solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## IMPORTY\n",
    "# wykresy będą wyświetlać się w notebooku nie osobnych oknach\n",
    "%matplotlib inline\n",
    "# biblioteka do rysowania wykresów\n",
    "import matplotlib.pyplot as plt\n",
    "# schematy kolorów\n",
    "from matplotlib import cm\n",
    "# moduły do generowania danych i obrabiania\n",
    "from sklearn.datasets import make_blobs, load_iris\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## GENEROWANIE DANYCH\n",
    "blobs_X, blobs_y = make_blobs()\n",
    "x = np.linspace(-10, 10,)\n",
    "sin_X, sin_y = x, np.sin(x)\n",
    "iris = load_iris()\n",
    "iris_X, iris_y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lmmFIkiQlmmFIkiQlmmFIkiQlmmFIkiQlmmFIkiQlmmFIkiQlmmFIkiQlmmFI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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc01c1d3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zbiór iris opisuje irysy trzech gatunków.\n",
    "# Każda kolumna w iris.data zawiera informację o wielkości danej cechy konkretnego kwiatu.\n",
    "# iris.target zawiera informację o gatunku konkretnego irysa\n",
    "\n",
    "# informację o każdym gatunku będziemy trzymać w osobnej macierzy\n",
    "iris_0 = iris_X[iris_y==0]\n",
    "iris_1 = iris_X[iris_y==1]\n",
    "iris_2 = iris_X[iris_y==2]\n",
    "\n",
    "# wyliczamy średnią każdej cechy dla każdego gatunku\n",
    "mean_0 = np.mean(iris_0, axis=0)\n",
    "mean_1 = np.mean(iris_1, axis=0)\n",
    "mean_2 = np.mean(iris_2, axis=0)\n",
    "\n",
    "# Oto są kolory \"Tableau 20\" w RGB.    \n",
    "tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),    \n",
    "             (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),    \n",
    "             (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),    \n",
    "             (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),    \n",
    "             (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]    \n",
    "  \n",
    "# Przeskaluj wartości RGB to skali [0, 1], by była akceptowana przez matplotliba.    \n",
    "for i in range(len(tableau20)):    \n",
    "    r, g, b = tableau20[i]    \n",
    "    tableau20[i] = (r / 255., g / 255., b / 255.) \n",
    "\n",
    "    \n",
    "plt.figure(figsize=(10, 7.5)) \n",
    "ax = plt.subplot()\n",
    "\n",
    "# zmienne, potrzebne do ustawienia słupków obok siebie\n",
    "N = len(mean_0)\n",
    "ind = np.arange(N)\n",
    "width = 1./N\n",
    "\n",
    "# właściwe rysowanie\n",
    "rects0 = ax.bar(ind, mean_0, width, color=tableau20[0], edgecolor = \"none\")\n",
    "rects1 = ax.bar(ind+width, mean_1, width, color=tableau20[1], edgecolor = \"none\")\n",
    "rects2 = ax.bar(ind+2*width, mean_2, width, color=tableau20[2], edgecolor = \"none\")\n",
    "\n",
    "# legenda\n",
    "ax.legend((rects0[0], rects1[0], rects2[0]), ('Setosa', 'Versicolour', 'Virginica'), frameon=False)\n",
    "\n",
    "# podpisanie diagramu\n",
    "ax.set_title('Average values in the Iris Dataset')\n",
    "\n",
    "# ustawiamy podpisy w osi X\n",
    "ax.set_xticks(ind + 1.5 * width)\n",
    "ax.set_xticklabels(('Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'))\n",
    "\n",
    "# chcemy nie mieć ticksów\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    top='off',         # ticks along the top edge are off\n",
    "    bottom='off',      # ticks along the bottom edge are off\n",
    "    labelbottom='on')  # labels along the bottom edge are on\n",
    "\n",
    "plt.tick_params(\n",
    "    axis='y',          # changes apply to the y-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    left='off',        # ticks along the left edge are off\n",
    "    right='off',       # ticks along the right edge are off\n",
    "    labelleft='off')   # labels along the bottom edge are off\n",
    "\n",
    "\n",
    "# Remove the plot frame lines. They are unnecessary chartjunk.    \n",
    "ax.spines[\"top\"].set_visible(False)    \n",
    "ax.spines[\"bottom\"].set_visible(False)    \n",
    "ax.spines[\"right\"].set_visible(False)    \n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "# wartość reprezentowana przez słupek zostanie wyświetlona nad słupkiem\n",
    "def autolabel(rects):\n",
    "    # attach some text labels\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.text(rect.get_x() + rect.get_width()/2., 0.05+height,\n",
    "                '%.2f' % (height),\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "autolabel(rects0)\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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