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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 1, |
| 6 | + "metadata": { |
| 7 | + "collapsed": true |
| 8 | + }, |
| 9 | + "outputs": [], |
| 10 | + "source": [ |
| 11 | + "import numpy as np" |
| 12 | + ] |
| 13 | + }, |
| 14 | + { |
| 15 | + "cell_type": "code", |
| 16 | + "execution_count": 2, |
| 17 | + "metadata": { |
| 18 | + "collapsed": false |
| 19 | + }, |
| 20 | + "outputs": [ |
| 21 | + { |
| 22 | + "data": { |
| 23 | + "text/plain": [ |
| 24 | + "'1.11.2'" |
| 25 | + ] |
| 26 | + }, |
| 27 | + "execution_count": 2, |
| 28 | + "metadata": {}, |
| 29 | + "output_type": "execute_result" |
| 30 | + } |
| 31 | + ], |
| 32 | + "source": [ |
| 33 | + "np.__version__" |
| 34 | + ] |
| 35 | + }, |
| 36 | + { |
| 37 | + "cell_type": "markdown", |
| 38 | + "metadata": {}, |
| 39 | + "source": [ |
| 40 | + "Q1. Search for docstrings of the numpy functions on linear algebra." |
| 41 | + ] |
| 42 | + }, |
| 43 | + { |
| 44 | + "cell_type": "code", |
| 45 | + "execution_count": 4, |
| 46 | + "metadata": { |
| 47 | + "collapsed": false |
| 48 | + }, |
| 49 | + "outputs": [ |
| 50 | + { |
| 51 | + "name": "stdout", |
| 52 | + "output_type": "stream", |
| 53 | + "text": [ |
| 54 | + "Search results for 'linear algebra'\n", |
| 55 | + "-----------------------------------\n", |
| 56 | + "numpy.linalg.solve\n", |
| 57 | + " Solve a linear matrix equation, or system of linear scalar equations.\n", |
| 58 | + "numpy.poly\n", |
| 59 | + " Find the coefficients of a polynomial with the given sequence of roots.\n", |
| 60 | + "numpy.restoredot\n", |
| 61 | + " Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS\n", |
| 62 | + "numpy.linalg.eig\n", |
| 63 | + " Compute the eigenvalues and right eigenvectors of a square array.\n", |
| 64 | + "numpy.linalg.cond\n", |
| 65 | + " Compute the condition number of a matrix.\n", |
| 66 | + "numpy.linalg.eigh\n", |
| 67 | + " Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.\n", |
| 68 | + "numpy.linalg.pinv\n", |
| 69 | + " Compute the (Moore-Penrose) pseudo-inverse of a matrix.\n", |
| 70 | + "numpy.linalg.LinAlgError\n", |
| 71 | + " Generic Python-exception-derived object raised by linalg functions.\n" |
| 72 | + ] |
| 73 | + } |
| 74 | + ], |
| 75 | + "source": [] |
| 76 | + }, |
| 77 | + { |
| 78 | + "cell_type": "markdown", |
| 79 | + "metadata": {}, |
| 80 | + "source": [ |
| 81 | + "Q2. Get help information for numpy dot function." |
| 82 | + ] |
| 83 | + }, |
| 84 | + { |
| 85 | + "cell_type": "code", |
| 86 | + "execution_count": 9, |
| 87 | + "metadata": { |
| 88 | + "collapsed": false |
| 89 | + }, |
| 90 | + "outputs": [ |
| 91 | + { |
| 92 | + "name": "stdout", |
| 93 | + "output_type": "stream", |
| 94 | + "text": [ |
| 95 | + "dot(a, b, out=None)\n", |
| 96 | + "\n", |
| 97 | + "Dot product of two arrays.\n", |
| 98 | + "\n", |
| 99 | + "For 2-D arrays it is equivalent to matrix multiplication, and for 1-D\n", |
| 100 | + "arrays to inner product of vectors (without complex conjugation). For\n", |
| 101 | + "N dimensions it is a sum product over the last axis of `a` and\n", |
| 102 | + "the second-to-last of `b`::\n", |
| 103 | + "\n", |
| 104 | + " dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\n", |
| 105 | + "\n", |
| 106 | + "Parameters\n", |
| 107 | + "----------\n", |
| 108 | + "a : array_like\n", |
| 109 | + " First argument.\n", |
| 110 | + "b : array_like\n", |
| 111 | + " Second argument.\n", |
| 112 | + "out : ndarray, optional\n", |
| 113 | + " Output argument. This must have the exact kind that would be returned\n", |
| 114 | + " if it was not used. In particular, it must have the right type, must be\n", |
| 115 | + " C-contiguous, and its dtype must be the dtype that would be returned\n", |
| 116 | + " for `dot(a,b)`. This is a performance feature. Therefore, if these\n", |
| 117 | + " conditions are not met, an exception is raised, instead of attempting\n", |
| 118 | + " to be flexible.\n", |
| 119 | + "\n", |
| 120 | + "Returns\n", |
| 121 | + "-------\n", |
| 122 | + "output : ndarray\n", |
| 123 | + " Returns the dot product of `a` and `b`. If `a` and `b` are both\n", |
| 124 | + " scalars or both 1-D arrays then a scalar is returned; otherwise\n", |
| 125 | + " an array is returned.\n", |
| 126 | + " If `out` is given, then it is returned.\n", |
| 127 | + "\n", |
| 128 | + "Raises\n", |
| 129 | + "------\n", |
| 130 | + "ValueError\n", |
| 131 | + " If the last dimension of `a` is not the same size as\n", |
| 132 | + " the second-to-last dimension of `b`.\n", |
| 133 | + "\n", |
| 134 | + "See Also\n", |
| 135 | + "--------\n", |
| 136 | + "vdot : Complex-conjugating dot product.\n", |
| 137 | + "tensordot : Sum products over arbitrary axes.\n", |
| 138 | + "einsum : Einstein summation convention.\n", |
| 139 | + "matmul : '@' operator as method with out parameter.\n", |
| 140 | + "\n", |
| 141 | + "Examples\n", |
| 142 | + "--------\n", |
| 143 | + ">>> np.dot(3, 4)\n", |
| 144 | + "12\n", |
| 145 | + "\n", |
| 146 | + "Neither argument is complex-conjugated:\n", |
| 147 | + "\n", |
| 148 | + ">>> np.dot([2j, 3j], [2j, 3j])\n", |
| 149 | + "(-13+0j)\n", |
| 150 | + "\n", |
| 151 | + "For 2-D arrays it is the matrix product:\n", |
| 152 | + "\n", |
| 153 | + ">>> a = [[1, 0], [0, 1]]\n", |
| 154 | + ">>> b = [[4, 1], [2, 2]]\n", |
| 155 | + ">>> np.dot(a, b)\n", |
| 156 | + "array([[4, 1],\n", |
| 157 | + " [2, 2]])\n", |
| 158 | + "\n", |
| 159 | + ">>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\n", |
| 160 | + ">>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\n", |
| 161 | + ">>> np.dot(a, b)[2,3,2,1,2,2]\n", |
| 162 | + "499128\n", |
| 163 | + ">>> sum(a[2,3,2,:] * b[1,2,:,2])\n", |
| 164 | + "499128\n" |
| 165 | + ] |
| 166 | + } |
| 167 | + ], |
| 168 | + "source": [] |
| 169 | + }, |
| 170 | + { |
| 171 | + "cell_type": "code", |
| 172 | + "execution_count": null, |
| 173 | + "metadata": { |
| 174 | + "collapsed": true |
| 175 | + }, |
| 176 | + "outputs": [], |
| 177 | + "source": [] |
| 178 | + } |
| 179 | + ], |
| 180 | + "metadata": { |
| 181 | + "kernelspec": { |
| 182 | + "display_name": "Python 2", |
| 183 | + "language": "python", |
| 184 | + "name": "python2" |
| 185 | + }, |
| 186 | + "language_info": { |
| 187 | + "codemirror_mode": { |
| 188 | + "name": "ipython", |
| 189 | + "version": 2 |
| 190 | + }, |
| 191 | + "file_extension": ".py", |
| 192 | + "mimetype": "text/x-python", |
| 193 | + "name": "python", |
| 194 | + "nbconvert_exporter": "python", |
| 195 | + "pygments_lexer": "ipython2", |
| 196 | + "version": "2.7.10" |
| 197 | + } |
| 198 | + }, |
| 199 | + "nbformat": 4, |
| 200 | + "nbformat_minor": 0 |
| 201 | +} |
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