Commit 9a5e5c23 authored by Danniene Wete's avatar Danniene Wete

test aggr. appraoch (a) with more clusters

parent 3f987622
......@@ -34,18 +34,29 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"(7352, 42)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_docs.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"serialize a train_docs to disk for later use\n",
"#serialize a train_docs to disk for later use\n",
"with open('data/corpus.train', 'wb') as fp:\n",
" pickle.dump(train_docs, fp)\n",
"\n",
......@@ -55,14 +66,10 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load train corpus from disk\n",
"#with open ('data/corpus.train', 'rb') as fp:\n",
" #train_docs = pickle.load(fp)"
]
"source": []
},
{
"cell_type": "code",
......@@ -187,9 +194,21 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "NameError",
"evalue": "name 'DONE' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-a6dc029c183b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m#with open('latexSearchModelDirWL30/' +filename, 'w') as f:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;31m# f.write(df_document_topics.to_latex())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDONE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'DONE' is not defined"
]
}
],
"source": [
"for rdst in range(0, 1500):\n",
" \n",
......@@ -215,7 +234,7 @@
" #filename = 'run'+str(rdst)+'.tex'\n",
" #with open('latexSearchModelDirWL30/' +filename, 'w') as f:\n",
" # f.write(df_document_topics.to_latex())\n",
"print (DONE)"
"print ('DONE')"
]
},
{
......
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -33,7 +33,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
......@@ -205,7 +205,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -234,7 +234,7 @@
" trainAcc_centroids = calc_centroids_array(trainAcc_window, n_cluster)\n",
" trainGyr_centroids = calc_centroids_array(trainGyr_window, n_cluster)\n",
" \n",
" Save centroids on disk for using later\n",
" #Save centroids on disk for using later\n",
" with open('data/trainAcc.centroids', 'wb') as fp:\n",
" pickle.dump(trainAcc_centroids, fp)\n",
" with open('data/trainGyr.centroids', 'wb') as fp: \n",
......
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
......@@ -23,29 +23,40 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"train_docs, test_docs = codebook_approach(30, 29) # best params: 30, 17"
"train_docs, test_docs = codebook_approach(35, 23) # best params: 30, 17"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 13,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"(7352, 36)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_docs.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"serialize a train_docs to disk for later use\n",
"#serialize a train_docs to disk for later use\n",
"with open('data/corpus.train', 'wb') as fp:\n",
" pickle.dump(train_docs, fp)\n",
"\n",
......@@ -55,18 +66,14 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load train corpus from disk\n",
"#with open ('data/corpus.train', 'rb') as fp:\n",
" #train_docs = pickle.load(fp)"
]
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
......@@ -76,7 +83,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
......@@ -101,7 +108,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
......@@ -159,7 +166,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 18,
"metadata": {},
"outputs": [
{
......@@ -168,7 +175,7 @@
"array([0, 1, 2, 3, 4, 5])"
]
},
"execution_count": 9,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
......@@ -187,9 +194,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DONE\n"
]
}
],
"source": [
"for rdst in range(0, 1500):\n",
" \n",
......@@ -215,7 +230,7 @@
" #filename = 'run'+str(rdst)+'.tex'\n",
" #with open('latexSearchModelDirWL30/' +filename, 'w') as f:\n",
" # f.write(df_document_topics.to_latex())\n",
"print (DONE)"
"print ('DONE')"
]
},
{
......
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"scores = np.loadtxt('randomSeed_Scores.txt')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8947225244831338"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(scores[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
......@@ -2,11 +2,11 @@
\toprule
{} & Precision & Recall & F1 score \\
\midrule
WALKING & 0.842 & 0.883 & 0.862 \\
WALKING\_UPSTAIRS & 0.789 & 0.856 & 0.821 \\
WALKING\_DOWNSTAIRS & 0.897 & 0.767 & 0.827 \\
SITTING & 0.638 & 0.485 & 0.551 \\
STANDING & 0.658 & 0.739 & 0.696 \\
LAYING & 0.915 & 1.000 & 0.956 \\
WALKING & 0.918 & 0.952 & 0.935 \\
WALKING\_UPSTAIRS & 0.911 & 0.890 & 0.900 \\
WALKING\_DOWNSTAIRS & 0.895 & 0.893 & 0.894 \\
SITTING & 0.692 & 0.434 & 0.533 \\
STANDING & 0.649 & 0.816 & 0.723 \\
LAYING & 0.912 & 0.980 & 0.945 \\
\bottomrule
\end{tabular}
......@@ -2,11 +2,11 @@
\toprule
{} & Precision & Recall & F1 score \\
\midrule
WALKING & 0.899 & 0.794 & 0.843 \\
WALKING\_UPSTAIRS & 0.771 & 0.917 & 0.838 \\
WALKING\_DOWNSTAIRS & 0.881 & 0.829 & 0.854 \\
SITTING & 0.771 & 0.537 & 0.633 \\
STANDING & 0.726 & 0.849 & 0.783 \\
LAYING & 0.902 & 1.000 & 0.948 \\
WALKING & 0.966 & 0.908 & 0.936 \\
WALKING\_UPSTAIRS & 0.880 & 0.938 & 0.908 \\
WALKING\_DOWNSTAIRS & 0.923 & 0.929 & 0.926 \\
SITTING & 0.782 & 0.494 & 0.605 \\
STANDING & 0.698 & 0.870 & 0.775 \\
LAYING & 0.901 & 0.986 & 0.942 \\
\bottomrule
\end{tabular}
......@@ -2,11 +2,11 @@
\toprule
{} & Top0 & Top1 & Top2 & Top3 & Top4 & Top5 \\
\midrule
Class0 & 8 & 0 & 0 & 438 & 0 & 50 \\
Class1 & 29 & 0 & 0 & 39 & 0 & 403 \\
Class2 & 322 & 0 & 0 & 40 & 0 & 58 \\
Class3 & 0 & 204 & 238 & 0 & 49 & 0 \\
Class4 & 0 & 393 & 135 & 3 & 1 & 0 \\
Class5 & 0 & 0 & 0 & 0 & 537 & 0 \\
Class0 & 472 & 0 & 0 & 12 & 0 & 12 \\
Class1 & 21 & 0 & 0 & 419 & 0 & 31 \\
Class2 & 18 & 0 & 0 & 27 & 0 & 375 \\
Class3 & 0 & 49 & 213 & 0 & 229 & 0 \\
Class4 & 3 & 2 & 91 & 2 & 434 & 0 \\
Class5 & 0 & 526 & 4 & 0 & 6 & 1 \\
\bottomrule
\end{tabular}
......@@ -2,11 +2,11 @@
\toprule
{} & Top0 & Top1 & Top2 & Top3 & Top4 & Top5 \\
\midrule
Class0 & 42 & 1 & 0 & 974 & 0 & 209 \\
Class1 & 67 & 0 & 0 & 22 & 0 & 984 \\
Class2 & 817 & 0 & 0 & 87 & 0 & 82 \\
Class3 & 0 & 440 & 691 & 1 & 153 & 1 \\
Class4 & 1 & 1167 & 205 & 0 & 0 & 1 \\
Class5 & 0 & 0 & 0 & 0 & 1407 & 0 \\
Class0 & 1113 & 0 & 0 & 90 & 0 & 23 \\
Class1 & 14 & 0 & 0 & 1006 & 0 & 53 \\
Class2 & 25 & 0 & 0 & 45 & 0 & 916 \\
Class3 & 0 & 152 & 635 & 1 & 498 & 0 \\
Class4 & 0 & 0 & 177 & 1 & 1196 & 0 \\
Class5 & 0 & 1388 & 0 & 0 & 19 & 0 \\
\bottomrule
\end{tabular}
......@@ -195,9 +195,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-048866324d96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrdst\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1500\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 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpred_topics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSearchBestModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_corpus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_id2word\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrdst\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 4\u001b[0m \u001b[0mCM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontingency_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_topics\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 df_document_topics = pd.DataFrame(CM, index=['Class'+str(i) for i in list(set(y_train))], \n",
"\u001b[0;32m<ipython-input-7-e6745180488f>\u001b[0m in \u001b[0;36mSearchBestModel\u001b[0;34m(corpus_train, train_vocab, rnd_numb)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.01\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus_train, alpha=alpha,\n\u001b[0;32m----> 8\u001b[0;31m num_topics=ntpc, id2word=train_vocab, random_seed=rnd_numb)\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mdoc_topics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mldamallet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcorpus_train\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mtopicList\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[0;32m/usr/local/lib/python3.7/dist-packages/gensim/models/wrappers/ldamallet.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, mallet_path, corpus, num_topics, alpha, id2word, workers, prefix, optimize_interval, iterations, topic_threshold, random_seed)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_seed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorpus\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 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfinferencer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/usr/local/lib/python3.7/dist-packages/gensim/models/wrappers/ldamallet.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, corpus)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;31m# NOTE \"--keep-sequence-bigrams\" / \"--use-ngrams true\" poorer results + runs out of memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"training MALLET LDA with %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m \u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshell\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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 285\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_topics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_word_topics\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 286\u001b[0m \u001b[0;31m# NOTE - we are still keeping the wordtopics variable to not break backward compatibility.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gensim/utils.py\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(stdout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 1908\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"COMMAND: %s %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1909\u001b[0m \u001b[0mprocess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mpopenargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1910\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munused_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\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 1911\u001b[0m \u001b[0mretcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\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 1912\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mretcode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.7/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m 924\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stdin_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 925\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m \u001b[0mstdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\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 927\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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 928\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"for rdst in range(0, 1500):\n",
" \n",
......
0.0 0.7909738717339667 174 0.0
0.1111111111111111 0.7909738717339667 174 0.0
0.2222222222222222 0.7899558873430608 174 0.017241379310344827
0.3333333333333333 0.7899558873430608 174 0.017241379310344827
0.4444444444444444 0.7899558873430608 174 0.017241379310344827
0.5555555555555556 0.7926705123854768 174 0.022988505747126436
0.6666666666666666 0.7712928401764506 174 0.034482758620689655
0.7777777777777777 0.7448252460128945 174 0.15517241379310345
0.8888888888888888 0.6793349168646081 174 0.28160919540229884
1.0 0.6233457753647778 174 0.46551724137931033
0.0 0.827621309806583 366 0.0
0.1111111111111111 0.827621309806583 366 0.0
0.2222222222222222 0.8266033254156769 366 0.00273224043715847
0.3333333333333333 0.827281981676281 366 0.00819672131147541
0.4444444444444444 0.827281981676281 366 0.00819672131147541
0.5555555555555556 0.8218527315914489 366 0.01092896174863388
0.6666666666666666 0.8187987784187309 366 0.01912568306010929
0.7777777777777777 0.8194774346793349 366 0.040983606557377046
0.8888888888888888 0.8198167628096369 366 0.07103825136612021
1.0 0.7380386834068544 366 0.14207650273224043
0.0 0.8215451577801959 174 0.0
0.1111111111111111 0.8215451577801959 174 0.0
0.2222222222222222 0.4352557127312296 174 0.017241379310344827
0.3333333333333333 0.4352557127312296 174 0.017241379310344827
0.4444444444444444 0.4352557127312296 174 0.017241379310344827
0.5555555555555556 0.3986670293797606 174 0.022988505747126436
0.6666666666666666 0.3986670293797606 174 0.022988505747126436
0.7777777777777777 0.3150163220892274 174 0.06321839080459771
0.8888888888888888 0.4129488574537541 174 0.21839080459770116
1.0 0.4208378672470076 174 0.4482758620689655
0.0 0.8506528835690969 366 0.0
0.1111111111111111 0.8506528835690969 366 0.0
0.2222222222222222 0.6651251360174102 366 0.00273224043715847
0.3333333333333333 0.5322361262241567 366 0.00819672131147541
0.4444444444444444 0.5322361262241567 366 0.00819672131147541
0.5555555555555556 0.5322361262241567 366 0.00819672131147541
0.6666666666666666 0.3103917301414581 366 0.01366120218579235
0.7777777777777777 0.34330794341675736 366 0.01912568306010929
0.8888888888888888 0.42723068552774757 366 0.03551912568306011
1.0 0.38071273122959737 366 0.09562841530054644
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......@@ -274,7 +274,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.7.4"
}
},
"nbformat": 4,
......
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
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