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RepTB_NBI_Algorithm_Snippet.ipynb
| 1 | { | File was deleted | |
| 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 | } | ||
| 80 | 1 | { |