{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from nltk.parse.stanford import StanfordParser\n", "import numpy as np\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.13.1'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Building a parse tree from a sentence " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sentence = 'The quick brown fox jumps over the lazy dog'\n", "\n", "# create parser object\n", "scp = StanfordParser(path_to_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser.jar',\n", " path_to_models_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser-3.5.2-models.jar')\n", "\n", "scp" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(ROOT\n", " (NP\n", " (NP (DT The) (JJ quick) (JJ brown) (NN fox))\n", " (NP\n", " (NP (NNS jumps))\n", " (PP (IN over) (NP (DT the) (JJ lazy) (NN dog))))))\n" ] } ], "source": [ "# get parse tree\n", "result = list(scp.raw_parse(sentence)) \n", "tree = result[0]\n", "\n", "# print the constituency parse tree\n", "print(tree) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# visualize constituency parse tree\n", "tree.draw() " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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PKdChx7nUvC3V36UQJICnrVzsffYwBluMYhxj3mKMwxdBAnhK+QIfaw9jUGIX3djzFnt8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\n", "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import Image\n", "\n", "Image('tree.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Math Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Vectors" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## vectors\n", "\n", "x = [1, 2, 3, 4, 5]\n", "x" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4 5]\n", "\n" ] } ], "source": [ "# using numpy\n", "import numpy as np\n", "x = np.array([1, 2, 3, 4, 5])\n", "\n", "print(x)\n", "print(type(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matrices" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 5 2]\n", " [4 7 4]\n", " [2 0 9]]\n", "(3, 3)\n" ] } ], "source": [ "## matrices\n", "\n", "m = np.array([[1, 5, 2],\n", " [4, 7, 4],\n", " [2, 0, 9]])\n", "\n", "# view matrix\n", "print(m)\n", "\n", "# view dimensions\n", "print(m.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matrix transpose" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix Transpose:\n", " [[1 4 2]\n", " [5 7 0]\n", " [2 4 9]] \n", "\n" ] } ], "source": [ "# matrix transpose\n", "print('Matrix Transpose:\\n', m.transpose(), '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matrix determinant" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix Determinant: -105.0 \n", "\n" ] } ], "source": [ "# matrix determinant\n", "print ('Matrix Determinant:', np.linalg.det(m), '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Matrix inverse" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Matrix inverse:\n", " [[-0.6 0.42857143 -0.05714286]\n", " [ 0.26666667 -0.04761905 -0.03809524]\n", " [ 0.13333333 -0.0952381 0.12380952]] \n", "\n" ] } ], "source": [ "# matrix inverse\n", "m_inv = np.linalg.inv(m)\n", "print ('Matrix inverse:\\n', m_inv, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Identity matrix" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Product of matrix and its inverse:\n", " [[ 1. 0. 0.]\n", " [ 0. 1. 0.]\n", " [ 0. 0. 1.]]\n" ] } ], "source": [ "# identity matrix (result of matrix x matrix_inverse)\n", "iden_m = np.dot(m, m_inv)\n", "iden_m = np.round(np.abs(iden_m), 0)\n", "print ('Product of matrix and its inverse:\\n', iden_m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Eigendecomposition" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Eigen Values: [ -1.32455532 11.32455532 7. ] \n", "\n", "Eigen Vectors:\n", " [[-0.91761521 0.46120352 -0.46829291]\n", " [ 0.35550789 0.79362022 -0.74926865]\n", " [ 0.17775394 0.39681011 0.46829291]]\n" ] } ], "source": [ "# eigendecomposition\n", "m = np.array([[1, 5, 2],\n", " [4, 7, 4],\n", " [2, 0, 9]])\n", "\n", "eigen_vals, eigen_vecs = np.linalg.eig(m)\n", "\n", "print('Eigen Values:', eigen_vals, '\\n')\n", "print('Eigen Vectors:\\n', eigen_vecs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# SVD" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Getting SVD outputs:-\n", "\n", "U:\n", " [[ 0.3831556 -0.39279153 0.83600634]\n", " [ 0.68811254 -0.48239977 -0.54202545]\n", " [ 0.61619228 0.78294653 0.0854506 ]] \n", "\n", "S:\n", " [ 12.10668383 6.91783499 1.25370079] \n", "\n", "VT:\n", " [[ 0.36079164 0.55610321 0.74871798]\n", " [-0.10935467 -0.7720271 0.62611158]\n", " [-0.92621323 0.30777163 0.21772844]] \n", "\n" ] } ], "source": [ "# SVD\n", "m = np.array([[1, 5, 2],\n", " [4, 7, 4],\n", " [2, 0, 9]])\n", "\n", "U, S, VT = np.linalg.svd(m)\n", "\n", "print ('Getting SVD outputs:-\\n')\n", "print('U:\\n', U, '\\n')\n", "print('S:\\n', S, '\\n')\n", "print('VT:\\n', VT, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Descriptive Statistics" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data: [ 6 13 3 8 16 14 15 6 11 16 16 9 2 18 1]\n" ] } ], "source": [ "# descriptive statistics\n", "import scipy as sp\n", "import numpy as np\n", "\n", "# get data\n", "nums = np.random.randint(1,20, size=(1,15))[0]\n", "print('Data: ', nums)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean: 10.2666666667\n", "Median: 11.0\n", "Mode: ModeResult(mode=array([16]), count=array([3]))\n", "Standard Deviation: 5.495048276\n", "Variance: 30.1955555556\n", "Skew: -0.283914098226024\n", "Kurtosis: -1.3113890167335824\n" ] } ], "source": [ "# get descriptive stats\n", "print ('Mean:', sp.mean(nums))\n", "print ('Median:', sp.median(nums))\n", "print ('Mode:', sp.stats.mode(nums))\n", "print ('Standard Deviation:', sp.std(nums))\n", "print ('Variance:', sp.var(nums))\n", "print ('Skew:', sp.stats.skew(nums))\n", "print ('Kurtosis:', sp.stats.kurtosis(nums))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }