Untitled.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import shelve\n",
"import numpy\n",
"import pandas\n",
"import sklearn.manifold\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data=shelve.open(\"./Sparse_mat.shelve\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"array_test=data[\"ASR\"][\"DEV\"].toarray()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(175, 1060)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"max_list=numpy.max(array_test,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a=pandas.DataFrame([numpy.divide(x,numpy.float(max(x))) for x in array_test])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"<p>5 rows × 1060 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 \\\n",
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"1 0.500000 0.000000 0.250000 0.0 0.000000 0 0 0 0.000000 \n",
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"4 0.500000 0.500000 0.000000 0.0 0.000000 0 0 0 0.000000 \n",
"\n",
" 9 ... 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 \n",
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"\n",
"[5 rows x 1060 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"trans=sklearn.manifold.TSNE()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tsne=trans.fit_transform(a.values)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f0e612a9e90>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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IlK+MK4bPu/vG8GcGwMw2ANcDG4D3A3eZWf2Lqb8CfMTdLwUuNbPxEmLoqlqt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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0e613b0fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter([x[0] for x in tsne],[y[1] for y in tsne])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'title' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-20-7cea0497790c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'title' is not defined"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0e612bef50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.axes()\n",
"plt.title(title)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i,pos in enumerate(zip([x[0] for x in tsne],[y[1] for y in tsne])):\n",
" plt.text(pos[0],pos[1],i)\n",
" if i > 30:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"enumerate(zip([x[0] for x in tsne],[y[1] for y in tsne])).next()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.axes()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}