0.1741, 6 | 0.0953]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/92_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3816, grad_fn=) 2 | Generator error: tensor(0.7571, grad_fn=) 3 | Points: tensor([[ 0.3440, 0.4867, 0.5532, 0.1698, 0.0670, 0.4172, 0.1402, 0.1587, 4 | -0.0121], 5 | [ 0.4692, 0.5876, 0.6077, 0.1676, 0.0290, 0.4804, 0.2088, 0.1834, 6 | -0.0829]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/94_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4493, grad_fn=) 2 | Generator error: tensor(0.8840, grad_fn=) 3 | Points: tensor([[ 0.0997, 0.5334, 0.4851, 0.2279, 0.1151, 0.4351, 0.0567, 0.0048, 4 | 0.0583], 5 | [ 0.0649, 0.4799, 0.4966, 0.2459, 0.1575, 0.4548, 0.0613, -0.0352, 6 | 0.0245]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/95_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1034, grad_fn=) 2 | Generator error: tensor(0.8254, grad_fn=) 3 | Points: tensor([[ 0.2340, 0.5668, 0.6360, 0.1083, 0.0640, 0.5095, 0.1255, 0.3363, 4 | -0.0299], 5 | [ 0.2967, 0.5543, 0.6034, 0.0264, 0.0333, 0.4767, 0.1387, 0.3297, 6 | -0.0070]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/98_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5669, grad_fn=) 2 | Generator error: tensor(0.8851, grad_fn=) 3 | Points: tensor([[ 0.0894, 0.5331, 0.5312, 0.2762, -0.0121, 0.7238, 0.3385, 0.0630, 4 | 0.0467], 5 | [ 0.1141, 0.6216, 0.5604, 0.2290, -0.0064, 0.7156, 0.3060, 0.0752, 6 | 0.0009]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/99_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5895, grad_fn=) 2 | Generator error: tensor(1.5432, grad_fn=) 3 | Points: tensor([[ 0.1285, 0.7245, 0.5306, -0.0988, -0.1256, 0.6665, -0.2413, 0.5654, 4 | 0.9888], 5 | [ 0.2211, 0.6634, 0.5348, 0.1481, 0.0465, 0.4462, 0.0052, 0.2682, 6 | 0.2539]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/100_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.9325, grad_fn=) 2 | Generator error: tensor(0.8124, grad_fn=) 3 | Points: tensor([[ 0.2000, 0.6714, 0.5417, 0.1442, 0.1062, 0.3207, 0.1050, 0.1412, 4 | 0.0490], 5 | [ 0.2341, 0.8002, 0.5673, 0.1070, 0.1146, 0.3928, 0.0398, 0.1485, 6 | -0.0627]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/101_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5180, grad_fn=) 2 | Generator error: tensor(0.8683, grad_fn=) 3 | Points: tensor([[ 0.2805, 0.4918, 0.7561, 0.2120, 0.1264, 0.7280, 0.2553, 0.2613, 4 | 0.0781], 5 | [ 0.4137, 0.4015, 0.6389, 0.2605, 0.1059, 0.6390, 0.1009, 0.2733, 6 | -0.0472]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/102_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.0549, grad_fn=) 2 | Generator error: tensor(0.8718, grad_fn=) 3 | Points: tensor([[-0.1216, 0.6135, 0.5511, 0.2528, 0.0239, 0.2375, 0.2928, 0.0288, 4 | 0.0077], 5 | [ 0.0050, 0.5549, 0.5397, 0.2176, 0.0243, 0.3975, 0.2704, 0.1307, 6 | -0.0117]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/103_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.8135, grad_fn=) 2 | Generator error: tensor(0.7377, grad_fn=) 3 | Points: tensor([[ 0.2652, 0.7573, 0.6526, 0.1367, 0.3027, 0.8540, 0.0341, 0.3881, 4 | 0.0117], 5 | [ 0.3710, 0.9688, 0.8550, 0.1018, 0.4658, 1.0245, -0.0331, 0.4974, 6 | 0.0976]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/104_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.7362, grad_fn=) 2 | Generator error: tensor(0.5830, grad_fn=) 3 | Points: tensor([[-0.0400, 0.9521, 0.4916, -0.0656, 0.1207, 0.5483, 0.3878, -0.1071, 4 | 0.5028], 5 | [-0.0217, 0.8662, 0.5050, 0.0280, 0.1861, 0.5752, 0.2895, -0.0804, 6 | 0.4181]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/105_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4033, grad_fn=) 2 | Generator error: tensor(0.9397, grad_fn=) 3 | Points: tensor([[ 0.2084, 0.6342, 0.7016, 0.1889, 0.1970, 0.5475, 0.1268, 0.0941, 4 | -0.2275], 5 | [ 0.3955, 0.8661, 0.8058, 0.1918, 0.1405, 0.6711, 0.3709, 0.0683, 6 | 0.3539]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/11_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4185, grad_fn=) 2 | Generator error: tensor(0.6878, grad_fn=) 3 | Points: tensor([[ 0.1961, 0.7192, 0.4434, 0.0267, 0.0614, 0.2452, 0.0980, -0.0488, 4 | 0.0954], 5 | [ 0.1745, 0.7819, 0.5065, 0.1272, 0.0855, 0.2826, 0.0564, -0.0180, 6 | 0.1389]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/12_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3825, grad_fn=) 2 | Generator error: tensor(0.8076, grad_fn=) 3 | Points: tensor([[ 0.6918, 0.3406, 0.5229, 0.3233, -0.2941, 0.9175, 0.5132, 0.3870, 4 | 1.0148], 5 | [ 0.8159, 0.4348, 0.6664, 0.4177, -0.3209, 1.1944, 0.6309, 0.5840, 6 | 1.2077]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/13_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4726, grad_fn=) 2 | Generator error: tensor(0.6932, grad_fn=) 3 | Points: tensor([[ 0.1387, 0.3474, 0.2079, 0.0733, -0.0293, 0.2730, 0.1969, 0.1381, 4 | 0.1170], 5 | [ 0.1828, 0.2945, 0.2372, 0.0762, -0.0273, 0.2517, 0.1995, 0.1452, 6 | 0.1311]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/15_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5421, grad_fn=) 2 | Generator error: tensor(0.6554, grad_fn=) 3 | Points: tensor([[ 0.1408, 0.6473, 0.5020, 0.1610, 0.0721, 0.4887, 0.0218, 0.2292, 4 | 0.3389], 5 | [ 0.0565, 0.5692, 0.3906, 0.2077, 0.0860, 0.4305, -0.0118, 0.1524, 6 | 0.2467]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/17_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3653, grad_fn=) 2 | Generator error: tensor(0.7816, grad_fn=) 3 | Points: tensor([[ 0.5829, 0.5451, 0.5422, -0.1635, 0.0656, 0.3758, 0.4004, 0.4916, 4 | 1.5153], 5 | [ 0.6819, 0.5533, 0.4519, -0.2610, -0.1902, 0.3764, 0.2257, 0.5318, 6 | 1.9321]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/18_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3895, grad_fn=) 2 | Generator error: tensor(0.6536, grad_fn=) 3 | Points: tensor([[ 0.1408, 0.5176, 0.5483, 0.1986, 0.0622, 0.4181, 0.2009, 0.1566, 4 | -0.0692], 5 | [ 0.2124, 0.6179, 0.5654, 0.2309, 0.1214, 0.4210, 0.3261, 0.1873, 6 | 0.0260]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/1_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1974, grad_fn=) 2 | Generator error: tensor(1.2049, grad_fn=) 3 | Points: tensor([[ 0.3813, 0.3765, -0.0838, 0.4082, 0.6939, -0.0652, 0.4916, 0.6328, 4 | 0.4631], 5 | [ 0.3344, 0.4698, -0.0112, 0.3778, 0.7474, 0.0366, 0.4725, 0.6401, 6 | 0.4642]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/20_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3817, grad_fn=) 2 | Generator error: tensor(0.6569, grad_fn=) 3 | Points: tensor([[-0.0102, 0.4669, 0.5456, 0.1334, -0.0925, 0.1456, 0.0542, -0.1105, 4 | -0.2764], 5 | [ 0.0170, 0.4566, 0.5628, 0.1880, 0.0090, 0.1808, 0.1053, -0.0766, 6 | -0.2422]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/22_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3495, grad_fn=) 2 | Generator error: tensor(0.6513, grad_fn=) 3 | Points: tensor([[ 0.1876, 0.8316, 0.6357, 0.0813, 0.1001, 0.5861, 0.1494, 0.0926, 4 | -0.0287], 5 | [ 0.1108, 0.7576, 0.5222, 0.1696, 0.1610, 0.5431, 0.2287, 0.0028, 6 | 0.0286]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/27_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3616, grad_fn=) 2 | Generator error: tensor(0.6674, grad_fn=) 3 | Points: tensor([[ 0.2388, 0.5734, 0.5673, 0.1871, 0.0651, 0.5034, 0.1104, 0.1125, 4 | 0.2894], 5 | [ 0.2951, 0.8262, 0.6976, 0.2927, -0.0294, 0.4208, 0.1602, -0.0641, 6 | 0.3563]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/29_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3213, grad_fn=) 2 | Generator error: tensor(0.6731, grad_fn=) 3 | Points: tensor([[ 0.4092, 0.5608, 0.6434, 0.1001, -0.1409, 0.5046, -0.0837, 0.3744, 4 | 0.6330], 5 | [ 0.4260, 0.5845, 0.6433, 0.1355, -0.1041, 0.5845, -0.1113, 0.3677, 6 | 0.6351]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/2_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3745, grad_fn=) 2 | Generator error: tensor(0.6844, grad_fn=) 3 | Points: tensor([[ 0.0596, 0.1586, 0.2358, -0.0233, -0.0659, 0.1434, 0.0103, 0.1839, 4 | 0.0713], 5 | [ 0.0744, 0.2637, 0.2073, 0.0018, -0.0255, 0.2214, -0.0065, 0.2199, 6 | 0.0940]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/30_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4229, grad_fn=) 2 | Generator error: tensor(0.6667, grad_fn=) 3 | Points: tensor([[ 0.4960, 0.6127, 0.9919, 0.1179, 0.0027, 0.7497, 0.1542, 0.4180, 4 | -1.0827], 5 | [ 0.4735, 0.6766, 0.8759, 0.1189, -0.0845, 0.5220, 0.1487, 0.3545, 6 | -0.8801]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/31_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3029, grad_fn=) 2 | Generator error: tensor(0.7607, grad_fn=) 3 | Points: tensor([[-0.1992, 0.5854, 0.3717, 0.2407, 0.2218, 0.5397, 0.2198, -0.2355, 4 | 0.8310], 5 | [-0.2300, 0.6572, 0.4172, 0.2487, 0.3337, 0.6444, 0.1335, -0.2597, 6 | 1.0066]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/35_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4991, grad_fn=) 2 | Generator error: tensor(0.7248, grad_fn=) 3 | Points: tensor([[ 0.3420, 0.6204, 0.6129, 0.4037, -0.0398, 0.5621, 0.1377, 0.3310, 4 | 0.5909], 5 | [ 0.3730, 0.6627, 0.7239, 0.3934, -0.0860, 0.4841, 0.1444, 0.3811, 6 | 0.8210]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/36_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4036, grad_fn=) 2 | Generator error: tensor(0.6133, grad_fn=) 3 | Points: tensor([[ 0.2528, 0.7015, 0.7243, 0.5868, -0.0337, 0.7735, 0.4549, 0.2351, 4 | 0.0526], 5 | [ 0.0542, 0.6773, 0.7136, 0.5379, -0.0517, 0.6719, 0.3316, 0.1880, 6 | -0.1182]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/38_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3896, grad_fn=) 2 | Generator error: tensor(0.6941, grad_fn=) 3 | Points: tensor([[ 0.2457, 0.7444, 0.5034, 0.8810, -0.3111, 0.3339, 0.4418, 0.4032, 4 | 1.0888], 5 | [ 0.2832, 0.8230, 0.5013, 1.2787, -0.6139, 0.2571, 0.5814, 0.6440, 6 | 1.5625]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/39_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3410, grad_fn=) 2 | Generator error: tensor(0.7495, grad_fn=) 3 | Points: tensor([[ 0.2050, 0.4993, 0.4168, -0.0312, 0.0560, 0.1112, 0.0497, 0.2669, 4 | -0.0591], 5 | [ 0.3356, 0.7673, 0.2808, 0.0630, -0.1385, -0.2780, 0.0645, 0.3091, 6 | -0.3287]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/41_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3279, grad_fn=) 2 | Generator error: tensor(0.6998, grad_fn=) 3 | Points: tensor([[ 0.1689, 0.4392, 0.2973, 0.0110, 0.0429, 0.4134, 0.0365, 0.1962, 4 | 0.3433], 5 | [ 0.1947, 0.5944, 0.3389, -0.0018, 0.0877, 0.5933, 0.0786, 0.2115, 6 | 0.4168]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/44_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3652, grad_fn=) 2 | Generator error: tensor(0.7918, grad_fn=) 3 | Points: tensor([[ 0.1199, 0.5782, 0.5414, 0.0354, 0.0553, 0.3859, 0.1919, 0.3090, 4 | 0.0313], 5 | [ 0.1093, 0.6820, 0.5717, 0.0189, 0.0681, 0.4056, 0.2308, 0.3606, 6 | -0.1804]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/46_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4301, grad_fn=) 2 | Generator error: tensor(0.7705, grad_fn=) 3 | Points: tensor([[ 0.3457, 0.5442, 0.4172, 0.2032, 0.2548, 0.3664, 0.2024, 0.5171, 4 | -0.3812], 5 | [ 0.3308, 0.5563, 0.3579, 0.1876, 0.2331, 0.2985, 0.1231, 0.4371, 6 | -0.2277]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/48_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4045, grad_fn=) 2 | Generator error: tensor(0.6532, grad_fn=) 3 | Points: tensor([[-0.0838, 0.7734, 0.5371, 0.2392, 0.0919, 0.9926, 0.6386, 0.2626, 4 | 0.9704], 5 | [-0.0927, 0.7884, 0.4633, 0.3362, 0.1467, 0.9784, 0.6000, 0.2386, 6 | 0.9340]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/49_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5066, grad_fn=) 2 | Generator error: tensor(0.6774, grad_fn=) 3 | Points: tensor([[ 0.1547, 0.5310, 0.3753, 0.3799, 0.3482, 0.6410, 0.4839, 0.1168, 4 | -0.1659], 5 | [ 0.1943, 0.5486, 0.4561, 0.3971, 0.3878, 0.6113, 0.3825, 0.1318, 6 | -0.1194]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/56_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3266, grad_fn=) 2 | Generator error: tensor(0.8273, grad_fn=) 3 | Points: tensor([[ 0.4079, 0.6566, 0.2798, 0.0291, -0.0760, 0.3019, 0.1016, 0.0726, 4 | 0.1014], 5 | [ 0.4165, 0.7094, 0.3098, 0.0778, -0.0363, 0.3336, 0.1502, 0.0598, 6 | 0.0293]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/59_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3810, grad_fn=) 2 | Generator error: tensor(0.6283, grad_fn=) 3 | Points: tensor([[-0.0371, 0.6032, 0.6380, 0.3414, 0.1029, 0.4522, 0.2210, 0.0894, 4 | 0.3963], 5 | [-0.1659, 0.4998, 0.6609, 0.4662, 0.1331, 0.4257, 0.1525, -0.0255, 6 | 0.4564]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/5_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.2623, grad_fn=) 2 | Generator error: tensor(0.7913, grad_fn=) 3 | Points: tensor([[ 0.1985, 0.7167, 0.6130, 0.0435, -0.1024, 0.4693, 0.0013, 0.1588, 4 | 0.6513], 5 | [ 0.1868, 0.5501, 0.5555, 0.0935, -0.0400, 0.4428, 0.0526, 0.1717, 6 | 0.5023]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/62_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3858, grad_fn=) 2 | Generator error: tensor(0.7818, grad_fn=) 3 | Points: tensor([[ 0.1633, 0.5877, 0.2447, 0.2688, -0.0742, 0.5093, 0.0831, 0.1787, 4 | 0.1219], 5 | [ 0.1751, 0.4720, 0.3367, 0.2550, -0.0189, 0.4719, 0.0797, 0.1554, 6 | 0.0894]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/64_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3824, grad_fn=) 2 | Generator error: tensor(0.7523, grad_fn=) 3 | Points: tensor([[ 0.3226, 0.7295, 0.8776, 0.1643, -0.0362, 0.4199, 0.3118, 0.3115, 4 | 0.5597], 5 | [ 0.4552, 0.6580, 0.8883, 0.2034, -0.0536, 0.3923, 0.3181, 0.2957, 6 | 0.5541]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/67_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4247, grad_fn=) 2 | Generator error: tensor(0.6511, grad_fn=) 3 | Points: tensor([[ 0.0661, 0.5456, 0.6932, 0.2247, -0.0382, 0.4563, 0.2092, 0.1077, 4 | 0.2728], 5 | [ 0.1253, 0.6763, 0.7284, 0.2050, 0.0024, 0.5438, 0.2816, 0.1917, 6 | 0.3795]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/68_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3939, grad_fn=) 2 | Generator error: tensor(0.8233, grad_fn=) 3 | Points: tensor([[ 0.4543, 0.6724, 0.3601, 0.1879, 0.0378, 0.4326, 0.2480, 0.5164, 4 | -0.1026], 5 | [ 0.3560, 0.6705, 0.2962, 0.1801, 0.0079, 0.3976, 0.2787, 0.5398, 6 | -0.1410]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/6_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4062, grad_fn=) 2 | Generator error: tensor(0.6151, grad_fn=) 3 | Points: tensor([[ 0.0679, 0.3575, 0.4354, 0.0882, 0.1458, 0.3678, 0.0166, 0.3376, 4 | 0.3243], 5 | [ 0.0780, 0.4198, 0.3793, 0.0435, -0.0059, 0.3504, 0.0234, 0.1783, 6 | 0.2472]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/70_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4159, grad_fn=) 2 | Generator error: tensor(0.8089, grad_fn=) 3 | Points: tensor([[ 0.3627, 0.6754, 0.5664, 0.1295, 0.0660, 0.4787, -0.0111, 0.2684, 4 | -0.0183], 5 | [ 0.2802, 0.7434, 0.5862, 0.2848, 0.1143, 0.4770, 0.1003, 0.2836, 6 | 0.1460]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/71_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3314, grad_fn=) 2 | Generator error: tensor(0.6281, grad_fn=) 3 | Points: tensor([[ 0.2927, 0.5267, 0.5777, 0.2757, 0.0652, 0.4424, 0.0743, 0.0697, 4 | 0.4818], 5 | [ 0.4101, 0.6419, 0.6566, 0.3613, -0.0288, 0.5453, 0.0717, 0.1278, 6 | 0.6812]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/72_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4048, grad_fn=) 2 | Generator error: tensor(0.6827, grad_fn=) 3 | Points: tensor([[-0.0676, 0.7242, 0.4631, 0.2539, 0.2124, 0.5180, 0.3605, 0.1754, 4 | -0.1460], 5 | [-0.0298, 0.6345, 0.4933, 0.1578, 0.1725, 0.4652, 0.2955, 0.1471, 6 | -0.0183]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/73_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3218, grad_fn=) 2 | Generator error: tensor(0.6312, grad_fn=) 3 | Points: tensor([[-0.3166, 0.8344, 0.4052, 0.4898, 0.2959, 0.6848, 0.1342, -0.0018, 4 | 0.5577], 5 | [-0.1871, 0.7378, 0.2692, 0.2345, 0.3256, 0.6095, 0.0467, -0.1604, 6 | 0.5901]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/74_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3451, grad_fn=) 2 | Generator error: tensor(0.7928, grad_fn=) 3 | Points: tensor([[ 0.3252, 0.2771, 0.4285, 0.0207, 0.0898, 0.3789, 0.0191, 0.1056, 4 | -0.0978], 5 | [ 0.3230, 0.4338, 0.5376, 0.2585, 0.1251, 0.5077, 0.1222, 0.1053, 6 | 0.1026]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/75_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4353, grad_fn=) 2 | Generator error: tensor(0.8723, grad_fn=) 3 | Points: tensor([[ 0.1244, 0.9924, 0.6466, 0.2439, -0.0809, 0.2750, 0.4433, 0.5247, 4 | 0.5715], 5 | [ 0.1025, 1.1361, 0.6748, 0.2206, -0.0835, 0.3254, 0.5720, 0.6490, 6 | 0.7284]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/79_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(2.0625, grad_fn=) 2 | Generator error: tensor(0.4941, grad_fn=) 3 | Points: tensor([[ 0.0627, 0.9172, 0.7337, 0.2135, 0.1160, 0.6493, 0.2839, 0.1675, 4 | -0.4549], 5 | [ 0.1830, 0.5721, 0.5201, 0.2007, 0.0935, 0.4902, 0.1729, 0.1298, 6 | -0.0526]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/80_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3834, grad_fn=) 2 | Generator error: tensor(0.6189, grad_fn=) 3 | Points: tensor([[ 0.2357, 0.8527, 0.3998, 0.0401, -0.0393, 0.5988, 0.1856, 0.0684, 4 | 0.9108], 5 | [ 0.1756, 0.6393, 0.3786, -0.0077, -0.0404, 0.4693, 0.1065, 0.0779, 6 | 0.7639]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/81_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.9804, grad_fn=) 2 | Generator error: tensor(0.6425, grad_fn=) 3 | Points: tensor([[ 0.0717, 0.4361, 0.5016, 0.2268, 0.0062, 0.3869, 0.1577, 0.0446, 4 | -0.2964], 5 | [ 0.1121, 0.4329, 0.5341, 0.2611, 0.0204, 0.4046, 0.1672, 0.0711, 6 | -0.2133]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/83_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5684, grad_fn=) 2 | Generator error: tensor(0.7897, grad_fn=) 3 | Points: tensor([[ 0.2291, 0.5808, 0.5884, 0.2172, 0.1784, 0.4527, 0.1576, 0.2862, 4 | -0.0439], 5 | [ 0.2003, 0.5700, 0.6351, 0.1675, 0.1623, 0.4413, 0.1037, 0.3535, 6 | 0.0442]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/85_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3471, grad_fn=) 2 | Generator error: tensor(1.8976, grad_fn=) 3 | Points: tensor([[ 1.3131, 0.5018, 0.7604, 0.6624, -0.2431, 1.2420, -1.0533, 0.7792, 4 | 1.3805], 5 | [ 0.8753, 0.4417, 0.5371, 0.5084, -0.0977, 0.8149, -0.3601, 0.5767, 6 | 0.7220]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/86_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3618, grad_fn=) 2 | Generator error: tensor(0.8981, grad_fn=) 3 | Points: tensor([[ 0.2283, 0.4217, 0.4069, 0.2261, 0.0817, 0.4599, 0.2717, 0.2932, 4 | 0.0971], 5 | [ 0.4430, 0.3155, 0.5727, 0.3241, 0.0056, 0.5408, -0.0179, 0.3613, 6 | -0.1674]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/87_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4518, grad_fn=) 2 | Generator error: tensor(0.8603, grad_fn=) 3 | Points: tensor([[-0.0612, 0.7350, 0.7725, 0.3098, 0.2511, 0.4870, 0.2194, -0.0107, 4 | 0.1384], 5 | [-0.0087, 0.5946, 0.6162, 0.2352, 0.1449, 0.5147, 0.2936, 0.0233, 6 | 0.1555]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/88_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.9970, grad_fn=) 2 | Generator error: tensor(0.8727, grad_fn=) 3 | Points: tensor([[ 0.3583, 0.7587, 0.7234, -0.0488, -0.0151, 0.4879, -0.1930, 0.1752, 4 | 0.3134], 5 | [ 0.2893, 0.5202, 0.5186, 0.1552, 0.0026, 0.4135, -0.0761, 0.1938, 6 | 0.2385]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/8_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1390, grad_fn=) 2 | Generator error: tensor(0.7722, grad_fn=) 3 | Points: tensor([[ 0.1034, 0.6577, 0.8363, 0.0904, -0.1354, 0.6196, -0.0182, 0.2977, 4 | -0.0264], 5 | [ 0.1037, 0.6024, 0.8193, 0.0729, -0.1180, 0.5779, -0.0158, 0.2412, 6 | -0.0027]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/90_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.0658, grad_fn=) 2 | Generator error: tensor(1.1643, grad_fn=) 3 | Points: tensor([[ 0.4228, 0.8672, 0.1818, 0.3371, 0.1788, 0.4853, -0.0359, 0.4547, 4 | 0.5936], 5 | [ 0.2321, 0.5651, 0.1710, 0.2772, 0.0854, 0.3390, 0.0541, 0.2681, 6 | 0.3851]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/91_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(0.7670, grad_fn=) 2 | Generator error: tensor(0.7796, grad_fn=) 3 | Points: tensor([[-0.0846, 0.5268, 0.4304, 0.1990, 0.1910, 0.5348, 0.3076, 0.2181, 4 | 0.0557], 5 | [-0.1110, 0.5785, 0.4781, 0.2680, 0.2037, 0.4931, 0.3777, 0.1741, 6 | 0.0953]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/92_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.3816, grad_fn=) 2 | Generator error: tensor(0.7571, grad_fn=) 3 | Points: tensor([[ 0.3440, 0.4867, 0.5532, 0.1698, 0.0670, 0.4172, 0.1402, 0.1587, 4 | -0.0121], 5 | [ 0.4692, 0.5876, 0.6077, 0.1676, 0.0290, 0.4804, 0.2088, 0.1834, 6 | -0.0829]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/94_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4493, grad_fn=) 2 | Generator error: tensor(0.8840, grad_fn=) 3 | Points: tensor([[ 0.0997, 0.5334, 0.4851, 0.2279, 0.1151, 0.4351, 0.0567, 0.0048, 4 | 0.0583], 5 | [ 0.0649, 0.4799, 0.4966, 0.2459, 0.1575, 0.4548, 0.0613, -0.0352, 6 | 0.0245]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/95_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.1034, grad_fn=) 2 | Generator error: tensor(0.8254, grad_fn=) 3 | Points: tensor([[ 0.2340, 0.5668, 0.6360, 0.1083, 0.0640, 0.5095, 0.1255, 0.3363, 4 | -0.0299], 5 | [ 0.2967, 0.5543, 0.6034, 0.0264, 0.0333, 0.4767, 0.1387, 0.3297, 6 | -0.0070]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/98_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5669, grad_fn=) 2 | Generator error: tensor(0.8851, grad_fn=) 3 | Points: tensor([[ 0.0894, 0.5331, 0.5312, 0.2762, -0.0121, 0.7238, 0.3385, 0.0630, 4 | 0.0467], 5 | [ 0.1141, 0.6216, 0.5604, 0.2290, -0.0064, 0.7156, 0.3060, 0.0752, 6 | 0.0009]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/99_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.5895, grad_fn=) 2 | Generator error: tensor(1.5432, grad_fn=) 3 | Points: tensor([[ 0.1285, 0.7245, 0.5306, -0.0988, -0.1256, 0.6665, -0.2413, 0.5654, 4 | 0.9888], 5 | [ 0.2211, 0.6634, 0.5348, 0.1481, 0.0465, 0.4462, 0.0052, 0.2682, 6 | 0.2539]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/9_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.6051, grad_fn=) 2 | Generator error: tensor(0.6125, grad_fn=) 3 | Points: tensor([[ 0.0697, 0.1853, 0.8212, 0.5049, -0.0649, 0.5449, -0.0111, 0.0386, 4 | -0.5204], 5 | [ 0.0247, 0.2246, 0.8630, 0.5235, 0.0331, 0.6347, -0.0953, 0.0856, 6 | -0.4123]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/61_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4207, grad_fn=) 2 | Generator error: tensor(0.6378, grad_fn=) 3 | Points: tensor([[1.2210e-01, 5.8123e-01, 6.6477e-01, 2.5984e-01, 2.5770e-04, 5.0613e-01, 4 | 1.8565e-01, 1.1439e-01, 3.0509e-01], 5 | [2.5483e-01, 6.7691e-01, 5.4736e-01, 2.4276e-01, 4.6633e-02, 5.1811e-01, 6 | 2.6127e-01, 3.9953e-02, 4.2825e-01]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/61_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4207, grad_fn=) 2 | Generator error: tensor(0.6378, grad_fn=) 3 | Points: tensor([[1.2210e-01, 5.8123e-01, 6.6477e-01, 2.5984e-01, 2.5770e-04, 5.0613e-01, 4 | 1.8565e-01, 1.1439e-01, 3.0509e-01], 5 | [2.5483e-01, 6.7691e-01, 5.4736e-01, 2.4276e-01, 4.6633e-02, 5.1811e-01, 6 | 2.6127e-01, 3.9953e-02, 4.2825e-01]], grad_fn=) 7 | 8 | 9 | -------------------------------------------------------------------------------- /diabetes_escalonated/0_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(2.0641, grad_fn=) 2 | Generator error: tensor(0.9702, grad_fn=) 3 | Points: tensor([[-8.7970e-02, 3.0089e+00, 9.0916e-02, -3.9213e-01, -1.0712e-01, 4 | 1.6574e-02, 5.9310e-02, 7.6971e-01, 2.8206e-03], 5 | [-1.1168e-01, 3.7632e+00, 1.6347e-01, -5.7346e-01, -1.5325e-01, 6 | 4.8964e-02, 4.0699e-02, 9.1624e-01, -8.4749e-03]], 7 | grad_fn=) 8 | 9 | 10 | -------------------------------------------------------------------------------- /diabetes_escalonated/51_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4527, grad_fn=) 2 | Generator error: tensor(0.6756, grad_fn=) 3 | Points: tensor([[ 1.9288e-01, 5.9314e-01, 7.9980e-01, -7.7504e-04, -8.7565e-02, 4 | 4.1252e-01, 1.2243e-01, 4.1201e-01, -2.9451e-01], 5 | [ 1.7681e-01, 4.7047e-01, 5.8462e-01, 1.9530e-01, 4.4693e-02, 6 | 4.3516e-01, 1.1302e-01, 2.2323e-01, 9.2669e-03]], 7 | grad_fn=) 8 | 9 | 10 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/0_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(2.0641, grad_fn=) 2 | Generator error: tensor(0.9702, grad_fn=) 3 | Points: tensor([[-8.7970e-02, 3.0089e+00, 9.0916e-02, -3.9213e-01, -1.0712e-01, 4 | 1.6574e-02, 5.9310e-02, 7.6971e-01, 2.8206e-03], 5 | [-1.1168e-01, 3.7632e+00, 1.6347e-01, -5.7346e-01, -1.5325e-01, 6 | 4.8964e-02, 4.0699e-02, 9.1624e-01, -8.4749e-03]], 7 | grad_fn=) 8 | 9 | 10 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/51_107.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: tensor(1.4527, grad_fn=) 2 | Generator error: tensor(0.6756, grad_fn=) 3 | Points: tensor([[ 1.9288e-01, 5.9314e-01, 7.9980e-01, -7.7504e-04, -8.7565e-02, 4 | 4.1252e-01, 1.2243e-01, 4.1201e-01, -2.9451e-01], 5 | [ 1.7681e-01, 4.7047e-01, 5.8462e-01, 1.9530e-01, 4.4693e-02, 6 | 4.3516e-01, 1.1302e-01, 2.2323e-01, 9.2669e-03]], 7 | grad_fn=) 8 | 9 | 10 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Generating a Dataset with GANs 2 | 3 | We used GANs to generate new data from an existing dataset for data augmentation. We use the dataset [Pima Indians Diabetes Database](https://www.kaggle.com/uciml/pima-indians-diabetes-database) from Kaggle. All of our code can be found in the notebook [Deep_Learning_From_Scratch_Original.ipynb](/Deep_Learning_From_Scratch_Original.ipynb). 4 | 5 | Our approach is based on the paper [Data Augmentation Using GANs](https://arxiv.org/abs/1904.09135) by Fabio Henrique K. dos S. Tanaka. 6 |
7 | ## Wanna know what we found? 8 | You can [read about our experiments and results](https://medium.com/swlh/generating-a-dataset-with-gans-1e994ff633fd) on Medium. 9 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | from torch.autograd.variable import Variable 2 | import torch 3 | 4 | def random_noise(size): 5 | n = Variable(torch.randn(size, 100)) 6 | if torch.cuda.is_available(): 7 | return n.cuda() 8 | return n 9 | 10 | def real_data_target(size): 11 | ''' 12 | Tensor containing ones, with shape = size 13 | ''' 14 | data = Variable(torch.ones(size, 1)) 15 | if torch.cuda.is_available(): return data.cuda() 16 | return data 17 | 18 | def fake_data_target(size): 19 | ''' 20 | Tensor containing zeros, with shape = size 21 | ''' 22 | data = Variable(torch.zeros(size, 1)) 23 | if torch.cuda.is_available(): return data.cuda() 24 | return data -------------------------------------------------------------------------------- /values_analysis/diabetes_analysis.txt: -------------------------------------------------------------------------------- 1 | Pima Indians Diabetes Database 2 | 3 | Normal: 65.1% of the dataset 4 | Diabets: 34.9% of the dataset 5 | 6 | Pregnancies: 7 | Normal Diabets 8 | min: 0 0 9 | max: 13 17 10 | mean: 3.298 4.866 11 | median: 2.0 4.0 12 | 13 | Glucose: 14 | Normal Diabets 15 | min: 0 0 16 | max: 197 199 17 | mean: 109.98 141.257 18 | median: 107.0 140.0 19 | 20 | BloodPressure: 21 | Normal Diabets 22 | min: 0 0 23 | max: 122 114 24 | mean: 68.184 70.825 25 | median: 70.0 74.0 26 | 27 | SkinThickness: 28 | Normal Diabets 29 | min: 0 0 30 | max: 60 99 31 | mean: 19.664 22.164 32 | median: 21.0 27.0 33 | 34 | Insulin: 35 | Normal Diabets 36 | min: 0 0 37 | max: 744 846 38 | mean: 68.792 100.336 39 | median: 39.0 0.0 40 | 41 | BMI: 42 | Normal Diabets 43 | min: 0.0 0.0 44 | max: 57.3 67.1 45 | mean: 30.304 35.143 46 | median: 30.05 34.25 47 | 48 | DiabetesPedigreeFunction: 49 | Normal Diabets 50 | min: 0.078 0.088 51 | max: 2.329 2.42 52 | mean: 0.43 0.55 53 | median: 0.336 0.449 54 | 55 | Age: 56 | Normal Diabets 57 | min: 21 21 58 | max: 81 70 59 | mean: 31.19 37.067 60 | median: 27.0 36.0 61 | 62 | -------------------------------------------------------------------------------- /values_analysis/diabetes_escalonated_analysis.txt: -------------------------------------------------------------------------------- 1 | Pima Indians Diabetes Database eSCALONATED 2 | 3 | Normal: 65.1% of the dataset 4 | Diabets: 34.9% of the dataset 5 | 6 | Pregnancies: 7 | Normal Diabets 8 | min: 0.0 0.0 9 | max: 0.765 1.0 10 | mean: 0.194 0.286 11 | median: 0.118 0.235 12 | 13 | Glucose: 14 | Normal Diabets 15 | min: 0.0 0.0 16 | max: 0.99 1.0 17 | mean: 0.553 0.71 18 | median: 0.538 0.704 19 | 20 | BloodPressure: 21 | Normal Diabets 22 | min: 0.0 0.0 23 | max: 1.0 0.934 24 | mean: 0.559 0.581 25 | median: 0.574 0.607 26 | 27 | SkinThickness: 28 | Normal Diabets 29 | min: 0.0 0.0 30 | max: 0.606 1.0 31 | mean: 0.199 0.224 32 | median: 0.212 0.273 33 | 34 | Insulin: 35 | Normal Diabets 36 | min: 0.0 0.0 37 | max: 0.879 1.0 38 | mean: 0.081 0.119 39 | median: 0.046 0.0 40 | 41 | BMI: 42 | Normal Diabets 43 | min: 0.0 0.0 44 | max: 0.854 1.0 45 | mean: 0.452 0.524 46 | median: 0.448 0.51 47 | 48 | DiabetesPedigreeFunction: 49 | Normal Diabets 50 | min: 0.0 0.004 51 | max: 0.961 1.0 52 | mean: 0.15 0.202 53 | median: 0.11 0.158 54 | 55 | Age: 56 | Normal Diabets 57 | min: 0.0 0.0 58 | max: 1.0 0.817 59 | mean: 0.17 0.268 60 | median: 0.1 0.25 61 | 62 | -------------------------------------------------------------------------------- /discriminator.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn, optim 3 | from torch.autograd.variable import Variable 4 | from torchvision import transforms, datasets 5 | from utils import real_data_target, fake_data_target 6 | 7 | class DiscriminatorNet(torch.nn.Module): 8 | """ 9 | A three hidden-layer discriminative neural network 10 | """ 11 | def __init__(self, in_features, leakyRelu=0.2, dropout=0.3, hidden_layers=[1024, 512, 256]): 12 | super(DiscriminatorNet, self).__init__() 13 | 14 | out_features = 1 15 | self.layers = hidden_layers.copy() 16 | self.layers.insert(0, in_features) 17 | 18 | for count in range(0, len(self.layers)-1): 19 | self.add_module("hidden_" + str(count), 20 | nn.Sequential( 21 | nn.Linear(self.layers[count], self.layers[count+1]), 22 | nn.LeakyReLU(leakyRelu), 23 | nn.Dropout(dropout) 24 | ) 25 | ) 26 | 27 | self.add_module("out", 28 | nn.Sequential( 29 | nn.Linear(self.layers[-1], out_features), 30 | torch.nn.Sigmoid() 31 | ) 32 | ) 33 | 34 | def forward(self, x): 35 | for name, module in self.named_children(): 36 | x = module(x) 37 | return x 38 | 39 | # train_discriminator(d_optimizer, discriminator, loss, real_data, fake_data) 40 | def train_discriminator(optimizer, discriminator, loss, real_data, fake_data): 41 | # Reset gradients 42 | optimizer.zero_grad() 43 | 44 | # 1.1 Train on Real Data 45 | prediction_real = discriminator(real_data) 46 | # Calculate error and backpropagate 47 | error_real = loss(prediction_real, real_data_target(real_data.size(0))) 48 | error_real.backward() 49 | 50 | # 1.2 Train on Fake Data 51 | prediction_fake = discriminator(fake_data) 52 | # Calculate error and backpropagate 53 | error_fake = loss(prediction_fake, fake_data_target(real_data.size(0))) 54 | error_fake.backward() 55 | 56 | # 1.3 Update weights with gradients 57 | optimizer.step() 58 | 59 | # Return error 60 | return error_real + error_fake, prediction_real, prediction_fake -------------------------------------------------------------------------------- /generator.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn, optim 3 | from torch.autograd.variable import Variable 4 | from torchvision import transforms, datasets 5 | from utils import real_data_target 6 | 7 | def noise(quantity, size): 8 | return Variable(torch.randn(quantity, size)) 9 | 10 | class GeneratorNet(torch.nn.Module): 11 | """ 12 | A three hidden-layer generative neural network 13 | """ 14 | def __init__(self, out_features, leakyRelu=0.2, hidden_layers=[256, 512, 1024], in_features=100, escalonate=False): 15 | super(GeneratorNet, self).__init__() 16 | 17 | self.in_features = in_features 18 | self.layers = hidden_layers.copy() 19 | self.layers.insert(0, self.in_features) 20 | 21 | for count in range(0, len(self.layers)-1): 22 | self.add_module("hidden_" + str(count), 23 | nn.Sequential( 24 | nn.Linear(self.layers[count], self.layers[count+1]), 25 | nn.LeakyReLU(leakyRelu) 26 | ) 27 | ) 28 | 29 | if not escalonate: 30 | self.add_module("out", 31 | nn.Sequential( 32 | nn.Linear(self.layers[-1], out_features) 33 | ) 34 | ) 35 | else: 36 | self.add_module("out", 37 | nn.Sequential( 38 | nn.Linear(self.layers[-1], out_features), 39 | escalonate 40 | ) 41 | ) 42 | 43 | def forward(self, x): 44 | for name, module in self.named_children(): 45 | x = module(x) 46 | return x 47 | 48 | def create_data(self, quantity): 49 | points = noise(quantity, self.in_features) 50 | try: 51 | data=self.forward(points.cuda()) 52 | except RuntimeError: 53 | data=self.forward(points.cpu()) 54 | return data.detach().numpy() 55 | 56 | def train_generator(optimizer, discriminator, loss, fake_data): 57 | # 2. Train Generator 58 | # Reset gradients 59 | optimizer.zero_grad() 60 | # Sample noise and generate fake data 61 | prediction = discriminator(fake_data) 62 | # Calculate error and backpropagate 63 | error = loss(prediction, real_data_target(prediction.size(0))) 64 | error.backward() 65 | # Update weights with gradients 66 | optimizer.step() 67 | # Return error 68 | return error -------------------------------------------------------------------------------- /values_analysis/creditcard_analysis.txt: -------------------------------------------------------------------------------- 1 | Time: 2 | No Frauds Frauds 3 | mean: 94838.202 80746.807 4 | median: 84711.0 75568.5 5 | 6 | V1: 7 | No Frauds Frauds 8 | mean: 0.008 -4.772 9 | median: 0.02 -2.342 10 | 11 | V2: 12 | No Frauds Frauds 13 | mean: -0.006 3.624 14 | median: 0.064 2.718 15 | 16 | V3: 17 | No Frauds Frauds 18 | mean: 0.012 -7.033 19 | median: 0.182 -5.075 20 | 21 | V4: 22 | No Frauds Frauds 23 | mean: -0.008 4.542 24 | median: -0.022 4.177 25 | 26 | V5: 27 | No Frauds Frauds 28 | mean: 0.005 -3.151 29 | median: -0.053 -1.523 30 | 31 | V6: 32 | No Frauds Frauds 33 | mean: 0.002 -1.398 34 | median: -0.273 -1.425 35 | 36 | V7: 37 | No Frauds Frauds 38 | mean: 0.01 -5.569 39 | median: 0.041 -3.034 40 | 41 | V8: 42 | No Frauds Frauds 43 | mean: -0.001 0.571 44 | median: 0.022 0.622 45 | 46 | V9: 47 | No Frauds Frauds 48 | mean: 0.004 -2.581 49 | median: -0.05 -2.209 50 | 51 | V10: 52 | No Frauds Frauds 53 | mean: 0.01 -5.677 54 | median: -0.092 -4.579 55 | 56 | V11: 57 | No Frauds Frauds 58 | mean: -0.007 3.8 59 | median: -0.035 3.586 60 | 61 | V12: 62 | No Frauds Frauds 63 | mean: 0.011 -6.259 64 | median: 0.142 -5.503 65 | 66 | V13: 67 | No Frauds Frauds 68 | mean: 0.0 -0.109 69 | median: -0.014 -0.066 70 | 71 | V14: 72 | No Frauds Frauds 73 | mean: 0.012 -6.972 74 | median: 0.052 -6.73 75 | 76 | V15: 77 | No Frauds Frauds 78 | mean: 0.0 -0.093 79 | median: 0.048 -0.057 80 | 81 | V16: 82 | No Frauds Frauds 83 | mean: 0.007 -4.14 84 | median: 0.067 -3.55 85 | 86 | V17: 87 | No Frauds Frauds 88 | mean: 0.012 -6.666 89 | median: -0.065 -5.303 90 | 91 | V18: 92 | No Frauds Frauds 93 | mean: 0.004 -2.246 94 | median: -0.003 -1.664 95 | 96 | V19: 97 | No Frauds Frauds 98 | mean: -0.001 0.681 99 | median: 0.003 0.647 100 | 101 | V20: 102 | No Frauds Frauds 103 | mean: -0.001 0.372 104 | median: -0.063 0.285 105 | 106 | V21: 107 | No Frauds Frauds 108 | mean: -0.001 0.714 109 | median: -0.03 0.592 110 | 111 | V22: 112 | No Frauds Frauds 113 | mean: -0.0 0.014 114 | median: 0.007 0.048 115 | 116 | V23: 117 | No Frauds Frauds 118 | mean: 0.0 -0.04 119 | median: -0.011 -0.073 120 | 121 | V24: 122 | No Frauds Frauds 123 | mean: 0.0 -0.105 124 | median: 0.041 -0.061 125 | 126 | V25: 127 | No Frauds Frauds 128 | mean: -0.0 0.041 129 | median: 0.016 0.088 130 | 131 | V26: 132 | No Frauds Frauds 133 | mean: -0.0 0.052 134 | median: -0.052 0.004 135 | 136 | V27: 137 | No Frauds Frauds 138 | mean: -0.0 0.171 139 | median: 0.001 0.395 140 | 141 | V28: 142 | No Frauds Frauds 143 | mean: -0.0 0.076 144 | median: 0.011 0.146 145 | 146 | Amount: 147 | No Frauds Frauds 148 | mean: 88.291 122.211 149 | median: 22.0 9.25 150 | 151 | -------------------------------------------------------------------------------- /values_analysis/data_analysis.txt: -------------------------------------------------------------------------------- 1 | id: 2 | Benign Malignant 3 | mean: 26543824.625 36818050.443 4 | median: 908916.0 895366.5 5 | 6 | radius_mean: 7 | Benign Malignant 8 | mean: 12.147 17.463 9 | median: 12.2 17.325 10 | 11 | texture_mean: 12 | Benign Malignant 13 | mean: 17.915 21.605 14 | median: 17.39 21.46 15 | 16 | perimeter_mean: 17 | Benign Malignant 18 | mean: 78.075 115.365 19 | median: 78.18 114.2 20 | 21 | area_mean: 22 | Benign Malignant 23 | mean: 462.79 978.376 24 | median: 458.4 932.0 25 | 26 | smoothness_mean: 27 | Benign Malignant 28 | mean: 0.092 0.103 29 | median: 0.091 0.102 30 | 31 | compactness_mean: 32 | Benign Malignant 33 | mean: 0.08 0.145 34 | median: 0.075 0.132 35 | 36 | concavity_mean: 37 | Benign Malignant 38 | mean: 0.046 0.161 39 | median: 0.037 0.151 40 | 41 | concave points_mean: 42 | Benign Malignant 43 | mean: 0.026 0.088 44 | median: 0.023 0.086 45 | 46 | symmetry_mean: 47 | Benign Malignant 48 | mean: 0.174 0.193 49 | median: 0.171 0.19 50 | 51 | fractal_dimension_mean: 52 | Benign Malignant 53 | mean: 0.063 0.063 54 | median: 0.062 0.062 55 | 56 | radius_se: 57 | Benign Malignant 58 | mean: 0.284 0.609 59 | median: 0.258 0.547 60 | 61 | texture_se: 62 | Benign Malignant 63 | mean: 1.22 1.211 64 | median: 1.108 1.102 65 | 66 | perimeter_se: 67 | Benign Malignant 68 | mean: 2.0 4.324 69 | median: 1.851 3.68 70 | 71 | area_se: 72 | Benign Malignant 73 | mean: 21.135 72.672 74 | median: 19.63 58.455 75 | 76 | smoothness_se: 77 | Benign Malignant 78 | mean: 0.007 0.007 79 | median: 0.007 0.006 80 | 81 | compactness_se: 82 | Benign Malignant 83 | mean: 0.021 0.032 84 | median: 0.016 0.029 85 | 86 | concavity_se: 87 | Benign Malignant 88 | mean: 0.026 0.042 89 | median: 0.018 0.037 90 | 91 | concave points_se: 92 | Benign Malignant 93 | mean: 0.01 0.015 94 | median: 0.009 0.014 95 | 96 | symmetry_se: 97 | Benign Malignant 98 | mean: 0.021 0.02 99 | median: 0.019 0.018 100 | 101 | fractal_dimension_se: 102 | Benign Malignant 103 | mean: 0.004 0.004 104 | median: 0.003 0.004 105 | 106 | radius_worst: 107 | Benign Malignant 108 | mean: 13.38 21.135 109 | median: 13.35 20.59 110 | 111 | texture_worst: 112 | Benign Malignant 113 | mean: 23.515 29.318 114 | median: 22.82 28.945 115 | 116 | perimeter_worst: 117 | Benign Malignant 118 | mean: 87.006 141.37 119 | median: 86.92 138.0 120 | 121 | area_worst: 122 | Benign Malignant 123 | mean: 558.899 1422.286 124 | median: 547.4 1303.0 125 | 126 | smoothness_worst: 127 | Benign Malignant 128 | mean: 0.125 0.145 129 | median: 0.125 0.143 130 | 131 | compactness_worst: 132 | Benign Malignant 133 | mean: 0.183 0.375 134 | median: 0.17 0.356 135 | 136 | concavity_worst: 137 | Benign Malignant 138 | mean: 0.166 0.451 139 | median: 0.141 0.405 140 | 141 | concave points_worst: 142 | Benign Malignant 143 | mean: 0.074 0.182 144 | median: 0.074 0.182 145 | 146 | symmetry_worst: 147 | Benign Malignant 148 | mean: 0.27 0.323 149 | median: 0.269 0.31 150 | 151 | fractal_dimension_worst: 152 | Benign Malignant 153 | mean: 0.079 0.092 154 | median: 0.077 0.088 155 | 156 | -------------------------------------------------------------------------------- /compare_data.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import seaborn as sns 3 | import numpy as np 4 | from scipy.stats import norm 5 | import ipywidgets as widgets 6 | import matplotlib.pyplot as plt 7 | import glob 8 | from data_treatment import DataAtts 9 | from IPython.display import display 10 | from sklearn.tree import DecisionTreeClassifier as DT 11 | from sklearn.tree import export_graphviz # Decision tree from sklearn 12 | import pydotplus # Decision tree plotting 13 | 14 | def compare_data (original_data, fake_data, size_of_fake, mode="save"): 15 | dataAtts = DataAtts(original_data) 16 | 17 | data = pd.read_csv(original_data) 18 | fake_data = pd.read_csv(fake_data).tail(size_of_fake) 19 | print(dataAtts.message, "\n") 20 | print(dataAtts.values_names[0], round(data[dataAtts.class_name].value_counts()[0]/len(data) * 100,2), '% of the dataset') 21 | print(dataAtts.values_names[1], round(data[dataAtts.class_name].value_counts()[1]/len(data) * 100,2), '% of the dataset') 22 | 23 | classes = list(data) 24 | 25 | for name in classes: 26 | if name=="Unnamed: 32": 27 | continue 28 | 29 | plt.xlabel('Values') 30 | plt.ylabel('Probability') 31 | plt.title(name + " distribution") 32 | real_dist = data[name].values 33 | fake_dist = fake_data[name].values 34 | plt.hist(real_dist, 50, density=True, alpha=0.5) 35 | plt.hist(fake_dist, 50, density=True, alpha=0.5, facecolor='r') 36 | if mode=="save": 37 | plt.savefig('fake_data/'+ dataAtts.fname + "/"+name+'_distribution.png') 38 | elif mode=="show": 39 | plt.show() 40 | plt.clf() 41 | 42 | def create_comparing_table(original_data_name, fake_data_name): 43 | 44 | dataAtts = DataAtts(original_data_name) 45 | data = pd.read_csv(original_data_name) 46 | fake_data = pd.read_csv(fake_data_name) 47 | fake_data.loc[getattr(fake_data, dataAtts.class_name) >= 0.5, dataAtts.class_name] = 1 48 | fake_data.loc[getattr(fake_data, dataAtts.class_name) < 0.5, dataAtts.class_name] = 0 49 | 50 | # Creates the training set 51 | training_data = [["original", data.head(int(data.shape[0]*0.7))]] 52 | fake_name = "fake" + str(fake_data_name).split("/")[2][0] 53 | training_data.append([fake_name, fake_data.head(int(fake_data.shape[0]*0.7))]) 54 | 55 | test = data.tail(int(data.shape[0]*0.3)) 56 | 57 | print("| Database \t| Proportion \t| Test Error \t|") 58 | print("| ---------\t| ---------: \t| :--------- \t|") 59 | 60 | for episode in training_data: 61 | name = episode[0] 62 | train = episode[1] 63 | try: 64 | positive=str(round(train[dataAtts.class_name].value_counts()[0]/len(train) * 100,2)) 65 | except: 66 | positive="0" 67 | try: 68 | negative=str(round(train[dataAtts.class_name].value_counts()[1]/len(train) * 100,2)) 69 | except: 70 | negative="0" 71 | 72 | 73 | trainX = train.drop(dataAtts.class_name, 1) 74 | testX = test.drop(dataAtts.class_name, 1) 75 | y_train = train[dataAtts.class_name] 76 | y_test = test[dataAtts.class_name] 77 | #trainX = pd.get_dummies(trainX) 78 | 79 | clf1 = DT(max_depth = 3, min_samples_leaf = 1) 80 | clf1 = clf1.fit(trainX,y_train) 81 | export_graphviz(clf1, out_file="models/tree.dot", feature_names=trainX.columns, class_names=["0","1"], filled=True, rounded=True) 82 | g = pydotplus.graph_from_dot_file(path="models/tree.dot") 83 | 84 | pred = clf1.predict_proba(testX) 85 | if pred.shape[1] > 1: 86 | pred = np.argmax(pred, axis=1) 87 | else: 88 | pred = pred.reshape((pred.shape[0])) 89 | if negative=="0": 90 | pred = pred-1 91 | 92 | mse = round(((pred - y_test.values)**2).mean(axis=0), 4) 93 | 94 | string="| " + name + " \t| " + positive + "/" + negative + " \t| " + str(mse) + " \t|" 95 | print(string) 96 | 97 | 98 | -------------------------------------------------------------------------------- /data_treatment.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pandas as pd 3 | import os 4 | from sklearn import preprocessing 5 | from sklearn.utils import shuffle 6 | from torch import nn, optim 7 | from torch.autograd.variable import Variable 8 | from torchvision import transforms, datasets, utils 9 | from torch.utils.data import Dataset, DataLoader 10 | 11 | class ToTensor(object): 12 | """Convert ndarrays in sample to Tensors.""" 13 | 14 | def __call__(self, sample): 15 | # print (sample.values[2]) 16 | # print (torch.from_numpy(sample.values)[2].item()) 17 | return torch.from_numpy(sample.values) 18 | 19 | class DataSet(Dataset): 20 | """Face Landmarks dataset.""" 21 | 22 | def __init__(self, csv_file, root_dir, transform=transforms.Compose([ToTensor()]), training_porcentage=0.7, shuffle_db=False): 23 | """ 24 | Args: 25 | csv_file (string): Path to the csv file with annotations. 26 | root_dir (string): Directory with all the images. 27 | transform (callable, optional): Optional transform to be applied 28 | on a sample. 29 | """ 30 | # self.data = pd.read_csv(csv_file).head(100000) 31 | self.file = pd.read_csv(csv_file) 32 | if (shuffle): 33 | self.file = shuffle(self.file) 34 | self.data = self.file.head(int(self.file.shape[0]*training_porcentage)) 35 | self.test_data = self.file.tail(int(self.file.shape[0]*(1-training_porcentage))) 36 | self.root_dir = root_dir 37 | self.transform = transform 38 | 39 | def __len__(self): 40 | return len(self.data) 41 | 42 | def __getitem__(self, idx): 43 | item = self.data.iloc[idx] 44 | if self.transform: 45 | item = self.transform(item) 46 | return item 47 | 48 | def get_columns(self): 49 | return self.data.columns 50 | 51 | class DataAtts(): 52 | def __init__(self, file_name): 53 | if file_name == "original_data/data.csv": 54 | self.message = "Breast Cancer Wisconsin (Diagnostic) Data Set" 55 | self.class_name = "diagnosis" 56 | self.values_names = {0: "Benign", 1: "Malignant"} 57 | self.class_len = 32 58 | self.fname="data" 59 | elif file_name == "original_data/creditcard.csv": 60 | self.message = "Credit Card Fraud Detection" 61 | self.class_name = "Class" 62 | self.values_names = {0: "No Frauds", 1: "Frauds"} 63 | self.class_len = 31 64 | self.fname="creditcard" 65 | elif file_name == "original_data/diabetes.csv": 66 | self.message="Pima Indians Diabetes Database" 67 | self.class_name = "Outcome" 68 | self.values_names = {0: "Normal", 1: "Diabets"} 69 | self.class_len = 9 70 | self.fname="diabetes" 71 | 72 | elif file_name == "original_data/data_escalonated.csv": 73 | self.message = "Breast Cancer Wisconsin (Diagnostic) Data Set eSCALONATED" 74 | self.class_name = "diagnosis" 75 | self.values_names = {0: "Benign", 1: "Malignant"} 76 | self.class_len = 32 77 | self.fname="data_escalonated" 78 | elif file_name == "original_data/creditcard_escalonated.csv": 79 | self.message = "Credit Card Fraud Detection eSCALONATED" 80 | self.class_name = "Class" 81 | self.values_names = {0: "No Frauds", 1: "Frauds"} 82 | self.class_len = 31 83 | self.fname="creditcard_escalonated" 84 | elif file_name == "original_data/diabetes_escalonated.csv": 85 | self.message="Pima Indians Diabetes Database eSCALONATED" 86 | self.class_name = "Outcome" 87 | self.values_names = {0: "Normal", 1: "Diabets"} 88 | self.class_len = 9 89 | self.fname="diabetes_escalonated" 90 | elif file_name == "original_data/creditcard_1s_escalonated.csv": 91 | self.message = "Credit Card Fraud Detection eSCALONATED" 92 | self.class_name = "Class" 93 | self.values_names = {0: "No Frauds", 1: "Frauds"} 94 | self.class_len = 31 95 | self.fname="creditcard_1s_escalonated" 96 | else: 97 | print("File not found, exiting") 98 | exit(1) 99 | 100 | 101 | -------------------------------------------------------------------------------- /train_generator_discriminator.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import pandas as pd 3 | from torch import nn, optim 4 | from torch.autograd.variable import Variable 5 | from torchvision import transforms, datasets 6 | from data_treatment import DataSet, DataAtts 7 | from discriminator import * 8 | from generator import * 9 | import os 10 | # import ipywidgets as widgets 11 | # from IPython.display import display 12 | # import matplotlib.pyplot as plt 13 | import glob 14 | from utils import * 15 | 16 | 17 | class Architecture(): 18 | def __init__(self, learning_rate, batch_size, loss, hidden_layers, name): 19 | self.learning_rate=learning_rate 20 | self.batch_size=batch_size 21 | self.loss=loss 22 | self.hidden_layers=hidden_layers 23 | self.name=name 24 | 25 | def save_model(name, epoch, attributes, dictionary, optimizer_dictionary, loss_function, db_name, arch_name): 26 | torch.save({ 27 | 'epoch': epoch, 28 | 'model_attributes': attributes, 29 | 'model_state_dict': dictionary, 30 | 'optimizer_state_dict': optimizer_dictionary, 31 | 'loss': loss_function 32 | }, "models/" + db_name + "/" + name + "_" + arch_name + ".pt") 33 | 34 | 35 | # Check if creditcard.csv exists and if so, create a scalonated version of it 36 | # escalonate_creditcard_db() 37 | if not os.path.isfile('./original_data/diabetes_escalonated.csv'): 38 | print("Database creditcard.csv not found, exiting...") 39 | exit() 40 | 41 | file_names=["original_data/diabetes_escalonated.csv"] 42 | num_epochs=[500] 43 | learning_rate=[0.0002] 44 | batch_size=[5] 45 | number_of_experiments = 5 46 | #hidden_layers=[[256, 512]] 47 | hidden_layers=[[256, 512], [256], [128, 256], [128]] 48 | # hidden_layers=[[256]] 49 | 50 | #create the different architetures 51 | architectures=[] 52 | count=0 53 | for lr in learning_rate: 54 | for b_size in batch_size: 55 | for hidden in hidden_layers: 56 | for i in range(number_of_experiments): 57 | name = "id-" + str(count) 58 | name += "_epochs-" + str(num_epochs[0]) 59 | name += "_layer-" + str(len(hidden)) 60 | name += "_lr-" + str(lr) 61 | name += "_batch-" + str(b_size) 62 | name += "_arc-" + ','.join(map(str, hidden)) 63 | architectures.append( Architecture( 64 | learning_rate=lr, 65 | batch_size=b_size, 66 | loss=nn.BCELoss(), 67 | hidden_layers=hidden, 68 | name=name 69 | ) 70 | ) 71 | count+=1 72 | 73 | 74 | #training process 75 | for file_name, epochs in zip(file_names, num_epochs): 76 | dataAtts = DataAtts(file_name) 77 | database = DataSet (csv_file=file_name, root_dir=".", shuffle_db=False) 78 | 79 | for arc in architectures: 80 | if ("escalonated" in file_name): 81 | esc = torch.nn.Sigmoid() 82 | else: 83 | esc = False 84 | 85 | generatorAtts = { 86 | 'out_features':dataAtts.class_len, 87 | 'leakyRelu':0.2, 88 | 'hidden_layers':arc.hidden_layers, 89 | 'in_features':100, 90 | 'escalonate':esc 91 | } 92 | generator = GeneratorNet(**generatorAtts) 93 | 94 | discriminatorAtts = { 95 | 'in_features':dataAtts.class_len, 96 | 'leakyRelu':0.2, 97 | 'dropout':0.3, 98 | 'hidden_layers':arc.hidden_layers[::-1] 99 | 100 | } 101 | discriminator = DiscriminatorNet(**discriminatorAtts) 102 | 103 | if torch.cuda.is_available(): 104 | discriminator.cuda() 105 | generator.cuda() 106 | d_optimizer = optim.Adam(discriminator.parameters(), lr=arc.learning_rate) 107 | g_optimizer = optim.Adam(generator.parameters(), lr=arc.learning_rate) 108 | loss = arc.loss 109 | data_loader = torch.utils.data.DataLoader(database, batch_size=arc.batch_size, shuffle=True) 110 | num_batches = len(data_loader) 111 | 112 | print(dataAtts.fname) 113 | print(arc.name) 114 | for epoch in range(epochs): 115 | if (epoch % 100 == 0): 116 | print("Epoch ", epoch) 117 | 118 | for n_batch, real_batch in enumerate(data_loader): 119 | # 1. Train DdataAtts.fnameiscriminator 120 | real_data = Variable(real_batch).float() 121 | if torch.cuda.is_available(): 122 | real_data = real_data.cuda() 123 | # Generate fake data 124 | fake_data = generator(random_noise(real_data.size(0))).detach() 125 | # Train D 126 | d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, discriminator, loss, real_data, fake_data) 127 | 128 | # 2. Train Generator 129 | # Generate fake data 130 | fake_data = generator(random_noise(real_batch.size(0))) 131 | # Train G 132 | g_error = train_generator(g_optimizer, discriminator, loss, fake_data) 133 | 134 | # Display Progress 135 | 136 | #if (n_batch) % print_interval == 0: 137 | 138 | # From this line on it's just the saving 139 | # save_model("generator", epoch, generatorAtts, generator.state_dict(), g_optimizer.state_dict(), loss, dataAtts.fname, arc.name) 140 | # save_model("discriminator", epoch, discriminatorAtts, discriminator.state_dict(), d_optimizer.state_dict(), loss, dataAtts.fname, arc.name) 141 | 142 | torch.save({ 143 | 'epoch': epoch, 144 | 'model_attributes': generatorAtts, 145 | 'model_state_dict': generator.state_dict(), 146 | 'optimizer_state_dict': g_optimizer.state_dict(), 147 | 'loss': loss 148 | }, "models/" + dataAtts.fname + "/generator_" + arc.name + ".pt") 149 | 150 | torch.save({ 151 | 'epoch': epoch, 152 | 'model_attributes': discriminatorAtts, 153 | 'model_state_dict': discriminator.state_dict(), 154 | 'optimizer_state_dict': d_optimizer.state_dict(), 155 | 'loss': loss 156 | }, "models/" + dataAtts.fname + "/discriminator_" + arc.name + ".pt") -------------------------------------------------------------------------------- /diabetes_escalonated/error_growth.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: [0, tensor(2.0641, grad_fn=), tensor(1.1974, grad_fn=), tensor(1.3745, grad_fn=), tensor(1.3459, grad_fn=), tensor(1.5805, grad_fn=), tensor(1.2623, grad_fn=), tensor(1.4062, grad_fn=), tensor(1.3281, grad_fn=), tensor(1.1390, grad_fn=), tensor(1.6051, grad_fn=), tensor(1.3854, grad_fn=), tensor(1.4185, grad_fn=), tensor(1.3825, grad_fn=), tensor(1.4726, grad_fn=), tensor(1.3430, grad_fn=), tensor(1.5421, grad_fn=), tensor(1.5321, grad_fn=), tensor(1.3653, grad_fn=), tensor(1.3895, grad_fn=), tensor(1.3347, grad_fn=), tensor(1.3817, grad_fn=), tensor(1.5338, grad_fn=), tensor(1.3495, grad_fn=), tensor(1.3422, grad_fn=), tensor(1.3877, grad_fn=), tensor(1.2924, grad_fn=), tensor(1.3686, grad_fn=), tensor(1.3616, grad_fn=), tensor(1.4217, grad_fn=), tensor(1.3213, grad_fn=), tensor(1.4229, grad_fn=), tensor(1.3029, grad_fn=), tensor(1.3717, grad_fn=), tensor(1.3926, grad_fn=), tensor(1.3948, grad_fn=), tensor(1.4991, grad_fn=), tensor(1.4036, grad_fn=), tensor(1.3417, grad_fn=), tensor(1.3896, grad_fn=), tensor(1.3410, grad_fn=), tensor(1.4145, grad_fn=), tensor(1.3279, grad_fn=), tensor(1.3951, grad_fn=), tensor(1.3914, grad_fn=), tensor(1.3652, grad_fn=), tensor(1.4254, grad_fn=), tensor(1.4301, grad_fn=), tensor(1.4202, grad_fn=), tensor(1.4045, grad_fn=), tensor(1.5066, grad_fn=), tensor(1.3821, grad_fn=), tensor(1.4527, grad_fn=), tensor(1.4502, grad_fn=), tensor(1.2733, grad_fn=), tensor(1.2885, grad_fn=), tensor(1.2891, grad_fn=), tensor(1.3266, grad_fn=), tensor(1.4450, grad_fn=), tensor(1.3807, grad_fn=), tensor(1.3810, grad_fn=), tensor(1.3933, grad_fn=), tensor(1.4207, grad_fn=), tensor(1.3858, grad_fn=), tensor(1.3668, grad_fn=), tensor(1.3824, grad_fn=), tensor(1.5890, grad_fn=), tensor(1.5525, grad_fn=), tensor(1.4247, grad_fn=), tensor(1.3939, grad_fn=), tensor(1.4859, grad_fn=), tensor(1.4159, grad_fn=), tensor(1.3314, grad_fn=), tensor(1.4048, grad_fn=), tensor(1.3218, grad_fn=), tensor(1.3451, grad_fn=), tensor(1.4353, grad_fn=), tensor(1.3822, grad_fn=), tensor(1.1195, grad_fn=), tensor(1.2318, grad_fn=), tensor(2.0625, grad_fn=), tensor(1.3834, grad_fn=), tensor(1.9804, grad_fn=), tensor(1.4415, grad_fn=), tensor(1.5684, grad_fn=), tensor(1.0176, grad_fn=), tensor(1.3471, grad_fn=), tensor(1.3618, grad_fn=), tensor(1.4518, grad_fn=), tensor(0.9970, grad_fn=), tensor(1.4125, grad_fn=), tensor(1.0658, grad_fn=), tensor(0.7670, grad_fn=), tensor(1.3816, grad_fn=), tensor(1.7080, grad_fn=), tensor(1.4493, grad_fn=), tensor(1.1034, grad_fn=), tensor(1.3452, grad_fn=), tensor(1.2170, grad_fn=), tensor(1.5669, grad_fn=), tensor(1.5895, grad_fn=), tensor(0.9325, grad_fn=), tensor(1.5180, grad_fn=), tensor(1.0549, grad_fn=), tensor(0.8135, grad_fn=), tensor(0.7362, grad_fn=), tensor(1.4033, grad_fn=)] 2 | 3 | 4 | 5 | Generator error: [0, tensor(0.9702, grad_fn=), tensor(1.2049, grad_fn=), tensor(0.6844, grad_fn=), tensor(0.8369, grad_fn=), tensor(0.7174, grad_fn=), tensor(0.7913, grad_fn=), tensor(0.6151, grad_fn=), tensor(0.7110, grad_fn=), tensor(0.7722, grad_fn=), tensor(0.6125, grad_fn=), tensor(0.6746, grad_fn=), tensor(0.6878, grad_fn=), tensor(0.8076, grad_fn=), tensor(0.6932, grad_fn=), tensor(0.6997, grad_fn=), tensor(0.6554, grad_fn=), tensor(0.6723, grad_fn=), tensor(0.7816, grad_fn=), tensor(0.6536, grad_fn=), tensor(0.6661, grad_fn=), tensor(0.6569, grad_fn=), tensor(0.7044, grad_fn=), tensor(0.6513, grad_fn=), tensor(1.5392, grad_fn=), tensor(0.6531, grad_fn=), tensor(0.6784, grad_fn=), tensor(0.8698, grad_fn=), tensor(0.6674, grad_fn=), tensor(0.7522, grad_fn=), tensor(0.6731, grad_fn=), tensor(0.6667, grad_fn=), tensor(0.7607, grad_fn=), tensor(0.8018, grad_fn=), tensor(0.7112, grad_fn=), tensor(0.6439, grad_fn=), tensor(0.7248, grad_fn=), tensor(0.6133, grad_fn=), tensor(0.6854, grad_fn=), tensor(0.6941, grad_fn=), tensor(0.7495, grad_fn=), tensor(0.6907, grad_fn=), tensor(0.6998, grad_fn=), tensor(0.7034, grad_fn=), tensor(0.7700, grad_fn=), tensor(0.7918, grad_fn=), tensor(0.7436, grad_fn=), tensor(0.7705, grad_fn=), tensor(0.6769, grad_fn=), tensor(0.6532, grad_fn=), tensor(0.6774, grad_fn=), tensor(0.7435, grad_fn=), tensor(0.6756, grad_fn=), tensor(0.7720, grad_fn=), tensor(0.7634, grad_fn=), tensor(0.6820, grad_fn=), tensor(0.6777, grad_fn=), tensor(0.8273, grad_fn=), tensor(0.5904, grad_fn=), tensor(0.7401, grad_fn=), tensor(0.6283, grad_fn=), tensor(0.7798, grad_fn=), tensor(0.6378, grad_fn=), tensor(0.7818, grad_fn=), tensor(0.5927, grad_fn=), tensor(0.7523, grad_fn=), tensor(0.6170, grad_fn=), tensor(0.7877, grad_fn=), tensor(0.6511, grad_fn=), tensor(0.8233, grad_fn=), tensor(0.6239, grad_fn=), tensor(0.8089, grad_fn=), tensor(0.6281, grad_fn=), tensor(0.6827, grad_fn=), tensor(0.6312, grad_fn=), tensor(0.7928, grad_fn=), tensor(0.8723, grad_fn=), tensor(0.7395, grad_fn=), tensor(0.6893, grad_fn=), tensor(0.7641, grad_fn=), tensor(0.4941, grad_fn=), tensor(0.6189, grad_fn=), tensor(0.6425, grad_fn=), tensor(0.6555, grad_fn=), tensor(0.7897, grad_fn=), tensor(0.8942, grad_fn=), tensor(1.8976, grad_fn=), tensor(0.8981, grad_fn=), tensor(0.8603, grad_fn=), tensor(0.8727, grad_fn=), tensor(0.6860, grad_fn=), tensor(1.1643, grad_fn=), tensor(0.7796, grad_fn=), tensor(0.7571, grad_fn=), tensor(0.9132, grad_fn=), tensor(0.8840, grad_fn=), tensor(0.8254, grad_fn=), tensor(0.8110, grad_fn=), tensor(1.0021, grad_fn=), tensor(0.8851, grad_fn=), tensor(1.5432, grad_fn=), tensor(0.8124, grad_fn=), tensor(0.8683, grad_fn=), tensor(0.8718, grad_fn=), tensor(0.7377, grad_fn=), tensor(0.5830, grad_fn=), tensor(0.9397, grad_fn=)] 6 | -------------------------------------------------------------------------------- /results/diabetes_escalonated/error_growth.txt: -------------------------------------------------------------------------------- 1 | Discriminator error: [0, tensor(2.0641, grad_fn=), tensor(1.1974, grad_fn=), tensor(1.3745, grad_fn=), tensor(1.3459, grad_fn=), tensor(1.5805, grad_fn=), tensor(1.2623, grad_fn=), tensor(1.4062, grad_fn=), tensor(1.3281, grad_fn=), tensor(1.1390, grad_fn=), tensor(1.6051, grad_fn=), tensor(1.3854, grad_fn=), tensor(1.4185, grad_fn=), tensor(1.3825, grad_fn=), tensor(1.4726, grad_fn=), tensor(1.3430, grad_fn=), tensor(1.5421, grad_fn=), tensor(1.5321, grad_fn=), tensor(1.3653, grad_fn=), tensor(1.3895, grad_fn=), tensor(1.3347, grad_fn=), tensor(1.3817, grad_fn=), tensor(1.5338, grad_fn=), tensor(1.3495, grad_fn=), tensor(1.3422, grad_fn=), tensor(1.3877, grad_fn=), tensor(1.2924, grad_fn=), tensor(1.3686, grad_fn=), tensor(1.3616, grad_fn=), tensor(1.4217, grad_fn=), tensor(1.3213, grad_fn=), tensor(1.4229, grad_fn=), tensor(1.3029, grad_fn=), tensor(1.3717, grad_fn=), tensor(1.3926, grad_fn=), tensor(1.3948, grad_fn=), tensor(1.4991, grad_fn=), tensor(1.4036, grad_fn=), tensor(1.3417, grad_fn=), tensor(1.3896, grad_fn=), tensor(1.3410, grad_fn=), tensor(1.4145, grad_fn=), tensor(1.3279, grad_fn=), tensor(1.3951, grad_fn=), tensor(1.3914, grad_fn=), tensor(1.3652, grad_fn=), tensor(1.4254, grad_fn=), tensor(1.4301, grad_fn=), tensor(1.4202, grad_fn=), tensor(1.4045, grad_fn=), tensor(1.5066, grad_fn=), tensor(1.3821, grad_fn=), tensor(1.4527, grad_fn=), tensor(1.4502, grad_fn=), tensor(1.2733, grad_fn=), tensor(1.2885, grad_fn=), tensor(1.2891, grad_fn=), tensor(1.3266, grad_fn=), tensor(1.4450, grad_fn=), tensor(1.3807, grad_fn=), tensor(1.3810, grad_fn=), tensor(1.3933, grad_fn=), tensor(1.4207, grad_fn=), tensor(1.3858, grad_fn=), tensor(1.3668, grad_fn=), tensor(1.3824, grad_fn=), tensor(1.5890, grad_fn=), tensor(1.5525, grad_fn=), tensor(1.4247, grad_fn=), tensor(1.3939, grad_fn=), tensor(1.4859, grad_fn=), tensor(1.4159, grad_fn=), tensor(1.3314, grad_fn=), tensor(1.4048, grad_fn=), tensor(1.3218, grad_fn=), tensor(1.3451, grad_fn=), tensor(1.4353, grad_fn=), tensor(1.3822, grad_fn=), tensor(1.1195, grad_fn=), tensor(1.2318, grad_fn=), tensor(2.0625, grad_fn=), tensor(1.3834, grad_fn=), tensor(1.9804, grad_fn=), tensor(1.4415, grad_fn=), tensor(1.5684, grad_fn=), tensor(1.0176, grad_fn=), tensor(1.3471, grad_fn=), tensor(1.3618, grad_fn=), tensor(1.4518, grad_fn=), tensor(0.9970, grad_fn=), tensor(1.4125, grad_fn=), tensor(1.0658, grad_fn=), tensor(0.7670, grad_fn=), tensor(1.3816, grad_fn=), tensor(1.7080, grad_fn=), tensor(1.4493, grad_fn=), tensor(1.1034, grad_fn=), tensor(1.3452, grad_fn=), tensor(1.2170, grad_fn=), tensor(1.5669, grad_fn=), tensor(1.5895, grad_fn=), tensor(0.9325, grad_fn=), tensor(1.5180, grad_fn=), tensor(1.0549, grad_fn=), tensor(0.8135, grad_fn=), tensor(0.7362, grad_fn=), tensor(1.4033, grad_fn=)] 2 | 3 | 4 | 5 | Generator error: [0, tensor(0.9702, grad_fn=), tensor(1.2049, grad_fn=), tensor(0.6844, grad_fn=), tensor(0.8369, grad_fn=), tensor(0.7174, grad_fn=), tensor(0.7913, grad_fn=), tensor(0.6151, grad_fn=), tensor(0.7110, grad_fn=), tensor(0.7722, grad_fn=), tensor(0.6125, grad_fn=), tensor(0.6746, grad_fn=), tensor(0.6878, grad_fn=), tensor(0.8076, grad_fn=), tensor(0.6932, grad_fn=), tensor(0.6997, grad_fn=), tensor(0.6554, grad_fn=), tensor(0.6723, grad_fn=), tensor(0.7816, grad_fn=), tensor(0.6536, grad_fn=), tensor(0.6661, grad_fn=), tensor(0.6569, grad_fn=), tensor(0.7044, grad_fn=), tensor(0.6513, grad_fn=), tensor(1.5392, grad_fn=), tensor(0.6531, grad_fn=), tensor(0.6784, grad_fn=), tensor(0.8698, grad_fn=), tensor(0.6674, grad_fn=), tensor(0.7522, grad_fn=), tensor(0.6731, grad_fn=), tensor(0.6667, grad_fn=), tensor(0.7607, grad_fn=), tensor(0.8018, grad_fn=), tensor(0.7112, grad_fn=), tensor(0.6439, grad_fn=), tensor(0.7248, grad_fn=), tensor(0.6133, grad_fn=), tensor(0.6854, grad_fn=), tensor(0.6941, grad_fn=), tensor(0.7495, grad_fn=), tensor(0.6907, grad_fn=), tensor(0.6998, grad_fn=), tensor(0.7034, grad_fn=), tensor(0.7700, grad_fn=), tensor(0.7918, grad_fn=), tensor(0.7436, grad_fn=), tensor(0.7705, grad_fn=), tensor(0.6769, grad_fn=), tensor(0.6532, grad_fn=), tensor(0.6774, grad_fn=), tensor(0.7435, grad_fn=), tensor(0.6756, grad_fn=), tensor(0.7720, grad_fn=), tensor(0.7634, grad_fn=), tensor(0.6820, grad_fn=), tensor(0.6777, grad_fn=), tensor(0.8273, grad_fn=), tensor(0.5904, grad_fn=), tensor(0.7401, grad_fn=), tensor(0.6283, grad_fn=), tensor(0.7798, grad_fn=), tensor(0.6378, grad_fn=), tensor(0.7818, grad_fn=), tensor(0.5927, grad_fn=), tensor(0.7523, grad_fn=), tensor(0.6170, grad_fn=), tensor(0.7877, grad_fn=), tensor(0.6511, grad_fn=), tensor(0.8233, grad_fn=), tensor(0.6239, grad_fn=), tensor(0.8089, grad_fn=), tensor(0.6281, grad_fn=), tensor(0.6827, grad_fn=), tensor(0.6312, grad_fn=), tensor(0.7928, grad_fn=), tensor(0.8723, grad_fn=), tensor(0.7395, grad_fn=), tensor(0.6893, grad_fn=), tensor(0.7641, grad_fn=), tensor(0.4941, grad_fn=), tensor(0.6189, grad_fn=), tensor(0.6425, grad_fn=), tensor(0.6555, grad_fn=), tensor(0.7897, grad_fn=), tensor(0.8942, grad_fn=), tensor(1.8976, grad_fn=), tensor(0.8981, grad_fn=), tensor(0.8603, grad_fn=), tensor(0.8727, grad_fn=), tensor(0.6860, grad_fn=), tensor(1.1643, grad_fn=), tensor(0.7796, grad_fn=), tensor(0.7571, grad_fn=), tensor(0.9132, grad_fn=), tensor(0.8840, grad_fn=), tensor(0.8254, grad_fn=), tensor(0.8110, grad_fn=), tensor(1.0021, grad_fn=), tensor(0.8851, grad_fn=), tensor(1.5432, grad_fn=), tensor(0.8124, grad_fn=), tensor(0.8683, grad_fn=), tensor(0.8718, grad_fn=), tensor(0.7377, grad_fn=), tensor(0.5830, grad_fn=), tensor(0.9397, grad_fn=)] 6 | -------------------------------------------------------------------------------- /data_analysis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "colab_type": "text", 7 | "id": "1QSZzzI9CNTc" 8 | }, 9 | "source": [ 10 | "# Data Analysis" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 5, 16 | "metadata": { 17 | "colab": {}, 18 | "colab_type": "code", 19 | "id": "u_F2uoysCNTe" 20 | }, 21 | "outputs": [], 22 | "source": [ 23 | "import pandas as pd\n", 24 | "import seaborn as sns\n", 25 | "import numpy as np\n", 26 | "from data_treatment import DataAtts\n", 27 | "from scipy.stats import norm\n", 28 | "import ipywidgets as widgets\n", 29 | "import matplotlib.pyplot as plt\n", 30 | "import glob\n", 31 | "from IPython.display import display" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 6, 37 | "metadata": { 38 | "colab": { 39 | "base_uri": "https://localhost:8080/", 40 | "height": 49, 41 | "referenced_widgets": [ 42 | "9f6fa67984b54a0a8221440c871ce41a", 43 | "42fd8ac8651d402f924064b4e409b3ca", 44 | "a9e212199163413296b7c65cf44362a0" 45 | ] 46 | }, 47 | "colab_type": "code", 48 | "id": "eUf0dMBtCNTg", 49 | "outputId": "8f6ba935-d877-4370-8666-78b32e190a30" 50 | }, 51 | "outputs": [ 52 | { 53 | "data": { 54 | "application/vnd.jupyter.widget-view+json": { 55 | "model_id": "0a3afa4de9344f5f989fc01ebd0cfd9f", 56 | "version_major": 2, 57 | "version_minor": 0 58 | }, 59 | "text/plain": [ 60 | "Dropdown(description='File:', options=('original_data/diabetes_escalonated.csv', 'original_data/data.csv', 'or…" 61 | ] 62 | }, 63 | "metadata": {}, 64 | "output_type": "display_data" 65 | } 66 | ], 67 | "source": [ 68 | "# Folder structure needed to run original_data, values_analysis, images/diabetes_escalonated (In case files recycle)\n", 69 | "# Run data_treatment.py\n", 70 | "# Change dataset here\n", 71 | "files_dropdown = widgets.Dropdown(\n", 72 | " options=glob.glob(\"original_data/*.csv\"),\n", 73 | " description='File:',\n", 74 | " value = 'original_data/diabetes_escalonated.csv', \n", 75 | " disabled=False,\n", 76 | ")\n", 77 | "display(files_dropdown)\n" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 7, 83 | "metadata": { 84 | "colab": { 85 | "base_uri": "https://localhost:8080/", 86 | "height": 85 87 | }, 88 | "colab_type": "code", 89 | "id": "bMT6Zp8uCNTj", 90 | "outputId": "19fcddbd-a352-4162-d212-098630e9de4c" 91 | }, 92 | "outputs": [ 93 | { 94 | "name": "stdout", 95 | "output_type": "stream", 96 | "text": [ 97 | "Pima Indians Diabetes Database eSCALONATED \n", 98 | "\n", 99 | "Normal 65.1 % of the dataset\n", 100 | "Diabets 34.9 % of the dataset\n" 101 | ] 102 | } 103 | ], 104 | "source": [ 105 | "file_name=files_dropdown.value\n", 106 | "dataAtts = DataAtts(file_name)\n", 107 | " \n", 108 | " \n", 109 | "data = pd.read_csv(file_name)\n", 110 | "print(dataAtts.message, \"\\n\")\n", 111 | "print(dataAtts.values_names[0], round(data[dataAtts.class_name].value_counts()[0]/len(data) * 100,2), '% of the dataset')\n", 112 | "print(dataAtts.values_names[1], round(data[dataAtts.class_name].value_counts()[1]/len(data) * 100,2), '% of the dataset')" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 8, 118 | "metadata": { 119 | "colab": { 120 | "base_uri": "https://localhost:8080/", 121 | "height": 34 122 | }, 123 | "colab_type": "code", 124 | "id": "up3I8QiPCNTl", 125 | "outputId": "4445b7be-dc5f-401a-ac39-32baa234b91f" 126 | }, 127 | "outputs": [ 128 | { 129 | "data": { 130 | "text/plain": [ 131 | "
" 132 | ] 133 | }, 134 | "metadata": {}, 135 | "output_type": "display_data" 136 | } 137 | ], 138 | "source": [ 139 | "#Add subfolder to images to save all distribution figures i.e. images/\"dataset_name\"\n", 140 | "classes = list(data)\n", 141 | "file = open(\"values_analysis/\" + dataAtts.fname + \"_analysis.txt\", \"w\")\n", 142 | "\n", 143 | "file.write(dataAtts.message + \"\\n\\n\")\n", 144 | "file.write(dataAtts.values_names[0] + \": \" + str(round(data[dataAtts.class_name].value_counts()[0]/len(data) * 100, 2)) + '% of the dataset' + \"\\n\")\n", 145 | "file.write(dataAtts.values_names[1] + \": \" + str(round(data[dataAtts.class_name].value_counts()[1]/len(data) * 100, 2)) + '% of the dataset' + \"\\n\\n\")\n", 146 | "\n", 147 | "for name in classes:\n", 148 | " if name==dataAtts.class_name or name==\"Unnamed: 32\":\n", 149 | " continue\n", 150 | " plt.xlabel('Values')\n", 151 | " plt.ylabel('Probability')\n", 152 | " plt.title(name + \" distribution\")\n", 153 | " #Collecting corresponding class values where the Outcome is 0/1 (Color red being 1 and blue being 0)\n", 154 | " fraud_dist = data[name].loc[data[dataAtts.class_name] == 1].values\n", 155 | " common_dist = data[name].loc[data[dataAtts.class_name] == 0].values\n", 156 | " plt.hist(common_dist, 50, density=True, alpha=0.6)\n", 157 | " plt.hist(fraud_dist, 50, density=True, alpha=0.6, facecolor='r')\n", 158 | " plt.savefig('images/'+ dataAtts.fname + \"/\"+name+'_distribution.png')\n", 159 | " plt.clf()\n", 160 | " \n", 161 | " file.write(name + \":\\n\")\n", 162 | " file.write(\"\\t\\t\\t\" + dataAtts.values_names[0] + \"\\t\\t\\t\\t\\t\"+dataAtts.values_names[1]+\"\\n\")\n", 163 | " file.write(\"min:\\t\\t\" + str(round(common_dist.min(), 3)) + \"\\t\\t\\t\\t\\t\" + str(round(fraud_dist.min(), 3))+\"\\n\")\n", 164 | " file.write(\"max:\\t\\t\" + str(round(common_dist.max(), 3)) + \"\\t\\t\\t\\t\\t\" + str(round(fraud_dist.max(), 3))+\"\\n\")\n", 165 | " file.write(\"mean:\\t\\t\" + str(round(common_dist.mean(), 3)) + \"\\t\\t\\t\\t\\t\" + str(round(fraud_dist.mean(), 3))+\"\\n\")\n", 166 | " file.write(\"median:\\t\\t\" + str(round(np.median(common_dist), 3)) + \"\\t\\t\\t\\t\\t\" + str(round(np.median(fraud_dist), 3))+\"\\n\\n\")" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": { 173 | "colab": {}, 174 | "colab_type": "code", 175 | "id": "8VUml_AmCNTo" 176 | }, 177 | "outputs": [], 178 | "source": [] 179 | } 180 | ], 181 | "metadata": { 182 | "colab": { 183 | "name": "Copy of data_analysis.ipynb", 184 | "provenance": [] 185 | }, 186 | "kernelspec": { 187 | "display_name": "Python 3", 188 | "language": "python", 189 | "name": "python3" 190 | }, 191 | "language_info": { 192 | "codemirror_mode": { 193 | "name": "ipython", 194 | "version": 3 195 | }, 196 | "file_extension": ".py", 197 | "mimetype": "text/x-python", 198 | "name": "python", 199 | "nbconvert_exporter": "python", 200 | "pygments_lexer": "ipython3", 201 | "version": "3.7.3" 202 | }, 203 | "widgets": { 204 | "application/vnd.jupyter.widget-state+json": { 205 | "42fd8ac8651d402f924064b4e409b3ca": { 206 | "model_module": "@jupyter-widgets/controls", 207 | "model_name": "DescriptionStyleModel", 208 | "state": { 209 | "_model_module": "@jupyter-widgets/controls", 210 | "_model_module_version": "1.5.0", 211 | "_model_name": "DescriptionStyleModel", 212 | "_view_count": null, 213 | "_view_module": "@jupyter-widgets/base", 214 | "_view_module_version": "1.2.0", 215 | "_view_name": "StyleView", 216 | "description_width": "" 217 | } 218 | }, 219 | "9f6fa67984b54a0a8221440c871ce41a": { 220 | "model_module": "@jupyter-widgets/controls", 221 | "model_name": "DropdownModel", 222 | "state": { 223 | "_dom_classes": [], 224 | "_model_module": "@jupyter-widgets/controls", 225 | "_model_module_version": "1.5.0", 226 | "_model_name": "DropdownModel", 227 | "_options_labels": [ 228 | "original_data/creditcard_1s_escalonated.csv", 229 | "original_data/data_escalonated.csv", 230 | "original_data/diabetes_escalonated.csv", 231 | "original_data/data.csv", 232 | "original_data/diabetes.csv" 233 | ], 234 | "_view_count": null, 235 | "_view_module": "@jupyter-widgets/controls", 236 | "_view_module_version": "1.5.0", 237 | "_view_name": "DropdownView", 238 | "description": "File:", 239 | "description_tooltip": null, 240 | "disabled": false, 241 | "index": 2, 242 | "layout": "IPY_MODEL_a9e212199163413296b7c65cf44362a0", 243 | "style": "IPY_MODEL_42fd8ac8651d402f924064b4e409b3ca" 244 | } 245 | }, 246 | "a9e212199163413296b7c65cf44362a0": { 247 | "model_module": "@jupyter-widgets/base", 248 | "model_name": "LayoutModel", 249 | "state": { 250 | "_model_module": "@jupyter-widgets/base", 251 | "_model_module_version": "1.2.0", 252 | "_model_name": "LayoutModel", 253 | "_view_count": null, 254 | "_view_module": "@jupyter-widgets/base", 255 | "_view_module_version": "1.2.0", 256 | "_view_name": "LayoutView", 257 | "align_content": null, 258 | "align_items": null, 259 | "align_self": null, 260 | "border": null, 261 | "bottom": null, 262 | "display": null, 263 | "flex": null, 264 | "flex_flow": null, 265 | "grid_area": null, 266 | "grid_auto_columns": null, 267 | "grid_auto_flow": null, 268 | "grid_auto_rows": null, 269 | "grid_column": null, 270 | "grid_gap": null, 271 | "grid_row": null, 272 | "grid_template_areas": null, 273 | "grid_template_columns": null, 274 | "grid_template_rows": null, 275 | "height": null, 276 | "justify_content": null, 277 | "justify_items": null, 278 | "left": null, 279 | "margin": null, 280 | "max_height": null, 281 | "max_width": null, 282 | "min_height": null, 283 | "min_width": null, 284 | "object_fit": null, 285 | "object_position": null, 286 | "order": null, 287 | "overflow": null, 288 | "overflow_x": null, 289 | "overflow_y": null, 290 | "padding": null, 291 | "right": null, 292 | "top": null, 293 | "visibility": null, 294 | "width": null 295 | } 296 | } 297 | } 298 | } 299 | }, 300 | "nbformat": 4, 301 | "nbformat_minor": 1 302 | } 303 | -------------------------------------------------------------------------------- /create_fake_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 19, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import torch\n", 10 | "import pandas as pd\n", 11 | "from torch import nn, optim\n", 12 | "from torch.autograd.variable import Variable\n", 13 | "from torchvision import transforms, datasets\n", 14 | "from data_treatment import DataSet, DataAtts\n", 15 | "from discriminator import *\n", 16 | "from generator import *\n", 17 | "import ipywidgets as widgets\n", 18 | "from IPython.display import display\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "import glob" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 20, 26 | "metadata": {}, 27 | "outputs": [ 28 | { 29 | "data": { 30 | "application/vnd.jupyter.widget-view+json": { 31 | "model_id": "c5bce4ab38d94e638f4f0b25dd3d7c58", 32 | "version_major": 2, 33 | "version_minor": 0 34 | }, 35 | "text/plain": [ 36 | "Dropdown(description='Folder:', options=('models/diabetes_escalonated',), value='models/diabetes_escalonated')" 37 | ] 38 | }, 39 | "metadata": {}, 40 | "output_type": "display_data" 41 | } 42 | ], 43 | "source": [ 44 | "#Run train_generator_discriminator.py before running this\n", 45 | "\n", 46 | "folder = widgets.Dropdown(\n", 47 | " options=glob.glob(\"models/*\"),\n", 48 | " description='Folder:',\n", 49 | " value=\"models/diabetes_escalonated\",\n", 50 | " disabled=False,\n", 51 | ")\n", 52 | "display(folder)" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 21, 58 | "metadata": {}, 59 | "outputs": [ 60 | { 61 | "data": { 62 | "application/vnd.jupyter.widget-view+json": { 63 | "model_id": "d0434bb26f6345b9bd79ec020b7f42d7", 64 | "version_major": 2, 65 | "version_minor": 0 66 | }, 67 | "text/plain": [ 68 | "Dropdown(description='Generator:', options=('models/diabetes_escalonated/generator_id-2_epochs-500_layer-2_lr-…" 69 | ] 70 | }, 71 | "metadata": {}, 72 | "output_type": "display_data" 73 | } 74 | ], 75 | "source": [ 76 | "folder_name = folder.value+\"/generator*.pt\"\n", 77 | "model_widget = widgets.Dropdown(\n", 78 | " options=glob.glob(folder_name),\n", 79 | " description='Generator:',\n", 80 | " disabled=False,\n", 81 | ")\n", 82 | "display(model_widget)" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 22, 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "original_db_name = folder.value[7:]\n", 92 | "original_db_path = \"original_data/\" + original_db_name + \".csv\"\n", 93 | "original_db = pd.read_csv(original_db_path)\n", 94 | "original_db_size=original_db.shape[0]" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 23, 100 | "metadata": {}, 101 | "outputs": [ 102 | { 103 | "name": "stdout", 104 | "output_type": "stream", 105 | "text": [ 106 | "models/diabetes_escalonated/generator_id-2_epochs-500_layer-2_lr-0.0002_batch-5_arc-256,512.pt\n" 107 | ] 108 | }, 109 | { 110 | "data": { 111 | "text/plain": [ 112 | "" 113 | ] 114 | }, 115 | "execution_count": 23, 116 | "metadata": {}, 117 | "output_type": "execute_result" 118 | } 119 | ], 120 | "source": [ 121 | "try:\n", 122 | " checkpoint= torch.load(model_widget.value, map_location='cuda')\n", 123 | "except:\n", 124 | " checkpoint= torch.load(model_widget.value, map_location='cpu')\n", 125 | "print(model_widget.value)\n", 126 | "checkpoint['model_attributes']['out_features'] = len(original_db.columns)\n", 127 | "generator = GeneratorNet(**checkpoint['model_attributes'])\n", 128 | "generator.load_state_dict(checkpoint['model_state_dict'])" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 24, 134 | "metadata": {}, 135 | "outputs": [ 136 | { 137 | "ename": "AssertionError", 138 | "evalue": "Torch not compiled with CUDA enabled", 139 | "output_type": "error", 140 | "traceback": [ 141 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 142 | "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", 143 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moriginal_db_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moriginal_db\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#Changes the name to be easier to read\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_widget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"_size-\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 144 | "\u001b[0;32m~/Desktop/Deep-Final/generator.py\u001b[0m in \u001b[0;36mcreate_data\u001b[0;34m(self, quantity)\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0mpoints\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnoise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquantity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 145 | "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m 190\u001b[0m raise RuntimeError(\n\u001b[1;32m 191\u001b[0m \"Cannot re-initialize CUDA in forked subprocess. \" + msg)\n\u001b[0;32m--> 192\u001b[0;31m \u001b[0m_check_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 193\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0m_cudart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_cudart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 146 | "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_check_driver\u001b[0;34m()\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_cuda_isDriverSufficient'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Torch not compiled with CUDA enabled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_isDriverSufficient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_getDriverVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 147 | "\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled" 148 | ] 149 | } 150 | ], 151 | "source": [ 152 | "size = original_db_size\n", 153 | "new_data = generator.create_data(size)\n", 154 | "df = pd.DataFrame(new_data, columns=original_db.columns)\n", 155 | "#Changes the name to be easier to read\n", 156 | "name = model_widget.value.split(\"/\")[-1][10:-4] + \"_size-\" + str(size)\n", 157 | "df.to_csv( \"fake_data/\" + original_db_name + \"/\" + name + \".csv\", index=False)" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": null, 163 | "metadata": {}, 164 | "outputs": [], 165 | "source": [ 166 | "#Do the same thing as the cells above but for all the files in the directory\n", 167 | "import glob\n", 168 | "for file in glob.glob(folder_name):\n", 169 | " name = file.split(\"/\")[-1][10:-4]\n", 170 | " print(name)\n", 171 | " try:\n", 172 | " checkpoint= torch.load(file, map_location='cuda')\n", 173 | " except:\n", 174 | " checkpoint= torch.load(file, map_location='cpu')\n", 175 | " generator = GeneratorNet(**checkpoint['model_attributes'])\n", 176 | " generator.load_state_dict(checkpoint['model_state_dict'])\n", 177 | " size = original_db_size\n", 178 | " new_data = generator.create_data(size)\n", 179 | " df = pd.DataFrame(new_data, columns=original_db.columns)\n", 180 | " name = name + \"_size-\" + str(size)\n", 181 | " df.to_csv( \"fake_data/\" + original_db_name + \"/\" + name + \".csv\", index=False)" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": null, 187 | "metadata": {}, 188 | "outputs": [], 189 | "source": [] 190 | } 191 | ], 192 | "metadata": { 193 | "kernelspec": { 194 | "display_name": "Python 3", 195 | "language": "python", 196 | "name": "python3" 197 | }, 198 | "language_info": { 199 | "codemirror_mode": { 200 | "name": "ipython", 201 | "version": 3 202 | }, 203 | "file_extension": ".py", 204 | "mimetype": "text/x-python", 205 | "name": "python", 206 | "nbconvert_exporter": "python", 207 | "pygments_lexer": "ipython3", 208 | "version": "3.7.3" 209 | } 210 | }, 211 | "nbformat": 4, 212 | "nbformat_minor": 2 213 | } 214 | -------------------------------------------------------------------------------- /original_data/diabetes.csv: -------------------------------------------------------------------------------- 1 | Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome 2 | 6,148,72,35,0,33.6,0.627,50,1 3 | 1,85,66,29,0,26.6,0.351,31,0 4 | 8,183,64,0,0,23.3,0.672,32,1 5 | 1,89,66,23,94,28.1,0.167,21,0 6 | 0,137,40,35,168,43.1,2.288,33,1 7 | 5,116,74,0,0,25.6,0.201,30,0 8 | 3,78,50,32,88,31,0.248,26,1 9 | 10,115,0,0,0,35.3,0.134,29,0 10 | 2,197,70,45,543,30.5,0.158,53,1 11 | 8,125,96,0,0,0,0.232,54,1 12 | 4,110,92,0,0,37.6,0.191,30,0 13 | 10,168,74,0,0,38,0.537,34,1 14 | 10,139,80,0,0,27.1,1.441,57,0 15 | 1,189,60,23,846,30.1,0.398,59,1 16 | 5,166,72,19,175,25.8,0.587,51,1 17 | 7,100,0,0,0,30,0.484,32,1 18 | 0,118,84,47,230,45.8,0.551,31,1 19 | 7,107,74,0,0,29.6,0.254,31,1 20 | 1,103,30,38,83,43.3,0.183,33,0 21 | 1,115,70,30,96,34.6,0.529,32,1 22 | 3,126,88,41,235,39.3,0.704,27,0 23 | 8,99,84,0,0,35.4,0.388,50,0 24 | 7,196,90,0,0,39.8,0.451,41,1 25 | 9,119,80,35,0,29,0.263,29,1 26 | 11,143,94,33,146,36.6,0.254,51,1 27 | 10,125,70,26,115,31.1,0.205,41,1 28 | 7,147,76,0,0,39.4,0.257,43,1 29 | 1,97,66,15,140,23.2,0.487,22,0 30 | 13,145,82,19,110,22.2,0.245,57,0 31 | 5,117,92,0,0,34.1,0.337,38,0 32 | 5,109,75,26,0,36,0.546,60,0 33 | 3,158,76,36,245,31.6,0.851,28,1 34 | 3,88,58,11,54,24.8,0.267,22,0 35 | 6,92,92,0,0,19.9,0.188,28,0 36 | 10,122,78,31,0,27.6,0.512,45,0 37 | 4,103,60,33,192,24,0.966,33,0 38 | 11,138,76,0,0,33.2,0.42,35,0 39 | 9,102,76,37,0,32.9,0.665,46,1 40 | 2,90,68,42,0,38.2,0.503,27,1 41 | 4,111,72,47,207,37.1,1.39,56,1 42 | 3,180,64,25,70,34,0.271,26,0 43 | 7,133,84,0,0,40.2,0.696,37,0 44 | 7,106,92,18,0,22.7,0.235,48,0 45 | 9,171,110,24,240,45.4,0.721,54,1 46 | 7,159,64,0,0,27.4,0.294,40,0 47 | 0,180,66,39,0,42,1.893,25,1 48 | 1,146,56,0,0,29.7,0.564,29,0 49 | 2,71,70,27,0,28,0.586,22,0 50 | 7,103,66,32,0,39.1,0.344,31,1 51 | 7,105,0,0,0,0,0.305,24,0 52 | 1,103,80,11,82,19.4,0.491,22,0 53 | 1,101,50,15,36,24.2,0.526,26,0 54 | 5,88,66,21,23,24.4,0.342,30,0 55 | 8,176,90,34,300,33.7,0.467,58,1 56 | 7,150,66,42,342,34.7,0.718,42,0 57 | 1,73,50,10,0,23,0.248,21,0 58 | 7,187,68,39,304,37.7,0.254,41,1 59 | 0,100,88,60,110,46.8,0.962,31,0 60 | 0,146,82,0,0,40.5,1.781,44,0 61 | 0,105,64,41,142,41.5,0.173,22,0 62 | 2,84,0,0,0,0,0.304,21,0 63 | 8,133,72,0,0,32.9,0.27,39,1 64 | 5,44,62,0,0,25,0.587,36,0 65 | 2,141,58,34,128,25.4,0.699,24,0 66 | 7,114,66,0,0,32.8,0.258,42,1 67 | 5,99,74,27,0,29,0.203,32,0 68 | 0,109,88,30,0,32.5,0.855,38,1 69 | 2,109,92,0,0,42.7,0.845,54,0 70 | 1,95,66,13,38,19.6,0.334,25,0 71 | 4,146,85,27,100,28.9,0.189,27,0 72 | 2,100,66,20,90,32.9,0.867,28,1 73 | 5,139,64,35,140,28.6,0.411,26,0 74 | 13,126,90,0,0,43.4,0.583,42,1 75 | 4,129,86,20,270,35.1,0.231,23,0 76 | 1,79,75,30,0,32,0.396,22,0 77 | 1,0,48,20,0,24.7,0.14,22,0 78 | 7,62,78,0,0,32.6,0.391,41,0 79 | 5,95,72,33,0,37.7,0.37,27,0 80 | 0,131,0,0,0,43.2,0.27,26,1 81 | 2,112,66,22,0,25,0.307,24,0 82 | 3,113,44,13,0,22.4,0.14,22,0 83 | 2,74,0,0,0,0,0.102,22,0 84 | 7,83,78,26,71,29.3,0.767,36,0 85 | 0,101,65,28,0,24.6,0.237,22,0 86 | 5,137,108,0,0,48.8,0.227,37,1 87 | 2,110,74,29,125,32.4,0.698,27,0 88 | 13,106,72,54,0,36.6,0.178,45,0 89 | 2,100,68,25,71,38.5,0.324,26,0 90 | 15,136,70,32,110,37.1,0.153,43,1 91 | 1,107,68,19,0,26.5,0.165,24,0 92 | 1,80,55,0,0,19.1,0.258,21,0 93 | 4,123,80,15,176,32,0.443,34,0 94 | 7,81,78,40,48,46.7,0.261,42,0 95 | 4,134,72,0,0,23.8,0.277,60,1 96 | 2,142,82,18,64,24.7,0.761,21,0 97 | 6,144,72,27,228,33.9,0.255,40,0 98 | 2,92,62,28,0,31.6,0.13,24,0 99 | 1,71,48,18,76,20.4,0.323,22,0 100 | 6,93,50,30,64,28.7,0.356,23,0 101 | 1,122,90,51,220,49.7,0.325,31,1 102 | 1,163,72,0,0,39,1.222,33,1 103 | 1,151,60,0,0,26.1,0.179,22,0 104 | 0,125,96,0,0,22.5,0.262,21,0 105 | 1,81,72,18,40,26.6,0.283,24,0 106 | 2,85,65,0,0,39.6,0.93,27,0 107 | 1,126,56,29,152,28.7,0.801,21,0 108 | 1,96,122,0,0,22.4,0.207,27,0 109 | 4,144,58,28,140,29.5,0.287,37,0 110 | 3,83,58,31,18,34.3,0.336,25,0 111 | 0,95,85,25,36,37.4,0.247,24,1 112 | 3,171,72,33,135,33.3,0.199,24,1 113 | 8,155,62,26,495,34,0.543,46,1 114 | 1,89,76,34,37,31.2,0.192,23,0 115 | 4,76,62,0,0,34,0.391,25,0 116 | 7,160,54,32,175,30.5,0.588,39,1 117 | 4,146,92,0,0,31.2,0.539,61,1 118 | 5,124,74,0,0,34,0.22,38,1 119 | 5,78,48,0,0,33.7,0.654,25,0 120 | 4,97,60,23,0,28.2,0.443,22,0 121 | 4,99,76,15,51,23.2,0.223,21,0 122 | 0,162,76,56,100,53.2,0.759,25,1 123 | 6,111,64,39,0,34.2,0.26,24,0 124 | 2,107,74,30,100,33.6,0.404,23,0 125 | 5,132,80,0,0,26.8,0.186,69,0 126 | 0,113,76,0,0,33.3,0.278,23,1 127 | 1,88,30,42,99,55,0.496,26,1 128 | 3,120,70,30,135,42.9,0.452,30,0 129 | 1,118,58,36,94,33.3,0.261,23,0 130 | 1,117,88,24,145,34.5,0.403,40,1 131 | 0,105,84,0,0,27.9,0.741,62,1 132 | 4,173,70,14,168,29.7,0.361,33,1 133 | 9,122,56,0,0,33.3,1.114,33,1 134 | 3,170,64,37,225,34.5,0.356,30,1 135 | 8,84,74,31,0,38.3,0.457,39,0 136 | 2,96,68,13,49,21.1,0.647,26,0 137 | 2,125,60,20,140,33.8,0.088,31,0 138 | 0,100,70,26,50,30.8,0.597,21,0 139 | 0,93,60,25,92,28.7,0.532,22,0 140 | 0,129,80,0,0,31.2,0.703,29,0 141 | 5,105,72,29,325,36.9,0.159,28,0 142 | 3,128,78,0,0,21.1,0.268,55,0 143 | 5,106,82,30,0,39.5,0.286,38,0 144 | 2,108,52,26,63,32.5,0.318,22,0 145 | 10,108,66,0,0,32.4,0.272,42,1 146 | 4,154,62,31,284,32.8,0.237,23,0 147 | 0,102,75,23,0,0,0.572,21,0 148 | 9,57,80,37,0,32.8,0.096,41,0 149 | 2,106,64,35,119,30.5,1.4,34,0 150 | 5,147,78,0,0,33.7,0.218,65,0 151 | 2,90,70,17,0,27.3,0.085,22,0 152 | 1,136,74,50,204,37.4,0.399,24,0 153 | 4,114,65,0,0,21.9,0.432,37,0 154 | 9,156,86,28,155,34.3,1.189,42,1 155 | 1,153,82,42,485,40.6,0.687,23,0 156 | 8,188,78,0,0,47.9,0.137,43,1 157 | 7,152,88,44,0,50,0.337,36,1 158 | 2,99,52,15,94,24.6,0.637,21,0 159 | 1,109,56,21,135,25.2,0.833,23,0 160 | 2,88,74,19,53,29,0.229,22,0 161 | 17,163,72,41,114,40.9,0.817,47,1 162 | 4,151,90,38,0,29.7,0.294,36,0 163 | 7,102,74,40,105,37.2,0.204,45,0 164 | 0,114,80,34,285,44.2,0.167,27,0 165 | 2,100,64,23,0,29.7,0.368,21,0 166 | 0,131,88,0,0,31.6,0.743,32,1 167 | 6,104,74,18,156,29.9,0.722,41,1 168 | 3,148,66,25,0,32.5,0.256,22,0 169 | 4,120,68,0,0,29.6,0.709,34,0 170 | 4,110,66,0,0,31.9,0.471,29,0 171 | 3,111,90,12,78,28.4,0.495,29,0 172 | 6,102,82,0,0,30.8,0.18,36,1 173 | 6,134,70,23,130,35.4,0.542,29,1 174 | 2,87,0,23,0,28.9,0.773,25,0 175 | 1,79,60,42,48,43.5,0.678,23,0 176 | 2,75,64,24,55,29.7,0.37,33,0 177 | 8,179,72,42,130,32.7,0.719,36,1 178 | 6,85,78,0,0,31.2,0.382,42,0 179 | 0,129,110,46,130,67.1,0.319,26,1 180 | 5,143,78,0,0,45,0.19,47,0 181 | 5,130,82,0,0,39.1,0.956,37,1 182 | 6,87,80,0,0,23.2,0.084,32,0 183 | 0,119,64,18,92,34.9,0.725,23,0 184 | 1,0,74,20,23,27.7,0.299,21,0 185 | 5,73,60,0,0,26.8,0.268,27,0 186 | 4,141,74,0,0,27.6,0.244,40,0 187 | 7,194,68,28,0,35.9,0.745,41,1 188 | 8,181,68,36,495,30.1,0.615,60,1 189 | 1,128,98,41,58,32,1.321,33,1 190 | 8,109,76,39,114,27.9,0.64,31,1 191 | 5,139,80,35,160,31.6,0.361,25,1 192 | 3,111,62,0,0,22.6,0.142,21,0 193 | 9,123,70,44,94,33.1,0.374,40,0 194 | 7,159,66,0,0,30.4,0.383,36,1 195 | 11,135,0,0,0,52.3,0.578,40,1 196 | 8,85,55,20,0,24.4,0.136,42,0 197 | 5,158,84,41,210,39.4,0.395,29,1 198 | 1,105,58,0,0,24.3,0.187,21,0 199 | 3,107,62,13,48,22.9,0.678,23,1 200 | 4,109,64,44,99,34.8,0.905,26,1 201 | 4,148,60,27,318,30.9,0.15,29,1 202 | 0,113,80,16,0,31,0.874,21,0 203 | 1,138,82,0,0,40.1,0.236,28,0 204 | 0,108,68,20,0,27.3,0.787,32,0 205 | 2,99,70,16,44,20.4,0.235,27,0 206 | 6,103,72,32,190,37.7,0.324,55,0 207 | 5,111,72,28,0,23.9,0.407,27,0 208 | 8,196,76,29,280,37.5,0.605,57,1 209 | 5,162,104,0,0,37.7,0.151,52,1 210 | 1,96,64,27,87,33.2,0.289,21,0 211 | 7,184,84,33,0,35.5,0.355,41,1 212 | 2,81,60,22,0,27.7,0.29,25,0 213 | 0,147,85,54,0,42.8,0.375,24,0 214 | 7,179,95,31,0,34.2,0.164,60,0 215 | 0,140,65,26,130,42.6,0.431,24,1 216 | 9,112,82,32,175,34.2,0.26,36,1 217 | 12,151,70,40,271,41.8,0.742,38,1 218 | 5,109,62,41,129,35.8,0.514,25,1 219 | 6,125,68,30,120,30,0.464,32,0 220 | 5,85,74,22,0,29,1.224,32,1 221 | 5,112,66,0,0,37.8,0.261,41,1 222 | 0,177,60,29,478,34.6,1.072,21,1 223 | 2,158,90,0,0,31.6,0.805,66,1 224 | 7,119,0,0,0,25.2,0.209,37,0 225 | 7,142,60,33,190,28.8,0.687,61,0 226 | 1,100,66,15,56,23.6,0.666,26,0 227 | 1,87,78,27,32,34.6,0.101,22,0 228 | 0,101,76,0,0,35.7,0.198,26,0 229 | 3,162,52,38,0,37.2,0.652,24,1 230 | 4,197,70,39,744,36.7,2.329,31,0 231 | 0,117,80,31,53,45.2,0.089,24,0 232 | 4,142,86,0,0,44,0.645,22,1 233 | 6,134,80,37,370,46.2,0.238,46,1 234 | 1,79,80,25,37,25.4,0.583,22,0 235 | 4,122,68,0,0,35,0.394,29,0 236 | 3,74,68,28,45,29.7,0.293,23,0 237 | 4,171,72,0,0,43.6,0.479,26,1 238 | 7,181,84,21,192,35.9,0.586,51,1 239 | 0,179,90,27,0,44.1,0.686,23,1 240 | 9,164,84,21,0,30.8,0.831,32,1 241 | 0,104,76,0,0,18.4,0.582,27,0 242 | 1,91,64,24,0,29.2,0.192,21,0 243 | 4,91,70,32,88,33.1,0.446,22,0 244 | 3,139,54,0,0,25.6,0.402,22,1 245 | 6,119,50,22,176,27.1,1.318,33,1 246 | 2,146,76,35,194,38.2,0.329,29,0 247 | 9,184,85,15,0,30,1.213,49,1 248 | 10,122,68,0,0,31.2,0.258,41,0 249 | 0,165,90,33,680,52.3,0.427,23,0 250 | 9,124,70,33,402,35.4,0.282,34,0 251 | 1,111,86,19,0,30.1,0.143,23,0 252 | 9,106,52,0,0,31.2,0.38,42,0 253 | 2,129,84,0,0,28,0.284,27,0 254 | 2,90,80,14,55,24.4,0.249,24,0 255 | 0,86,68,32,0,35.8,0.238,25,0 256 | 12,92,62,7,258,27.6,0.926,44,1 257 | 1,113,64,35,0,33.6,0.543,21,1 258 | 3,111,56,39,0,30.1,0.557,30,0 259 | 2,114,68,22,0,28.7,0.092,25,0 260 | 1,193,50,16,375,25.9,0.655,24,0 261 | 11,155,76,28,150,33.3,1.353,51,1 262 | 3,191,68,15,130,30.9,0.299,34,0 263 | 3,141,0,0,0,30,0.761,27,1 264 | 4,95,70,32,0,32.1,0.612,24,0 265 | 3,142,80,15,0,32.4,0.2,63,0 266 | 4,123,62,0,0,32,0.226,35,1 267 | 5,96,74,18,67,33.6,0.997,43,0 268 | 0,138,0,0,0,36.3,0.933,25,1 269 | 2,128,64,42,0,40,1.101,24,0 270 | 0,102,52,0,0,25.1,0.078,21,0 271 | 2,146,0,0,0,27.5,0.24,28,1 272 | 10,101,86,37,0,45.6,1.136,38,1 273 | 2,108,62,32,56,25.2,0.128,21,0 274 | 3,122,78,0,0,23,0.254,40,0 275 | 1,71,78,50,45,33.2,0.422,21,0 276 | 13,106,70,0,0,34.2,0.251,52,0 277 | 2,100,70,52,57,40.5,0.677,25,0 278 | 7,106,60,24,0,26.5,0.296,29,1 279 | 0,104,64,23,116,27.8,0.454,23,0 280 | 5,114,74,0,0,24.9,0.744,57,0 281 | 2,108,62,10,278,25.3,0.881,22,0 282 | 0,146,70,0,0,37.9,0.334,28,1 283 | 10,129,76,28,122,35.9,0.28,39,0 284 | 7,133,88,15,155,32.4,0.262,37,0 285 | 7,161,86,0,0,30.4,0.165,47,1 286 | 2,108,80,0,0,27,0.259,52,1 287 | 7,136,74,26,135,26,0.647,51,0 288 | 5,155,84,44,545,38.7,0.619,34,0 289 | 1,119,86,39,220,45.6,0.808,29,1 290 | 4,96,56,17,49,20.8,0.34,26,0 291 | 5,108,72,43,75,36.1,0.263,33,0 292 | 0,78,88,29,40,36.9,0.434,21,0 293 | 0,107,62,30,74,36.6,0.757,25,1 294 | 2,128,78,37,182,43.3,1.224,31,1 295 | 1,128,48,45,194,40.5,0.613,24,1 296 | 0,161,50,0,0,21.9,0.254,65,0 297 | 6,151,62,31,120,35.5,0.692,28,0 298 | 2,146,70,38,360,28,0.337,29,1 299 | 0,126,84,29,215,30.7,0.52,24,0 300 | 14,100,78,25,184,36.6,0.412,46,1 301 | 8,112,72,0,0,23.6,0.84,58,0 302 | 0,167,0,0,0,32.3,0.839,30,1 303 | 2,144,58,33,135,31.6,0.422,25,1 304 | 5,77,82,41,42,35.8,0.156,35,0 305 | 5,115,98,0,0,52.9,0.209,28,1 306 | 3,150,76,0,0,21,0.207,37,0 307 | 2,120,76,37,105,39.7,0.215,29,0 308 | 10,161,68,23,132,25.5,0.326,47,1 309 | 0,137,68,14,148,24.8,0.143,21,0 310 | 0,128,68,19,180,30.5,1.391,25,1 311 | 2,124,68,28,205,32.9,0.875,30,1 312 | 6,80,66,30,0,26.2,0.313,41,0 313 | 0,106,70,37,148,39.4,0.605,22,0 314 | 2,155,74,17,96,26.6,0.433,27,1 315 | 3,113,50,10,85,29.5,0.626,25,0 316 | 7,109,80,31,0,35.9,1.127,43,1 317 | 2,112,68,22,94,34.1,0.315,26,0 318 | 3,99,80,11,64,19.3,0.284,30,0 319 | 3,182,74,0,0,30.5,0.345,29,1 320 | 3,115,66,39,140,38.1,0.15,28,0 321 | 6,194,78,0,0,23.5,0.129,59,1 322 | 4,129,60,12,231,27.5,0.527,31,0 323 | 3,112,74,30,0,31.6,0.197,25,1 324 | 0,124,70,20,0,27.4,0.254,36,1 325 | 13,152,90,33,29,26.8,0.731,43,1 326 | 2,112,75,32,0,35.7,0.148,21,0 327 | 1,157,72,21,168,25.6,0.123,24,0 328 | 1,122,64,32,156,35.1,0.692,30,1 329 | 10,179,70,0,0,35.1,0.2,37,0 330 | 2,102,86,36,120,45.5,0.127,23,1 331 | 6,105,70,32,68,30.8,0.122,37,0 332 | 8,118,72,19,0,23.1,1.476,46,0 333 | 2,87,58,16,52,32.7,0.166,25,0 334 | 1,180,0,0,0,43.3,0.282,41,1 335 | 12,106,80,0,0,23.6,0.137,44,0 336 | 1,95,60,18,58,23.9,0.26,22,0 337 | 0,165,76,43,255,47.9,0.259,26,0 338 | 0,117,0,0,0,33.8,0.932,44,0 339 | 5,115,76,0,0,31.2,0.343,44,1 340 | 9,152,78,34,171,34.2,0.893,33,1 341 | 7,178,84,0,0,39.9,0.331,41,1 342 | 1,130,70,13,105,25.9,0.472,22,0 343 | 1,95,74,21,73,25.9,0.673,36,0 344 | 1,0,68,35,0,32,0.389,22,0 345 | 5,122,86,0,0,34.7,0.29,33,0 346 | 8,95,72,0,0,36.8,0.485,57,0 347 | 8,126,88,36,108,38.5,0.349,49,0 348 | 1,139,46,19,83,28.7,0.654,22,0 349 | 3,116,0,0,0,23.5,0.187,23,0 350 | 3,99,62,19,74,21.8,0.279,26,0 351 | 5,0,80,32,0,41,0.346,37,1 352 | 4,92,80,0,0,42.2,0.237,29,0 353 | 4,137,84,0,0,31.2,0.252,30,0 354 | 3,61,82,28,0,34.4,0.243,46,0 355 | 1,90,62,12,43,27.2,0.58,24,0 356 | 3,90,78,0,0,42.7,0.559,21,0 357 | 9,165,88,0,0,30.4,0.302,49,1 358 | 1,125,50,40,167,33.3,0.962,28,1 359 | 13,129,0,30,0,39.9,0.569,44,1 360 | 12,88,74,40,54,35.3,0.378,48,0 361 | 1,196,76,36,249,36.5,0.875,29,1 362 | 5,189,64,33,325,31.2,0.583,29,1 363 | 5,158,70,0,0,29.8,0.207,63,0 364 | 5,103,108,37,0,39.2,0.305,65,0 365 | 4,146,78,0,0,38.5,0.52,67,1 366 | 4,147,74,25,293,34.9,0.385,30,0 367 | 5,99,54,28,83,34,0.499,30,0 368 | 6,124,72,0,0,27.6,0.368,29,1 369 | 0,101,64,17,0,21,0.252,21,0 370 | 3,81,86,16,66,27.5,0.306,22,0 371 | 1,133,102,28,140,32.8,0.234,45,1 372 | 3,173,82,48,465,38.4,2.137,25,1 373 | 0,118,64,23,89,0,1.731,21,0 374 | 0,84,64,22,66,35.8,0.545,21,0 375 | 2,105,58,40,94,34.9,0.225,25,0 376 | 2,122,52,43,158,36.2,0.816,28,0 377 | 12,140,82,43,325,39.2,0.528,58,1 378 | 0,98,82,15,84,25.2,0.299,22,0 379 | 1,87,60,37,75,37.2,0.509,22,0 380 | 4,156,75,0,0,48.3,0.238,32,1 381 | 0,93,100,39,72,43.4,1.021,35,0 382 | 1,107,72,30,82,30.8,0.821,24,0 383 | 0,105,68,22,0,20,0.236,22,0 384 | 1,109,60,8,182,25.4,0.947,21,0 385 | 1,90,62,18,59,25.1,1.268,25,0 386 | 1,125,70,24,110,24.3,0.221,25,0 387 | 1,119,54,13,50,22.3,0.205,24,0 388 | 5,116,74,29,0,32.3,0.66,35,1 389 | 8,105,100,36,0,43.3,0.239,45,1 390 | 5,144,82,26,285,32,0.452,58,1 391 | 3,100,68,23,81,31.6,0.949,28,0 392 | 1,100,66,29,196,32,0.444,42,0 393 | 5,166,76,0,0,45.7,0.34,27,1 394 | 1,131,64,14,415,23.7,0.389,21,0 395 | 4,116,72,12,87,22.1,0.463,37,0 396 | 4,158,78,0,0,32.9,0.803,31,1 397 | 2,127,58,24,275,27.7,1.6,25,0 398 | 3,96,56,34,115,24.7,0.944,39,0 399 | 0,131,66,40,0,34.3,0.196,22,1 400 | 3,82,70,0,0,21.1,0.389,25,0 401 | 3,193,70,31,0,34.9,0.241,25,1 402 | 4,95,64,0,0,32,0.161,31,1 403 | 6,137,61,0,0,24.2,0.151,55,0 404 | 5,136,84,41,88,35,0.286,35,1 405 | 9,72,78,25,0,31.6,0.28,38,0 406 | 5,168,64,0,0,32.9,0.135,41,1 407 | 2,123,48,32,165,42.1,0.52,26,0 408 | 4,115,72,0,0,28.9,0.376,46,1 409 | 0,101,62,0,0,21.9,0.336,25,0 410 | 8,197,74,0,0,25.9,1.191,39,1 411 | 1,172,68,49,579,42.4,0.702,28,1 412 | 6,102,90,39,0,35.7,0.674,28,0 413 | 1,112,72,30,176,34.4,0.528,25,0 414 | 1,143,84,23,310,42.4,1.076,22,0 415 | 1,143,74,22,61,26.2,0.256,21,0 416 | 0,138,60,35,167,34.6,0.534,21,1 417 | 3,173,84,33,474,35.7,0.258,22,1 418 | 1,97,68,21,0,27.2,1.095,22,0 419 | 4,144,82,32,0,38.5,0.554,37,1 420 | 1,83,68,0,0,18.2,0.624,27,0 421 | 3,129,64,29,115,26.4,0.219,28,1 422 | 1,119,88,41,170,45.3,0.507,26,0 423 | 2,94,68,18,76,26,0.561,21,0 424 | 0,102,64,46,78,40.6,0.496,21,0 425 | 2,115,64,22,0,30.8,0.421,21,0 426 | 8,151,78,32,210,42.9,0.516,36,1 427 | 4,184,78,39,277,37,0.264,31,1 428 | 0,94,0,0,0,0,0.256,25,0 429 | 1,181,64,30,180,34.1,0.328,38,1 430 | 0,135,94,46,145,40.6,0.284,26,0 431 | 1,95,82,25,180,35,0.233,43,1 432 | 2,99,0,0,0,22.2,0.108,23,0 433 | 3,89,74,16,85,30.4,0.551,38,0 434 | 1,80,74,11,60,30,0.527,22,0 435 | 2,139,75,0,0,25.6,0.167,29,0 436 | 1,90,68,8,0,24.5,1.138,36,0 437 | 0,141,0,0,0,42.4,0.205,29,1 438 | 12,140,85,33,0,37.4,0.244,41,0 439 | 5,147,75,0,0,29.9,0.434,28,0 440 | 1,97,70,15,0,18.2,0.147,21,0 441 | 6,107,88,0,0,36.8,0.727,31,0 442 | 0,189,104,25,0,34.3,0.435,41,1 443 | 2,83,66,23,50,32.2,0.497,22,0 444 | 4,117,64,27,120,33.2,0.23,24,0 445 | 8,108,70,0,0,30.5,0.955,33,1 446 | 4,117,62,12,0,29.7,0.38,30,1 447 | 0,180,78,63,14,59.4,2.42,25,1 448 | 1,100,72,12,70,25.3,0.658,28,0 449 | 0,95,80,45,92,36.5,0.33,26,0 450 | 0,104,64,37,64,33.6,0.51,22,1 451 | 0,120,74,18,63,30.5,0.285,26,0 452 | 1,82,64,13,95,21.2,0.415,23,0 453 | 2,134,70,0,0,28.9,0.542,23,1 454 | 0,91,68,32,210,39.9,0.381,25,0 455 | 2,119,0,0,0,19.6,0.832,72,0 456 | 2,100,54,28,105,37.8,0.498,24,0 457 | 14,175,62,30,0,33.6,0.212,38,1 458 | 1,135,54,0,0,26.7,0.687,62,0 459 | 5,86,68,28,71,30.2,0.364,24,0 460 | 10,148,84,48,237,37.6,1.001,51,1 461 | 9,134,74,33,60,25.9,0.46,81,0 462 | 9,120,72,22,56,20.8,0.733,48,0 463 | 1,71,62,0,0,21.8,0.416,26,0 464 | 8,74,70,40,49,35.3,0.705,39,0 465 | 5,88,78,30,0,27.6,0.258,37,0 466 | 10,115,98,0,0,24,1.022,34,0 467 | 0,124,56,13,105,21.8,0.452,21,0 468 | 0,74,52,10,36,27.8,0.269,22,0 469 | 0,97,64,36,100,36.8,0.6,25,0 470 | 8,120,0,0,0,30,0.183,38,1 471 | 6,154,78,41,140,46.1,0.571,27,0 472 | 1,144,82,40,0,41.3,0.607,28,0 473 | 0,137,70,38,0,33.2,0.17,22,0 474 | 0,119,66,27,0,38.8,0.259,22,0 475 | 7,136,90,0,0,29.9,0.21,50,0 476 | 4,114,64,0,0,28.9,0.126,24,0 477 | 0,137,84,27,0,27.3,0.231,59,0 478 | 2,105,80,45,191,33.7,0.711,29,1 479 | 7,114,76,17,110,23.8,0.466,31,0 480 | 8,126,74,38,75,25.9,0.162,39,0 481 | 4,132,86,31,0,28,0.419,63,0 482 | 3,158,70,30,328,35.5,0.344,35,1 483 | 0,123,88,37,0,35.2,0.197,29,0 484 | 4,85,58,22,49,27.8,0.306,28,0 485 | 0,84,82,31,125,38.2,0.233,23,0 486 | 0,145,0,0,0,44.2,0.63,31,1 487 | 0,135,68,42,250,42.3,0.365,24,1 488 | 1,139,62,41,480,40.7,0.536,21,0 489 | 0,173,78,32,265,46.5,1.159,58,0 490 | 4,99,72,17,0,25.6,0.294,28,0 491 | 8,194,80,0,0,26.1,0.551,67,0 492 | 2,83,65,28,66,36.8,0.629,24,0 493 | 2,89,90,30,0,33.5,0.292,42,0 494 | 4,99,68,38,0,32.8,0.145,33,0 495 | 4,125,70,18,122,28.9,1.144,45,1 496 | 3,80,0,0,0,0,0.174,22,0 497 | 6,166,74,0,0,26.6,0.304,66,0 498 | 5,110,68,0,0,26,0.292,30,0 499 | 2,81,72,15,76,30.1,0.547,25,0 500 | 7,195,70,33,145,25.1,0.163,55,1 501 | 6,154,74,32,193,29.3,0.839,39,0 502 | 2,117,90,19,71,25.2,0.313,21,0 503 | 3,84,72,32,0,37.2,0.267,28,0 504 | 6,0,68,41,0,39,0.727,41,1 505 | 7,94,64,25,79,33.3,0.738,41,0 506 | 3,96,78,39,0,37.3,0.238,40,0 507 | 10,75,82,0,0,33.3,0.263,38,0 508 | 0,180,90,26,90,36.5,0.314,35,1 509 | 1,130,60,23,170,28.6,0.692,21,0 510 | 2,84,50,23,76,30.4,0.968,21,0 511 | 8,120,78,0,0,25,0.409,64,0 512 | 12,84,72,31,0,29.7,0.297,46,1 513 | 0,139,62,17,210,22.1,0.207,21,0 514 | 9,91,68,0,0,24.2,0.2,58,0 515 | 2,91,62,0,0,27.3,0.525,22,0 516 | 3,99,54,19,86,25.6,0.154,24,0 517 | 3,163,70,18,105,31.6,0.268,28,1 518 | 9,145,88,34,165,30.3,0.771,53,1 519 | 7,125,86,0,0,37.6,0.304,51,0 520 | 13,76,60,0,0,32.8,0.18,41,0 521 | 6,129,90,7,326,19.6,0.582,60,0 522 | 2,68,70,32,66,25,0.187,25,0 523 | 3,124,80,33,130,33.2,0.305,26,0 524 | 6,114,0,0,0,0,0.189,26,0 525 | 9,130,70,0,0,34.2,0.652,45,1 526 | 3,125,58,0,0,31.6,0.151,24,0 527 | 3,87,60,18,0,21.8,0.444,21,0 528 | 1,97,64,19,82,18.2,0.299,21,0 529 | 3,116,74,15,105,26.3,0.107,24,0 530 | 0,117,66,31,188,30.8,0.493,22,0 531 | 0,111,65,0,0,24.6,0.66,31,0 532 | 2,122,60,18,106,29.8,0.717,22,0 533 | 0,107,76,0,0,45.3,0.686,24,0 534 | 1,86,66,52,65,41.3,0.917,29,0 535 | 6,91,0,0,0,29.8,0.501,31,0 536 | 1,77,56,30,56,33.3,1.251,24,0 537 | 4,132,0,0,0,32.9,0.302,23,1 538 | 0,105,90,0,0,29.6,0.197,46,0 539 | 0,57,60,0,0,21.7,0.735,67,0 540 | 0,127,80,37,210,36.3,0.804,23,0 541 | 3,129,92,49,155,36.4,0.968,32,1 542 | 8,100,74,40,215,39.4,0.661,43,1 543 | 3,128,72,25,190,32.4,0.549,27,1 544 | 10,90,85,32,0,34.9,0.825,56,1 545 | 4,84,90,23,56,39.5,0.159,25,0 546 | 1,88,78,29,76,32,0.365,29,0 547 | 8,186,90,35,225,34.5,0.423,37,1 548 | 5,187,76,27,207,43.6,1.034,53,1 549 | 4,131,68,21,166,33.1,0.16,28,0 550 | 1,164,82,43,67,32.8,0.341,50,0 551 | 4,189,110,31,0,28.5,0.68,37,0 552 | 1,116,70,28,0,27.4,0.204,21,0 553 | 3,84,68,30,106,31.9,0.591,25,0 554 | 6,114,88,0,0,27.8,0.247,66,0 555 | 1,88,62,24,44,29.9,0.422,23,0 556 | 1,84,64,23,115,36.9,0.471,28,0 557 | 7,124,70,33,215,25.5,0.161,37,0 558 | 1,97,70,40,0,38.1,0.218,30,0 559 | 8,110,76,0,0,27.8,0.237,58,0 560 | 11,103,68,40,0,46.2,0.126,42,0 561 | 11,85,74,0,0,30.1,0.3,35,0 562 | 6,125,76,0,0,33.8,0.121,54,1 563 | 0,198,66,32,274,41.3,0.502,28,1 564 | 1,87,68,34,77,37.6,0.401,24,0 565 | 6,99,60,19,54,26.9,0.497,32,0 566 | 0,91,80,0,0,32.4,0.601,27,0 567 | 2,95,54,14,88,26.1,0.748,22,0 568 | 1,99,72,30,18,38.6,0.412,21,0 569 | 6,92,62,32,126,32,0.085,46,0 570 | 4,154,72,29,126,31.3,0.338,37,0 571 | 0,121,66,30,165,34.3,0.203,33,1 572 | 3,78,70,0,0,32.5,0.27,39,0 573 | 2,130,96,0,0,22.6,0.268,21,0 574 | 3,111,58,31,44,29.5,0.43,22,0 575 | 2,98,60,17,120,34.7,0.198,22,0 576 | 1,143,86,30,330,30.1,0.892,23,0 577 | 1,119,44,47,63,35.5,0.28,25,0 578 | 6,108,44,20,130,24,0.813,35,0 579 | 2,118,80,0,0,42.9,0.693,21,1 580 | 10,133,68,0,0,27,0.245,36,0 581 | 2,197,70,99,0,34.7,0.575,62,1 582 | 0,151,90,46,0,42.1,0.371,21,1 583 | 6,109,60,27,0,25,0.206,27,0 584 | 12,121,78,17,0,26.5,0.259,62,0 585 | 8,100,76,0,0,38.7,0.19,42,0 586 | 8,124,76,24,600,28.7,0.687,52,1 587 | 1,93,56,11,0,22.5,0.417,22,0 588 | 8,143,66,0,0,34.9,0.129,41,1 589 | 6,103,66,0,0,24.3,0.249,29,0 590 | 3,176,86,27,156,33.3,1.154,52,1 591 | 0,73,0,0,0,21.1,0.342,25,0 592 | 11,111,84,40,0,46.8,0.925,45,1 593 | 2,112,78,50,140,39.4,0.175,24,0 594 | 3,132,80,0,0,34.4,0.402,44,1 595 | 2,82,52,22,115,28.5,1.699,25,0 596 | 6,123,72,45,230,33.6,0.733,34,0 597 | 0,188,82,14,185,32,0.682,22,1 598 | 0,67,76,0,0,45.3,0.194,46,0 599 | 1,89,24,19,25,27.8,0.559,21,0 600 | 1,173,74,0,0,36.8,0.088,38,1 601 | 1,109,38,18,120,23.1,0.407,26,0 602 | 1,108,88,19,0,27.1,0.4,24,0 603 | 6,96,0,0,0,23.7,0.19,28,0 604 | 1,124,74,36,0,27.8,0.1,30,0 605 | 7,150,78,29,126,35.2,0.692,54,1 606 | 4,183,0,0,0,28.4,0.212,36,1 607 | 1,124,60,32,0,35.8,0.514,21,0 608 | 1,181,78,42,293,40,1.258,22,1 609 | 1,92,62,25,41,19.5,0.482,25,0 610 | 0,152,82,39,272,41.5,0.27,27,0 611 | 1,111,62,13,182,24,0.138,23,0 612 | 3,106,54,21,158,30.9,0.292,24,0 613 | 3,174,58,22,194,32.9,0.593,36,1 614 | 7,168,88,42,321,38.2,0.787,40,1 615 | 6,105,80,28,0,32.5,0.878,26,0 616 | 11,138,74,26,144,36.1,0.557,50,1 617 | 3,106,72,0,0,25.8,0.207,27,0 618 | 6,117,96,0,0,28.7,0.157,30,0 619 | 2,68,62,13,15,20.1,0.257,23,0 620 | 9,112,82,24,0,28.2,1.282,50,1 621 | 0,119,0,0,0,32.4,0.141,24,1 622 | 2,112,86,42,160,38.4,0.246,28,0 623 | 2,92,76,20,0,24.2,1.698,28,0 624 | 6,183,94,0,0,40.8,1.461,45,0 625 | 0,94,70,27,115,43.5,0.347,21,0 626 | 2,108,64,0,0,30.8,0.158,21,0 627 | 4,90,88,47,54,37.7,0.362,29,0 628 | 0,125,68,0,0,24.7,0.206,21,0 629 | 0,132,78,0,0,32.4,0.393,21,0 630 | 5,128,80,0,0,34.6,0.144,45,0 631 | 4,94,65,22,0,24.7,0.148,21,0 632 | 7,114,64,0,0,27.4,0.732,34,1 633 | 0,102,78,40,90,34.5,0.238,24,0 634 | 2,111,60,0,0,26.2,0.343,23,0 635 | 1,128,82,17,183,27.5,0.115,22,0 636 | 10,92,62,0,0,25.9,0.167,31,0 637 | 13,104,72,0,0,31.2,0.465,38,1 638 | 5,104,74,0,0,28.8,0.153,48,0 639 | 2,94,76,18,66,31.6,0.649,23,0 640 | 7,97,76,32,91,40.9,0.871,32,1 641 | 1,100,74,12,46,19.5,0.149,28,0 642 | 0,102,86,17,105,29.3,0.695,27,0 643 | 4,128,70,0,0,34.3,0.303,24,0 644 | 6,147,80,0,0,29.5,0.178,50,1 645 | 4,90,0,0,0,28,0.61,31,0 646 | 3,103,72,30,152,27.6,0.73,27,0 647 | 2,157,74,35,440,39.4,0.134,30,0 648 | 1,167,74,17,144,23.4,0.447,33,1 649 | 0,179,50,36,159,37.8,0.455,22,1 650 | 11,136,84,35,130,28.3,0.26,42,1 651 | 0,107,60,25,0,26.4,0.133,23,0 652 | 1,91,54,25,100,25.2,0.234,23,0 653 | 1,117,60,23,106,33.8,0.466,27,0 654 | 5,123,74,40,77,34.1,0.269,28,0 655 | 2,120,54,0,0,26.8,0.455,27,0 656 | 1,106,70,28,135,34.2,0.142,22,0 657 | 2,155,52,27,540,38.7,0.24,25,1 658 | 2,101,58,35,90,21.8,0.155,22,0 659 | 1,120,80,48,200,38.9,1.162,41,0 660 | 11,127,106,0,0,39,0.19,51,0 661 | 3,80,82,31,70,34.2,1.292,27,1 662 | 10,162,84,0,0,27.7,0.182,54,0 663 | 1,199,76,43,0,42.9,1.394,22,1 664 | 8,167,106,46,231,37.6,0.165,43,1 665 | 9,145,80,46,130,37.9,0.637,40,1 666 | 6,115,60,39,0,33.7,0.245,40,1 667 | 1,112,80,45,132,34.8,0.217,24,0 668 | 4,145,82,18,0,32.5,0.235,70,1 669 | 10,111,70,27,0,27.5,0.141,40,1 670 | 6,98,58,33,190,34,0.43,43,0 671 | 9,154,78,30,100,30.9,0.164,45,0 672 | 6,165,68,26,168,33.6,0.631,49,0 673 | 1,99,58,10,0,25.4,0.551,21,0 674 | 10,68,106,23,49,35.5,0.285,47,0 675 | 3,123,100,35,240,57.3,0.88,22,0 676 | 8,91,82,0,0,35.6,0.587,68,0 677 | 6,195,70,0,0,30.9,0.328,31,1 678 | 9,156,86,0,0,24.8,0.23,53,1 679 | 0,93,60,0,0,35.3,0.263,25,0 680 | 3,121,52,0,0,36,0.127,25,1 681 | 2,101,58,17,265,24.2,0.614,23,0 682 | 2,56,56,28,45,24.2,0.332,22,0 683 | 0,162,76,36,0,49.6,0.364,26,1 684 | 0,95,64,39,105,44.6,0.366,22,0 685 | 4,125,80,0,0,32.3,0.536,27,1 686 | 5,136,82,0,0,0,0.64,69,0 687 | 2,129,74,26,205,33.2,0.591,25,0 688 | 3,130,64,0,0,23.1,0.314,22,0 689 | 1,107,50,19,0,28.3,0.181,29,0 690 | 1,140,74,26,180,24.1,0.828,23,0 691 | 1,144,82,46,180,46.1,0.335,46,1 692 | 8,107,80,0,0,24.6,0.856,34,0 693 | 13,158,114,0,0,42.3,0.257,44,1 694 | 2,121,70,32,95,39.1,0.886,23,0 695 | 7,129,68,49,125,38.5,0.439,43,1 696 | 2,90,60,0,0,23.5,0.191,25,0 697 | 7,142,90,24,480,30.4,0.128,43,1 698 | 3,169,74,19,125,29.9,0.268,31,1 699 | 0,99,0,0,0,25,0.253,22,0 700 | 4,127,88,11,155,34.5,0.598,28,0 701 | 4,118,70,0,0,44.5,0.904,26,0 702 | 2,122,76,27,200,35.9,0.483,26,0 703 | 6,125,78,31,0,27.6,0.565,49,1 704 | 1,168,88,29,0,35,0.905,52,1 705 | 2,129,0,0,0,38.5,0.304,41,0 706 | 4,110,76,20,100,28.4,0.118,27,0 707 | 6,80,80,36,0,39.8,0.177,28,0 708 | 10,115,0,0,0,0,0.261,30,1 709 | 2,127,46,21,335,34.4,0.176,22,0 710 | 9,164,78,0,0,32.8,0.148,45,1 711 | 2,93,64,32,160,38,0.674,23,1 712 | 3,158,64,13,387,31.2,0.295,24,0 713 | 5,126,78,27,22,29.6,0.439,40,0 714 | 10,129,62,36,0,41.2,0.441,38,1 715 | 0,134,58,20,291,26.4,0.352,21,0 716 | 3,102,74,0,0,29.5,0.121,32,0 717 | 7,187,50,33,392,33.9,0.826,34,1 718 | 3,173,78,39,185,33.8,0.97,31,1 719 | 10,94,72,18,0,23.1,0.595,56,0 720 | 1,108,60,46,178,35.5,0.415,24,0 721 | 5,97,76,27,0,35.6,0.378,52,1 722 | 4,83,86,19,0,29.3,0.317,34,0 723 | 1,114,66,36,200,38.1,0.289,21,0 724 | 1,149,68,29,127,29.3,0.349,42,1 725 | 5,117,86,30,105,39.1,0.251,42,0 726 | 1,111,94,0,0,32.8,0.265,45,0 727 | 4,112,78,40,0,39.4,0.236,38,0 728 | 1,116,78,29,180,36.1,0.496,25,0 729 | 0,141,84,26,0,32.4,0.433,22,0 730 | 2,175,88,0,0,22.9,0.326,22,0 731 | 2,92,52,0,0,30.1,0.141,22,0 732 | 3,130,78,23,79,28.4,0.323,34,1 733 | 8,120,86,0,0,28.4,0.259,22,1 734 | 2,174,88,37,120,44.5,0.646,24,1 735 | 2,106,56,27,165,29,0.426,22,0 736 | 2,105,75,0,0,23.3,0.56,53,0 737 | 4,95,60,32,0,35.4,0.284,28,0 738 | 0,126,86,27,120,27.4,0.515,21,0 739 | 8,65,72,23,0,32,0.6,42,0 740 | 2,99,60,17,160,36.6,0.453,21,0 741 | 1,102,74,0,0,39.5,0.293,42,1 742 | 11,120,80,37,150,42.3,0.785,48,1 743 | 3,102,44,20,94,30.8,0.4,26,0 744 | 1,109,58,18,116,28.5,0.219,22,0 745 | 9,140,94,0,0,32.7,0.734,45,1 746 | 13,153,88,37,140,40.6,1.174,39,0 747 | 12,100,84,33,105,30,0.488,46,0 748 | 1,147,94,41,0,49.3,0.358,27,1 749 | 1,81,74,41,57,46.3,1.096,32,0 750 | 3,187,70,22,200,36.4,0.408,36,1 751 | 6,162,62,0,0,24.3,0.178,50,1 752 | 4,136,70,0,0,31.2,1.182,22,1 753 | 1,121,78,39,74,39,0.261,28,0 754 | 3,108,62,24,0,26,0.223,25,0 755 | 0,181,88,44,510,43.3,0.222,26,1 756 | 8,154,78,32,0,32.4,0.443,45,1 757 | 1,128,88,39,110,36.5,1.057,37,1 758 | 7,137,90,41,0,32,0.391,39,0 759 | 0,123,72,0,0,36.3,0.258,52,1 760 | 1,106,76,0,0,37.5,0.197,26,0 761 | 6,190,92,0,0,35.5,0.278,66,1 762 | 2,88,58,26,16,28.4,0.766,22,0 763 | 9,170,74,31,0,44,0.403,43,1 764 | 9,89,62,0,0,22.5,0.142,33,0 765 | 10,101,76,48,180,32.9,0.171,63,0 766 | 2,122,70,27,0,36.8,0.34,27,0 767 | 5,121,72,23,112,26.2,0.245,30,0 768 | 1,126,60,0,0,30.1,0.349,47,1 769 | 1,93,70,31,0,30.4,0.315,23,0 --------------------------------------------------------------------------------