Tugas1_GriesYulianti.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Tulis jawaban ditempat yang sudah disediakan</h3>\n",
    "\n",
    "Data Iris terdiri atas 150 baris dan 4 kolom (sepal length, sepal width, petal length, dan petal width). Jawablah pertanyaan berikut menggunakan dataset Iris yang tersedia pada tool Python scikit-learn (atau sclearn) dan lakukan perhitungan dan pengolahan data Iris menggunakan kode program Python. Perhitungan tidak boleh menggunakan fungsi dari library, misalnya Anda tidak boleh menghitung covariance menggunakan numpy.cov(...,...)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><i>Data processing</i></b>\n",
    "\n",
    "1. Berapa banyak data Iris? Dan berapa dimensi?\n",
    "2. Hitunglah mean dan variance dari masing-masing kolom!\n",
    "3. Buatlah scatter plot 2D dari data petal length dan petal width!\n",
    "4. Dengan data petal length dan petal width, ubah data menjadi centered data matrix! \n",
    "5. Buatlah scatter plot 2D dari centered data matrix!\n",
    "6. Hitunglah mean petal length dan petal width dari centered data matrix dan berikan warna atau simbol berbeda untuk kelas yang berbeda (Versicolor, Setosa, Virginica)!\n",
    "7. Hitunglah dan buatlah covariant matrix petal length dan petal width dari centered data matrix!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Jawaban:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data matrik n x d\n",
      "(150L, 4L)\n",
      "Banyak data adalah n = 150\n",
      "Dimensi adalah jumlah atribut (d) = 4\n"
     ]
    }
   ],
   "source": [
    "#1.\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "dataIris = iris.data[:]\n",
    "\n",
    "print \"Data matrik n x d\"\n",
    "print dataIris.shape\n",
    "\n",
    "print \"Banyak data adalah n = 150\"\n",
    "\n",
    "print \"Dimensi adalah jumlah atribut (d) = 4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean Sepal Length\n",
      "5.843333333333335\n",
      "Varian Sepal Length\n",
      "0.681122222222222\n",
      "\n",
      "Mean Sepal Width\n",
      "3.0540000000000007\n",
      "Varian Sepal Width\n",
      "0.1867506666666667\n",
      "\n",
      "Mean Petal Length\n",
      "3.7586666666666693\n",
      "Varian Petal Length\n",
      "3.0924248888888854\n",
      "\n",
      "Mean Petal Width\n",
      "1.1986666666666672\n",
      "Varian Petal Width\n",
      "0.5785315555555559\n"
     ]
    }
   ],
   "source": [
    "#2.\n",
    "\n",
    "def mean(listKolom):\n",
    "    meanList = sum(listKolom)/len(listKolom)\n",
    "    \n",
    "    return meanList\n",
    "\n",
    "def varian(listKolom,meanX):\n",
    "    list_operasiX = []\n",
    "    for x in listKolom:\n",
    "        operasiX = (x-meanX)**2\n",
    "        list_operasiX.append(operasiX)\n",
    "    \n",
    "    varianX = sum(list_operasiX)/len(listKolom)\n",
    "    return varianX\n",
    "\n",
    "# --- Sepal Length\n",
    "\n",
    "sepal_length = [row[0] for row in dataIris] \n",
    "#sepal_length = dataIris[:,:1] \n",
    "#print (sepal_length)\n",
    "#print type(sepal_length)\n",
    "\n",
    "mean_sepal_length = mean(sepal_length)\n",
    "print \"\\nMean Sepal Length\"\n",
    "print mean_sepal_length\n",
    "\n",
    "var_sepal_length = varian(sepal_length,mean_sepal_length)\n",
    "print \"Varian Sepal Length\"\n",
    "print var_sepal_length\n",
    "    \n",
    "\n",
    "# --- Sepal Width\n",
    "\n",
    "sepal_width = [row[1] for row in dataIris] \n",
    "#print (sepal_width)\n",
    "\n",
    "mean_sepal_width = mean(sepal_width)\n",
    "print \"\\nMean Sepal Width\"\n",
    "print mean_sepal_width\n",
    "\n",
    "var_sepal_width = varian(sepal_width,mean_sepal_width)\n",
    "print \"Varian Sepal Width\"\n",
    "print var_sepal_width\n",
    " \n",
    "\n",
    "# --- Petal Length\n",
    "petal_length = [row[2] for row in dataIris] \n",
    "#print (petal_length)\n",
    "\n",
    "mean_petal_length = mean(petal_length)\n",
    "print \"\\nMean Petal Length\"\n",
    "print mean_petal_length\n",
    "\n",
    "var_petal_length = varian(petal_length,mean_petal_length)\n",
    "print \"Varian Petal Length\"\n",
    "print var_petal_length\n",
    "\n",
    "\n",
    "# --- Petal Width\n",
    "\n",
    "petal_width = [row[3] for row in dataIris] \n",
    "#print (petal_width)\n",
    "mean_petal_width = mean(petal_width)\n",
    "print \"\\nMean Petal Width\"\n",
    "print mean_petal_width\n",
    "\n",
    "var_petal_width = varian(petal_width,mean_petal_width)\n",
    "print \"Varian Petal Width\"\n",
    "print var_petal_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.scatter(petal_length,petal_width)\n",
    "plt.title('scatter plot 2D dari data petal length dan petal width')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.3586666666666694, -0.9986666666666673], [-2.3586666666666694, -0.9986666666666673], [-2.4586666666666694, -0.9986666666666673], [-2.2586666666666693, -0.9986666666666673], [-2.3586666666666694, -0.9986666666666673], [-2.058666666666669, -0.7986666666666672], [-2.3586666666666694, -0.8986666666666672], [-2.2586666666666693, -0.9986666666666673], [-2.3586666666666694, -0.9986666666666673], [-2.2586666666666693, -1.0986666666666671], [-2.2586666666666693, -0.9986666666666673], [-2.158666666666669, -0.9986666666666673], [-2.3586666666666694, -1.0986666666666671], [-2.658666666666669, -1.0986666666666671], [-2.558666666666669, -0.9986666666666673], [-2.2586666666666693, -0.7986666666666672], [-2.4586666666666694, -0.7986666666666672], [-2.3586666666666694, -0.8986666666666672], [-2.058666666666669, -0.8986666666666672], [-2.2586666666666693, -0.8986666666666672], [-2.058666666666669, -0.9986666666666673], [-2.2586666666666693, -0.7986666666666672], [-2.7586666666666693, -0.9986666666666673], [-2.058666666666669, -0.6986666666666672], [-1.8586666666666694, -0.9986666666666673], [-2.158666666666669, -0.9986666666666673], [-2.158666666666669, -0.7986666666666672], [-2.2586666666666693, -0.9986666666666673], [-2.3586666666666694, -0.9986666666666673], [-2.158666666666669, -0.9986666666666673], [-2.158666666666669, -0.9986666666666673], [-2.2586666666666693, -0.7986666666666672], [-2.2586666666666693, -1.0986666666666671], [-2.3586666666666694, -0.9986666666666673], [-2.2586666666666693, -1.0986666666666671], [-2.558666666666669, -0.9986666666666673], [-2.4586666666666694, -0.9986666666666673], [-2.2586666666666693, -1.0986666666666671], [-2.4586666666666694, -0.9986666666666673], [-2.2586666666666693, -0.9986666666666673], [-2.4586666666666694, -0.8986666666666672], [-2.4586666666666694, -0.8986666666666672], [-2.4586666666666694, -0.9986666666666673], [-2.158666666666669, -0.5986666666666672], [-1.8586666666666694, -0.7986666666666672], [-2.3586666666666694, -0.8986666666666672], [-2.158666666666669, -0.9986666666666673], [-2.3586666666666694, -0.9986666666666673], [-2.2586666666666693, -0.9986666666666673], [-2.3586666666666694, -0.9986666666666673], [0.9413333333333309, 0.2013333333333327], [0.7413333333333307, 0.3013333333333328], [1.141333333333331, 0.3013333333333328], [0.24133333333333074, 0.10133333333333283], [0.8413333333333304, 0.3013333333333328], [0.7413333333333307, 0.10133333333333283], [0.9413333333333309, 0.4013333333333329], [-0.45866666666666944, -0.1986666666666672], [0.8413333333333304, 0.10133333333333283], [0.14133333333333065, 0.2013333333333327], [-0.25866666666666926, -0.1986666666666672], [0.4413333333333309, 0.3013333333333328], [0.24133333333333074, -0.1986666666666672], [0.9413333333333309, 0.2013333333333327], [-0.15866666666666918, 0.10133333333333283], [0.6413333333333311, 0.2013333333333327], [0.7413333333333307, 0.3013333333333328], [0.3413333333333304, -0.1986666666666672], [0.7413333333333307, 0.3013333333333328], [0.14133333333333065, -0.09866666666666712], [1.0413333333333306, 0.6013333333333328], [0.24133333333333074, 0.10133333333333283], [1.141333333333331, 0.3013333333333328], [0.9413333333333309, 0.0013333333333327424], [0.5413333333333306, 0.10133333333333283], [0.6413333333333311, 0.2013333333333327], [1.0413333333333306, 0.2013333333333327], [1.2413333333333307, 0.5013333333333327], [0.7413333333333307, 0.3013333333333328], [-0.25866666666666926, -0.1986666666666672], [0.04133333333333056, -0.09866666666666712], [-0.05866666666666909, -0.1986666666666672], [0.14133333333333065, 0.0013333333333327424], [1.3413333333333304, 0.4013333333333329], [0.7413333333333307, 0.3013333333333328], [0.7413333333333307, 0.4013333333333329], [0.9413333333333309, 0.3013333333333328], [0.6413333333333311, 0.10133333333333283], [0.3413333333333304, 0.10133333333333283], [0.24133333333333074, 0.10133333333333283], [0.6413333333333311, 0.0013333333333327424], [0.8413333333333304, 0.2013333333333327], [0.24133333333333074, 0.0013333333333327424], [-0.45866666666666944, -0.1986666666666672], [0.4413333333333309, 0.10133333333333283], [0.4413333333333309, 0.0013333333333327424], [0.4413333333333309, 0.10133333333333283], [0.5413333333333306, 0.10133333333333283], [-0.7586666666666693, -0.09866666666666712], [0.3413333333333304, 0.10133333333333283], [2.2413333333333307, 1.3013333333333328], [1.3413333333333304, 0.7013333333333327], [2.141333333333331, 0.9013333333333329], [1.8413333333333304, 0.6013333333333328], [2.0413333333333306, 1.001333333333333], [2.8413333333333304, 0.9013333333333329], [0.7413333333333307, 0.5013333333333327], [2.5413333333333306, 0.6013333333333328], [2.0413333333333306, 0.6013333333333328], [2.3413333333333304, 1.3013333333333328], [1.3413333333333304, 0.8013333333333328], [1.5413333333333306, 0.7013333333333327], [1.7413333333333307, 0.9013333333333329], [1.2413333333333307, 0.8013333333333328], [1.3413333333333304, 1.2013333333333327], [1.5413333333333306, 1.1013333333333326], [1.7413333333333307, 0.6013333333333328], [2.941333333333331, 1.001333333333333], [3.141333333333331, 1.1013333333333326], [1.2413333333333307, 0.3013333333333328], [1.941333333333331, 1.1013333333333326], [1.141333333333331, 0.8013333333333328], [2.941333333333331, 0.8013333333333328], [1.141333333333331, 0.6013333333333328], [1.941333333333331, 0.9013333333333329], [2.2413333333333307, 0.6013333333333328], [1.0413333333333306, 0.6013333333333328], [1.141333333333331, 0.6013333333333328], [1.8413333333333304, 0.9013333333333329], [2.0413333333333306, 0.4013333333333329], [2.3413333333333304, 0.7013333333333327], [2.641333333333331, 0.8013333333333328], [1.8413333333333304, 1.001333333333333], [1.3413333333333304, 0.3013333333333328], [1.8413333333333304, 0.2013333333333327], [2.3413333333333304, 1.1013333333333326], [1.8413333333333304, 1.2013333333333327], [1.7413333333333307, 0.6013333333333328], [1.0413333333333306, 0.6013333333333328], [1.641333333333331, 0.9013333333333329], [1.8413333333333304, 1.2013333333333327], [1.3413333333333304, 1.1013333333333326], [1.3413333333333304, 0.7013333333333327], [2.141333333333331, 1.1013333333333326], [1.941333333333331, 1.3013333333333328], [1.441333333333331, 1.1013333333333326], [1.2413333333333307, 0.7013333333333327], [1.441333333333331, 0.8013333333333328], [1.641333333333331, 1.1013333333333326], [1.3413333333333304, 0.6013333333333328]]\n"
     ]
    }
   ],
   "source": [
    "#4.\n",
    "\n",
    "def data_center(listKolom,mean):\n",
    "    dCenter = []\n",
    "    for y in listKolom:\n",
    "        operasiDat = y - mean\n",
    "        dCenter.append(operasiDat)\n",
    "    return dCenter\n",
    "\n",
    "# -- List Data Center Petal Length\n",
    "data_center_petal_length = data_center(petal_length, mean_petal_length)\n",
    "#print data_central_petal_length\n",
    "\n",
    "# -- List Data Center Petal Width\n",
    "data_center_petal_width = data_center(petal_width, mean_petal_width)\n",
    "#print data_central_petal_width\n",
    "\n",
    "CenterDataMatrixPetal = []\n",
    "\n",
    "for i in range(len(data_center_petal_length)):\n",
    "    templist = [data_center_petal_length [i], data_center_petal_width [i]]\n",
    "    CenterDataMatrixPetal.append(templist)\n",
    "    \n",
    "print CenterDataMatrixPetal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5.\n",
    "\n",
    "plt.scatter(data_center_petal_length,data_center_petal_width)\n",
    "plt.title('scatter plot 2D dari centered data matrix')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean Data Center Petal Length\n",
      "-3.2388906371731232e-15\n",
      "Mean Data Center Petal Width\n",
      "-6.069219201284189e-16\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legend\n",
      "Warna ungu = Setosa\n",
      "Warna hijau = Versicolor\n",
      "Warna kuning = Virginica\n"
     ]
    }
   ],
   "source": [
    "#6.\n",
    "\n",
    "def data_center(listKolom,mean):\n",
    "    dCenter = []\n",
    "    for y in listKolom:\n",
    "        operasiDat = y - mean\n",
    "        dCenter.append(operasiDat)\n",
    "    return dCenter\n",
    "\n",
    "mean_data_center_petal_length = sum(data_center_petal_length)/len(data_center_petal_length)\n",
    "mean_data_center_petal_width = sum(data_center_petal_width)/len(data_center_petal_width)\n",
    "\n",
    "print \"\\nMean Data Center Petal Length\"\n",
    "print mean_data_center_petal_length\n",
    "print \"Mean Data Center Petal Width\"\n",
    "print mean_data_center_petal_width\n",
    "\n",
    "\n",
    "y = iris.target\n",
    "\n",
    "plt.scatter(data_center_petal_length,data_center_petal_width, c=y, edgecolors='k')\n",
    "    \n",
    "plt.grid(True)\n",
    "plt.title('scatter plot 2D dari centered data matrix')\n",
    "plt.xlabel('Data Center Petal Length')\n",
    "plt.ylabel('Data Center Petal Width')\n",
    "plt.show()\n",
    "\n",
    "print 'Legend'\n",
    "print 'Warna ungu = Setosa'\n",
    "print 'Warna hijau = Versicolor'\n",
    "print 'Warna kuning = Virginica'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Covariant matrix petal length dan petal width dari centered data matrix\n",
      "[[3.092424888888888, 1.2877448888888894], [1.2877448888888894, 0.5785315555555556]]\n"
     ]
    }
   ],
   "source": [
    "#7.\n",
    "\n",
    "def covarian(list1, mean1, list2, mean2):\n",
    "    listcov = []\n",
    "    for x in range(len(list1)):\n",
    "        op1 = list1[x]-mean1        \n",
    "        op2 = list2[x]-mean2\n",
    "        opcov = op1*op2\n",
    "        listcov.append(opcov)\n",
    "        \n",
    "    meanCov = sum(listcov)/len(listcov)\n",
    "    \n",
    "    return meanCov\n",
    "\n",
    "var_datCen_petal_length = varian(data_center_petal_length,mean_data_center_petal_length)\n",
    "var_datCen_petal_width = varian(data_center_petal_width, mean_data_center_petal_width)\n",
    "\n",
    "covarian_datCen_Petal_width_length = covarian(data_center_petal_length,mean_data_center_petal_length,data_center_petal_width,mean_data_center_petal_width)\n",
    "\n",
    "matrix_cov_datCen_petal = [[var_datCen_petal_length,covarian_datCen_Petal_width_length],[covarian_datCen_Petal_width_length, var_datCen_petal_width]]\n",
    "\n",
    "print \"\\nCovariant matrix petal length dan petal width dari centered data matrix\"\n",
    "print matrix_cov_datCen_petal\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><i>Algebraic and Geometric Views</i></b>\n",
    "\n",
    "8. Lakukan proyeksi ortogonal data petal length dan petal width terhadap persamaan x1=x2 (serupa dengan persamaan linier y=x untuk sumbu x dan y)!\n",
    "9. Buatlah scatter plot 2D dari hasil proyeksi ortogonal dengan warna/simbol berbeda untuk kelas yang berbeda!\n",
    "10. Apakah garis x1=x2 berhasil memisahkan data suatu kelas? Jelaskan jawaban Anda dengan menganalisis hasil plot Anda!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Jawaban:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "XP1\n",
      "[-1.6786666666666683, -1.6786666666666685, -1.7286666666666684, -1.628666666666668, -1.6786666666666683, -1.428666666666668, -1.6286666666666683, -1.6286666666666683, -1.678666666666668, -1.678666666666668, -1.6286666666666683, -1.578666666666668, -1.7286666666666684, -1.878666666666668, -1.778666666666668, -1.5286666666666684, -1.6286666666666683, -1.6286666666666685, -1.478666666666668, -1.5786666666666682, -1.528666666666668, -1.5286666666666682, -1.8786666666666685, -1.3786666666666685, -1.4286666666666683, -1.5786666666666684, -1.4786666666666681, -1.6286666666666685, -1.6786666666666685, -1.5786666666666684, -1.5786666666666682, -1.5286666666666684, -1.6786666666666679, -1.6786666666666683, -1.6786666666666683, -1.7786666666666684, -1.7286666666666684, -1.678666666666668, -1.7286666666666684, -1.6286666666666683, -1.6786666666666685, -1.6786666666666683, -1.7286666666666686, -1.3786666666666683, -1.3286666666666682, -1.6286666666666683, -1.5786666666666682, -1.6786666666666683, -1.6286666666666685, -1.678666666666668, 0.5713333333333318, 0.5213333333333318, 0.7213333333333319, 0.17133333333333178, 0.5713333333333317, 0.4213333333333318, 0.6713333333333319, -0.32866666666666833, 0.4713333333333316, 0.17133333333333167, -0.22866666666666824, 0.3713333333333319, 0.021333333333331754, 0.5713333333333318, -0.02866666666666817, 0.4213333333333319, 0.5213333333333318, 0.07133333333333158, 0.5213333333333318, 0.021333333333331757, 0.8213333333333317, 0.17133333333333178, 0.7213333333333318, 0.4713333333333319, 0.32133333333333164, 0.42133333333333195, 0.6213333333333316, 0.8713333333333316, 0.5213333333333318, -0.22866666666666827, -0.028666666666668287, -0.12866666666666815, 0.07133333333333171, 0.8713333333333316, 0.5213333333333318, 0.5713333333333318, 0.6213333333333318, 0.3713333333333319, 0.22133333333333166, 0.17133333333333176, 0.3213333333333319, 0.5213333333333315, 0.12133333333333174, -0.32866666666666833, 0.27133333333333187, 0.22133333333333186, 0.27133333333333187, 0.32133333333333164, -0.42866666666666814, 0.22133333333333158, 1.7713333333333316, 1.0213333333333314, 1.5213333333333323, 1.2213333333333316, 1.5213333333333316, 1.8713333333333317, 0.6213333333333316, 1.5713333333333317, 1.321333333333332, 1.8213333333333317, 1.0713333333333315, 1.1213333333333317, 1.3213333333333317, 1.0213333333333319, 1.2713333333333314, 1.3213333333333315, 1.1713333333333318, 1.971333333333332, 2.121333333333332, 0.7713333333333318, 1.5213333333333316, 0.971333333333332, 1.8713333333333322, 0.8713333333333321, 1.421333333333332, 1.4213333333333318, 0.8213333333333317, 0.8713333333333321, 1.3713333333333317, 1.2213333333333318, 1.5213333333333316, 1.7213333333333323, 1.4213333333333316, 0.8213333333333315, 1.0213333333333316, 1.7213333333333316, 1.5213333333333314, 1.171333333333332, 0.8213333333333316, 1.2713333333333319, 1.5213333333333316, 1.2213333333333316, 1.0213333333333316, 1.6213333333333317, 1.621333333333332, 1.2713333333333316, 0.9713333333333317, 1.121333333333332, 1.3713333333333317, 0.9713333333333317]\n",
      "\n",
      "XP2\n",
      "[-1.6786666666666683, -1.6786666666666685, -1.7286666666666684, -1.628666666666668, -1.6786666666666683, -1.428666666666668, -1.6286666666666683, -1.6286666666666683, -1.678666666666668, -1.678666666666668, -1.6286666666666683, -1.578666666666668, -1.7286666666666684, -1.878666666666668, -1.778666666666668, -1.5286666666666684, -1.6286666666666683, -1.6286666666666685, -1.478666666666668, -1.5786666666666682, -1.528666666666668, -1.5286666666666682, -1.8786666666666685, -1.3786666666666685, -1.4286666666666683, -1.5786666666666684, -1.4786666666666681, -1.6286666666666685, -1.6786666666666685, -1.5786666666666684, -1.5786666666666682, -1.5286666666666684, -1.6786666666666679, -1.6786666666666683, -1.6786666666666683, -1.7786666666666684, -1.7286666666666684, -1.678666666666668, -1.7286666666666684, -1.6286666666666683, -1.6786666666666685, -1.6786666666666683, -1.7286666666666686, -1.3786666666666683, -1.3286666666666682, -1.6286666666666683, -1.5786666666666682, -1.6786666666666683, -1.6286666666666685, -1.678666666666668, 0.5713333333333318, 0.5213333333333318, 0.7213333333333319, 0.17133333333333178, 0.5713333333333317, 0.4213333333333318, 0.6713333333333319, -0.32866666666666833, 0.4713333333333316, 0.17133333333333167, -0.22866666666666824, 0.3713333333333319, 0.021333333333331754, 0.5713333333333318, -0.02866666666666817, 0.4213333333333319, 0.5213333333333318, 0.07133333333333158, 0.5213333333333318, 0.021333333333331757, 0.8213333333333317, 0.17133333333333178, 0.7213333333333318, 0.4713333333333319, 0.32133333333333164, 0.42133333333333195, 0.6213333333333316, 0.8713333333333316, 0.5213333333333318, -0.22866666666666827, -0.028666666666668287, -0.12866666666666815, 0.07133333333333171, 0.8713333333333316, 0.5213333333333318, 0.5713333333333318, 0.6213333333333318, 0.3713333333333319, 0.22133333333333166, 0.17133333333333176, 0.3213333333333319, 0.5213333333333315, 0.12133333333333174, -0.32866666666666833, 0.27133333333333187, 0.22133333333333186, 0.27133333333333187, 0.32133333333333164, -0.42866666666666814, 0.22133333333333158, 1.7713333333333316, 1.0213333333333314, 1.5213333333333323, 1.2213333333333316, 1.5213333333333316, 1.8713333333333317, 0.6213333333333316, 1.5713333333333317, 1.321333333333332, 1.8213333333333317, 1.0713333333333315, 1.1213333333333317, 1.3213333333333317, 1.0213333333333319, 1.2713333333333314, 1.3213333333333315, 1.1713333333333318, 1.971333333333332, 2.121333333333332, 0.7713333333333318, 1.5213333333333316, 0.971333333333332, 1.8713333333333322, 0.8713333333333321, 1.421333333333332, 1.4213333333333318, 0.8213333333333317, 0.8713333333333321, 1.3713333333333317, 1.2213333333333318, 1.5213333333333316, 1.7213333333333323, 1.4213333333333316, 0.8213333333333315, 1.0213333333333316, 1.7213333333333316, 1.5213333333333314, 1.171333333333332, 0.8213333333333316, 1.2713333333333319, 1.5213333333333316, 1.2213333333333316, 1.0213333333333316, 1.6213333333333317, 1.621333333333332, 1.2713333333333316, 0.9713333333333317, 1.121333333333332, 1.3713333333333317, 0.9713333333333317]\n"
     ]
    }
   ],
   "source": [
    "#8.\n",
    "\n",
    "import random\n",
    "\n",
    "rand_list = []\n",
    "for s in range(len(petal_length)):\n",
    "    randomValue = random.uniform(0.0,1.0)\n",
    "    rand_list.append(randomValue)\n",
    "    \n",
    "#print cons_list\n",
    "    \n",
    "basis_vector = 1\n",
    "    \n",
    "#c1 = -2.00\n",
    "#c2 = 2.50\n",
    "\n",
    "xp1_list = []\n",
    "xp2_list = []\n",
    "\n",
    "\n",
    "for rr in range(len(rand_list)):\n",
    "    a1_op = basis_vector*rand_list[rr]\n",
    "#    a1_list.append(a1_op)\n",
    "    a2_op = basis_vector*rand_list[rr]\n",
    "#    a2_list.append(a2_op)\n",
    "        \n",
    "    az_op = ((a1_op*data_center_petal_length[rr])+(a2_op*data_center_petal_width[rr]))\n",
    "    aa_op = ((a1_op**2)+(a2_op**2))\n",
    "    \n",
    "    cc_op = az_op/aa_op\n",
    "    \n",
    "    xp1_op = a1_op*cc_op\n",
    "    xp1_list.append(xp1_op)\n",
    "    \n",
    "    xp2_op = a2_op*cc_op\n",
    "    xp2_list.append(xp2_op)\n",
    "\n",
    "print \"\\nXP1\"\n",
    "print xp1_list\n",
    "print \"\\nXP2\"\n",
    "print xp2_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RaeBkBmOMyczMJKVt9DHjO54ewbezDtLr8n38viKfzz6fVumLBoC4387qYAB34fjyOIeqPgHSgHTgHlVdXsp8hgJDARITE1MmTpx4TJvDd3wGGsvlG8vlG8t17HIzMzOJi4sjMjISgBUrVlBYmEODBsnERm53N1RYsSaPvPwg6tevT7Vq1RA57tuuyzRzWW+r1NTUharawavGqupoBzQAlpUyrQoQ4+nvA6z1Zp4pKSlakpkzZ5Y43mmWyzeWyzeWy2337t1aKzFGIyNEmzUK1Yhw0fr1ampubq7m5uZqbIzomDee17lf1NWfpybr3/tEa2yM6O7du/2asyT+2FbAAvXyczugL8dV1UxVzfb0TwVCRaTGCX7MGGOO0b59S1o3K2Trkoas/LEBGxc0IK5KJh07nkFYWBgzZv5CdrbS98p0el22lW9n5vDZ598THx/vdPSAE9AH60SkFrBdVVVEOuG+72S3w7GMMeXEokWLGHbLjWzevIrMrEM883+JVK0SDEBCjRBefzqBXpetAqBDhw5kZ2ezb3+hk5HLBacvx/0A6AHUEJE04CEgFEBVRwMDgX+JSAFwCBjs2aUyxpjjmj9/Puf0PItrLovlkbvjWLkmmjse2ElePlx+USwAjeqFkJtnHym+cvqqqstPMP1V3JfrGmOMV9asWcOjjz7KzOlf8c+rq/DMQzUB6Nktio5nRDBwSAaDLowhJET4+ItsYmNCHU5c/gT0oSpjjPFFamp35v08l9NahnHgYD6D/17nqOmdzohAVZn4WSZrNuTz/Kh9PPzfpxxKW35Z4TDGVAgvvvgiCxfMZcG39WjZLIx67TewcUs+Ke3+fCRIVraLPXtd3PHALiIjYnl91Ntce+21zoUup6xwGGPKrf3793PWWWeSvnUN6oL4asFEuW/N4LIBsdz78C46tougfnIoOTkubhuxg7p1a7F2Xbqzwcs5KxzGmHKrSeNatGgMr75dm+ioIF5+ax8dzt/C2nn1eeahmqxZn0erv22mXt0Q0rcVUrNGdWbNmed07HIvoO/jMMaY4vbv388VV1xB/fr1UVce335Uh7PPiqLD6RGMfyWRFk3DGP6Y+6r9N55NRFXpc+EtfD/9JzZs2kG9evUcXoPyzwqHMabcGDduHHWS4ljx+yfUrLadc7tHER7+58eYiDCwXwzTfzjIS2/upf25f9CwYQNeeOEFOnfu7FjuisYKhzGm3Ljj9ht4YkR1Fn1fn0fuq86GzfnHtFm0NJet2woZ+VoOFw8cyspVG0uYkzkZVjiMMQHtgQceIDEhirAwQVD+dY37fd7nnR1F9gHlsRd2k5PjwuVSPpicxSdfZvP11zPI2JbF66+/7nD6islOjhtjAtb999/PG6OfZuwLiSRUr87gm7YR5Pm6GxwsTH0/icuGZvDUy3sJDoagIOHue0bQo0cPR3NXdFY4jDEBxeVy8dprr/H555/z80+zGPtiAv0viMHlUsJChW9mHqRXT/e7M5LrhFCzejA1E5IYNWoMF1xwAcHBwc6uQCVghcMYEzAOHjxIm9YNyc/dw2V/jyFUw7l9xE7q1w2lc/sI3ng2gcFDM7jwghjaNA/j/U+zWLuxgMVLfqBhw4ZOx680rHAYYwJCfn4+N954AzXjM5k9uQFhYe6XJn36VTbX3raN5XPqk9o1ijefT+Dyf24nPr4qp7Xtwbcz37dHn/uZnRw3xjhq3bp1XNivJzExkUyb+iHDb407UjQALuoTTX6+snRlLguX5HDnA7u59LIr2Jq+l6+//tqKhgOscBhjHJOVlUXP1LPonrKc3SsbEB1Z8qtZD+UoHc7fwjkD0znrbxfzzjsT/JzUFGWHqowxfvfggw8SHx9H/wvPo2oV4dYhyYSFBdG9SxRPvbKH3j2jj+x1TJ56gIOHhO3bdxIfH09QkH3fdZr9BowxftWyRTNeeP4xasQLI+6oSmioknzGJvLyCnnjmQR27Cqk6Zkbue/RnQy4Jp2rb93O8y+MpkaNGlY0AoSjvwUReVtEdojIslKmi4i8LCLrROR3EWnv74zGmFNj/fr1PP7446zfsJZfvq5H3aQQ7r0lnuWz61M9PpjBN20nKiqI1T/WI78gjBnzGhGXeDGrVm1kyJAhTsc3RTh9qGoc7jf8lXbAsjfQ1NN1BkZ5/jXGlBM7d+7kjNObsWv3fqIjhdAQ4cmX93D9UPf08PAgbh1SjUef38PaDXk89kI2zVu0ZtbsXxAp+ZyHcZajexyqOgfYc5wmA4AJ6jYPqCYitf2TzhhzKrQ/oxmtmuaxaUEDdq5szNwvkvlu1kG2ZhQcaZN9wMWBgy56DsyiZvLlfPnVDCsaAUxUnX1Ru4g0AL5U1TYlTPsSeEpV53qGpwP3qeqCEtoOBYYCJCYmpkycOPGYZWVnZxMTE3NK858Klss3lss3TubKzc1l+fJltG0VTkiR4xt797nIdyWSEL+T/HxYsSaX2Ng4GjVq5EjOwyrz7zA1NXWhqnbwqrGqOtoBDYBlpUz7CuhWZHg6kHKieaakpGhJZs6cWeJ4p1ku31gu3ziZa9KkSRpfLUgLM5oe1S2bXV9feP5ZvahPtEZFitauVc2xjEVV5t8hsEC9/NwO9EsU0oDkIsN1AXvnozEBaNOmTdxwww0888wzR8ZdcMEF5OQqS5bnHtX2y++yAVi6pjYvv/Im6Rl7/ZrVnJxALxxTgKs9V1edCexX1QynQxljjta6dUtatmzEzO8n8MRjw6laJZgJEyYQExPDGe070WvwVj6aksWK1bk889oe/vvcHpLqJLN27Vq7YqoccvSqKhH5AOgB1BCRNOAhIBRAVUcDU4E+wDrgIHCdM0mNMaW5/vrr2Zq2hp+/SqZtq3AKCpRHX9jDsFuu44orrmDu3HkMHjyYYcM/oaDQRXh4BCNHvkxCQoLT0c1f5GjhUNXLTzBdgVv8FMcY46Vdu3bRoUMKWfvTUKBhvRCaNAgFICREeOjueMZM2M8999zDiy++SEkXq8yaNcu/oc0pE+iHqowxAaagoIDGjWqRGL+dt15IZOwLiYSHBXFa6mZcLhfgfqFScp0QVq1a5XBaUxascBhjvOJyuZg7dy7du3enSqww+7NkBvSKYUCvGGZNrktBPrz2diYAaen5LF+Vx/333+9walMWnL5z3BhTDqxZs4YB/c8nNDiTtev3ccv1VY969HlYmDD4ohjGTdxPbGwQDzy1i2rx8XTv3t3B1Kas2B6HMea4VJVLLu7DsOty+O376rRrHcaqdXnHtFu1Np/la/K4++FdNGnWlYz0XQ6kNf5ghcMYU6Inn3ySaokJhERGsnfPH9x0dSwiwpvPJzLjh0N88mXWkRvCPvkyi+k/HGT27J/Yu7eA2bNnOx3flCE7VGWMOcbtd97Jq2PGUP2SvxNXUEj15V8eeXZU6+bh3DG0GtffsZ3bRuwE3M+auvSyq+nSpYuTsY2fWOEwxhzx22+/MW/ePF4bPZqEG68jskljXLm5rP1iMqvX5dG8SRgAjw6vzsKliiu4A23btuXxxx8nIiLC4fTGX6xwGGPIz8/nksGXMfOHuYQ3boiroICIxu4HDgaFhxN7YX+6XvQFdw2JIblOCP/7tIADuQ34fvoUIiMjHU5v/M0KhzGGF196ibmrV1Nj+N0gwt6lyyjYs4fQ6tUBiD7zTPbnFfDka1O5sM/5DLqiP1dddZXtZVRSVjiMqaTef/99ht16IwezDxEcAoVh0cQcOEBI1apU6daVXe9/RM2rriCkWlXytm0nc+ZsbvjH9YwaNcrp6MZhdlWVMZXQvHnzuOGGf3D7kEg2/NqA+VOTOfsMZc+zTwNQrdd5hNevR9qjT7L5Pw+S8dxL9D27hxUNA1jhMKZSGT16NImJifytezcuvCCWB+6Kp1ZCCK2ah/P5uNqEUsD+H+YiQUGE1UmicdOm/PDdd+zfvZvPPv3U6fgmQNihKmMqiabNm7N+40ai259OZEEWvVKPPqkdFiZ0Solixg8/ErRtB3nLV/L+N9/QsWNHhxKbQGV7HMZUcIWFhTz99NOs37CBOv++i5pXXIarZhJz5uUUa6cs+v0QZ9RvyD0XDmDNypVWNEyJrHAYU0GtWLGC4IgIFi9ZzPD77ycoKpKCffsAqNq/PxMnZzJ6/H5yc11s31nA1bftIC8/mF/mz+e+f/+bxMREh9fABCpHC4eI9BKR1SKyTkSGlzD9WhHZKSKLPd0NTuQ0pjxq0749YQ3qE1a7Ng2ee4rqAy9mx/j3yE3bSnjdOlQZ/A+GP7WPmEbrqZ+ykelzg5g3b7HTsU054FjhEJFg4DWgN9AKuFxEWpXQ9ENVPd3TveXXkMaUM5s3b+biiy+mZu3a4HKReN1VSHAwEhREdLvTqNqzB5mzfwAgonEjDuQGM2TIjWRn57Jj+15atmzp7AqYcsHJk+OdgHWqugFARCYCA4AVDmYyptyaMWMG5/XpTUTz5riqVSG4sJCgYnd1h9evR+YPP7Hrw0lkL/yNqrGxjBkzxqHEprwS99tZHViwyECgl6re4Bm+CuisqsOKtLkWeBLYCawB7lTVLaXMbygwFCAxMTGlpFdVZmdnExMTc4rX5ORZLt9YrqOpKvv27WPzH39AdDQhVWJx5eWRv2MnYUm1qRUewfZ892PQC/ZnUpiVRUhwMAkJCdSuXdvveQ8LxN9jIGYC/+RKTU1dqKodvGp8+LHI/u6AQcBbRYavAl4p1qY6EO7pvwmY4c28U1JStCQzZ84scbzTLJdvLNefzjnvPJWwMA2KilJCQjS6Q3utP/JxbfjSsxocH6dhdZL0pXcnaL3HHtL4S/6uEhqq119/vd9zliQQf4+BmEnVP7mABerl57eTJ8fTgOQiw3WB9KINVHW3quZ6Bt8EUvyUzZiAN2zYMGb+OJfaw26i/pOPUO/RB8HlYteHnwBQ5z/34iooIH/HTv54+HH2Tv2avhdcwNixYx1Obso7J89x/Ao0FZGGwFZgMHBF0QYiUltVMzyD/YGV/o1oTGDJycnhrrvu4tOvvmLnjh1Ed+pAWD3396/gqChqXDaQLf99nMKsLIKiowmvU4eIiAg0P9/h5KYicaxwqGqBiAwDvgGCgbdVdbmIPIJ7l2kKcJuI9AcKgD3AtU7lNcZpBQUFJDduTKZAlbP/RlxeHvtnzmFvRDjxfXsDEBQRQVBEBDvem0h+xjaiQkJo1rSpw8lNRePofRyqOlVVm6lqY1V93DPuQU/RQFX/o6qtVbWdqqaq6ion8xrjhMLCQl555RXOOecc9ubmknT37cR27kiVv3Ul6e7byPrxZ/J37wYgf9cuCjOz6Nu6Da+PHMm+HTsIDQ11eA1MRWPPqjImgI0dO5ahw4ZBSDBBYeG4srLYO+1b4vu59zCCo6OJaNaU7F8WEpZUm92ffk6z5s2ZNGmSw8lNRWaFw5gAdejQIW68+Wbi+velStcuSFAQORs3se31MUQ0aURUi+YAFO7dx75lKwgJC6N3aipffPGFw8lNRWfPqjImwLzyyivUql+PKjWqExJXjap/64oEuf+rRjRsQGzXLuz7+jsADixdTn56Bru3bSM/O9uKhvEL2+MwJoD881//4q0JE4jr24voffvJy9h2TJvQGtXJmv8raU+MpHDffsa8/jrx8fEOpDWVlRUOYwLAhAkTmDRpEl9Om0btO4YRnlyX/D17SH/mRQoPHiQ4Kgpw37Cb/csCGiQmcvvtt/PPf/7T3vtt/M4KhzEO2rNnD3UbNSInN4fw5GQkLIy9U78m4Zp/EBofT+yZHcl46TWqnXcOQZERZM75kYLtO5j/xx/UrFnT6fimkrLCYYyDTk9JQRNqUm/oEILCQtHCQnZ+8BF7pnxFjUsvIa5/P9Sl7P5kMkFBwTSpV49vVq+2omEcddyT4yJygYgMEZEGxcZfX5ahjKnIRo8eTVhMDEGREaRlpFP9ov4EhbnvtZDgYKr370f2goWoy0XBnj0cXPw7d98yjPysLFYuX069evUcXgNT2ZVaOETkCWAEcBowXURuLTJ5WMk/ZYw5nokTJ3LzbbcR1aUzSXfeCoUugoo99TQoKhLNyyft8afZ+uSznNetGyNHjnQosTHHOt4ex4VAT1W9A/fDBXuLyAueaVLIt80NAAAa6ElEQVTmyYypQHbs2ME777zDNddfT/QZbYm/sA9htWoRHFeNrHnzj2qbvWARwZGRjH3uebalpTHVLrE1AeZ45zhCVLUAQFX3iciFwBgR+RgI80s6YyqAt8aO5bY77ySmZQuCG9TjwO/LiWy1mJgzTqfGpZew/c23Kdi5i8iWLcjZuInseb/w3wce4Oqrr3Y6ujElOl7hWC8iZ6vqbABVLQSGiMhjwCV+SWdMObdx40Zuv/suqt92M6EJNYkB8tIzyHjldSIaNyKyWVNq3zaM9BdfIXfVampWi+O9SZPo37+/09GNKdXxCsegkkaq6v+JyKgyymNMuZednc35vXsz/9dfceXnEXtmJ0IT/rwKKiypNpEtW3BgyVJiUtqTOXsOQaGh5GVmOZjaGO+VWjhU9dDhfhG5GOgGKDBXVSf7IZsx5VKLtm3ZHRpC7TtvJftX99VRxUlwCHs+m8Kez6YQEhbOvNmzHUhqzF9zwmdVicjruF/buhRYBvxTRF4r62DGlCc7duxg8+bNtGvXjvT0dBJvvJ6w2rWIbn86Bxb9RuGBg0faFmRm4lqzhpFPPMkvP/1M/oEDdOzY0cH0xvjGmxsAzwbaeN5Ji4iMx11EjDHAd999R68LL+SZ555lXZAQVjfpyH0Z4XXrENOxA1uffpaYzp0IESF3wSL+fdfd3HvvvQ4nN+av8ebpuKuBonccJQO/n4qFi0gvEVktIutEZHgJ08NF5EPP9PnFb0Q0xkkul4tt27Zx8eDBVO19AWE1axLX5wIKdu5CCwqOtIu/sA/hDRsSsXwF17U9ndnffstDDzzgYHJjTo43haM6sFJEZonILGAFkCAiU0Rkyl9dsIgEA68BvYFWwOUi0qpYsyHAXlVtArwAPP1Xl2fMqXT7nXcSHhtL7Tp1OJCVhebmokBYYgLh9euz870PKNi3Hy0oIOvn+RxavoLJH0/imZEjad++vdPxjTkp3hyq+gh4sNg4wX2i/GR0Atap6gYAEZkIDMBdmA4bADzs6Z8EvCoicviwmTFOeOyxx3j1zTEkXH81EU0ak79jJ7s++JDC/ZkA1Lz6CnZ98BFbHnkCXC5i4+N595136NKli8PJjTk15ESfwSKyDHgXGAlEeP7toKon9b9ARAYCvVT1Bs/wVUBnVR1WpM0yT5s0z/B6T5tdJcxvKDAUIDExMWXixInHLDM7O5uYYo93CASWyzdO5/ptyRKCqlY58qhzAC0oJCEkhB3qAhEKdu0mIiiIFs2bI+Lsgxac3l6lCcRcgZgJ/JMrNTV1oap28KatN3scnXEfIvoJiAX+B3T96/GOKOl/U/Eq5k0b90jVMcAYgA4dOmiPHj2OaTNr1ixKGu80y+Ubp3Od27s3te+9k9Aa1Y8af0uB8PAnH5O7dj1hBfmsW7GSWrVqOZTyT05vr9IEYq5AzASBl8ubcxz5wCEgEvcex0ZVPfbCdN+l4T7RflhdIL20NiISAlQF9pyCZRvjlYyMDNLS0o4aVzWuGjlr1x01Li89A4BOEVE8dPvt7NuxMyCKhjFlwZvC8SvuwtER902Al4vIpFOw7F+BpiLSUETCgMFA8ZPtU4BrPP0DgRl2fsP4w5o1a2h/Zmcat2hOszZtaHV6OxYvXgzAkw//l92TPyfrlwUUZmdzaPUatr/5NtWqVWP69OmMGDGCkBB71Y2puLz56x6iqgs8/duAAZ7zESdFVQtEZBjwDRAMvK2qy0XkEWCBqk4BxgLvisg63Hsag092ucacSE5ODt179qTwzI4kXnYJiLBrwSJSzzuPjWvXMnToUAoKCvjPww+x+8NJhEZGct3gwTRu1Mjp6Mb4xQn3OIoUjaLj3j0VC1fVqaraTFUbq+rjnnEPeooGqpqjqoNUtYmqdjp8BZYxZWnKlCkUxscR270bEhyMBAUR06kDIY0a8MEHHwBw8803s3/HTlz5+eRmZjJmzBiHUxvjP94cqjKmUklLS0OLnfgGKKhRnT+2bHEgkTGBxQqHqdQWL17MqFGjWLly5ZFxnTp1omDNWrSw8Mg4dbkIWrOOMzt3diKmMQHFzuCZSik7O5vT2rdn86ZNhNaoTv7tt9OyVSt+++UXunbtSkqb01g87j3CznYfrsqd+xMNqtegb9++Tkc3xnG2x2Eqpe49e7I9SKj32EPUGX4Pyf99gPX79tJvwABEhKmff869V1xJ1Vlzif52Brf0vZA506fb1VLGYHscphJyuVws/u036tz/b4IiIgAIjo6i+qCLmfHaGwCEh4cz/L77GH7ffU5GNSYg2R6HqXRycnLQwkJCqlU9anxIfByu/DyHUhlTfljhMBXaH3/8wYgRIxg5ciQHD7pfphQVFUVk1aocWHL0a2WyF/5GXM2aJc3GGFOEFQ5TYV13/fU0aNyY5z94nwdefIHY+DjGjh0LwMhHH2XXBx+x95vvObR6LXu+mMreL75i1IsvOZzamMBn5zhMhTR58mQmfPA+Sf++i7DEBAAOLF3O0JtvZtCgQQwbNozk5GTu+c9/yJj/K/WSkvh02tf07NnT4eTGBD7b4zAV0mNPPklst65HigZA9GmtCa1di6eeegqAAQMGsHbFCrJ37WLF779b0TDGS1Y4TIWUdeAAwbGxx4wPjo1lzx57wLIxJ8MKhynX8vLy+Oyzzxg1ahRLliw5Mn7ggAFk/fTzUXd/F+zP5NDqNQwdOtSJqMZUGHaOw5Rbq1evpse551IQG4vUqM6hBx+k13nnMfHdd3n44Yd5c/x40p99kdi/dcWVk0Pm9Fmkpvawd34bc5Jsj8OUW4OuuILCLp2IvWkIMQP/TvX77mb6woW8+eabhIWFsWX9em4aOIiIXxcSv3odLzzxBNO/+dbp2MaUe1Y4TLm0ceNGNmzaSPRZZx4ZFxQWSliPbrw5YTwAERERvPTSS6St38C6lSu59dZbnYprTIXiSOEQkXgR+U5E1nr+jSulXaGILPZ0xd8OaCqxvLw8gkNCQY5+Lb2EhpKbm+tQKmMqB6f2OIYD01W1KTDdM1ySQ6p6uqfr7794JtA1a9aMqjExHFy24sg4dbnI+3k+l18y0MFkxlR8Tp0cHwD08PSPB2YB9jQ54zUR4f3x4+l94YW4Vq2mMD4OWbWGBnHx3HH77U7HM6ZCc6pwJKpqBoCqZohIQintIkRkAVAAPKWqn/ktoQl43bp1Y+3KlYyfMIEtaWn0uOpaBgwYQGhoqNPRjKnQRFXLZsYi3wO1Spg0AhivqtWKtN2rqsec5xCRJFVNF5FGwAzgHFVdX8ryhgJDARITE1MmTpx4TJvs7GxiYmL+0vqUJcvlG8vlG8vlvUDMBP7JlZqaulBVO3jVWFX93gGrgdqe/trAai9+Zhww0Jv5p6SkaElmzpxZ4ninWS7fWC7fWC7vBWImVf/kAhaol5/hTp0cnwJc4+m/Bvi8eAMRiRORcE9/DaArsKJ4O2OMMf7lVOF4CjhPRNYC53mGEZEOIvKWp01LYIGILAFm4j7HYYXDGGMc5sjJcVXdDZxTwvgFwA2e/p+A0/wczRhjzAnYnePGGGN8YoXDGGOMT6xwGGOM8YkVDmOMMT6xwmGMMcYnVjiMMcb4xAqHMcYYn1jhMMYY4xMrHMYYY3xihcMYY4xPrHAYY4zxiRUOY4wxPrHCYYwxxidWOIwxxvjECocxxhifWOEwxhjjE0cKh4gMEpHlIuISkVJfji4ivURktYisE5Hh/sxojDGmZE7tcSwDLgbmlNZARIKB14DeQCvgchFp5Z94xhhjSuPUq2NXAojI8Zp1Atap6gZP24nAAMDeO26MMQ4SVXVu4SKzgHs87xovPm0g0EtVb/AMXwV0VtVhpcxrKDAUIDExMWXixInHtMnOziYmJubUrcApYrl8Y7l8Y7m8F4iZwD+5UlNTF6pqqacOjqKqZdIB3+M+JFW8G1CkzSygQyk/Pwh4q8jwVcAr3iw7JSVFSzJz5swSxzvNcvnGcvnGcnkvEDOp+icXsEC9/Hwvs0NVqnruSc4iDUguMlwXSD/JeRpjjDlJgXw57q9AUxFpKCJhwGBgisOZjDGm0nPqctyLRCQN6AJ8JSLfeMYnichUAFUtAIYB3wArgY9UdbkTeY0xxvzJqauqJgOTSxifDvQpMjwVmOrHaMYYY04gkA9VGWOMCUBWOIwxxvjECocxxhifWOEwxhjjEyscxhhjfGKFwxhjjE+scJQzqsqSJUv46aefyM3NdTqOMaYSssJRjqxatYoWTVuQ2vUcLuk9iFoJtfj444+djmWMqWQcuQHQ+K6goIDzep5H7LYE2utpiAiZuoch1w6hTZs2tGzZ0umIxphKwvY4Atzzzz9PrRq1iYmIZd/2LOrQ6Mh7TKpIPAl5ybw15i2HUxpjKhPb4whg//znTbwz5h2acBrRxLOfPVDs3VehBRFsy9juTEBjTKVkhSMATZ06la+//pqxY8aSwtlUkThy9CB/sJY8zSVMwgH3ifL90Tvp1fcOhxMbYyoTKxwBJDs7mxZNWrJ9+3ZiqEIQQVSROAAiJIq62pgFzKSBtiCEMHZHbaVOs1pceumlDic3xlQmdo4jgPTt05dD2/PoTj/a0RVFKdD8I9Mb05qaJLE+aBlVzgzm30/exZwfZxMeHu5gamNMZWN7HA6bM2cOy5Yu4/ye5+NSJZkmCEGESjA1NYlVLKKFphAiIRziANvYwm1338rIkSOdjm6MqaScepHTIBFZLiIuESn15egisklElorIYhFZ4M+M/rB48WJ69jgH8oQO2pN2nMUetrOOpQC04AwUmMMXzNWpzOM7Luh3nhUNY4yjnDpUtQy4GJjjRdtUVT1dVUstMOXNpEmTaNSoEWeeeSY1NYlwIomWWOIlgdPpxlY2kqc5BEsI8SQQER7B+A/fYfvObXz+xedOxzfGVHJOvQFwJXDkfoTKpHHDxmzatIka1KYK8ewknXzyjkwPk3AiNYbNrKVA89jGH4wfO95OgBtjAkagn+NQ4FsRUeANVR3jdKCT8corr7Bp02bO5DyiJBaAA5pJDgfJ0UNESCQuLeQgWYTHh9CyVQs+e+EjOnSoMDtbxpgKQFS1bGYs8j1Qq4RJI1T1c0+bWcA9qlri+QsRSVLVdBFJAL4DblXVEg9vichQYChAYmJiysSJE49pk52dTUxMzF9Znb8sJyeHFctXoLi3cxBBRBJDMMFH2lStG8POtF2EEk4OB9EQF+3atfNrzpI4sb28Ybl8Y7m8F4iZwD+5UlNTF3p9SkBVHeuAWUAHL9s+jLvInLBtSkqKlmTmzJklji9LwYRoPAnahQu0BwO0Ge00jHDtRh89VwbquTJQX3r2ZRVEgwjSBskNdevWrX7PWRIntpc3LJdvLJf3AjGTqn9yAQvUy8/ugL2PQ0SiRdzHc0QkGjgf90n1gPfmm28SFBSEiKC4OI0uREssIRJKPWlKIsmksQGAXD1EPvncfsftZGzPYOMfG0hKSnJ4DYwxpnROXY57kYikAV2Ar0TkG8/4JBGZ6mmWCMwVkSXAL8BXqvq1E3l9ccYZZ3DT0H8Rpwk0ohXRVGERc8jXP0+AV6MGO0lnrf7Oz3xLSHAwL7zwAgkJCQ4mN8YY7zh1VdVkYHIJ49OBPp7+DYDzB/q9lJmZyZ133snvi5fSmNbUl2YANNSWrGAhG1lJM8/q7GMXB8liTwzcMuRm2p1eblbTGGMC/qqqckFECCYEFy4EyCcPVUVEEBHqa1N+Yy6NtQ0ZbGIrG7nr7rt49tlnAZg1a5aj+Y0xxhdWOE5SUFAQoYTRjrOoSnUOcYAVLGA9y2lCGwD3M6fIZyaTCSGU83udd6RoGGNMeROwJ8cD2f79+2nSpAmhQWEEawjNOYNqUgMRIUpiaEMntrKBQi1EVdnACgopJCsri3zNY9q0aU6vgjHG/GW2x+GjXbt2UatmbSKIpBGtWc8yqlDtqDYREoWqsorf2MdO8shl3Lh3AvL6cGOM8ZUVDh/Mnz+f/v37E0k0nTiHIAniD13LbrYTReyRdtm6HxcuMthEVFQUmTv3ExUV5WByY4w5daxweGHatGkMuuhScnJzAKEZbQkS91G+RrRkNb8RpMHUoBZZ7GclC1Fch29cNMaYCsUKxwlkZmbSv+8AGmtr6tKYH/jyqIcSJkkDVF2s5XdWs5gggigg34qGMabCspPjJ/Dwww8To1VIliaICIkks5nV5OqhI21CCceFi+07t5GveVY0jDEVmu1xnMDGjRuJpuqR4eZyOrt1Oz8yjThNII8cDpBFv/59qVGjhoNJjTHGP2yPo4hff/2Vl156icWLFx8Z169fP3aSjktdR8Z14XzCCCc3JpvkVkmsWbeazz+3FywZYyoH2+PAfR6jbau2pG1NI4pYDpBFk8ZNWLJsMddddx0jhv8fi3bNoaG2JIggNrOG0MhQduzcTkREhNPxjTHGr2yPA+h5dk+yt+bQnQvpLOfSnX7sWL+Lvr37EhQUxIbN6+ne/yzWhP3GitBfaZvakg1/rLeiYYyplCr9HofL5eK3xb/RhQsIFvfmCJFQmusZzJk9G4CoqCg7FGWMMR6Vfo8jLy8PFy7CiTxqfARRFGqBQ6mMMSZwVfrCERERQWxkFbaz5ajxGfxB9WrVHUpljDGBq9IXDoBnX3yGVSxinS5jl25jrf7OepYy6s1RTkczxpiA49QbAJ8RkVUi8ruITBaRaqW06yUiq0VknYgML6s8Q4cO5YupXxDVPIhN0cup2jqSGbNnMHDgwLJapDHGlFtOnRz/DviPqhaIyNPAf4D7ijYQkWDgNeA8IA34VUSmqOqKsgjUu3dvevfuXRazNsaYCsWRPQ5V/Vb1yJnneUDdEpp1Atap6gZVzQMmAgP8ldEYY0zJxOnnKonIF8CHqvpesfEDgV6qeoNn+Cqgs6oOK2U+Q4GhAImJiSkTJ048pk12dnZAvhPDcvnGcvnGcnkvEDOBf3KlpqYuVNUOXjVW1TLpgO+BZSV0A4q0GQFMxlPAiv38IOCtIsNXAa94s+yUlBQtycyZM0sc7zTL5RvL5RvL5b1AzKTqn1zAAvXy873MznGo6rnHmy4i1wD9gHM8oYtLA5KLDNcF0k9dQmOMMX+FU1dV9cJ9Mry/qh4spdmvQFMRaSgiYcBgYIq/MhpjjCmZU/dxvArEAt+JyGIRGQ0gIkkiMhVA3SfPhwHfACuBj1R1uUN5jTHGeDh+crwsiMhOYHMJk2oAu/wcxxuWyzeWyzeWy3uBmAn8k6u+qtb0pmGFLBylEZEF6u1VA35kuXxjuXxjubwXiJkg8HLZI0eMMcb4xAqHMcYYn1S2wjHG6QClsFy+sVy+sVzeC8RMEGC5KtU5DmOMMSevsu1xGGOMOUlWOIwxxvikQheOQHvvR5HlDRKR5SLiEpFSL7ETkU0istRzk+SCAMrl7+0VLyLfichaz79xpbQr9GyrxSJSZk8ZONH6i0i4iHzomT5fRBqUVRYfMl0rIjuLbJ8byjqTZ7lvi8gOEVlWynQRkZc9uX8XkfYBkquHiOwvsr0e9EOmZBGZKSIrPf8Pby+hjSPb6xjePtSqPHbA+UCIp/9p4OkS2gQD64FGQBiwBGhVxrlaAs2BWUCH47TbBNTw4/Y6YS6HttdIYLinf3hJv0fPtGw/bKMTrj9wMzDa0z8Y99Ofnc50LfCqv/6Wiiy3O9AeWFbK9D7ANECAM4H5AZKrB/Cln7dVbaC9pz8WWFPC79GR7VW8q9B7HBqg7/1Q1ZWqurosl/FXeJnLifekDADGe/rHA38v4+UdjzfrXzTvJOAcERGHMzlCVecAe47TZAAwQd3mAdVEpHYA5PI7Vc1Q1UWe/izcj1qqU6yZI9uruApdOIq5HnelLq4OsKXIcBrH/rKcosC3IrLQ876RQODE9kpU1Qxw/+cCEkppFyEiC0RknoiUVXHxZv2PtPF8cdkPVC+jPN5mArjEc3hjkogklzDdCYH8/6+LiCwRkWki0tqfC/Yc3jwDmF9sUkBsL6deHXvKiMj3QK0SJo1Q1c89bUYABcD/SppFCeNO+hplb3J5oauqpotIAu4HQq7yfFNyMpfft5cPs6nn2V6NgBkislRV159stmK8Wf8y2UbH4c3yvgA+UNVcEbkJ9x5RzzLM5C1/bytvLcL97KZsEekDfAY09ceCRSQG+AS4Q1Uzi08u4Uf8vr3KfeHQAH3vx4lyeTmPdM+/O0RkMu5DEidVOE5BLr9vLxHZLiK1VTXDs1u+o5R5HN5eG0RkFu5vbKe6cHiz/ofbpIlICFCVsj0scsJMqrq7yOCbuM/5BYKAfO9O0Q9sVZ0qIq+LSA1VLdMHDYpIKO6i8T9V/bSEJgGxvSr0oSopx+/9EJFoEYk93I/7RH+JV4D4mRPbawpwjaf/GuCYPSMRiRORcE9/DaArsKIMsniz/kXzDgRmlPKlxW+Zih0H74/7+HkgmAJc7bla6Exg/+HDkk4SkVqHz0uJSCfcn5W7j/9TJ71MAcYCK1X1+VKaBcb2cuKMvL86YB3u44GLPd3hK12SgKlF2vXBfQXDetyHbMo610W4vznkAtuBb4rnwn2FzBJPtzxQcjm0vaoD04G1nn/jPeM74Hm9MHAWsNSzvZYCQ8owzzHrDzyC+wsKQATwsefv7xegkR+20YkyPen5O1oCzARalHUmz3I/ADKAfM/f1hDgJuAmz3QBXvPkXspxrjL0c65hRbbXPOAsP2Tqhvuw0+9FPrP6BML2Kt7ZI0eMMcb4pEIfqjLGGHPqWeEwxhjjEyscxhhjfGKFwxhjjE+scBhjjPGJFQ5jHCIij4vIFhHJdjqLMb6wwmGMc77A/TQAY8oVKxzGnCIi0tHzEMEIz53/y0VkmIjMEff7YFaIyGgRCQJQ1XkaAHdJG+MruwHQmFNIRB7Dfed4JO47kn8GvgZaAZs9/W+o6qQiP5OtqjEOxDXmL7E9DmNOrUeA83A/DmWkZ9wv6n5XRiHuR110cyqcMadCuX86rjEBJh6IAUJx73nAsY+9tt18U67ZHocxp9YY4AHc7345/OjyTp4n1wYBlwFznQpnzKlghcOYU0RErgYKVPV94CmgI+7/Yz97hpcBG4HJnvYjRSQNiBKRNBF52JHgxvjITo4bU4ZEpAdwj6r2czqLMaeK7XEYY4zxie1xGGOM8YntcRhjjPGJFQ5jjDE+scJhjDHGJ1Y4jDHG+MQKhzHGGJ/8P9nX+Xk93gv4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legend\n",
      "Warna ungu = Setosa\n",
      "Warna hijau = Versicolor\n",
      "Warna kuning = Virginica\n"
     ]
    }
   ],
   "source": [
    "#9.\n",
    "\n",
    "plt.scatter(xp1_list,xp2_list, c = y, edgecolors='k')   \n",
    "\n",
    "plt.grid(True)\n",
    "plt.title('scatter plot 2D nomor 9')\n",
    "plt.xlabel('xp1')\n",
    "plt.ylabel('xp2')\n",
    "plt.show()\n",
    "\n",
    "print 'Legend'\n",
    "print 'Warna ungu = Setosa'\n",
    "print 'Warna hijau = Versicolor'\n",
    "print 'Warna kuning = Virginica'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "10. Garis x1=x2 berhasil memisahkan data suatu kelas.\n",
    "Dari gambar proyeksi orthogonal pada soal nomor 9 menunjukan proyeksi setiap titik yang dapat ditarik menjadi sebuah garis yang memaksimalkan pemisahan antara kelas iris-setosa (warna ungu) dari dua kelas lainnya yaitu iris-versicolor (warna hijau) dan iris-virginica (warna kuning)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b><i>Probabilistic View</i></b>\n",
    "\n",
    "11. Dengan satu kali percobaan, hitunglah probabilitas dimana Anda dapat memilih Iris yang memiliki sepal width lebih dari 3 cm!\n",
    "12. Dengan 30 kali percobaan, hitunglah probabilitas dimana Anda dapat memilih 5 Iris yang memiliki sepal width lebih dari 3 cm!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Jawaban:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilitas sepal width\n",
      "0.446666666667\n"
     ]
    }
   ],
   "source": [
    "#11.\n",
    "\n",
    "listPro_sepal_width = []\n",
    "for t in sepal_width:\n",
    "    if t > 3:\n",
    "        listPro_sepal_width.append(t)\n",
    "\n",
    "\n",
    "probabilitas_sepal_width = float(len(listPro_sepal_width)) / float(len(sepal_width))\n",
    "\n",
    "print \"Probabilitas sepal width\"\n",
    "print probabilitas_sepal_width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilitas\n",
      "0.000951528282119\n"
     ]
    }
   ],
   "source": [
    "#12.\n",
    "def faktorial(n):\n",
    "    i = 1\n",
    "    hasil = 1\n",
    "    while i <= n:\n",
    "        #kalikan hasil ke i simpan kembali ke hasil\n",
    "        hasil = hasil * i\n",
    "        # naikan nilai i sebesar 1\n",
    "        i = i + 1\n",
    "    return hasil\n",
    "    \n",
    "    \n",
    "\n",
    "m = 30\n",
    "k = 5\n",
    "mk = m-k\n",
    "\n",
    "\n",
    "kFak = faktorial(k)\n",
    "mFak = faktorial(m)\n",
    "mkFak = faktorial(mk)\n",
    "\n",
    "combMK = mFak/(kFak*(mkFak))\n",
    "\n",
    "#print combMK\n",
    "\n",
    "probAnswer = (combMK)*(probabilitas_sepal_width**k) * ((1-probabilitas_sepal_width)**(m-k))\n",
    "\n",
    "print \"Probabilitas\"\n",
    "print probAnswer"
   ]
  }
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