Transformasi_fitur_numerik.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.4 3.9 1.7 0.4]\n",
      " [4.6 3.4 1.4 0.3]\n",
      " [5.  3.4 1.5 0.2]\n",
      " [4.4 2.9 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.4 3.7 1.5 0.2]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [4.3 3.  1.1 0.1]\n",
      " [5.8 4.  1.2 0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.1 3.5 1.4 0.3]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [5.4 3.4 1.7 0.2]\n",
      " [5.1 3.7 1.5 0.4]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.1 3.3 1.7 0.5]\n",
      " [4.8 3.4 1.9 0.2]\n",
      " [5.  3.  1.6 0.2]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.2 3.4 1.4 0.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.5 4.2 1.4 0.2]\n",
      " [4.9 3.1 1.5 0.2]\n",
      " [5.  3.2 1.2 0.2]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.6 1.4 0.1]\n",
      " [4.4 3.  1.3 0.2]\n",
      " [5.1 3.4 1.5 0.2]\n",
      " [5.  3.5 1.3 0.3]\n",
      " [4.5 2.3 1.3 0.3]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [5.  3.5 1.6 0.6]\n",
      " [5.1 3.8 1.9 0.4]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [5.1 3.8 1.6 0.2]\n",
      " [4.6 3.2 1.4 0.2]\n",
      " [5.3 3.7 1.5 0.2]\n",
      " [5.  3.3 1.4 0.2]\n",
      " [7.  3.2 4.7 1.4]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [6.9 3.1 4.9 1.5]\n",
      " [5.5 2.3 4.  1.3]\n",
      " [6.5 2.8 4.6 1.5]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [4.9 2.4 3.3 1. ]\n",
      " [6.6 2.9 4.6 1.3]\n",
      " [5.2 2.7 3.9 1.4]\n",
      " [5.  2.  3.5 1. ]\n",
      " [5.9 3.  4.2 1.5]\n",
      " [6.  2.2 4.  1. ]\n",
      " [6.1 2.9 4.7 1.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [5.6 3.  4.5 1.5]\n",
      " [5.8 2.7 4.1 1. ]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.9 3.2 4.8 1.8]\n",
      " [6.1 2.8 4.  1.3]\n",
      " [6.3 2.5 4.9 1.5]\n",
      " [6.1 2.8 4.7 1.2]\n",
      " [6.4 2.9 4.3 1.3]\n",
      " [6.6 3.  4.4 1.4]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [6.7 3.  5.  1.7]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [5.7 2.6 3.5 1. ]\n",
      " [5.5 2.4 3.8 1.1]\n",
      " [5.5 2.4 3.7 1. ]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.  2.7 5.1 1.6]\n",
      " [5.4 3.  4.5 1.5]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [6.3 2.3 4.4 1.3]\n",
      " [5.6 3.  4.1 1.3]\n",
      " [5.5 2.5 4.  1.3]\n",
      " [5.5 2.6 4.4 1.2]\n",
      " [6.1 3.  4.6 1.4]\n",
      " [5.8 2.6 4.  1.2]\n",
      " [5.  2.3 3.3 1. ]\n",
      " [5.6 2.7 4.2 1.3]\n",
      " [5.7 3.  4.2 1.2]\n",
      " [5.7 2.9 4.2 1.3]\n",
      " [6.2 2.9 4.3 1.3]\n",
      " [5.1 2.5 3.  1.1]\n",
      " [5.7 2.8 4.1 1.3]\n",
      " [6.3 3.3 6.  2.5]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [7.1 3.  5.9 2.1]\n",
      " [6.3 2.9 5.6 1.8]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [7.6 3.  6.6 2.1]\n",
      " [4.9 2.5 4.5 1.7]\n",
      " [7.3 2.9 6.3 1.8]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [7.2 3.6 6.1 2.5]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [6.4 2.7 5.3 1.9]\n",
      " [6.8 3.  5.5 2.1]\n",
      " [5.7 2.5 5.  2. ]\n",
      " [5.8 2.8 5.1 2.4]\n",
      " [6.4 3.2 5.3 2.3]\n",
      " [6.5 3.  5.5 1.8]\n",
      " [7.7 3.8 6.7 2.2]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.2 5.  1.5]\n",
      " [6.9 3.2 5.7 2.3]\n",
      " [5.6 2.8 4.9 2. ]\n",
      " [7.7 2.8 6.7 2. ]\n",
      " [6.3 2.7 4.9 1.8]\n",
      " [6.7 3.3 5.7 2.1]\n",
      " [7.2 3.2 6.  1.8]\n",
      " [6.2 2.8 4.8 1.8]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.2 3.  5.8 1.6]\n",
      " [7.4 2.8 6.1 1.9]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [6.3 2.8 5.1 1.5]\n",
      " [6.1 2.6 5.6 1.4]\n",
      " [7.7 3.  6.1 2.3]\n",
      " [6.3 3.4 5.6 2.4]\n",
      " [6.4 3.1 5.5 1.8]\n",
      " [6.  3.  4.8 1.8]\n",
      " [6.9 3.1 5.4 2.1]\n",
      " [6.7 3.1 5.6 2.4]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [6.7 3.3 5.7 2.5]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.5 3.  5.2 2. ]\n",
      " [6.2 3.4 5.4 2.3]\n",
      " [5.9 3.  5.1 1.8]]\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "data = iris.data\n",
    "target = iris.target\n",
    "print(data)\n",
    "print(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Approximation result (150, 2)\n",
      "Detail results (150, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6.08111832 1.13137085]\n",
      " [5.58614357 1.13137085]\n",
      " [5.58614357 1.06066017]\n",
      " [5.44472222 1.20208153]\n",
      " [6.08111832 1.13137085]\n",
      " [6.57609307 1.48492424]\n",
      " [5.65685425 1.20208153]\n",
      " [5.93969696 1.20208153]\n",
      " [5.1618795  1.13137085]\n",
      " [5.65685425 1.13137085]\n",
      " [6.43467171 1.20208153]\n",
      " [5.79827561 1.27279221]\n",
      " [5.51543289 1.06066017]\n",
      " [5.1618795  0.84852814]\n",
      " [6.92964646 0.98994949]\n",
      " [7.14177849 1.34350288]\n",
      " [6.57609307 1.20208153]\n",
      " [6.08111832 1.20208153]\n",
      " [6.71751442 1.41421356]\n",
      " [6.29325035 1.27279221]\n",
      " [6.22253967 1.34350288]\n",
      " [6.22253967 1.34350288]\n",
      " [5.79827561 0.84852814]\n",
      " [5.93969696 1.55563492]\n",
      " [5.79827561 1.48492424]\n",
      " [5.65685425 1.27279221]\n",
      " [5.93969696 1.41421356]\n",
      " [6.151829   1.20208153]\n",
      " [6.08111832 1.13137085]\n",
      " [5.58614357 1.27279221]\n",
      " [5.58614357 1.27279221]\n",
      " [6.22253967 1.34350288]\n",
      " [6.57609307 1.13137085]\n",
      " [6.85893578 1.13137085]\n",
      " [5.65685425 1.20208153]\n",
      " [5.79827561 0.98994949]\n",
      " [6.36396103 1.06066017]\n",
      " [6.01040764 1.06066017]\n",
      " [5.23259018 1.06066017]\n",
      " [6.01040764 1.20208153]\n",
      " [6.01040764 1.13137085]\n",
      " [4.80832611 1.13137085]\n",
      " [5.37401154 1.06066017]\n",
      " [6.01040764 1.55563492]\n",
      " [6.29325035 1.6263456 ]\n",
      " [5.51543289 1.20208153]\n",
      " [6.29325035 1.27279221]\n",
      " [5.51543289 1.13137085]\n",
      " [6.36396103 1.20208153]\n",
      " [5.86898628 1.13137085]\n",
      " [7.21248917 4.31335137]\n",
      " [6.7882251  4.24264069]\n",
      " [7.07106781 4.5254834 ]\n",
      " [5.51543289 3.74766594]\n",
      " [6.57609307 4.31335137]\n",
      " [6.01040764 4.10121933]\n",
      " [6.7882251  4.45477272]\n",
      " [5.1618795  3.04055916]\n",
      " [6.71751442 4.17193001]\n",
      " [5.58614357 3.74766594]\n",
      " [4.94974747 3.18198052]\n",
      " [6.29325035 4.03050865]\n",
      " [5.79827561 3.53553391]\n",
      " [6.36396103 4.31335137]\n",
      " [6.01040764 3.46482323]\n",
      " [6.92964646 4.10121933]\n",
      " [6.08111832 4.24264069]\n",
      " [6.01040764 3.60624458]\n",
      " [5.93969696 4.24264069]\n",
      " [5.72756493 3.53553391]\n",
      " [6.43467171 4.66690476]\n",
      " [6.29325035 3.74766594]\n",
      " [6.22253967 4.5254834 ]\n",
      " [6.29325035 4.17193001]\n",
      " [6.57609307 3.95979797]\n",
      " [6.7882251  4.10121933]\n",
      " [6.7882251  4.38406204]\n",
      " [6.85893578 4.73761543]\n",
      " [6.29325035 4.24264069]\n",
      " [5.86898628 3.18198052]\n",
      " [5.58614357 3.46482323]\n",
      " [5.58614357 3.32340187]\n",
      " [6.01040764 3.60624458]\n",
      " [6.151829   4.73761543]\n",
      " [5.93969696 4.24264069]\n",
      " [6.64680374 4.31335137]\n",
      " [6.92964646 4.38406204]\n",
      " [6.08111832 4.03050865]\n",
      " [6.08111832 3.81837662]\n",
      " [5.65685425 3.74766594]\n",
      " [5.72756493 3.95979797]\n",
      " [6.43467171 4.24264069]\n",
      " [5.93969696 3.67695526]\n",
      " [5.1618795  3.04055916]\n",
      " [5.86898628 3.8890873 ]\n",
      " [6.151829   3.81837662]\n",
      " [6.08111832 3.8890873 ]\n",
      " [6.43467171 3.95979797]\n",
      " [5.37401154 2.8991378 ]\n",
      " [6.01040764 3.81837662]\n",
      " [6.7882251  6.01040764]\n",
      " [6.01040764 4.94974747]\n",
      " [7.14177849 5.65685425]\n",
      " [6.50538239 5.23259018]\n",
      " [6.71751442 5.65685425]\n",
      " [7.49533188 6.151829  ]\n",
      " [5.23259018 4.38406204]\n",
      " [7.21248917 5.72756493]\n",
      " [6.50538239 5.37401154]\n",
      " [7.63675324 6.08111832]\n",
      " [6.85893578 5.02045815]\n",
      " [6.43467171 5.09116882]\n",
      " [6.92964646 5.37401154]\n",
      " [5.79827561 4.94974747]\n",
      " [6.08111832 5.30330086]\n",
      " [6.7882251  5.37401154]\n",
      " [6.71751442 5.1618795 ]\n",
      " [8.13172798 6.29325035]\n",
      " [7.28319985 6.50538239]\n",
      " [5.79827561 4.59619408]\n",
      " [7.14177849 5.65685425]\n",
      " [5.93969696 4.87903679]\n",
      " [7.4246212  6.151829  ]\n",
      " [6.36396103 4.73761543]\n",
      " [7.07106781 5.51543289]\n",
      " [7.35391052 5.51543289]\n",
      " [6.36396103 4.66690476]\n",
      " [6.43467171 4.73761543]\n",
      " [6.50538239 5.44472222]\n",
      " [7.21248917 5.23259018]\n",
      " [7.21248917 5.65685425]\n",
      " [8.27314934 5.93969696]\n",
      " [6.50538239 5.51543289]\n",
      " [6.43467171 4.66690476]\n",
      " [6.151829   4.94974747]\n",
      " [7.56604256 5.93969696]\n",
      " [6.85893578 5.65685425]\n",
      " [6.71751442 5.1618795 ]\n",
      " [6.36396103 4.66690476]\n",
      " [7.07106781 5.30330086]\n",
      " [6.92964646 5.65685425]\n",
      " [7.07106781 5.23259018]\n",
      " [6.01040764 4.94974747]\n",
      " [7.07106781 5.79827561]\n",
      " [7.07106781 5.79827561]\n",
      " [6.85893578 5.30330086]\n",
      " [6.22253967 4.87903679]\n",
      " [6.71751442 5.09116882]\n",
      " [6.7882251  5.44472222]\n",
      " [6.29325035 4.87903679]]\n"
     ]
    }
   ],
   "source": [
    "import pywt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "(cA, cD) = pywt.dwt(data, 'db1')\n",
    "print('Approximation result',cA.shape)\n",
    "print('Detail results',cD.shape)\n",
    "\n",
    "plt.scatter(cD[:, 0], cD[:, 1], marker='o', c=target)\n",
    "plt.show()\n",
    "print(cA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sumber: https://sebastianraschka.com/Articles/2014_pca_step_by_step.html\n",
    "\n",
    "Principle Component Analysis (PCA) Steps:\n",
    "\n",
    "1. Taking the whole dataset ignoring the class labels.\n",
    "2. Computing the d-dimensional mean vector.\n",
    "3. Computing the Covariance Matrix.\n",
    "4. Computing eigenvectors and corresponding eigenvalues.\n",
    "5. Sorting the eigenvectors by decreasing eigenvalues.\n",
    "6. Choosing k eigenvectors with the largest eigenvalues.\n",
    "7. Transforming the samples onto the new subspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.68412563  0.31939725]\n",
      " [-2.71414169 -0.17700123]\n",
      " [-2.88899057 -0.14494943]\n",
      " [-2.74534286 -0.31829898]\n",
      " [-2.72871654  0.32675451]\n",
      " [-2.28085963  0.74133045]\n",
      " [-2.82053775 -0.08946138]\n",
      " [-2.62614497  0.16338496]\n",
      " [-2.88638273 -0.57831175]\n",
      " [-2.6727558  -0.11377425]\n",
      " [-2.50694709  0.6450689 ]\n",
      " [-2.61275523  0.01472994]\n",
      " [-2.78610927 -0.235112  ]\n",
      " [-3.22380374 -0.51139459]\n",
      " [-2.64475039  1.17876464]\n",
      " [-2.38603903  1.33806233]\n",
      " [-2.62352788  0.81067951]\n",
      " [-2.64829671  0.31184914]\n",
      " [-2.19982032  0.87283904]\n",
      " [-2.5879864   0.51356031]\n",
      " [-2.31025622  0.39134594]\n",
      " [-2.54370523  0.43299606]\n",
      " [-3.21593942  0.13346807]\n",
      " [-2.30273318  0.09870885]\n",
      " [-2.35575405 -0.03728186]\n",
      " [-2.50666891 -0.14601688]\n",
      " [-2.46882007  0.13095149]\n",
      " [-2.56231991  0.36771886]\n",
      " [-2.63953472  0.31203998]\n",
      " [-2.63198939 -0.19696122]\n",
      " [-2.58739848 -0.20431849]\n",
      " [-2.4099325   0.41092426]\n",
      " [-2.64886233  0.81336382]\n",
      " [-2.59873675  1.09314576]\n",
      " [-2.63692688 -0.12132235]\n",
      " [-2.86624165  0.06936447]\n",
      " [-2.62523805  0.59937002]\n",
      " [-2.80068412  0.26864374]\n",
      " [-2.98050204 -0.48795834]\n",
      " [-2.59000631  0.22904384]\n",
      " [-2.77010243  0.26352753]\n",
      " [-2.84936871 -0.94096057]\n",
      " [-2.99740655 -0.34192606]\n",
      " [-2.40561449  0.18887143]\n",
      " [-2.20948924  0.43666314]\n",
      " [-2.71445143 -0.2502082 ]\n",
      " [-2.53814826  0.50377114]\n",
      " [-2.83946217 -0.22794557]\n",
      " [-2.54308575  0.57941002]\n",
      " [-2.70335978  0.10770608]\n",
      " [ 1.28482569  0.68516047]\n",
      " [ 0.93248853  0.31833364]\n",
      " [ 1.46430232  0.50426282]\n",
      " [ 0.18331772 -0.82795901]\n",
      " [ 1.08810326  0.07459068]\n",
      " [ 0.64166908 -0.41824687]\n",
      " [ 1.09506066  0.28346827]\n",
      " [-0.74912267 -1.00489096]\n",
      " [ 1.04413183  0.2283619 ]\n",
      " [-0.0087454  -0.72308191]\n",
      " [-0.50784088 -1.26597119]\n",
      " [ 0.51169856 -0.10398124]\n",
      " [ 0.26497651 -0.55003646]\n",
      " [ 0.98493451 -0.12481785]\n",
      " [-0.17392537 -0.25485421]\n",
      " [ 0.92786078  0.46717949]\n",
      " [ 0.66028376 -0.35296967]\n",
      " [ 0.23610499 -0.33361077]\n",
      " [ 0.94473373 -0.54314555]\n",
      " [ 0.04522698 -0.58383438]\n",
      " [ 1.11628318 -0.08461685]\n",
      " [ 0.35788842 -0.06892503]\n",
      " [ 1.29818388 -0.32778731]\n",
      " [ 0.92172892 -0.18273779]\n",
      " [ 0.71485333  0.14905594]\n",
      " [ 0.90017437  0.32850447]\n",
      " [ 1.33202444  0.24444088]\n",
      " [ 1.55780216  0.26749545]\n",
      " [ 0.81329065 -0.1633503 ]\n",
      " [-0.30558378 -0.36826219]\n",
      " [-0.06812649 -0.70517213]\n",
      " [-0.18962247 -0.68028676]\n",
      " [ 0.13642871 -0.31403244]\n",
      " [ 1.38002644 -0.42095429]\n",
      " [ 0.58800644 -0.48428742]\n",
      " [ 0.80685831  0.19418231]\n",
      " [ 1.22069088  0.40761959]\n",
      " [ 0.81509524 -0.37203706]\n",
      " [ 0.24595768 -0.2685244 ]\n",
      " [ 0.16641322 -0.68192672]\n",
      " [ 0.46480029 -0.67071154]\n",
      " [ 0.8908152  -0.03446444]\n",
      " [ 0.23054802 -0.40438585]\n",
      " [-0.70453176 -1.01224823]\n",
      " [ 0.35698149 -0.50491009]\n",
      " [ 0.33193448 -0.21265468]\n",
      " [ 0.37621565 -0.29321893]\n",
      " [ 0.64257601  0.01773819]\n",
      " [-0.90646986 -0.75609337]\n",
      " [ 0.29900084 -0.34889781]\n",
      " [ 2.53119273 -0.00984911]\n",
      " [ 1.41523588 -0.57491635]\n",
      " [ 2.61667602  0.34390315]\n",
      " [ 1.97153105 -0.1797279 ]\n",
      " [ 2.35000592 -0.04026095]\n",
      " [ 3.39703874  0.55083667]\n",
      " [ 0.52123224 -1.19275873]\n",
      " [ 2.93258707  0.3555    ]\n",
      " [ 2.32122882 -0.2438315 ]\n",
      " [ 2.91675097  0.78279195]\n",
      " [ 1.66177415  0.24222841]\n",
      " [ 1.80340195 -0.21563762]\n",
      " [ 2.1655918   0.21627559]\n",
      " [ 1.34616358 -0.77681835]\n",
      " [ 1.58592822 -0.53964071]\n",
      " [ 1.90445637  0.11925069]\n",
      " [ 1.94968906  0.04194326]\n",
      " [ 3.48705536  1.17573933]\n",
      " [ 3.79564542  0.25732297]\n",
      " [ 1.30079171 -0.76114964]\n",
      " [ 2.42781791  0.37819601]\n",
      " [ 1.19900111 -0.60609153]\n",
      " [ 3.49992004  0.4606741 ]\n",
      " [ 1.38876613 -0.20439933]\n",
      " [ 2.2754305   0.33499061]\n",
      " [ 2.61409047  0.56090136]\n",
      " [ 1.25850816 -0.17970479]\n",
      " [ 1.29113206 -0.11666865]\n",
      " [ 2.12360872 -0.20972948]\n",
      " [ 2.38800302  0.4646398 ]\n",
      " [ 2.84167278  0.37526917]\n",
      " [ 3.23067366  1.37416509]\n",
      " [ 2.15943764 -0.21727758]\n",
      " [ 1.44416124 -0.14341341]\n",
      " [ 1.78129481 -0.49990168]\n",
      " [ 3.07649993  0.68808568]\n",
      " [ 2.14424331  0.1400642 ]\n",
      " [ 1.90509815  0.04930053]\n",
      " [ 1.16932634 -0.16499026]\n",
      " [ 2.10761114  0.37228787]\n",
      " [ 2.31415471  0.18365128]\n",
      " [ 1.9222678   0.40920347]\n",
      " [ 1.41523588 -0.57491635]\n",
      " [ 2.56301338  0.2778626 ]\n",
      " [ 2.41874618  0.3047982 ]\n",
      " [ 1.94410979  0.1875323 ]\n",
      " [ 1.52716661 -0.37531698]\n",
      " [ 1.76434572  0.07885885]\n",
      " [ 1.90094161  0.11662796]\n",
      " [ 1.39018886 -0.28266094]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA as sklearnPCA\n",
    "\n",
    "pca = sklearnPCA(n_components=2)\n",
    "X_pca = pca.fit_transform(data)\n",
    "\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], marker='o', c=target)\n",
    "plt.show()\n",
    "print(X_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.91274714e+00  2.30203322e+00]\n",
      " [ 5.57248242e+00  1.97182599e+00]\n",
      " [ 5.44697714e+00  2.09520636e+00]\n",
      " [ 5.43645948e+00  1.87038151e+00]\n",
      " [ 5.87564494e+00  2.32829018e+00]\n",
      " [ 6.47759822e+00  2.32464996e+00]\n",
      " [ 5.51597520e+00  2.07090423e+00]\n",
      " [ 5.85092859e+00  2.14807482e+00]\n",
      " [ 5.15891972e+00  1.77506408e+00]\n",
      " [ 5.64500117e+00  1.99000106e+00]\n",
      " [ 6.26539771e+00  2.42576813e+00]\n",
      " [ 5.75200785e+00  2.02037338e+00]\n",
      " [ 5.48058085e+00  1.97777558e+00]\n",
      " [ 4.95112411e+00  2.04828749e+00]\n",
      " [ 6.52596417e+00  2.91606081e+00]\n",
      " [ 6.79037199e+00  2.82500759e+00]\n",
      " [ 6.27239468e+00  2.60811578e+00]\n",
      " [ 5.92953789e+00  2.26766614e+00]\n",
      " [ 6.64813130e+00  2.38959506e+00]\n",
      " [ 6.09486463e+00  2.36082303e+00]\n",
      " [ 6.25397363e+00  2.12001187e+00]\n",
      " [ 6.07364677e+00  2.27178150e+00]\n",
      " [ 5.36999813e+00  2.49808604e+00]\n",
      " [ 6.04100482e+00  1.87698371e+00]\n",
      " [ 5.90591050e+00  1.80777402e+00]\n",
      " [ 5.75019501e+00  1.85851056e+00]\n",
      " [ 5.93581099e+00  2.00847420e+00]\n",
      " [ 6.03915884e+00  2.25958425e+00]\n",
      " [ 5.94984934e+00  2.27577626e+00]\n",
      " [ 5.60087980e+00  1.88260699e+00]\n",
      " [ 5.63798200e+00  1.85635003e+00]\n",
      " [ 6.18495337e+00  2.19301062e+00]\n",
      " [ 6.25041979e+00  2.62199803e+00]\n",
      " [ 6.47925073e+00  2.79842433e+00]\n",
      " [ 5.66179193e+00  1.95563398e+00]\n",
      " [ 5.62100870e+00  2.25132529e+00]\n",
      " [ 6.16188952e+00  2.48656963e+00]\n",
      " [ 5.78374337e+00  2.33423977e+00]\n",
      " [ 5.14562746e+00  1.90060499e+00]\n",
      " [ 5.92603941e+00  2.17649231e+00]\n",
      " [ 5.80312619e+00  2.31011510e+00]\n",
      " [ 4.97146870e+00  1.51193425e+00]\n",
      " [ 5.22164469e+00  2.00995389e+00]\n",
      " [ 6.00740111e+00  1.99441449e+00]\n",
      " [ 6.31685893e+00  2.04299013e+00]\n",
      " [ 5.51416236e+00  1.90904141e+00]\n",
      " [ 6.12937476e+00  2.32432366e+00]\n",
      " [ 5.42316721e+00  1.99592242e+00]\n",
      " [ 6.19028689e+00  2.39735064e+00]\n",
      " [ 5.76161909e+00  2.16426683e+00]\n",
      " [ 9.12024508e+00 -7.30558200e-02]\n",
      " [ 8.58376916e+00 -1.36194931e-01]\n",
      " [ 9.12651817e+00 -3.32247752e-01]\n",
      " [ 7.27560832e+00 -4.60955955e-01]\n",
      " [ 8.55814639e+00 -3.97341697e-01]\n",
      " [ 7.87237747e+00 -4.85081002e-01]\n",
      " [ 8.66605949e+00 -2.86037963e-01]\n",
      " [ 6.45347358e+00  2.23799845e-02]\n",
      " [ 8.63768432e+00 -2.45515595e-01]\n",
      " [ 7.16780021e+00 -2.91011251e-01]\n",
      " [ 6.47915170e+00 -3.09633237e-01]\n",
      " [ 7.97829519e+00 -1.75031916e-01]\n",
      " [ 7.56278153e+00 -2.70441712e-01]\n",
      " [ 8.33022188e+00 -4.92836582e-01]\n",
      " [ 7.37356730e+00  1.78974058e-01]\n",
      " [ 8.70300135e+00 -3.83374315e-04]\n",
      " [ 7.90686540e+00 -4.72883753e-01]\n",
      " [ 7.65390386e+00 -1.24770897e-01]\n",
      " [ 8.05346136e+00 -7.39774413e-01]\n",
      " [ 7.34185398e+00 -1.83588948e-01]\n",
      " [ 8.41249000e+00 -5.93982991e-01]\n",
      " [ 7.91631631e+00 -1.70787632e-02]\n",
      " [ 8.44780157e+00 -8.30799394e-01]\n",
      " [ 8.25863175e+00 -4.78776871e-01]\n",
      " [ 8.33356003e+00 -8.97512089e-02]\n",
      " [ 8.58988192e+00 -8.34753146e-02]\n",
      " [ 8.86928986e+00 -4.19455056e-01]\n",
      " [ 9.02317031e+00 -5.83357800e-01]\n",
      " [ 8.16930004e+00 -4.13888242e-01]\n",
      " [ 7.23297912e+00  2.17335895e-01]\n",
      " [ 7.17743366e+00 -1.95814432e-01]\n",
      " [ 7.10934202e+00 -9.05808962e-02]\n",
      " [ 7.58488360e+00 -5.17721477e-02]\n",
      " [ 8.41787888e+00 -9.82802956e-01]\n",
      " [ 7.75664376e+00 -5.29718734e-01]\n",
      " [ 8.37613388e+00 -1.74883073e-01]\n",
      " [ 8.87369476e+00 -2.47349822e-01]\n",
      " [ 8.08169840e+00 -5.17081855e-01]\n",
      " [ 7.66808034e+00 -1.20683770e-01]\n",
      " [ 7.35162556e+00 -3.51607055e-01]\n",
      " [ 7.57804696e+00 -5.46031346e-01]\n",
      " [ 8.31692961e+00 -3.67295677e-01]\n",
      " [ 7.59817587e+00 -1.77313053e-01]\n",
      " [ 6.49057578e+00 -3.87697543e-03]\n",
      " [ 7.60535538e+00 -3.55573575e-01]\n",
      " [ 7.77770129e+00 -1.28765654e-01]\n",
      " [ 7.75648343e+00 -2.17807185e-01]\n",
      " [ 8.18333840e+00 -1.46586189e-01]\n",
      " [ 6.50459193e+00  3.12121701e-01]\n",
      " [ 7.66717393e+00 -2.01615180e-01]\n",
      " [ 9.48408779e+00 -1.51660561e+00]\n",
      " [ 8.31802951e+00 -1.14273918e+00]\n",
      " [ 9.85248456e+00 -1.24495426e+00]\n",
      " [ 9.00931450e+00 -1.21126802e+00]\n",
      " [ 9.36730953e+00 -1.37895983e+00]\n",
      " [ 1.05871448e+01 -1.59893200e+00]\n",
      " [ 7.22462810e+00 -1.01391260e+00]\n",
      " [ 1.01195289e+01 -1.42315831e+00]\n",
      " [ 9.26032507e+00 -1.45802877e+00]\n",
      " [ 1.03254119e+01 -1.16769130e+00]\n",
      " [ 9.05063906e+00 -7.04811578e-01]\n",
      " [ 8.87129618e+00 -1.11396715e+00]\n",
      " [ 9.42194857e+00 -1.04674091e+00]\n",
      " [ 8.13239132e+00 -1.24400619e+00]\n",
      " [ 8.43999189e+00 -1.25990013e+00]\n",
      " [ 9.12850228e+00 -9.78063221e-01]\n",
      " [ 9.14624386e+00 -1.02889214e+00]\n",
      " [ 1.10344162e+01 -1.23835244e+00]\n",
      " [ 1.06977054e+01 -2.07054584e+00]\n",
      " [ 8.15974415e+00 -1.15094167e+00]\n",
      " [ 9.70925990e+00 -1.11944159e+00]\n",
      " [ 8.12000547e+00 -1.03753388e+00]\n",
      " [ 1.06207486e+01 -1.71636278e+00]\n",
      " [ 8.57419106e+00 -8.24551736e-01]\n",
      " [ 9.56346538e+00 -1.05286796e+00]\n",
      " [ 1.00045412e+01 -1.07495308e+00]\n",
      " [ 8.48578798e+00 -7.27428321e-01]\n",
      " [ 8.53799528e+00 -7.17363366e-01]\n",
      " [ 9.09678896e+00 -1.34062623e+00]\n",
      " [ 9.79234073e+00 -9.73834912e-01]\n",
      " [ 1.00708200e+01 -1.34204944e+00]\n",
      " [ 1.09971537e+01 -9.00183935e-01]\n",
      " [ 9.11357971e+00 -1.37499331e+00]\n",
      " [ 8.66442919e+00 -8.08508955e-01]\n",
      " [ 8.67790400e+00 -1.29465803e+00]\n",
      " [ 1.04393327e+01 -1.28491639e+00]\n",
      " [ 9.30010211e+00 -1.14409826e+00]\n",
      " [ 9.10914166e+00 -1.00263518e+00]\n",
      " [ 8.41158358e+00 -6.74914401e-01]\n",
      " [ 9.48376712e+00 -8.92782514e-01]\n",
      " [ 9.48651952e+00 -1.19445165e+00]\n",
      " [ 9.36344597e+00 -7.48917309e-01]\n",
      " [ 8.31802951e+00 -1.14273918e+00]\n",
      " [ 9.73675086e+00 -1.28959199e+00]\n",
      " [ 9.63062839e+00 -1.19033628e+00]\n",
      " [ 9.22651660e+00 -9.31293195e-01]\n",
      " [ 8.56626547e+00 -1.03913417e+00]\n",
      " [ 9.02592271e+00 -8.85026933e-01]\n",
      " [ 9.10559876e+00 -9.96415757e-01]\n",
      " [ 8.49037542e+00 -9.15931258e-01]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "svd = TruncatedSVD(n_components=2)\n",
    "X_svd = svd.fit_transform(data)\n",
    "\n",
    "plt.scatter(X_svd[:, 0], X_svd[:, 1], marker='o', c=target)\n",
    "plt.show()\n",
    "print(X_svd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "Sumber: https://sebastianraschka.com/Articles/2014_python_lda.html\n",
    "\n",
    "Linear Discriminant Analysis (LDA) Steps:\n",
    "1. Compute the d-dimensional mean vectors for the different classes from the dataset.\n",
    "2. Compute the scatter matrices (in-between-class and within-class scatter matrix).\n",
    "3. Compute the eigenvectors (ee1,ee2,...,eed) and corresponding eigenvalues (λλ1,λλ2,...,λλd) for the scatter matrices.\n",
    "4. Sort the eigenvectors by decreasing eigenvalues and choose k eigenvectors with the largest eigenvalues to form a d×k dimensional matrix WW (where every column represents an eigenvector).\n",
    "5. Use this d×k eigenvector matrix to transform the samples onto the new subspace. This can be summarized by the matrix multiplication: YY=XX×WW (where XX is a n×d-dimensional matrix representing the n samples, and yy are the transformed n×k-dimensional samples in the new subspace).\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-8.06179978e+00  3.00420621e-01]\n",
      " [-7.12868772e+00 -7.86660426e-01]\n",
      " [-7.48982797e+00 -2.65384488e-01]\n",
      " [-6.81320057e+00 -6.70631068e-01]\n",
      " [-8.13230933e+00  5.14462530e-01]\n",
      " [-7.70194674e+00  1.46172097e+00]\n",
      " [-7.21261762e+00  3.55836209e-01]\n",
      " [-7.60529355e+00 -1.16338380e-02]\n",
      " [-6.56055159e+00 -1.01516362e+00]\n",
      " [-7.34305989e+00 -9.47319209e-01]\n",
      " [-8.39738652e+00  6.47363392e-01]\n",
      " [-7.21929685e+00 -1.09646389e-01]\n",
      " [-7.32679599e+00 -1.07298943e+00]\n",
      " [-7.57247066e+00 -8.05464137e-01]\n",
      " [-9.84984300e+00  1.58593698e+00]\n",
      " [-9.15823890e+00  2.73759647e+00]\n",
      " [-8.58243141e+00  1.83448945e+00]\n",
      " [-7.78075375e+00  5.84339407e-01]\n",
      " [-8.07835876e+00  9.68580703e-01]\n",
      " [-8.02097451e+00  1.14050366e+00]\n",
      " [-7.49680227e+00 -1.88377220e-01]\n",
      " [-7.58648117e+00  1.20797032e+00]\n",
      " [-8.68104293e+00  8.77590154e-01]\n",
      " [-6.25140358e+00  4.39696367e-01]\n",
      " [-6.55893336e+00 -3.89222752e-01]\n",
      " [-6.77138315e+00 -9.70634453e-01]\n",
      " [-6.82308032e+00  4.63011612e-01]\n",
      " [-7.92461638e+00  2.09638715e-01]\n",
      " [-7.99129024e+00  8.63787128e-02]\n",
      " [-6.82946447e+00 -5.44960851e-01]\n",
      " [-6.75895493e+00 -7.59002759e-01]\n",
      " [-7.37495254e+00  5.65844592e-01]\n",
      " [-9.12634625e+00  1.22443267e+00]\n",
      " [-9.46768199e+00  1.82522635e+00]\n",
      " [-7.06201386e+00 -6.63400423e-01]\n",
      " [-7.95876243e+00 -1.64961722e-01]\n",
      " [-8.61367201e+00  4.03253602e-01]\n",
      " [-8.33041759e+00  2.28133530e-01]\n",
      " [-6.93412007e+00 -7.05519379e-01]\n",
      " [-7.68823131e+00 -9.22362309e-03]\n",
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      " [-5.66188065e+00 -1.93435524e+00]\n",
      " [-7.24101468e+00 -2.72615132e-01]\n",
      " [-6.41443556e+00  1.24730131e+00]\n",
      " [-6.85944381e+00  1.05165396e+00]\n",
      " [-6.76470393e+00 -5.05151855e-01]\n",
      " [-8.08189937e+00  7.63392750e-01]\n",
      " [-7.18676904e+00 -3.60986823e-01]\n",
      " [-8.31444876e+00  6.44953177e-01]\n",
      " [-7.67196741e+00 -1.34893840e-01]\n",
      " [ 1.45927545e+00  2.85437643e-02]\n",
      " [ 1.79770574e+00  4.84385502e-01]\n",
      " [ 2.41694888e+00 -9.27840307e-02]\n",
      " [ 2.26247349e+00 -1.58725251e+00]\n",
      " [ 2.54867836e+00 -4.72204898e-01]\n",
      " [ 2.42996725e+00 -9.66132066e-01]\n",
      " [ 2.44848456e+00  7.95961954e-01]\n",
      " [ 2.22666513e-01 -1.58467318e+00]\n",
      " [ 1.75020123e+00 -8.21180130e-01]\n",
      " [ 1.95842242e+00 -3.51563753e-01]\n",
      " [ 1.19376031e+00 -2.63445570e+00]\n",
      " [ 1.85892567e+00  3.19006544e-01]\n",
      " [ 1.15809388e+00 -2.64340991e+00]\n",
      " [ 2.66605725e+00 -6.42504540e-01]\n",
      " [ 3.78367218e-01  8.66389312e-02]\n",
      " [ 1.20117255e+00  8.44373592e-02]\n",
      " [ 2.76810246e+00  3.21995363e-02]\n",
      " [ 7.76854039e-01 -1.65916185e+00]\n",
      " [ 3.49805433e+00 -1.68495616e+00]\n",
      " [ 1.09042788e+00 -1.62658350e+00]\n",
      " [ 3.71589615e+00  1.04451442e+00]\n",
      " [ 9.97610366e-01 -4.90530602e-01]\n",
      " [ 3.83525931e+00 -1.40595806e+00]\n",
      " [ 2.25741249e+00 -1.42679423e+00]\n",
      " [ 1.25571326e+00 -5.46424197e-01]\n",
      " [ 1.43755762e+00 -1.34424979e-01]\n",
      " [ 2.45906137e+00 -9.35277280e-01]\n",
      " [ 3.51848495e+00  1.60588866e-01]\n",
      " [ 2.58979871e+00 -1.74611728e-01]\n",
      " [-3.07487884e-01 -1.31887146e+00]\n",
      " [ 1.10669179e+00 -1.75225371e+00]\n",
      " [ 6.05524589e-01 -1.94298038e+00]\n",
      " [ 8.98703769e-01 -9.04940034e-01]\n",
      " [ 4.49846635e+00 -8.82749915e-01]\n",
      " [ 2.93397799e+00  2.73791065e-02]\n",
      " [ 2.10360821e+00  1.19156767e+00]\n",
      " [ 2.14258208e+00  8.87797815e-02]\n",
      " [ 2.47945603e+00 -1.94073927e+00]\n",
      " [ 1.32552574e+00 -1.62869550e-01]\n",
      " [ 1.95557887e+00 -1.15434826e+00]\n",
      " [ 2.40157020e+00 -1.59458341e+00]\n",
      " [ 2.29248878e+00 -3.32860296e-01]\n",
      " [ 1.27227224e+00 -1.21458428e+00]\n",
      " [ 2.93176055e-01 -1.79871509e+00]\n",
      " [ 2.00598883e+00 -9.05418042e-01]\n",
      " [ 1.18166311e+00 -5.37570242e-01]\n",
      " [ 1.61615645e+00 -4.70103580e-01]\n",
      " [ 1.42158879e+00 -5.51244626e-01]\n",
      " [-4.75973788e-01 -7.99905482e-01]\n",
      " [ 1.54948259e+00 -5.93363582e-01]\n",
      " [ 7.83947399e+00  2.13973345e+00]\n",
      " [ 5.50747997e+00 -3.58139892e-02]\n",
      " [ 6.29200850e+00  4.67175777e-01]\n",
      " [ 5.60545633e+00 -3.40738058e-01]\n",
      " [ 6.85055995e+00  8.29825394e-01]\n",
      " [ 7.41816784e+00 -1.73117995e-01]\n",
      " [ 4.67799541e+00 -4.99095015e-01]\n",
      " [ 6.31692685e+00 -9.68980756e-01]\n",
      " [ 6.32773684e+00 -1.38328993e+00]\n",
      " [ 6.85281335e+00  2.71758963e+00]\n",
      " [ 4.44072512e+00  1.34723692e+00]\n",
      " [ 5.45009572e+00 -2.07736942e-01]\n",
      " [ 5.66033713e+00  8.32713617e-01]\n",
      " [ 5.95823722e+00 -9.40175447e-02]\n",
      " [ 6.75926282e+00  1.60023206e+00]\n",
      " [ 5.80704331e+00  2.01019882e+00]\n",
      " [ 5.06601233e+00 -2.62733839e-02]\n",
      " [ 6.60881882e+00  1.75163587e+00]\n",
      " [ 9.17147486e+00 -7.48255067e-01]\n",
      " [ 4.76453569e+00 -2.15573720e+00]\n",
      " [ 6.27283915e+00  1.64948141e+00]\n",
      " [ 5.36071189e+00  6.46120732e-01]\n",
      " [ 7.58119982e+00 -9.80722934e-01]\n",
      " [ 4.37150279e+00 -1.21297458e-01]\n",
      " [ 5.72317531e+00  1.29327553e+00]\n",
      " [ 5.27915920e+00 -4.24582377e-02]\n",
      " [ 4.08087208e+00  1.85936572e-01]\n",
      " [ 4.07703640e+00  5.23238483e-01]\n",
      " [ 6.51910397e+00  2.96976389e-01]\n",
      " [ 4.58371942e+00 -8.56815813e-01]\n",
      " [ 6.22824009e+00 -7.12719638e-01]\n",
      " [ 5.22048773e+00  1.46819509e+00]\n",
      " [ 6.80015000e+00  5.80895175e-01]\n",
      " [ 3.81515972e+00 -9.42985932e-01]\n",
      " [ 5.10748966e+00 -2.13059000e+00]\n",
      " [ 6.79671631e+00  8.63090395e-01]\n",
      " [ 6.52449599e+00  2.44503527e+00]\n",
      " [ 4.99550279e+00  1.87768525e-01]\n",
      " [ 3.93985300e+00  6.14020389e-01]\n",
      " [ 5.20383090e+00  1.14476808e+00]\n",
      " [ 6.65308685e+00  1.80531976e+00]\n",
      " [ 5.10555946e+00  1.99218201e+00]\n",
      " [ 5.50747997e+00 -3.58139892e-02]\n",
      " [ 6.79601924e+00  1.46068695e+00]\n",
      " [ 6.84735943e+00  2.42895067e+00]\n",
      " [ 5.64500346e+00  1.67771734e+00]\n",
      " [ 5.17956460e+00 -3.63475041e-01]\n",
      " [ 4.96774090e+00  8.21140550e-01]\n",
      " [ 5.88614539e+00  2.34509051e+00]\n",
      " [ 4.68315426e+00  3.32033811e-01]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA #newer version of sklearn\n",
    "\n",
    "lda = LDA(n_components=2)\n",
    "X_lda = lda.fit(data, target).transform(data)\n",
    "\n",
    "plt.scatter(X_lda[:, 0], X_lda[:, 1], marker='o', c=target)\n",
    "plt.show()\n",
    "print(X_lda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def calc(data,label):\n",
    "    clf_DT = tree.DecisionTreeClassifier(random_state=0)\n",
    "    scores_DT = cross_val_score(clf_DT, data, label, cv=5)\n",
    "    print(scores_DT)\n",
    "    print(np.mean(scores_DT),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Fitur Wavelet\n",
      "[0.96666667 0.96666667 0.9        0.93333333 0.96666667]\n",
      "0.9466666666666667 \n",
      "\n",
      "Accuracy Fitur PCA\n",
      "[0.93333333 0.9        0.96666667 0.96666667 0.93333333]\n",
      "0.9400000000000001 \n",
      "\n",
      "Accuracy Fitur SVD\n",
      "[0.96666667 0.93333333 0.96666667 0.93333333 1.        ]\n",
      "0.96 \n",
      "\n",
      "Accuracy Fitur LDA\n",
      "[1.  1.  0.9 0.9 1. ]\n",
      "0.96 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy Fitur Wavelet')\n",
    "calc(cA,target)\n",
    "print('Accuracy Fitur PCA')\n",
    "calc(X_pca,target)\n",
    "print('Accuracy Fitur SVD')\n",
    "calc(X_svd,target)\n",
    "print('Accuracy Fitur LDA')\n",
    "calc(X_lda,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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