Ex1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    ":set -ilib\n",
    ":l lib/Nat.hs\n",
    "\n",
    "import Cp\n",
    "import Nat\n",
    "import Criterion.Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fIneficiente a b c 0 = 0\n",
    "fIneficiente a b c 1 = 1\n",
    "fIneficiente a b c 2 = 1\n",
    "fIneficiente a b c n = a * fIneficiente a b c (n - 1) + b * fIneficiente a b c (n - 2) + c * fIneficiente a b c (n - 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fEficiente a b c = p1 . for (split (p1 . p2) (split (p2 . p2) (k2 a b c))) (0, (1,1))\n",
    "    where k2 a b c (x, (y, z)) = a * z + b * y + c * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fIneficiente 3 3 3 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fEficiente 3 3 3 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import Criterion.Main\n",
    "import Criterion.Measurement as CM\n",
    "import Criterion.Measurement.Types\n",
    "\n",
    "iterations = 10\n",
    "\n",
    "average :: [Double] -> Double\n",
    "average = uncurry (/) . split sum (fromIntegral . length)\n",
    "\n",
    "measureNTimes 0 _ _ = return []\n",
    "measureNTimes n f x = do\n",
    "    res <- measureNTimes (n - 1) f x\n",
    "    (m, d) <- CM.measure (nf f x) 1\n",
    "    return (measTime m : res)\n",
    "\n",
    "measureTime f n = do\n",
    "    values <- measureNTimes iterations f n\n",
    "    return (average values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "--Example adapted from https://github.com/timbod7/haskell-chart/wiki/example-2\n",
    "\n",
    "import System.Environment(getArgs)\n",
    "import Data.Colour.Names\n",
    "import Data.Colour\n",
    "import Control.Lens\n",
    "import Data.Default.Class\n",
    "import Data.Time.LocalTime\n",
    "import Graphics.Rendering.Chart\n",
    "import Graphics.Rendering.Chart.Backend.Cairo\n",
    "\n",
    "a = 2\n",
    "b = 3\n",
    "c = 4\n",
    "\n",
    "pointsAux :: [Int] -> (Int -> Int) -> IO [(Int,Double)]\n",
    "pointsAux [] _ = return []\n",
    "pointsAux (h:t) f = do\n",
    "    t' <- pointsAux t f\n",
    "    d  <- measureTime f h\n",
    "    return ((h,d):t')\n",
    "\n",
    "points :: (Int -> Int) -> Int -> IO [(Int,Double)]\n",
    "points f maxValue = pointsAux [1..maxValue] f\n",
    "\n",
    "pGood <- points 10000 (fEficiente a b c)\n",
    "pBad <- points 20 (fIneficiente a b c)\n",
    "\n",
    "chart1 = toRenderable layout \n",
    "  where\n",
    "\n",
    "    bad = plot_lines_style . line_color .~ opaque blue\n",
    "           $ plot_lines_values .~ [pBad]\n",
    "           $ plot_lines_title .~ \"Implementação Ineficiente\"\n",
    "           $ def\n",
    "\n",
    "    \n",
    "\n",
    "    layout = layoutlr_title .~\"Desempenho da implementação ineficiente de f\"\n",
    "           $ layoutlr_left_axis . laxis_override .~ axisGridHide\n",
    "           $ layoutlr_right_axis . laxis_override .~ axisGridHide\n",
    "           $ layoutlr_x_axis . laxis_override .~ axisGridHide\n",
    "           $ layoutlr_plots .~ [Left (toPlot bad)]\n",
    "           $ layoutlr_grid_last .~ False\n",
    "           $ def\n",
    "\n",
    "chart2 = toRenderable layout\n",
    "    where\n",
    "    good = plot_lines_style . line_color .~ opaque green\n",
    "           $ plot_lines_values .~ [ pGood ]\n",
    "           $ plot_lines_title .~ \"Implementação Eficiente\"\n",
    "           $ def\n",
    "           \n",
    "    \n",
    "    layout = layoutlr_title .~\"Desempenho da implementação eficiente de f\"\n",
    "           $ layoutlr_left_axis . laxis_override .~ axisGridHide\n",
    "           $ layoutlr_right_axis . laxis_override .~ axisGridHide\n",
    "           $ layoutlr_x_axis . laxis_override .~ axisGridHide\n",
    "           $ layoutlr_plots .~ [Left (toPlot good)]\n",
    "           $ layoutlr_grid_last .~ False\n",
    "           $ def\n",
    "\n",
    "chart1\n",
    "\n",
    "chart2\n"
   ]
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