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February 17, 2016 09:17
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k-means clustering example
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 38, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "import numpy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h1> Generating data </h1>\n", | |
| "<p>Data is generated for 200 patients with their response for 10 symptoms each." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 64, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "[[ 4. 2. 3. 3. 3. 0. 2. 4. 3. 1.]\n", | |
| " [ 4. 3. 1. 0. 1. 1. 3. 3. 1. 3.]\n", | |
| " [ 4. 0. 4. 3. 1. 2. 0. 1. 4. 4.]\n", | |
| " [ 0. 4. 4. 0. 3. 0. 3. 1. 1. 1.]\n", | |
| " [ 3. 4. 3. 3. 4. 2. 2. 4. 1. 2.]\n", | |
| " [ 4. 1. 3. 3. 2. 1. 3. 0. 3. 0.]\n", | |
| " [ 1. 3. 1. 1. 3. 3. 0. 3. 4. 3.]\n", | |
| " [ 1. 1. 0. 0. 2. 1. 0. 4. 3. 0.]\n", | |
| " [ 2. 3. 0. 1. 2. 1. 1. 2. 3. 0.]\n", | |
| " [ 3. 4. 0. 3. 3. 2. 2. 4. 3. 3.]]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "sample_sym = numpy.random.randint(5,size=(10,10))\n", | |
| "sym = numpy.random.randint(5,size=(1000,10))\n", | |
| "sym = sym.astype(float)\n", | |
| "\n", | |
| "print sample_sym.astype(float)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 57, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "numpy.ndarray" | |
| ] | |
| }, | |
| "execution_count": 57, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "type(sym)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h1> K-means clustering <h1>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 65, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from scipy.cluster.vq import *\n", | |
| "\n", | |
| "centroid ,labels = kmeans2(sym,12) #assuming 12 clusters" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h1> visualizing the clusters </h1>\n", | |
| "<p> matplotlib is used to visualize the clusters, 2d and 3d plots are drawn" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 59, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import matplotlib\n", | |
| "matplotlib.use('Agg')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 97, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# 3 features are chosen to represent the 3 axis in the plots\n", | |
| "# change the variables to choose different features\n", | |
| "x = 0\n", | |
| "y = 5\n", | |
| "z = 9" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h2> cluster centroid of each disease </h2>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 107, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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R0+LfuYYWMszk/xQX+WatPPo71+TbnchdLAEk6Ane0XdAZxBYj0Y33+jxpCtl2fJ8Z0sc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\nbABPAbhkNCZS77ECk5sXZnAZdvF/X+54pUk2F60AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0xf7544f0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "from mpl_toolkits.mplot3d import Axes3D\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "fig = plt.figure()\n", | |
| "ax = fig.add_subplot(111, projection='3d')\n", | |
| "\n", | |
| "#ax.scatter(sym[:,0], sym[:,1], sym[:,2], c=colors)\n", | |
| "\n", | |
| "ax.scatter(centroid[:,x],centroid[:,y],centroid[:,z] ,marker='o', linewidths=10)\n", | |
| "\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "<h2> individual disease cluster <h2>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 108, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "choose disease from 0 to 110\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "cluster_num = int(raw_input(\"choose disease from 0 to 11\"))\n", | |
| "cn = cluster_num" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 109, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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nHCxcuBC9e/c25Jh5SbrkaSVHgN4vWSvpku2KIjmyf+VyDiRnUI+CTLYLKioq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\nD1EcXEIAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x103cdb10>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sym_clus = sym[labels == cluster_num]\n", | |
| "\n", | |
| "fig = plt.figure()\n", | |
| "ax = fig.add_subplot(111, projection='3d')\n", | |
| "\n", | |
| "ax.scatter(sym_clus[:,x], sym_clus[:,y], sym_clus[:,z], c=[1,0,0])\n", | |
| "ax.scatter(centroid[cn,x],centroid[cn,y],centroid[cn,z] ,marker='o', linewidths=10, c=colors)\n", | |
| "\n", | |
| "plt.show()" | |
| ] | |
| } | |
| ], | |
| "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": "2.7.11" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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