TipsAndTricks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# In case I ever revisit to learn tips and tricks!\n",
    "\n",
    "# First of all, the inline pylab is set up using\n",
    "# c = get_config()\n",
    "# c.InteractiveShellApp.matplotlib = \"inline\"\n",
    "# in ~/.ipython/profile_default/startup/ipython_kernel_config.py\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-3.13277902  0.58423566 -0.41126174 -0.11024437 -0.75800303]]\n",
      "[[-3.13277902  0.58423566 -0.41126174 -0.11024437 -0.75800303]]\n",
      "[-3.13277902  0.58423566 -0.41126174 -0.11024437 -0.75800303]\n"
     ]
    }
   ],
   "source": [
    "# If you want to end up with an ndarray after slicing,\n",
    "# not a vector, you have to use a range, even a one-item range\n",
    "x = np.random.randn(1, 5)\n",
    "print(x[0:1, :])\n",
    "print(x[:1, :])\n",
    "print(x[0, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2)\n",
      "(1, 2)\n"
     ]
    }
   ],
   "source": [
    "# Watch out!\n",
    "bad = np.zeros(2) - np.random.randn(2, 1)\n",
    "print(bad.shape)\n",
    "\n",
    "okay = np.zeros(2) - np.random.randn(1,2)\n",
    "print(okay.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n",
      "(4, 1)\n"
     ]
    }
   ],
   "source": [
    "# adding singleton dimension\n",
    "a = np.array([1, 2, 3, 4])\n",
    "print(a.shape)\n",
    "print(a[:,None].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# label your dimensions\n",
    "a_td = np.random.random((100, 3)) # time by dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What's summing doing? It removes that dimension\n",
    "a_td.sum(axis=0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 4, 5)\n",
      "(1, 2, 1, 4, 5)\n"
     ]
    }
   ],
   "source": [
    "# Max, min, mean median all sum out the dimensions to obliterate\n",
    "oblit = np.random.randn(1, 2, 3, 4, 5).sum(axis=2)\n",
    "reduce = np.random.randn(1, 2, 3, 4, 5).sum(axis=2, keepdims=True)\n",
    "\n",
    "print(oblit.shape)\n",
    "print(reduce.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Workflow tips\n",
    "\n",
    "# hoj generally works in an ipy session,\n",
    "# makes something a function once it gets repeated,\n",
    "# makes a file once there are a few functions\n",
    "\n",
    "# !! you can start an embedded \n",
    "# import IPython; Ipython.embed()\n",
    "\n",
    "# move things out of ipy notebooks as fast as possible to avoid\n",
    "# variables still in scope from doing wacky things\n",
    "\n",
    "# if you're developing an algorithm, the thing to think about\n",
    "# is what to print at each iteration:\n",
    "# - policy gradient: the distance between last and new policy\n",
    "# - for evolution, look at populations. eg. rank of the mean\n",
    "# - L2 norm of parameters in neural net\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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OUkqfBF4MDAL/GxgF3pBS+r2i2pTUnqrVKouLZxgaukBn5yH27r2Xzs5DDA1d8DZGqQBF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\nbAh3TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10885fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting\n",
    "plt.plot(np.arange(15), np.arange(15)-2, 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10848e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ylabel is rows and xlabel is columns\n",
    "a = np.zeros([10, 10])\n",
    "a[0][2] = 1\n",
    "plt.imshow(a, interpolation='nearest', origin='upper')\n",
    "plt.ylabel('Rows'), plt.xlabel('Columns')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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
}