Commit c46592cac8c3c0f672971ca6c7ae8f8eb23c5c56
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RepTB_NBI_Algorithm_Snippet.ipynb
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| 1 | -{ | |
| 2 | - "cells": [ | |
| 3 | - { | |
| 4 | - "cell_type": "code", | |
| 5 | - "execution_count": null, | |
| 6 | - "metadata": { | |
| 7 | - "collapsed": true | |
| 8 | - }, | |
| 9 | - "outputs": [], | |
| 10 | - "source": [ | |
| 11 | - "import numpy as np\n", | |
| 12 | - "import csv\n", | |
| 13 | - "import numpy.matlib\n", | |
| 14 | - "from operator import itemgetter, attrgetter\n", | |
| 15 | - "from sklearn.model_selection import KFold\n", | |
| 16 | - "from sklearn.metrics import roc_curve, auc\n", | |
| 17 | - "import matplotlib.pyplot as plt" | |
| 18 | - ] | |
| 19 | - }, | |
| 20 | - { | |
| 21 | - "cell_type": "code", | |
| 22 | - "execution_count": null, | |
| 23 | - "metadata": { | |
| 24 | - "collapsed": true | |
| 25 | - }, | |
| 26 | - "outputs": [], | |
| 27 | - "source": [ | |
| 28 | - "#NBI calculation for A (adjacent matrix)\n", | |
| 29 | - "\n", | |
| 30 | - "K = np.diag((1/sum(A))) # Create a diagonal matrix \n", | |
| 31 | - "n = A.shape[0] # Number of rows of adjacent martix A\n", | |
| 32 | - "m = A.shape[1] # Number of columns in adjacent matrix A\n", | |
| 33 | - "#print n, m, Ky.shape\n", | |
| 34 | - "K[np.isinf(K) | np.isnan(K)] = 0\n", | |
| 35 | - "kk = np.transpose(np.sum(A,1))\n", | |
| 36 | - "#print kx.shape\n", | |
| 37 | - "N = np.matlib.repmat(1/kk,n,1)\n", | |
| 38 | - "N[np.isinf(N) | np.isnan(N)] = 0\n", | |
| 39 | - "#kx[np.isinf(kx) | np.isnan(kx)] = 0\n", | |
| 40 | - "W = np.transpose(np.dot(A, K)) # Create the weight matrix \n", | |
| 41 | - "W1 = np.dot(A, W)\n", | |
| 42 | - "W2 = np.multiply(N, W1) # Create the scaled up weight matrix\n", | |
| 43 | - "print W2.shape\n", | |
| 44 | - "NBIscore = np.dot(W2, A) # Create the Final Resource matrix in accordance with (R = W.A)\n", | |
| 45 | - "print NBIscore.shape" | |
| 46 | - ] | |
| 47 | - }, | |
| 48 | - { | |
| 49 | - "cell_type": "code", | |
| 50 | - "execution_count": null, | |
| 51 | - "metadata": { | |
| 52 | - "collapsed": true | |
| 53 | - }, | |
| 54 | - "outputs": [], | |
| 55 | - "source": [] | |
| 56 | - } | |
| 57 | - ], | |
| 58 | - "metadata": { | |
| 59 | - "kernelspec": { | |
| 60 | - "display_name": "Python 2", | |
| 61 | - "language": "python", | |
| 62 | - "name": "python2" | |
| 63 | - }, | |
| 64 | - "language_info": { | |
| 65 | - "codemirror_mode": { | |
| 66 | - "name": "ipython", | |
| 67 | - "version": 2 | |
| 68 | - }, | |
| 69 | - "file_extension": ".py", | |
| 70 | - "mimetype": "text/x-python", | |
| 71 | - "name": "python", | |
| 72 | - "nbconvert_exporter": "python", | |
| 73 | - "pygments_lexer": "ipython2", | |
| 74 | - "version": "2.7.6" | |
| 75 | - } | |
| 76 | - }, | |
| 77 | - "nbformat": 4, | |
| 78 | - "nbformat_minor": 2 | |
| 79 | -} |