PythagorasMatrix-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pythagoras via Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import random\n",
    "import operator\n",
    "import warnings\n",
    "import numpy as np\n",
    "\n",
    "from geneticprogramming import Baseset\n",
    "from geneticprogramming import Individual\n",
    "from geneticprogramming import Population\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training data \n",
    "\n",
    "* a number of random matrices (2D floating points) on a plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SAMPLE_SIZE = 50\n",
    "PLANE_SIZE = 20.0\n",
    "RANDOMPOINTS = [PLANE_SIZE * np.random.random_sample((1, 2)) for _ in range(SAMPLE_SIZE)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Base functions\n",
    "\n",
    "* _get_ a value at a location from a matrix\n",
    "* an _ephemeral integer_ - either 0 or 1\n",
    "* _addition_ and _subtraction_ of floats\n",
    "* _square_ and _square root_ of floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# setup the baseset\n",
    "def getValue(ndarray, idx):\n",
    "    \"\"\"Return the indexed value from the 1x2 numpy array\"\"\"\n",
    "    return ndarray[0][idx]\n",
    "\n",
    "def if_then_else(input, output1, output2):\n",
    "    if input:\n",
    "        return output1\n",
    "    else:\n",
    "        return output2\n",
    "    \n",
    "def prog2(a, b):\n",
    "    return a\n",
    "\n",
    "def do(condition, value):\n",
    "    return value\n",
    "\n",
    "def let_t(float):\n",
    "    return float\n",
    "\n",
    "square = lambda x: x ** 2\n",
    "sqrt = lambda x: math.sqrt(abs(x))\n",
    "\n",
    "bset = Baseset()\n",
    "bset.add_ephemeral('EM', lambda: np.random.rand(1, 2), np.ndarray)\n",
    "bset.add_ephemeral('EF', lambda: random.random(), float)\n",
    "bset.add_ephemeral('EI', lambda: random.randint(0, 1), int)\n",
    "\n",
    "bset.add_primitive(getValue, [np.ndarray, int], float, name=\"get\")\n",
    "bset.add_primitive(operator.add, [float, float], float, name=\"add\")\n",
    "bset.add_primitive(operator.sub, [float, float], float, name=\"sub\")\n",
    "bset.add_primitive(square, [float], float, name=\"square\")\n",
    "bset.add_primitive(sqrt, [float], float, name=\"sqrt\")\n",
    "\n",
    "bset.add_primitive(operator.lt, [float, float], bool, name=\"lt\")\n",
    "bset.add_primitive(if_then_else, [bool, float, float], float, name=\"IF\")\n",
    "bset.add_primitive(prog2, [float, float], float, name=\"PROG2\")\n",
    "bset.add_primitive(do, [bool, float], float, name='DO')\n",
    "bset.add_primitive(let_t, [float], float, name=\"LET_T\")\n",
    "\n",
    "bset.add_terminal(1.0, float, 'T')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Individuals\n",
    "* consume a matrix (a 1x2 array of floats - x and y coordinates)\n",
    "* return a float (the distance from the origin)\n",
    "* scored by accumulating the error when applying the Training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Individual.INTYPES = [np.ndarray]\n",
    "Individual.OUTTYPE = float\n",
    "\n",
    "def evaluate(individual):\n",
    "    \"\"\"sum of application of all the random points\"\"\"\n",
    "    program = individual.compile()\n",
    "    score = 0\n",
    "    try:\n",
    "        for point in RANDOMPOINTS:\n",
    "            program_distance = program(point)\n",
    "            true_distance = math.hypot(point[0][0], point[0][1])\n",
    "            score += abs(true_distance - program_distance)\n",
    "    except (OverflowError, RuntimeWarning):\n",
    "        pass\n",
    "    if math.isnan(score) or score == 0:\n",
    "        score = 100000\n",
    "\n",
    "    # count the number of nodes and scale it. (Adds 1 whole unit to the score at 250 nodes).\n",
    "    # nodes = sum([len(tree) for tree in individual.trees])\n",
    "    # modifier = 1 + (-2 ** - (nodes / 250))\n",
    "\n",
    "    # accumulate the number of nodes actually used during a run by calling the adfs in the rpb\n",
    "    nodes = 0\n",
    "    for node in individual.trees[-1]:\n",
    "        if node.name[:1] != 'F':\n",
    "            nodes += 1\n",
    "        else:\n",
    "            nodes += len(individual.trees[int(node.name[1])])\n",
    "    modifier = 1 + (-2 ** - (nodes / 250))\n",
    "    \n",
    "    return score + modifier,\n",
    "\n",
    "Individual.evaluate = evaluate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population Evolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gen\tavg    \tstd   \tmin    \tmax    \n",
      "0  \t936.774\t1185.3\t203.846\t7121.46\n",
      "1  \t641.449\t814.411\t203.76 \t6031.32\n",
      "2  \t534.723\t823.879\t203.76 \t6031.32\n",
      "3  \t355.103\t208.107\t192.244\t857.174\n",
      "4  \t247.054\t33.3773\t192.136\t327.895\n",
      "5  \t260.03 \t96.3322\t192.096\t762.019\n",
      "6  \t257.839\t101.429\t192.096\t745.81 \n",
      "7  \t262.067\t126.652\t192.026\t790.284\n",
      "8  \t223.53 \t34.7894\t192.026\t315.116\n",
      "9  \t243.167\t108.819\t192.026\t746.653\n",
      "10 \t235.667\t108.874\t118.842\t746.653\n",
      "11 \t218.947\t80.4913\t118.842\t746.814\n",
      "12 \t238.119\t131.026\t118.842\t762.263\n",
      "13 \t203.995\t41.5613\t118.842\t390.488\n",
      "14 \t201.781\t54.1502\t118.842\t390.515\n",
      "15 \t188.956\t47.3239\t118.842\t390.504\n",
      "16 \t187.713\t78.1279\t118.842\t670.013\n",
      "17 \t180.643\t48.5125\t118.74 \t301.892\n",
      "18 \t193.981\t97.345 \t118.74 \t755.164\n",
      "19 \t204.504\t124.984\t118.74 \t746.756\n"
     ]
    },
    {
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keVmSvyulXNbksgAAACaFco7swAAAAOOulPKqJB9J8tUkb621PtLkkialI7Nr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XjXJ0mSJC0oSy3qBUiSJEnSEmBl4I5BHf2JmiTrJvlW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\nJEnSIvT/AdEUrnIXFvQaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f27122c6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# run the evolution\n",
    "Population.POPULATION_SIZE = 50  # Number of individuals in a generation\n",
    "Population.MATE_MUTATE_CLONE = (70, 25, 5)  # ratio of individuals to mate, mutate, or clone\n",
    "Population.CLONE_BEST = 1  # Number of best individuals to seed directly into offspring\n",
    "Population.MAX_MATE_ATTEMPTS = 15 # Maximum number of paraents to try before error message\n",
    "\n",
    "Individual.MAX_ADFS = 1  # The maximum number of ADFs to generate\n",
    "Individual.ADF_NARGS = (1, 3)  # min, max number of input arguments to adfs\n",
    "Individual.GROWTH_TERM_PB = 0.3  # Probability of terminal when growing:\n",
    "Individual.GROWTH_MAX_INIT_DEPTH = 12  # Maximum depth of initial growth\n",
    "Individual.GROWTH_MAX_MUT_DEPTH = 5  # Maximum depth of mutation growth\n",
    "Individual.GROWTH_MAX_ATTEMPTS = 50  # maximum number of times to attempt to grow a complete adf before abandoning\n",
    "Individual.GROWTH_MAX_SIGNATURES = 1000  # maximum number of signatures to try before ultimately giving up\n",
    "\n",
    "NUMBER_OF_GENERATIONS = 20\n",
    "\n",
    "population = Population(bset)\n",
    "for gen in range(NUMBER_OF_GENERATIONS):\n",
    "    population.evolve()\n",
    "    if population[0].fitness.values[0] < 0.8:\n",
    "        break\n",
    "\n",
    "best = population[0]\n",
    "best.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAIN:add(sqrt(get(IN0, E170)), PROG2(add(sub(add(LET_T(PROG2(LET_T(get(IN0, lt(get(E168, E170), get(E168, E170)))), DO(lt(E169, T), E169))), E169), T), IF(lt(DO(lt(add(E169, add(PROG2(LET_T(T), LET_T(T)), E169)), square(T)), PROG2(LET_T(get(IN0, lt(get(E168, E170), get(E168, E170)))), DO(lt(E169, T), E169))), square(get(E168, lt(T, T)))), add(PROG2(sqrt(T), T), DO(lt(T, square(E169)), LET_T(E169))), E169)), add(T, E169)))\n"
     ]
    }
   ],
   "source": [
    "print(best)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}