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beta-vae-normalizing-flows
Commit 0beeeb5e authored by Kaan Güney Keklikçi's avatar Kaan Güney Keklikçi
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2-d ks test sample from initial distributions, fixed critial bug

parent c1661dc9
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beta-vae-normalizing-flows/thoracic-surgery/beta-vae-maf-thoracic-surgery_v1.1.ipynb
View file @ 0beeeb5e
......@@ -560,7 +560,7 @@
{
"cell_type": "code",
"execution_count": 21,
"id": "c4c0042a",
"id": "6e2bf63d",
"metadata": {},
"outputs": [
{
......@@ -1382,21 +1382,17 @@
},
{
"cell_type": "code",
"execution_count": 60,
"id": "2ba3a0a4",
"execution_count": 91,
"id": "0ba092f7",
"metadata": {},
"outputs": [],
"source": [
"# KS-test\n",
"prior_sample = prior.sample(X_train_copy.shape[0]).numpy()\n",
"post_sample = trainable_dist.sample(X_train_copy.shape[0]).numpy()\n",
"\n",
"with open('ks-test.txt', 'a') as f:\n",
" f.write('MAF[SHIFT-SCALE] FLOW\\n')\n",
" f.write('p-val[training - qtrainable] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[training - qtrained] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], post_sample[...,0], post_sample[...,1])))\n",
" \n",
" f.write('p-val[testing - qtrainable] = {}\\n'.format(ks2d2s(X_test_np[...,0], X_test_np[...,1], prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[testing - qtrained] = {}\\n'.format(ks2d2s(X_test_np[...,0], X_test_np[...,1], post_sample[...,0], post_sample[...,1])))\n",
"f.close()"
]
......
beta-vae-normalizing-flows/thoracic-surgery/beta-vae-nice-thoracic-surgery.ipynb
View file @ 0beeeb5e
......@@ -1359,21 +1359,17 @@
},
{
"cell_type": "code",
"execution_count": 226,
"execution_count": 239,
"id": "b31a919e",
"metadata": {},
"outputs": [],
"source": [
"# KS-test\n",
"prior_sample = prior.sample(X_train_copy.shape[0]).numpy()\n",
"post_sample = trainable_dist.sample(X_train_copy.shape[0]).numpy()\n",
"\n",
"with open('ks-test.txt', 'a') as f:\n",
" f.write('NICE FLOW\\n')\n",
" f.write('p-val[training - qtrainable] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[training - qtrained] = {}\\n'.format(ks2d2s(X_train_np[...,0], X_train_np[...,1], post_sample[...,0], post_sample[...,1])))\n",
" \n",
" f.write('p-val[testing - qtrainable] = {}\\n'.format(ks2d2s(X_test_np[...,0], X_test_np[...,1], prior_sample[...,0], prior_sample[...,1])))\n",
" f.write('p-val[testing - qtrained] = {}\\n'.format(ks2d2s(X_test_np[...,0], X_test_np[...,1], post_sample[...,0], post_sample[...,1])))\n",
"f.close()"
]
......
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