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jamesmcm/gist:0d54ae55b35f1e5cb5c9

Created August 4, 2014 09:57
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gistfile1.txt
{
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{
"cell_type": "code",
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"input": [
"import GPy\n",
"import numpy as np\n",
"import pylab\n",
"\n",
"data = np.load(\"./gpdata.npy\")"
],
"language": "python",
"metadata": {},
"outputs": [],
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{
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"input": [
"kernel = GPy.kern.rbf(2, ARD=True) + GPy.kern.linear(2)#+ GPy.kern.bias(2)\n",
"m2 = GPy.models.GPLVM(data, 2, kernel=kernel)\n",
"#m.data_labels = np.array([0]*40 + [1]*43)\n",
"m2.optimize('scg', messages=1)\n",
"print m2\n",
"m2.plot_latent()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Y is not zero mean, centering it locally (GPy.util.linalg.pca)\n",
" I F Scale |g| "
]
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"text": [
" \n",
"maxiter exceeded\n",
"\n",
"Log-likelihood: 1.728e+07, Log prior: -1.280e+03, LL+prior = -1.280e+03\n",
"\n",
" Name | Value | Constraints | Ties | prior \n",
"--------------------------------------------------------------------------\n",
" X_0_0 | -0.7082 | | | N(0.0, 1.0) \n",
" X_0_1 | 0.1529 | | | N(0.0, 1.0) \n",
" X_1_0 | -1.1857 | | | N(0.0, 1.0) \n",
" X_1_1 | 0.0046 | | | N(0.0, 1.0) \n",
" X_2_0 | 0.9089 | | | N(0.0, 1.0) \n",
" X_2_1 | 1.4416 | | | N(0.0, 1.0) \n",
" X_3_0 | 0.5030 | | | N(0.0, 1.0) \n",
" X_3_1 | 1.3534 | | | N(0.0, 1.0) \n",
" X_4_0 | 0.9230 | | | N(0.0, 1.0) \n",
" X_4_1 | 1.5116 | | | N(0.0, 1.0) \n",
" X_5_0 | -0.9743 | | | N(0.0, 1.0) \n",
" X_5_1 | -0.5030 | | | N(0.0, 1.0) \n",
" X_6_0 | -1.0417 | | | N(0.0, 1.0) \n",
" X_6_1 | -0.5575 | | | N(0.0, 1.0) \n",
" X_7_0 | -1.1046 | | | N(0.0, 1.0) \n",
" X_7_1 | -0.5766 | | | N(0.0, 1.0) \n",
" X_8_0 | -0.7746 | | | N(0.0, 1.0) \n",
" X_8_1 | 0.1318 | | | N(0.0, 1.0) \n",
" X_9_0 | 0.5975 | | | N(0.0, 1.0) \n",
" X_9_1 | 1.2578 | | | N(0.0, 1.0) \n",
" X_10_0 | -1.1171 | | | N(0.0, 1.0) \n",
" X_10_1 | -0.1676 | | | N(0.0, 1.0) \n",
" X_11_0 | -1.2010 | | | N(0.0, 1.0) \n",
" X_11_1 | -0.1390 | | | N(0.0, 1.0) \n",
" X_12_0 | -1.1256 | | | N(0.0, 1.0) \n",
" X_12_1 | -0.6021 | | | N(0.0, 1.0) \n",
" X_13_0 | -0.7136 | | | N(0.0, 1.0) \n",
" X_13_1 | 0.0396 | | | N(0.0, 1.0) \n",
" X_14_0 | -0.9635 | | | N(0.0, 1.0) \n",
" X_14_1 | -0.5142 | | | N(0.0, 1.0) \n",
" X_15_0 | -1.1506 | | | N(0.0, 1.0) \n",
" X_15_1 | -0.9296 | | | N(0.0, 1.0) \n",
" X_16_0 | 0.3921 | | | N(0.0, 1.0) \n",
" X_16_1 | 1.1754 | | | N(0.0, 1.0) \n",
" X_17_0 | -0.8299 | | | N(0.0, 1.0) \n",
" X_17_1 | 0.3664 | | | N(0.0, 1.0) \n",
" X_18_0 | 0.6224 | | | N(0.0, 1.0) \n",
" X_18_1 | 1.4029 | | | N(0.0, 1.0) \n",
" X_19_0 | -0.9492 | | | N(0.0, 1.0) \n",
" X_19_1 | -0.4050 | | | N(0.0, 1.0) \n",
" X_20_0 | -1.3038 | | | N(0.0, 1.0) \n",
" X_20_1 | -0.3488 | | | N(0.0, 1.0) \n",
" X_21_0 | -1.0756 | | | N(0.0, 1.0) \n",
" X_21_1 | -0.2590 | | | N(0.0, 1.0) \n",
" X_22_0 | -1.0045 | | | N(0.0, 1.0) \n",
" X_22_1 | -0.3950 | | | N(0.0, 1.0) \n",
" X_23_0 | 0.1895 | | | N(0.0, 1.0) \n",
" X_23_1 | 1.0892 | | | N(0.0, 1.0) \n",
" X_24_0 | -1.2595 | | | N(0.0, 1.0) \n",
" X_24_1 | -0.3837 | | | N(0.0, 1.0) \n",
" X_25_0 | 0.7517 | | | N(0.0, 1.0) \n",
" X_25_1 | 1.4557 | | | N(0.0, 1.0) \n",
" X_26_0 | -0.9395 | | | N(0.0, 1.0) \n",
" X_26_1 | -0.6089 | | | N(0.0, 1.0) \n",
" X_27_0 | -0.8728 | | | N(0.0, 1.0) \n",
" X_27_1 | -0.0647 | | | N(0.0, 1.0) \n",
" X_28_0 | -0.9848 | | | N(0.0, 1.0) \n",
" X_28_1 | -0.0212 | | | N(0.0, 1.0) \n",
" X_29_0 | 0.3264 | | | N(0.0, 1.0) \n",
" X_29_1 | 1.1105 | | | N(0.0, 1.0) \n",
" X_30_0 | -1.3116 | | | N(0.0, 1.0) \n",
" X_30_1 | -0.9746 | | | N(0.0, 1.0) \n",
" X_31_0 | -0.7389 | | | N(0.0, 1.0) \n",
" X_31_1 | -1.1052 | | | N(0.0, 1.0) \n",
" X_32_0 | 0.5173 | | | N(0.0, 1.0) \n",
" X_32_1 | 1.2429 | | | N(0.0, 1.0) \n",
" X_33_0 | -0.9609 | | | N(0.0, 1.0) \n",
" X_33_1 | -0.5649 | | | N(0.0, 1.0) \n",
" X_34_0 | -1.1425 | | | N(0.0, 1.0) \n",
" X_34_1 | -0.1838 | | | N(0.0, 1.0) \n",
" X_35_0 | -1.0877 | | | N(0.0, 1.0) \n",
" X_35_1 | -0.6112 | | | N(0.0, 1.0) \n",
" X_36_0 | -1.2369 | | | N(0.0, 1.0) \n",
" X_36_1 | -0.3564 | | | N(0.0, 1.0) \n",
" X_37_0 | -1.0539 | | | N(0.0, 1.0) \n",
" X_37_1 | 0.0300 | | | N(0.0, 1.0) \n",
" X_38_0 | -0.3029 | | | N(0.0, 1.0) \n",
" X_38_1 | 0.7291 | | | N(0.0, 1.0) \n",
" X_39_0 | -1.0683 | | | N(0.0, 1.0) \n",
" X_39_1 | -0.0463 | | | N(0.0, 1.0) \n",
" X_40_0 | -0.4094 | | | N(0.0, 1.0) \n",
" X_40_1 | 0.6440 | | | N(0.0, 1.0) \n",
" X_41_0 | -1.1338 | | | N(0.0, 1.0) \n",
" X_41_1 | -0.2976 | | | N(0.0, 1.0) \n",
" X_42_0 | -0.6634 | | | N(0.0, 1.0) \n",
" X_42_1 | 0.2768 | | | N(0.0, 1.0) \n",
" X_43_0 | -0.8398 | | | N(0.0, 1.0) \n",
" X_43_1 | 0.1155 | | | N(0.0, 1.0) \n",
" X_44_0 | -0.8208 | | | N(0.0, 1.0) \n",
" X_44_1 | 0.1075 | | | N(0.0, 1.0) \n",
" X_45_0 | -0.9434 | | | N(0.0, 1.0) \n",
" X_45_1 | 0.0391 | | | N(0.0, 1.0) \n",
" X_46_0 | -1.2868 | | | N(0.0, 1.0) \n",
" X_46_1 | -0.4690 | | | N(0.0, 1.0) \n",
" X_47_0 | -0.9585 | | | N(0.0, 1.0) \n",
" X_47_1 | -0.7733 | | | N(0.0, 1.0) \n",
" X_48_0 | -1.0132 | | | N(0.0, 1.0) \n",
" X_48_1 | -0.2784 | | | N(0.0, 1.0) \n",
" X_49_0 | -1.3321 | | | N(0.0, 1.0) \n",
" X_49_1 | -0.8886 | | | N(0.0, 1.0) \n",
" X_50_0 | -1.1005 | | | N(0.0, 1.0) \n",
" X_50_1 | 0.0830 | | | N(0.0, 1.0) \n",
" X_51_0 | -1.0391 | | | N(0.0, 1.0) \n",
" X_51_1 | 0.1106 | | | N(0.0, 1.0) \n",
" X_52_0 | -0.8015 | | | N(0.0, 1.0) \n",
" X_52_1 | 0.3622 | | | N(0.0, 1.0) \n",
" X_53_0 | -1.1083 | | | N(0.0, 1.0) \n",
" X_53_1 | 0.1096 | | | N(0.0, 1.0) \n",
" X_54_0 | -1.2406 | | | N(0.0, 1.0) \n",
" X_54_1 | -0.1768 | | | N(0.0, 1.0) \n",
" X_55_0 | -0.8772 | | | N(0.0, 1.0) \n",
" X_55_1 | 0.1910 | | | N(0.0, 1.0) \n",
" X_56_0 | -0.8778 | | | N(0.0, 1.0) \n",
" X_56_1 | 0.0041 | | | N(0.0, 1.0) \n",
" X_57_0 | -1.1565 | | | N(0.0, 1.0) \n",
" X_57_1 | 0.1803 | | | N(0.0, 1.0) \n",
" X_58_0 | -1.1588 | | | N(0.0, 1.0) \n",
" X_58_1 | -0.7772 | | | N(0.0, 1.0) \n",
" X_59_0 | -1.0548 | | | N(0.0, 1.0) \n",
" X_59_1 | -0.4755 | | | N(0.0, 1.0) \n",
" X_60_0 | 0.7068 | | | N(0.0, 1.0) \n",
" X_60_1 | 1.3949 | | | N(0.0, 1.0) \n",
" X_61_0 | -1.1602 | | | N(0.0, 1.0) \n",
" X_61_1 | -0.4417 | | | N(0.0, 1.0) \n",
" X_62_0 | -1.2715 | | | N(0.0, 1.0) \n",
" X_62_1 | -0.3916 | | | N(0.0, 1.0) \n",
" X_63_0 | -1.3157 | | | N(0.0, 1.0) \n",
" X_63_1 | -0.4227 | | | N(0.0, 1.0) \n",
" X_64_0 | 0.3381 | | | N(0.0, 1.0) \n",
" X_64_1 | 1.3098 | | | N(0.0, 1.0) \n",
" X_65_0 | -0.9028 | | | N(0.0, 1.0) \n",
" X_65_1 | 0.0647 | | | N(0.0, 1.0) \n",
" X_66_0 | -1.0300 | | | N(0.0, 1.0) \n",
" X_66_1 | 0.3165 | | | N(0.0, 1.0) \n",
" X_67_0 | -1.2311 | | | N(0.0, 1.0) \n",
" X_67_1 | -0.2159 | | | N(0.0, 1.0) \n",
" X_68_0 | -1.0974 | | | N(0.0, 1.0) \n",
" X_68_1 | -0.1491 | | | N(0.0, 1.0) \n",
" X_69_0 | 0.5589 | | | N(0.0, 1.0) \n",
" X_69_1 | 1.3190 | | | N(0.0, 1.0) \n",
" X_70_0 | -1.1713 | | | N(0.0, 1.0) \n",
" X_70_1 | -0.1986 | | | N(0.0, 1.0) \n",
" X_71_0 | -1.0277 | | | N(0.0, 1.0) \n",
" X_71_1 | 0.0654 | | | N(0.0, 1.0) \n",
" X_72_0 | -1.1342 | | | N(0.0, 1.0) \n",
" X_72_1 | -0.2413 | | | N(0.0, 1.0) \n",
" X_73_0 | -0.8572 | | | N(0.0, 1.0) \n",
" X_73_1 | 0.1816 | | | N(0.0, 1.0) \n",
" X_74_0 | -1.1374 | | | N(0.0, 1.0) \n",
" X_74_1 | -0.1052 | | | N(0.0, 1.0) \n",
" X_75_0 | -0.9704 | | | N(0.0, 1.0) \n",
" X_75_1 | -0.0800 | | | N(0.0, 1.0) \n",
" X_76_0 | -1.0127 | | | N(0.0, 1.0) \n",
" X_76_1 | 0.1928 | | | N(0.0, 1.0) \n",
" X_77_0 | 0.5899 | | | N(0.0, 1.0) \n",
" X_77_1 | 1.4655 | | | N(0.0, 1.0) \n",
" X_78_0 | -1.3390 | | | N(0.0, 1.0) \n",
" X_78_1 | -0.5887 | | | N(0.0, 1.0) \n",
" X_79_0 | 0.8710 | | | N(0.0, 1.0) \n",
" X_79_1 | 1.4290 | | | N(0.0, 1.0) \n",
" X_80_0 | 0.7024 | | | N(0.0, 1.0) \n",
" X_80_1 | 1.4359 | | | N(0.0, 1.0) \n",
" X_81_0 | 0.8395 | | | N(0.0, 1.0) \n",
" X_81_1 | 1.4457 | | | N(0.0, 1.0) \n",
" X_82_0 | -1.1647 | | | N(0.0, 1.0) \n",
" X_82_1 | -0.1171 | | | N(0.0, 1.0) \n",
" X_83_0 | -1.2694 | | | N(0.0, 1.0) \n",
" X_83_1 | -1.1863 | | | N(0.0, 1.0) \n",
" X_84_0 | -1.1340 | | | N(0.0, 1.0) \n",
" X_84_1 | -0.2531 | | | N(0.0, 1.0) \n",
" X_85_0 | -1.1639 | | | N(0.0, 1.0) \n",
" X_85_1 | -0.3011 | | | N(0.0, 1.0) \n",
" X_86_0 | -1.1643 | | | N(0.0, 1.0) \n",
" X_86_1 | -0.8113 | | | N(0.0, 1.0) \n",
" X_87_0 | -1.3027 | | | N(0.0, 1.0) \n",
" X_87_1 | -0.8350 | | | N(0.0, 1.0) \n",
" X_88_0 | 0.1835 | | | N(0.0, 1.0) \n",
" X_88_1 | 1.1682 | | | N(0.0, 1.0) \n",
" X_89_0 | -1.0538 | | | N(0.0, 1.0) \n",
" X_89_1 | -0.0398 | | | N(0.0, 1.0) \n",
" X_90_0 | 0.3360 | | | N(0.0, 1.0) \n",
" X_90_1 | 1.2774 | | | N(0.0, 1.0) \n",
" X_91_0 | -0.8995 | | | N(0.0, 1.0) \n",
" X_91_1 | 0.0156 | | | N(0.0, 1.0) \n",
" X_92_0 | -1.1638 | | | N(0.0, 1.0) \n",
" X_92_1 | -0.2934 | | | N(0.0, 1.0) \n",
" X_93_0 | -1.2564 | | | N(0.0, 1.0) \n",
" X_93_1 | -0.3502 | | | N(0.0, 1.0) \n",
" X_94_0 | -1.1270 | | | N(0.0, 1.0) \n",
" X_94_1 | -0.4666 | | | N(0.0, 1.0) \n",
" X_95_0 | 0.7022 | | | N(0.0, 1.0) \n",
" X_95_1 | 1.4338 | | | N(0.0, 1.0) \n",
" X_96_0 | -1.0314 | | | N(0.0, 1.0) \n",
" X_96_1 | -0.0708 | | | N(0.0, 1.0) \n",
" X_97_0 | -1.1010 | | | N(0.0, 1.0) \n",
" X_97_1 | -0.4635 | | | N(0.0, 1.0) \n",
" X_98_0 | -1.0856 | | | N(0.0, 1.0) \n",
" X_98_1 | -0.0539 | | | N(0.0, 1.0) \n",
" X_99_0 | -1.0100 | | | N(0.0, 1.0) \n",
" X_99_1 | -0.1557 | | | N(0.0, 1.0) \n",
" X_100_0 | -1.2395 | | | N(0.0, 1.0) \n",
" X_100_1 | -0.0901 | | | N(0.0, 1.0) \n",
" X_101_0 | -1.4095 | | | N(0.0, 1.0) \n",
" X_101_1 | -0.3998 | | | N(0.0, 1.0) \n",
" X_102_0 | 0.3629 | | | N(0.0, 1.0) \n",
" X_102_1 | 1.4462 | | | N(0.0, 1.0) \n",
" X_103_0 | 0.7019 | | | N(0.0, 1.0) \n",
" X_103_1 | 1.4210 | | | N(0.0, 1.0) \n",
" X_104_0 | -0.9783 | | | N(0.0, 1.0) \n",
" X_104_1 | 0.0750 | | | N(0.0, 1.0) \n",
" X_105_0 | -1.1884 | | | N(0.0, 1.0) \n",
" X_105_1 | -1.2492 | | | N(0.0, 1.0) \n",
" X_106_0 | 0.3087 | | | N(0.0, 1.0) \n",
" X_106_1 | 1.2260 | | | N(0.0, 1.0) \n",
" X_107_0 | 0.3492 | | | N(0.0, 1.0) \n",
" X_107_1 | 1.3196 | | | N(0.0, 1.0) \n",
" X_108_0 | -1.3116 | | | N(0.0, 1.0) \n",
" X_108_1 | -0.3017 | | | N(0.0, 1.0) \n",
" X_109_0 | -1.2276 | | | N(0.0, 1.0) \n",
" X_109_1 | -0.1124 | | | N(0.0, 1.0) \n",
" X_110_0 | -1.0608 | | | N(0.0, 1.0) \n",
" X_110_1 | 0.0056 | | | N(0.0, 1.0) \n",
" X_111_0 | -0.8536 | | | N(0.0, 1.0) \n",
" X_111_1 | 0.3831 | | | N(0.0, 1.0) \n",
" X_112_0 | 0.4566 | | | N(0.0, 1.0) \n",
" X_112_1 | 1.2868 | | | N(0.0, 1.0) \n",
" X_113_0 | -1.2347 | | | N(0.0, 1.0) \n",
" X_113_1 | -0.3870 | | | N(0.0, 1.0) \n",
" X_114_0 | -1.1740 | | | N(0.0, 1.0) \n",
" X_114_1 | -0.2190 | | | N(0.0, 1.0) \n",
" X_115_0 | -1.0408 | | | N(0.0, 1.0) \n",
" X_115_1 | -0.0627 | | | N(0.0, 1.0) \n",
" X_116_0 | 0.6696 | | | N(0.0, 1.0) \n",
" X_116_1 | 1.4532 | | | N(0.0, 1.0) \n",
" X_117_0 | -1.3279 | | | N(0.0, 1.0) \n",
" X_117_1 | -0.7578 | | | N(0.0, 1.0) \n",
" X_118_0 | -1.1393 | | | N(0.0, 1.0) \n",
" X_118_1 | -0.4091 | | | N(0.0, 1.0) \n",
" X_119_0 | -0.8411 | | | N(0.0, 1.0) \n",
" X_119_1 | -1.2540 | | | N(0.0, 1.0) \n",
" X_120_0 | -1.2273 | | | N(0.0, 1.0) \n",
" X_120_1 | -0.2674 | | | N(0.0, 1.0) \n",
" X_121_0 | -1.2880 | | | N(0.0, 1.0) \n",
" X_121_1 | -0.3247 | | | N(0.0, 1.0) \n",
" X_122_0 | -1.1097 | | | N(0.0, 1.0) \n",
" X_122_1 | -0.3256 | | | N(0.0, 1.0) \n",
" X_123_0 | -0.8870 | | | N(0.0, 1.0) \n",
" X_123_1 | -0.0589 | | | N(0.0, 1.0) \n",
" X_124_0 | -1.2273 | | | N(0.0, 1.0) \n",
" X_124_1 | -0.4764 | | | N(0.0, 1.0) \n",
" X_125_0 | -1.0224 | | | N(0.0, 1.0) \n",
" X_125_1 | -0.2869 | | | N(0.0, 1.0) \n",
" X_126_0 | -1.1763 | | | N(0.0, 1.0) \n",
" X_126_1 | -0.1663 | | | N(0.0, 1.0) \n",
" X_127_0 | -1.0332 | | | N(0.0, 1.0) \n",
" X_127_1 | 0.0521 | | | N(0.0, 1.0) \n",
" X_128_0 | -1.1853 | | | N(0.0, 1.0) \n",
" X_128_1 | -0.4502 | | | N(0.0, 1.0) \n",
" X_129_0 | -0.9095 | | | N(0.0, 1.0) \n",
" X_129_1 | -0.0005 | | | N(0.0, 1.0) \n",
" X_130_0 | -0.9772 | | | N(0.0, 1.0) \n",
" X_130_1 | -0.3197 | | | N(0.0, 1.0) \n",
" X_131_0 | -1.2226 | | | N(0.0, 1.0) \n",
" X_131_1 | -0.4721 | | | N(0.0, 1.0) \n",
" X_132_0 | 0.8059 | | | N(0.0, 1.0) \n",
" X_132_1 | 1.3800 | | | N(0.0, 1.0) \n",
" X_133_0 | -0.8607 | | | N(0.0, 1.0) \n",
" X_133_1 | -0.9187 | | | N(0.0, 1.0) \n",
" X_134_0 | -0.7949 | | | N(0.0, 1.0) \n",
" X_134_1 | 0.1631 | | | N(0.0, 1.0) \n",
" X_135_0 | -1.0191 | | | N(0.0, 1.0) \n",
" X_135_1 | -0.3389 | | | N(0.0, 1.0) \n",
" X_136_0 | -0.9446 | | | N(0.0, 1.0) \n",
" X_136_1 | -0.2563 | | | N(0.0, 1.0) \n",
" X_137_0 | -1.0391 | | | N(0.0, 1.0) \n",
" X_137_1 | 0.0087 | | | N(0.0, 1.0) \n",
" X_138_0 | -0.8370 | | | N(0.0, 1.0) \n",
" X_138_1 | -0.5311 | | | N(0.0, 1.0) \n",
" X_139_0 | -0.9356 | | | N(0.0, 1.0) \n",
" X_139_1 | -0.1502 | | | N(0.0, 1.0) \n",
" X_140_0 | 0.7441 | | | N(0.0, 1.0) \n",
" X_140_1 | 1.4420 | | | N(0.0, 1.0) \n",
" X_141_0 | 0.9168 | | | N(0.0, 1.0) \n",
" X_141_1 | 1.5190 | | | N(0.0, 1.0) \n",
" X_142_0 | -0.9268 | | | N(0.0, 1.0) \n",
" X_142_1 | -0.3669 | | | N(0.0, 1.0) \n",
" X_143_0 | -1.0093 | | | N(0.0, 1.0) \n",
" X_143_1 | 0.1661 | | | N(0.0, 1.0) \n",
" X_144_0 | -1.1331 | | | N(0.0, 1.0) \n",
" X_144_1 | -0.3337 | | | N(0.0, 1.0) \n",
" X_145_0 | -1.0247 | | | N(0.0, 1.0) \n",
" X_145_1 | -0.4848 | | | N(0.0, 1.0) \n",
" X_146_0 | -1.0230 | | | N(0.0, 1.0) \n",
" X_146_1 | 0.0320 | | | N(0.0, 1.0) \n",
" X_147_0 | -0.5730 | | | N(0.0, 1.0) \n",
" X_147_1 | 0.4977 | | | N(0.0, 1.0) \n",
" X_148_0 | -1.1693 | | | N(0.0, 1.0) \n",
" X_148_1 | -0.4149 | | | N(0.0, 1.0) \n",
" X_149_0 | -1.2604 | | | N(0.0, 1.0) \n",
" X_149_1 | -0.4961 | | | N(0.0, 1.0) \n",
" X_150_0 | -1.2410 | | | N(0.0, 1.0) \n",
" X_150_1 | 0.1050 | | | N(0.0, 1.0) \n",
" X_151_0 | 0.1687 | | | N(0.0, 1.0) \n",
" X_151_1 | 1.1597 | | | N(0.0, 1.0) \n",
" X_152_0 | -1.0900 | | | N(0.0, 1.0) \n",
" X_152_1 | -0.1897 | | | N(0.0, 1.0) \n",
" X_153_0 | -0.9773 | | | N(0.0, 1.0) \n",
" X_153_1 | 0.1780 | | | N(0.0, 1.0) \n",
" X_154_0 | -1.0085 | | | N(0.0, 1.0) \n",
" X_154_1 | 0.0691 | | | N(0.0, 1.0) \n",
" X_155_0 | -0.9550 | | | N(0.0, 1.0) \n",
" X_155_1 | 0.2609 | | | N(0.0, 1.0) \n",
" X_156_0 | -1.4404 | | | N(0.0, 1.0) \n",
" X_156_1 | -0.2156 | | | N(0.0, 1.0) \n",
" X_157_0 | -1.1831 | | | N(0.0, 1.0) \n",
" X_157_1 | 0.1310 | | | N(0.0, 1.0) \n",
" X_158_0 | -1.0448 | | | N(0.0, 1.0) \n",
" X_158_1 | 0.1491 | | | N(0.0, 1.0) \n",
" X_159_0 | -1.1846 | | | N(0.0, 1.0) \n",
" X_159_1 | -0.2352 | | | N(0.0, 1.0) \n",
" X_160_0 | -1.1108 | | | N(0.0, 1.0) \n",
" X_160_1 | -0.0342 | | | N(0.0, 1.0) \n",
" X_161_0 | -0.9633 | | | N(0.0, 1.0) \n",
" X_161_1 | 0.2005 | | | N(0.0, 1.0) \n",
" X_162_0 | -1.0644 | | | N(0.0, 1.0) \n",
" X_162_1 | -0.0260 | | | N(0.0, 1.0) \n",
" X_163_0 | 0.2260 | | | N(0.0, 1.0) \n",
" X_163_1 | 1.3553 | | | N(0.0, 1.0) \n",
" X_164_0 | -0.2678 | | | N(0.0, 1.0) \n",
" X_164_1 | 0.7476 | | | N(0.0, 1.0) \n",
" X_165_0 | -1.1691 | | | N(0.0, 1.0) \n",
" X_165_1 | -0.1086 | | | N(0.0, 1.0) \n",
" X_166_0 | 0.5523 | | | N(0.0, 1.0) \n",
" X_166_1 | 1.4225 | | | N(0.0, 1.0) \n",
" X_167_0 | -0.8936 | | | N(0.0, 1.0) \n",
" X_167_1 | 0.2012 | | | N(0.0, 1.0) \n",
" X_168_0 | -0.9542 | | | N(0.0, 1.0) \n",
" X_168_1 | 0.1697 | | | N(0.0, 1.0) \n",
" X_169_0 | -1.0398 | | | N(0.0, 1.0) \n",
" X_169_1 | 0.0758 | | | N(0.0, 1.0) \n",
" X_170_0 | -0.7143 | | | N(0.0, 1.0) \n",
" X_170_1 | 0.2564 | | | N(0.0, 1.0) \n",
" X_171_0 | -0.9650 | | | N(0.0, 1.0) \n",
" X_171_1 | 0.2499 | | | N(0.0, 1.0) \n",
" X_172_0 | -1.0478 | | | N(0.0, 1.0) \n",
" X_172_1 | -0.1298 | | | N(0.0, 1.0) \n",
" X_173_0 | -0.9578 | | | N(0.0, 1.0) \n",
" X_173_1 | -0.4121 | | | N(0.0, 1.0) \n",
" X_174_0 | 0.6479 | | | N(0.0, 1.0) \n",
" X_174_1 | -0.8256 | | | N(0.0, 1.0) \n",
" X_175_0 | -0.0166 | | | N(0.0, 1.0) \n",
" X_175_1 | -0.8262 | | | N(0.0, 1.0) \n",
" X_176_0 | 0.2172 | | | N(0.0, 1.0) \n",
" X_176_1 | -0.8978 | | | N(0.0, 1.0) \n",
" X_177_0 | 0.6591 | | | N(0.0, 1.0) \n",
" X_177_1 | -1.0173 | | | N(0.0, 1.0) \n",
" X_178_0 | 1.0195 | | | N(0.0, 1.0) \n",
" X_178_1 | 1.4573 | | | N(0.0, 1.0) \n",
" X_179_0 | 1.4115 | | | N(0.0, 1.0) \n",
" X_179_1 | 1.1952 | | | N(0.0, 1.0) \n",
" X_180_0 | 0.5556 | | | N(0.0, 1.0) \n",
" X_180_1 | -0.9833 | | | N(0.0, 1.0) \n",
" X_181_0 | 0.4349 | | | N(0.0, 1.0) \n",
" X_181_1 | -0.9324 | | | N(0.0, 1.0) \n",
" X_182_0 | 0.6162 | | | N(0.0, 1.0) \n",
" X_182_1 | -1.1935 | | | N(0.0, 1.0) \n",
" X_183_0 | 0.6191 | | | N(0.0, 1.0) \n",
" X_183_1 | -1.0004 | | | N(0.0, 1.0) \n",
" X_184_0 | -1.1319 | | | N(0.0, 1.0) \n",
" X_184_1 | -0.8227 | | | N(0.0, 1.0) \n",
" X_185_0 | 1.2337 | | | N(0.0, 1.0) \n",
" X_185_1 | 1.2372 | | | N(0.0, 1.0) \n",
" X_186_0 | 0.2425 | | | N(0.0, 1.0) \n",
" X_186_1 | -1.1145 | | | N(0.0, 1.0) \n",
" X_187_0 | 0.5863 | | | N(0.0, 1.0) \n",
" X_187_1 | -0.9614 | | | N(0.0, 1.0) \n",
" X_188_0 | 0.7267 | | | N(0.0, 1.0) \n",
" X_188_1 | -0.8002 | | | N(0.0, 1.0) \n",
" X_189_0 | 0.4591 | | | N(0.0, 1.0) \n",
" X_189_1 | -0.8409 | | | N(0.0, 1.0) \n",
" X_190_0 | 1.4010 | | | N(0.0, 1.0) \n",
" X_190_1 | 1.2790 | | | N(0.0, 1.0) \n",
" X_191_0 | 1.2816 | | | N(0.0, 1.0) \n",
" X_191_1 | 1.3647 | | | N(0.0, 1.0) \n",
" X_192_0 | 0.0004 | | | N(0.0, 1.0) \n",
" X_192_1 | -0.8560 | | | N(0.0, 1.0) \n",
" X_193_0 | 0.2500 | | | N(0.0, 1.0) \n",
" X_193_1 | -0.7651 | | | N(0.0, 1.0) \n",
" X_194_0 | 0.6156 | | | N(0.0, 1.0) \n",
" X_194_1 | 1.2651 | | | N(0.0, 1.0) \n",
" X_195_0 | 0.6382 | | | N(0.0, 1.0) \n",
" X_195_1 | -0.8494 | | | N(0.0, 1.0) \n",
" X_196_0 | 0.1923 | | | N(0.0, 1.0) \n",
" X_196_1 | -1.0247 | | | N(0.0, 1.0) \n",
" X_197_0 | 0.4148 | | | N(0.0, 1.0) \n",
" X_197_1 | -0.9128 | | | N(0.0, 1.0) \n",
" X_198_0 | 0.4859 | | | N(0.0, 1.0) \n",
" X_198_1 | -0.8768 | | | N(0.0, 1.0) \n",
" X_199_0 | 0.5194 | | | N(0.0, 1.0) \n",
" X_199_1 | -0.8102 | | | N(0.0, 1.0) \n",
" X_200_0 | 0.5933 | | | N(0.0, 1.0) \n",
" X_200_1 | -0.9144 | | | N(0.0, 1.0) \n",
" X_201_0 | 0.7192 | | | N(0.0, 1.0) \n",
" X_201_1 | -0.8808 | | | N(0.0, 1.0) \n",
" X_202_0 | 0.6893 | | | N(0.0, 1.0) \n",
" X_202_1 | -0.9079 | | | N(0.0, 1.0) \n",
" X_203_0 | 1.2342 | | | N(0.0, 1.0) \n",
" X_203_1 | 1.5123 | | | N(0.0, 1.0) \n",
" X_204_0 | 1.1060 | | | N(0.0, 1.0) \n",
" X_204_1 | 1.2300 | | | N(0.0, 1.0) \n",
" X_205_0 | 0.3390 | | | N(0.0, 1.0) \n",
" X_205_1 | -0.7592 | | | N(0.0, 1.0) \n",
" X_206_0 | 1.5571 | | | N(0.0, 1.0) \n",
" X_206_1 | 1.2874 | | | N(0.0, 1.0) \n",
" X_207_0 | 0.3935 | | | N(0.0, 1.0) \n",
" X_207_1 | -0.9439 | | | N(0.0, 1.0) \n",
" X_208_0 | 0.4070 | | | N(0.0, 1.0) \n",
" X_208_1 | -0.8330 | | | N(0.0, 1.0) \n",
" X_209_0 | 0.8804 | | | N(0.0, 1.0) \n",
" X_209_1 | 1.2491 | | | N(0.0, 1.0) \n",
" X_210_0 | 0.5291 | | | N(0.0, 1.0) \n",
" X_210_1 | -0.8371 | | | N(0.0, 1.0) \n",
" X_211_0 | 0.5599 | | | N(0.0, 1.0) \n",
" X_211_1 | -0.8766 | | | N(0.0, 1.0) \n",
" X_212_0 | -0.7945 | | | N(0.0, 1.0) \n",
" X_212_1 | -0.7207 | | | N(0.0, 1.0) \n",
" X_213_0 | -0.8926 | | | N(0.0, 1.0) \n",
" X_213_1 | -0.0844 | | | N(0.0, 1.0) \n",
" X_214_0 | -0.7639 | | | N(0.0, 1.0) \n",
" X_214_1 | -0.6176 | | | N(0.0, 1.0) \n",
" X_215_0 | 0.2520 | | | N(0.0, 1.0) \n",
" X_215_1 | -1.1062 | | | N(0.0, 1.0) \n",
" X_216_0 | 1.5783 | | | N(0.0, 1.0) \n",
" X_216_1 | 1.2567 | | | N(0.0, 1.0) \n",
" X_217_0 | -0.7296 | | | N(0.0, 1.0) \n",
" X_217_1 | -0.6057 | | | N(0.0, 1.0) \n",
" X_218_0 | 0.1451 | | | N(0.0, 1.0) \n",
" X_218_1 | -0.8666 | | | N(0.0, 1.0) \n",
" X_219_0 | 1.1909 | | | N(0.0, 1.0) \n",
" X_219_1 | 1.3169 | | | N(0.0, 1.0) \n",
" X_220_0 | 0.4007 | | | N(0.0, 1.0) \n",
" X_220_1 | -0.8324 | | | N(0.0, 1.0) \n",
" X_221_0 | 0.6805 | | | N(0.0, 1.0) \n",
" X_221_1 | -0.8921 | | | N(0.0, 1.0) \n",
" X_222_0 | 1.2118 | | | N(0.0, 1.0) \n",
" X_222_1 | 1.2301 | | | N(0.0, 1.0) \n",
" X_223_0 | 0.5740 | | | N(0.0, 1.0) \n",
" X_223_1 | -0.8687 | | | N(0.0, 1.0) \n",
" X_224_0 | 1.2506 | | | N(0.0, 1.0) \n",
" X_224_1 | 1.2339 | | | N(0.0, 1.0) \n",
" X_225_0 | 1.2246 | | | N(0.0, 1.0) \n",
" X_225_1 | 1.4955 | | | N(0.0, 1.0) \n",
" X_226_0 | 0.4580 | | | N(0.0, 1.0) \n",
" X_226_1 | -0.9012 | | | N(0.0, 1.0) \n",
" X_227_0 | 0.0080 | | | N(0.0, 1.0) \n",
" X_227_1 | -0.8869 | | | N(0.0, 1.0) \n",
" X_228_0 | 1.3367 | | | N(0.0, 1.0) \n",
" X_228_1 | 1.4623 | | | N(0.0, 1.0) \n",
" X_229_0 | 0.5350 | | | N(0.0, 1.0) \n",
" X_229_1 | -1.0276 | | | N(0.0, 1.0) \n",
" X_230_0 | 0.9697 | | | N(0.0, 1.0) \n",
" X_230_1 | 1.4525 | | | N(0.0, 1.0) \n",
" X_231_0 | -0.9701 | | | N(0.0, 1.0) \n",
" X_231_1 | -0.2950 | | | N(0.0, 1.0) \n",
" X_232_0 | 0.5962 | | | N(0.0, 1.0) \n",
" X_232_1 | -0.8951 | | | N(0.0, 1.0) \n",
" X_233_0 | 1.1268 | | | N(0.0, 1.0) \n",
" X_233_1 | 1.4005 | | | N(0.0, 1.0) \n",
" X_234_0 | 1.8187 | | | N(0.0, 1.0) \n",
" X_234_1 | 1.1642 | | | N(0.0, 1.0) \n",
" X_235_0 | 0.5705 | | | N(0.0, 1.0) \n",
" X_235_1 | -0.8503 | | | N(0.0, 1.0) \n",
" X_236_0 | 0.9429 | | | N(0.0, 1.0) \n",
" X_236_1 | 1.2712 | | | N(0.0, 1.0) \n",
" X_237_0 | 0.4911 | | | N(0.0, 1.0) \n",
" X_237_1 | 1.0330 | | | N(0.0, 1.0) \n",
" X_238_0 | 0.3987 | | | N(0.0, 1.0) \n",
" X_238_1 | -1.1265 | | | N(0.0, 1.0) \n",
" X_239_0 | 1.5828 | | | N(0.0, 1.0) \n",
" X_239_1 | 1.5236 | | | N(0.0, 1.0) \n",
" X_240_0 | 1.1018 | | | N(0.0, 1.0) \n",
" X_240_1 | 1.3214 | | | N(0.0, 1.0) \n",
" X_241_0 | 1.1568 | | | N(0.0, 1.0) \n",
" X_241_1 | 1.1573 | | | N(0.0, 1.0) \n",
" X_242_0 | 0.3061 | | | N(0.0, 1.0) \n",
" X_242_1 | -1.2715 | | | N(0.0, 1.0) \n",
" X_243_0 | 1.2128 | | | N(0.0, 1.0) \n",
" X_243_1 | 1.1218 | | | N(0.0, 1.0) \n",
" X_244_0 | 0.8259 | | | N(0.0, 1.0) \n",
" X_244_1 | -0.8353 | | | N(0.0, 1.0) \n",
" X_245_0 | 0.0662 | | | N(0.0, 1.0) \n",
" X_245_1 | -0.5046 | | | N(0.0, 1.0) \n",
" X_246_0 | 1.3463 | | | N(0.0, 1.0) \n",
" X_246_1 | 1.2443 | | | N(0.0, 1.0) \n",
" X_247_0 | 0.6656 | | | N(0.0, 1.0) \n",
" X_247_1 | -0.6144 | | | N(0.0, 1.0) \n",
" X_248_0 | 0.7622 | | | N(0.0, 1.0) \n",
" X_248_1 | -0.8091 | | | N(0.0, 1.0) \n",
" X_249_0 | 0.2496 | | | N(0.0, 1.0) \n",
" X_249_1 | -0.6971 | | | N(0.0, 1.0) \n",
" X_250_0 | 1.7916 | | | N(0.0, 1.0) \n",
" X_250_1 | 1.0433 | | | N(0.0, 1.0) \n",
" X_251_0 | -0.4386 | | | N(0.0, 1.0) \n",
" X_251_1 | 0.0601 | | | N(0.0, 1.0) \n",
" X_252_0 | -0.0152 | | | N(0.0, 1.0) \n",
" X_252_1 | -0.8528 | | | N(0.0, 1.0) \n",
" X_253_0 | 1.5053 | | | N(0.0, 1.0) \n",
" X_253_1 | 1.3301 | | | N(0.0, 1.0) \n",
" X_254_0 | 0.6350 | | | N(0.0, 1.0) \n",
" X_254_1 | -0.7690 | | | N(0.0, 1.0) \n",
" X_255_0 | 0.6915 | | | N(0.0, 1.0) \n",
" X_255_1 | -0.9501 | | | N(0.0, 1.0) \n",
" X_256_0 | -0.1719 | | | N(0.0, 1.0) \n",
" X_256_1 | -0.9123 | | | N(0.0, 1.0) \n",
" X_257_0 | 0.5168 | | | N(0.0, 1.0) \n",
" X_257_1 | -0.9129 | | | N(0.0, 1.0) \n",
" X_258_0 | -1.1325 | | | N(0.0, 1.0) \n",
" X_258_1 | 0.1868 | | | N(0.0, 1.0) \n",
" X_259_0 | 1.2580 | | | N(0.0, 1.0) \n",
" X_259_1 | 1.2151 | | | N(0.0, 1.0) \n",
" X_260_0 | -0.0803 | | | N(0.0, 1.0) \n",
" X_260_1 | -0.6981 | | | N(0.0, 1.0) \n",
" X_261_0 | 0.5907 | | | N(0.0, 1.0) \n",
" X_261_1 | -0.7843 | | | N(0.0, 1.0) \n",
" X_262_0 | 0.6709 | | | N(0.0, 1.0) \n",
" X_262_1 | -0.8643 | | | N(0.0, 1.0) \n",
" X_263_0 | 0.5463 | | | N(0.0, 1.0) \n",
" X_263_1 | -0.9882 | | | N(0.0, 1.0) \n",
" X_264_0 | 0.3102 | | | N(0.0, 1.0) \n",
" X_264_1 | -0.8368 | | | N(0.0, 1.0) \n",
" X_265_0 | 0.1396 | | | N(0.0, 1.0) \n",
" X_265_1 | -0.7257 | | | N(0.0, 1.0) \n",
" X_266_0 | 0.4731 | | | N(0.0, 1.0) \n",
" X_266_1 | -0.8754 | | | N(0.0, 1.0) \n",
" X_267_0 | 1.1337 | | | N(0.0, 1.0) \n",
" X_267_1 | 1.4554 | | | N(0.0, 1.0) \n",
" X_268_0 | -0.6815 | | | N(0.0, 1.0) \n",
" X_268_1 | -0.9358 | | | N(0.0, 1.0) \n",
" X_269_0 | -1.1129 | | | N(0.0, 1.0) \n",
" X_269_1 | -0.4245 | | | N(0.0, 1.0) \n",
" X_270_0 | -1.0021 | | | N(0.0, 1.0) \n",
" X_270_1 | -0.5214 | | | N(0.0, 1.0) \n",
" X_271_0 | 0.8252 | | | N(0.0, 1.0) \n",
" X_271_1 | 1.6954 | | | N(0.0, 1.0) \n",
" X_272_0 | -0.6350 | | | N(0.0, 1.0) \n",
" X_272_1 | -1.2511 | | | N(0.0, 1.0) \n",
" X_273_0 | 0.6507 | | | N(0.0, 1.0) \n",
" X_273_1 | 1.1884 | | | N(0.0, 1.0) \n",
" X_274_0 | 0.8705 | | | N(0.0, 1.0) \n",
" X_274_1 | 1.3821 | | | N(0.0, 1.0) \n",
" X_275_0 | 0.8472 | | | N(0.0, 1.0) \n",
" X_275_1 | 1.5422 | | | N(0.0, 1.0) \n",
" X_276_0 | 0.6796 | | | N(0.0, 1.0) \n",
" X_276_1 | 1.5044 | | | N(0.0, 1.0) \n",
" X_277_0 | -1.0632 | | | N(0.0, 1.0) \n",
" X_277_1 | -0.2400 | | | N(0.0, 1.0) \n",
" X_278_0 | 1.4737 | | | N(0.0, 1.0) \n",
" X_278_1 | 1.3088 | | | N(0.0, 1.0) \n",
" X_279_0 | -0.7763 | | | N(0.0, 1.0) \n",
" X_279_1 | -0.6269 | | | N(0.0, 1.0) \n",
" X_280_0 | 0.7362 | | | N(0.0, 1.0) \n",
" X_280_1 | 1.2298 | | | N(0.0, 1.0) \n",
" X_281_0 | 1.1320 | | | N(0.0, 1.0) \n",
" X_281_1 | 1.3440 | | | N(0.0, 1.0) \n",
" X_282_0 | 1.2660 | | | N(0.0, 1.0) \n",
" X_282_1 | 1.5128 | | | N(0.0, 1.0) \n",
" X_283_0 | 1.5121 | | | N(0.0, 1.0) \n",
" X_283_1 | 1.5574 | | | N(0.0, 1.0) \n",
" X_284_0 | 0.4651 | | | N(0.0, 1.0) \n",
" X_284_1 | -0.9766 | | | N(0.0, 1.0) \n",
" X_285_0 | 0.4983 | | | N(0.0, 1.0) \n",
" X_285_1 | -0.9493 | | | N(0.0, 1.0) \n",
" X_286_0 | 1.3639 | | | N(0.0, 1.0) \n",
" X_286_1 | 1.4973 | | | N(0.0, 1.0) \n",
" X_287_0 | -1.2055 | | | N(0.0, 1.0) \n",
" X_287_1 | 0.0614 | | | N(0.0, 1.0) \n",
" X_288_0 | 0.7936 | | | N(0.0, 1.0) \n",
" X_288_1 | 1.3294 | | | N(0.0, 1.0) \n",
" X_289_0 | 0.5770 | | | N(0.0, 1.0) \n",
" X_289_1 | -1.1162 | | | N(0.0, 1.0) \n",
" X_290_0 | 0.4368 | | | N(0.0, 1.0) \n",
" X_290_1 | -0.8960 | | | N(0.0, 1.0) \n",
" X_291_0 | -0.7950 | | | N(0.0, 1.0) \n",
" X_291_1 | -0.4938 | | | N(0.0, 1.0) \n",
" X_292_0 | 0.4037 | | | N(0.0, 1.0) \n",
" X_292_1 | -0.9349 | | | N(0.0, 1.0) \n",
" X_293_0 | -1.1102 | | | N(0.0, 1.0) \n",
" X_293_1 | -0.1308 | | | N(0.0, 1.0) \n",
" X_294_0 | 0.1697 | | | N(0.0, 1.0) \n",
" X_294_1 | -1.2808 | | | N(0.0, 1.0) \n",
" X_295_0 | -0.7139 | | | N(0.0, 1.0) \n",
" X_295_1 | -0.2233 | | | N(0.0, 1.0) \n",
" X_296_0 | 0.9942 | | | N(0.0, 1.0) \n",
" X_296_1 | 1.4499 | | | N(0.0, 1.0) \n",
" X_297_0 | 1.2068 | | | N(0.0, 1.0) \n",
" X_297_1 | 1.5168 | | | N(0.0, 1.0) \n",
" X_298_0 | 0.1763 | | | N(0.0, 1.0) \n",
" X_298_1 | -0.8143 | | | N(0.0, 1.0) \n",
" X_299_0 | 0.3133 | | | N(0.0, 1.0) \n",
" X_299_1 | -0.9710 | | | N(0.0, 1.0) \n",
" X_300_0 | 1.3996 | | | N(0.0, 1.0) \n",
" X_300_1 | 1.5052 | | | N(0.0, 1.0) \n",
" X_301_0 | 0.4449 | | | N(0.0, 1.0) \n",
" X_301_1 | -0.8665 | | | N(0.0, 1.0) \n",
" X_302_0 | -0.3045 | | | N(0.0, 1.0) \n",
" X_302_1 | -1.2015 | | | N(0.0, 1.0) \n",
" X_303_0 | 0.4161 | | | N(0.0, 1.0) \n",
" X_303_1 | -1.0804 | | | N(0.0, 1.0) \n",
" X_304_0 | -0.6758 | | | N(0.0, 1.0) \n",
" X_304_1 | -0.6095 | | | N(0.0, 1.0) \n",
" X_305_0 | 1.1342 | | | N(0.0, 1.0) \n",
" X_305_1 | 1.4275 | | | N(0.0, 1.0) \n",
" X_306_0 | -1.0868 | | | N(0.0, 1.0) \n",
" X_306_1 | -0.3523 | | | N(0.0, 1.0) \n",
" X_307_0 | -1.1902 | | | N(0.0, 1.0) \n",
" X_307_1 | -0.3179 | | | N(0.0, 1.0) \n",
" X_308_0 | 0.8347 | | | N(0.0, 1.0) \n",
" X_308_1 | 1.4061 | | | N(0.0, 1.0) \n",
" X_309_0 | 0.5103 | | | N(0.0, 1.0) \n",
" X_309_1 | -0.9000 | | | N(0.0, 1.0) \n",
" X_310_0 | 1.0839 | | | N(0.0, 1.0) \n",
" X_310_1 | 1.2443 | | | N(0.0, 1.0) \n",
" X_311_0 | 0.4577 | | | N(0.0, 1.0) \n",
" X_311_1 | -0.9240 | | | N(0.0, 1.0) \n",
" X_312_0 | -0.8645 | | | N(0.0, 1.0) \n",
" X_312_1 | -0.4950 | | | N(0.0, 1.0) \n",
" X_313_0 | 0.6269 | | | N(0.0, 1.0) \n",
" X_313_1 | -0.9839 | | | N(0.0, 1.0) \n",
" X_314_0 | 0.4934 | | | N(0.0, 1.0) \n",
" X_314_1 | -0.9316 | | | N(0.0, 1.0) \n",
" X_315_0 | -0.1999 | | | N(0.0, 1.0) \n",
" X_315_1 | -0.8256 | | | N(0.0, 1.0) \n",
" X_316_0 | 1.7565 | | | N(0.0, 1.0) \n",
" X_316_1 | 1.4010 | | | N(0.0, 1.0) \n",
" X_317_0 | 1.4349 | | | N(0.0, 1.0) \n",
" X_317_1 | 1.3773 | | | N(0.0, 1.0) \n",
" X_318_0 | 0.5263 | | | N(0.0, 1.0) \n",
" X_318_1 | -1.0649 | | | N(0.0, 1.0) \n",
" X_319_0 | 0.7954 | | | N(0.0, 1.0) \n",
" X_319_1 | 1.3509 | | | N(0.0, 1.0) \n",
" X_320_0 | -0.7870 | | | N(0.0, 1.0) \n",
" X_320_1 | -0.6864 | | | N(0.0, 1.0) \n",
" X_321_0 | 0.8884 | | | N(0.0, 1.0) \n",
" X_321_1 | 1.4011 | | | N(0.0, 1.0) \n",
" X_322_0 | 0.5321 | | | N(0.0, 1.0) \n",
" X_322_1 | -0.9951 | | | N(0.0, 1.0) \n",
" X_323_0 | -1.1016 | | | N(0.0, 1.0) \n",
" X_323_1 | -0.4172 | | | N(0.0, 1.0) \n",
" X_324_0 | 1.2888 | | | N(0.0, 1.0) \n",
" X_324_1 | 1.3805 | | | N(0.0, 1.0) \n",
" X_325_0 | 1.2640 | | | N(0.0, 1.0) \n",
" X_325_1 | 1.3949 | | | N(0.0, 1.0) \n",
" X_326_0 | 0.3513 | | | N(0.0, 1.0) \n",
" X_326_1 | -0.8695 | | | N(0.0, 1.0) \n",
" X_327_0 | 0.9396 | | | N(0.0, 1.0) \n",
" X_327_1 | 1.4133 | | | N(0.0, 1.0) \n",
" X_328_0 | 1.6318 | | | N(0.0, 1.0) \n",
" X_328_1 | 1.2112 | | | N(0.0, 1.0) \n",
" X_329_0 | 1.4056 | | | N(0.0, 1.0) \n",
" X_329_1 | 1.3848 | | | N(0.0, 1.0) \n",
" X_330_0 | 1.2924 | | | N(0.0, 1.0) \n",
" X_330_1 | 1.4382 | | | N(0.0, 1.0) \n",
" X_331_0 | 0.4587 | | | N(0.0, 1.0) \n",
" X_331_1 | -1.0039 | | | N(0.0, 1.0) \n",
" X_332_0 | 1.5052 | | | N(0.0, 1.0) \n",
" X_332_1 | 1.3059 | | | N(0.0, 1.0) \n",
" X_333_0 | 0.1024 | | | N(0.0, 1.0) \n",
" X_333_1 | -1.1791 | | | N(0.0, 1.0) \n",
" X_334_0 | 1.7724 | | | N(0.0, 1.0) \n",
" X_334_1 | 1.4535 | | | N(0.0, 1.0) \n",
" X_335_0 | 0.5527 | | | N(0.0, 1.0) \n",
" X_335_1 | -0.8863 | | | N(0.0, 1.0) \n",
" X_336_0 | 1.1055 | | | N(0.0, 1.0) \n",
" X_336_1 | 1.3883 | | | N(0.0, 1.0) \n",
" X_337_0 | 0.4916 | | | N(0.0, 1.0) \n",
" X_337_1 | 1.3652 | | | N(0.0, 1.0) \n",
" X_338_0 | 1.3109 | | | N(0.0, 1.0) \n",
" X_338_1 | 1.3739 | | | N(0.0, 1.0) \n",
" X_339_0 | 0.5312 | | | N(0.0, 1.0) \n",
" X_339_1 | -1.0925 | | | N(0.0, 1.0) \n",
" X_340_0 | 0.3271 | | | N(0.0, 1.0) \n",
" X_340_1 | -1.0907 | | | N(0.0, 1.0) \n",
" X_341_0 | -1.2340 | | | N(0.0, 1.0) \n",
" X_341_1 | -0.1285 | | | N(0.0, 1.0) \n",
" X_342_0 | -1.1188 | | | N(0.0, 1.0) \n",
" X_342_1 | 0.0872 | | | N(0.0, 1.0) \n",
" X_343_0 | -1.1025 | | | N(0.0, 1.0) \n",
" X_343_1 | 0.0162 | | | N(0.0, 1.0) \n",
" X_344_0 | 0.5834 | | | N(0.0, 1.0) \n",
" X_344_1 | -1.0228 | | | N(0.0, 1.0) \n",
" X_345_0 | -0.7656 | | | N(0.0, 1.0) \n",
" X_345_1 | -0.5591 | | | N(0.0, 1.0) \n",
" X_346_0 | -1.2057 | | | N(0.0, 1.0) \n",
" X_346_1 | -0.4245 | | | N(0.0, 1.0) \n",
" X_347_0 | -0.8502 | | | N(0.0, 1.0) \n",
" X_347_1 | -0.3197 | | | N(0.0, 1.0) \n",
" X_348_0 | 0.6102 | | | N(0.0, 1.0) \n",
" X_348_1 | -0.9618 | | | N(0.0, 1.0) \n",
" X_349_0 | -1.0939 | | | N(0.0, 1.0) \n",
" X_349_1 | -0.0035 | | | N(0.0, 1.0) \n",
" X_350_0 | 0.4577 | | | N(0.0, 1.0) \n",
" X_350_1 | -1.3071 | | | N(0.0, 1.0) \n",
" X_351_0 | -1.0107 | | | N(0.0, 1.0) \n",
" X_351_1 | -0.4037 | | | N(0.0, 1.0) \n",
" X_352_0 | -0.8964 | | | N(0.0, 1.0) \n",
" X_352_1 | -0.1567 | | | N(0.0, 1.0) \n",
" X_353_0 | -0.9207 | | | N(0.0, 1.0) \n",
" X_353_1 | -0.4958 | | | N(0.0, 1.0) \n",
" X_354_0 | 0.8781 | | | N(0.0, 1.0) \n",
" X_354_1 | 1.3232 | | | N(0.0, 1.0) \n",
" X_355_0 | -1.1065 | | | N(0.0, 1.0) \n",
" X_355_1 | -0.7041 | | | N(0.0, 1.0) \n",
" X_356_0 | 0.5363 | | | N(0.0, 1.0) \n",
" X_356_1 | -0.9073 | | | N(0.0, 1.0) \n",
" X_357_0 | 0.2631 | | | N(0.0, 1.0) \n",
" X_357_1 | 1.0493 | | | N(0.0, 1.0) \n",
" X_358_0 | 0.5999 | | | N(0.0, 1.0) \n",
" X_358_1 | -0.9880 | | | N(0.0, 1.0) \n",
" X_359_0 | 0.8818 | | | N(0.0, 1.0) \n",
" X_359_1 | 1.4279 | | | N(0.0, 1.0) \n",
" X_360_0 | 0.7077 | | | N(0.0, 1.0) \n",
" X_360_1 | -0.9962 | | | N(0.0, 1.0) \n",
" X_361_0 | -1.0466 | | | N(0.0, 1.0) \n",
" X_361_1 | -0.3486 | | | N(0.0, 1.0) \n",
" X_362_0 | -1.1245 | | | N(0.0, 1.0) \n",
" X_362_1 | -0.1697 | | | N(0.0, 1.0) \n",
" X_363_0 | -0.7981 | | | N(0.0, 1.0) \n",
" X_363_1 | -0.3640 | | | N(0.0, 1.0) \n",
" X_364_0 | -1.1650 | | | N(0.0, 1.0) \n",
" X_364_1 | -0.4170 | | | N(0.0, 1.0) \n",
" X_365_0 | 0.9367 | | | N(0.0, 1.0) \n",
" X_365_1 | 1.6608 | | | N(0.0, 1.0) \n",
" X_366_0 | -1.2705 | | | N(0.0, 1.0) \n",
" X_366_1 | -0.2649 | | | N(0.0, 1.0) \n",
" X_367_0 | -0.9140 | | | N(0.0, 1.0) \n",
" X_367_1 | -0.7148 | | | N(0.0, 1.0) \n",
" X_368_0 | -0.9621 | | | N(0.0, 1.0) \n",
" X_368_1 | -0.4212 | | | N(0.0, 1.0) \n",
" X_369_0 | 0.2198 | | | N(0.0, 1.0) \n",
" X_369_1 | -1.0010 | | | N(0.0, 1.0) \n",
" X_370_0 | 0.1986 | | | N(0.0, 1.0) \n",
" X_370_1 | -1.1492 | | | N(0.0, 1.0) \n",
" X_371_0 | -0.6775 | | | N(0.0, 1.0) \n",
" X_371_1 | -0.8049 | | | N(0.0, 1.0) \n",
" X_372_0 | 1.4284 | | | N(0.0, 1.0) \n",
" X_372_1 | 1.4305 | | | N(0.0, 1.0) \n",
" X_373_0 | 0.7826 | | | N(0.0, 1.0) \n",
" X_373_1 | 1.3498 | | | N(0.0, 1.0) \n",
" X_374_0 | -0.6593 | | | N(0.0, 1.0) \n",
" X_374_1 | -0.7486 | | | N(0.0, 1.0) \n",
" X_375_0 | 0.1024 | | | N(0.0, 1.0) \n",
" X_375_1 | -1.1658 | | | N(0.0, 1.0) \n",
" X_376_0 | 1.0967 | | | N(0.0, 1.0) \n",
" X_376_1 | 1.4689 | | | N(0.0, 1.0) \n",
" X_377_0 | 0.8377 | | | N(0.0, 1.0) \n",
" X_377_1 | 1.4966 | | | N(0.0, 1.0) \n",
" X_378_0 | 0.8372 | | | N(0.0, 1.0) \n",
" X_378_1 | 1.2900 | | | N(0.0, 1.0) \n",
" X_379_0 | 0.4819 | | | N(0.0, 1.0) \n",
" X_379_1 | -1.2511 | | | N(0.0, 1.0) \n",
" X_380_0 | -0.7933 | | | N(0.0, 1.0) \n",
" X_380_1 | -0.7353 | | | N(0.0, 1.0) \n",
" X_381_0 | 0.9709 | | | N(0.0, 1.0) \n",
" X_381_1 | 1.3560 | | | N(0.0, 1.0) \n",
" X_382_0 | 0.7457 | | | N(0.0, 1.0) \n",
" X_382_1 | 1.3200 | | | N(0.0, 1.0) \n",
" X_383_0 | 1.0994 | | | N(0.0, 1.0) \n",
" X_383_1 | 1.2878 | | | N(0.0, 1.0) \n",
" X_384_0 | 0.5062 | | | N(0.0, 1.0) \n",
" X_384_1 | -1.0135 | | | N(0.0, 1.0) \n",
" X_385_0 | 1.5981 | | | N(0.0, 1.0) \n",
" X_385_1 | 1.3840 | | | N(0.0, 1.0) \n",
" X_386_0 | 0.2770 | | | N(0.0, 1.0) \n",
" X_386_1 | -1.0006 | | | N(0.0, 1.0) \n",
" X_387_0 | 0.5127 | | | N(0.0, 1.0) \n",
" X_387_1 | -0.8827 | | | N(0.0, 1.0) \n",
" X_388_0 | 0.6515 | | | N(0.0, 1.0) \n",
" X_388_1 | -0.9287 | | | N(0.0, 1.0) \n",
" X_389_0 | 0.4182 | | | N(0.0, 1.0) \n",
" X_389_1 | -0.9268 | | | N(0.0, 1.0) \n",
" X_390_0 | 1.2814 | | | N(0.0, 1.0) \n",
" X_390_1 | 1.3540 | | | N(0.0, 1.0) \n",
" X_391_0 | 0.4502 | | | N(0.0, 1.0) \n",
" X_391_1 | -0.9126 | | | N(0.0, 1.0) \n",
" X_392_0 | 0.0926 | | | N(0.0, 1.0) \n",
" X_392_1 | -1.1701 | | | N(0.0, 1.0) \n",
" X_393_0 | 0.4182 | | | N(0.0, 1.0) \n",
" X_393_1 | -0.7942 | | | N(0.0, 1.0) \n",
" X_394_0 | 1.2227 | | | N(0.0, 1.0) \n",
" X_394_1 | 1.3512 | | | N(0.0, 1.0) \n",
" X_395_0 | 0.5024 | | | N(0.0, 1.0) \n",
" X_395_1 | -0.9059 | | | N(0.0, 1.0) \n",
" X_396_0 | 0.2703 | | | N(0.0, 1.0) \n",
" X_396_1 | -0.9894 | | | N(0.0, 1.0) \n",
" X_397_0 | 0.0724 | | | N(0.0, 1.0) \n",
" X_397_1 | -0.8885 | | | N(0.0, 1.0) \n",
" X_398_0 | 0.8713 | | | N(0.0, 1.0) \n",
" X_398_1 | -0.9348 | | | N(0.0, 1.0) \n",
" X_399_0 | 0.4671 | | | N(0.0, 1.0) \n",
" X_399_1 | -0.9052 | | | N(0.0, 1.0) \n",
" X_400_0 | 0.1095 | | | N(0.0, 1.0) \n",
" X_400_1 | -1.1997 | | | N(0.0, 1.0) \n",
" X_401_0 | 1.6490 | | | N(0.0, 1.0) \n",
" X_401_1 | 1.3092 | | | N(0.0, 1.0) \n",
" X_402_0 | 1.2285 | | | N(0.0, 1.0) \n",
" X_402_1 | 1.3087 | | | N(0.0, 1.0) \n",
" X_403_0 | 1.2304 | | | N(0.0, 1.0) \n",
" X_403_1 | 1.3163 | | | N(0.0, 1.0) \n",
" X_404_0 | 0.5488 | | | N(0.0, 1.0) \n",
" X_404_1 | -0.9178 | | | N(0.0, 1.0) \n",
" X_405_0 | 1.9116 | | | N(0.0, 1.0) \n",
" X_405_1 | 1.2339 | | | N(0.0, 1.0) \n",
" X_406_0 | 1.1074 | | | N(0.0, 1.0) \n",
" X_406_1 | 1.2299 | | | N(0.0, 1.0) \n",
" X_407_0 | 0.3957 | | | N(0.0, 1.0) \n",
" X_407_1 | -0.8146 | | | N(0.0, 1.0) \n",
" X_408_0 | 0.2943 | | | N(0.0, 1.0) \n",
" X_408_1 | -0.8893 | | | N(0.0, 1.0) \n",
" X_409_0 | 1.2476 | | | N(0.0, 1.0) \n",
" X_409_1 | 1.1988 | | | N(0.0, 1.0) \n",
" X_410_0 | 0.4707 | | | N(0.0, 1.0) \n",
" X_410_1 | -0.9649 | | | N(0.0, 1.0) \n",
" X_411_0 | 0.7309 | | | N(0.0, 1.0) \n",
" X_411_1 | -0.9259 | | | N(0.0, 1.0) \n",
" X_412_0 | 0.7802 | | | N(0.0, 1.0) \n",
" X_412_1 | -0.8837 | | | N(0.0, 1.0) \n",
" X_413_0 | 0.9115 | | | N(0.0, 1.0) \n",
" X_413_1 | 1.3835 | | | N(0.0, 1.0) \n",
" X_414_0 | 0.5747 | | | N(0.0, 1.0) \n",
" X_414_1 | -1.0389 | | | N(0.0, 1.0) \n",
" X_415_0 | 0.5980 | | | N(0.0, 1.0) \n",
" X_415_1 | -0.9224 | | | N(0.0, 1.0) \n",
" X_416_0 | 0.7495 | | | N(0.0, 1.0) \n",
" X_416_1 | 1.2158 | | | N(0.0, 1.0) \n",
" X_417_0 | 0.4026 | | | N(0.0, 1.0) \n",
" X_417_1 | -0.9641 | | | N(0.0, 1.0) \n",
" X_418_0 | 0.1336 | | | N(0.0, 1.0) \n",
" X_418_1 | -0.8077 | | | N(0.0, 1.0) \n",
" X_419_0 | 0.3899 | | | N(0.0, 1.0) \n",
" X_419_1 | -1.0219 | | | N(0.0, 1.0) \n",
" X_420_0 | 1.4735 | | | N(0.0, 1.0) \n",
" X_420_1 | 1.2511 | | | N(0.0, 1.0) \n",
" X_421_0 | 1.6505 | | | N(0.0, 1.0) \n",
" X_421_1 | 1.2856 | | | N(0.0, 1.0) \n",
" X_422_0 | 0.7431 | | | N(0.0, 1.0) \n",
" X_422_1 | -1.0557 | | | N(0.0, 1.0) \n",
" X_423_0 | 1.7289 | | | N(0.0, 1.0) \n",
" X_423_1 | 1.3205 | | | N(0.0, 1.0) \n",
" X_424_0 | 1.4678 | | | N(0.0, 1.0) \n",
" X_424_1 | 1.1661 | | | N(0.0, 1.0) \n",
" X_425_0 | 0.6958 | | | N(0.0, 1.0) \n",
" X_425_1 | -0.8754 | | | N(0.0, 1.0) \n",
" X_426_0 | 0.3103 | | | N(0.0, 1.0) \n",
" X_426_1 | -0.7758 | | | N(0.0, 1.0) \n",
" X_427_0 | 0.9314 | | | N(0.0, 1.0) \n",
" X_427_1 | 1.3056 | | | N(0.0, 1.0) \n",
" X_428_0 | 0.2987 | | | N(0.0, 1.0) \n",
" X_428_1 | -1.3646 | | | N(0.0, 1.0) \n",
" X_429_0 | 0.5667 | | | N(0.0, 1.0) \n",
" X_429_1 | -0.8222 | | | N(0.0, 1.0) \n",
" X_430_0 | 1.2235 | | | N(0.0, 1.0) \n",
" X_430_1 | 1.3364 | | | N(0.0, 1.0) \n",
" X_431_0 | 0.3345 | | | N(0.0, 1.0) \n",
" X_431_1 | -0.7893 | | | N(0.0, 1.0) \n",
" X_432_0 | 0.8879 | | | N(0.0, 1.0) \n",
" X_432_1 | 1.2964 | | | N(0.0, 1.0) \n",
" X_433_0 | 0.4954 | | | N(0.0, 1.0) \n",
" X_433_1 | -0.8359 | | | N(0.0, 1.0) \n",
" X_434_0 | 0.5429 | | | N(0.0, 1.0) \n",
" X_434_1 | -0.9210 | | | N(0.0, 1.0) \n",
" X_435_0 | -0.2149 | | | N(0.0, 1.0) \n",
" X_435_1 | -0.5697 | | | N(0.0, 1.0) \n",
" X_436_0 | 0.6437 | | | N(0.0, 1.0) \n",
" X_436_1 | -0.8002 | | | N(0.0, 1.0) \n",
" X_437_0 | 0.2416 | | | N(0.0, 1.0) \n",
" X_437_1 | -0.7033 | | | N(0.0, 1.0) \n",
" X_438_0 | 0.5381 | | | N(0.0, 1.0) \n",
" X_438_1 | -0.8854 | | | N(0.0, 1.0) \n",
" X_439_0 | 0.4274 | | | N(0.0, 1.0) \n",
" X_439_1 | -0.8993 | | | N(0.0, 1.0) \n",
" X_440_0 | 2.0522 | | | N(0.0, 1.0) \n",
" X_440_1 | 1.1680 | | | N(0.0, 1.0) \n",
" X_441_0 | -0.8902 | | | N(0.0, 1.0) \n",
" X_441_1 | -0.1525 | | | N(0.0, 1.0) \n",
" X_442_0 | 1.7322 | | | N(0.0, 1.0) \n",
" X_442_1 | 1.1749 | | | N(0.0, 1.0) \n",
" X_443_0 | 1.7260 | | | N(0.0, 1.0) \n",
" X_443_1 | 1.2079 | | | N(0.0, 1.0) \n",
" X_444_0 | 0.5189 | | | N(0.0, 1.0) \n",
" X_444_1 | -0.9081 | | | N(0.0, 1.0) \n",
" X_445_0 | 0.4341 | | | N(0.0, 1.0) \n",
" X_445_1 | -0.7971 | | | N(0.0, 1.0) \n",
" X_446_0 | 0.3268 | | | N(0.0, 1.0) \n",
" X_446_1 | -0.9016 | | | N(0.0, 1.0) \n",
" X_447_0 | 0.2708 | | | N(0.0, 1.0) \n",
" X_447_1 | -0.8550 | | | N(0.0, 1.0) \n",
" X_448_0 | -1.1525 | | | N(0.0, 1.0) \n",
" X_448_1 | -0.3663 | | | N(0.0, 1.0) \n",
" X_449_0 | -0.9997 | | | N(0.0, 1.0) \n",
" X_449_1 | -0.3789 | | | N(0.0, 1.0) \n",
" X_450_0 | -0.5868 | | | N(0.0, 1.0) \n",
" X_450_1 | 0.4269 | | | N(0.0, 1.0) \n",
" X_451_0 | -1.1289 | | | N(0.0, 1.0) \n",
" X_451_1 | -0.6493 | | | N(0.0, 1.0) \n",
" X_452_0 | 0.3412 | | | N(0.0, 1.0) \n",
" X_452_1 | -0.7731 | | | N(0.0, 1.0) \n",
" X_453_0 | 1.5281 | | | N(0.0, 1.0) \n",
" X_453_1 | 1.2654 | | | N(0.0, 1.0) \n",
" X_454_0 | 1.4888 | | | N(0.0, 1.0) \n",
" X_454_1 | 1.1916 | | | N(0.0, 1.0) \n",
" X_455_0 | 0.7528 | | | N(0.0, 1.0) \n",
" X_455_1 | 1.3387 | | | N(0.0, 1.0) \n",
" X_456_0 | -1.1337 | | | N(0.0, 1.0) \n",
" X_456_1 | -0.6983 | | | N(0.0, 1.0) \n",
" X_457_0 | -1.0105 | | | N(0.0, 1.0) \n",
" X_457_1 | -0.5357 | | | N(0.0, 1.0) \n",
" X_458_0 | 0.6193 | | | N(0.0, 1.0) \n",
" X_458_1 | -0.8053 | | | N(0.0, 1.0) \n",
" X_459_0 | -0.7768 | | | N(0.0, 1.0) \n",
" X_459_1 | -0.6092 | | | N(0.0, 1.0) \n",
" X_460_0 | 0.9865 | | | N(0.0, 1.0) \n",
" X_460_1 | 1.3377 | | | N(0.0, 1.0) \n",
" X_461_0 | 0.6034 | | | N(0.0, 1.0) \n",
" X_461_1 | -0.8144 | | | N(0.0, 1.0) \n",
" X_462_0 | 0.3992 | | | N(0.0, 1.0) \n",
" X_462_1 | -0.7986 | | | N(0.0, 1.0) \n",
" X_463_0 | 0.0089 | | | N(0.0, 1.0) \n",
" X_463_1 | -0.7494 | | | N(0.0, 1.0) \n",
" X_464_0 | 0.1099 | | | N(0.0, 1.0) \n",
" X_464_1 | -0.7386 | | | N(0.0, 1.0) \n",
" X_465_0 | 1.7386 | | | N(0.0, 1.0) \n",
" X_465_1 | 1.1559 | | | N(0.0, 1.0) \n",
" X_466_0 | -1.1997 | | | N(0.0, 1.0) \n",
" X_466_1 | -0.4080 | | | N(0.0, 1.0) \n",
" X_467_0 | 1.2710 | | | N(0.0, 1.0) \n",
" X_467_1 | 1.4418 | | | N(0.0, 1.0) \n",
" X_468_0 | 0.7544 | | | N(0.0, 1.0) \n",
" X_468_1 | -0.9862 | | | N(0.0, 1.0) \n",
" X_469_0 | 0.1796 | | | N(0.0, 1.0) \n",
" X_469_1 | -0.7951 | | | N(0.0, 1.0) \n",
" X_470_0 | 1.0964 | | | N(0.0, 1.0) \n",
" X_470_1 | 1.2282 | | | N(0.0, 1.0) \n",
" X_471_0 | 0.6180 | | | N(0.0, 1.0) \n",
" X_471_1 | -0.8972 | | | N(0.0, 1.0) \n",
" X_472_0 | 0.3087 | | | N(0.0, 1.0) \n",
" X_472_1 | -0.8450 | | | N(0.0, 1.0) \n",
" rbf_variance | 0.1680 | (+ve) | | \n",
" rbf_lengthscale_0 | 2.8445 | (+ve) | | \n",
" rbf_lengthscale_1 | 2.8504 | (+ve) | | \n",
" linear_variance | 0.0000 | (+ve) | | \n",
" noise_variance | 0.0008 | (+ve) | | \n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"<matplotlib.axes.AxesSubplot at 0x4d698d0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"m2.plot_latent()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['pylab']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.axes.AxesSubplot at 0x4d769d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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tP4GdFO/E8N5dsbHoKPaX19nyrIMsUhPjUNCtAxTlx16ChuKKOjz+9iYcPFIP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m0ixvfkzWKxeJue7rnnHtxwww34/PPPcfPNN+Pvf/87Ro0a5ZNndZ4896G+V49OJdq3b4/y8nJ0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\nJAYTJkyYMOGD/w89o8Q7mcZHWgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x4d79850>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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