{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-a45874165613>:36: RuntimeWarning: covariance is not positive-semidefinite.\n",
      "  datasetA = np.random.multivariate_normal(muA, covarianceA, size= sizeA)\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"
    }
   ],
   "source": [
    "# Data generation\n",
    "# if we want to repeat our results\n",
    "np.random.seed(1234)  \n",
    "\n",
    "# First we set mean for datasetA and datasetB\n",
    "muA = [-5,-5]\n",
    "muB = [5,5]\n",
    "muC = [-5,10]\n",
    "\n",
    "# Then we set standard deviation for datasetA and datasetB\n",
    "sigmaAx = 10.0\n",
    "sigmaAy = 3.0\n",
    "# covariance\n",
    "sigmaAxy = -10.0\n",
    "# covariance matrix\n",
    "covarianceA = [[sigmaAx, sigmaAxy], [sigmaAxy, sigmaAy]]\n",
    "\n",
    "sigmaBx = 3.0\n",
    "sigmaBy = 3.0\n",
    "# covariance \n",
    "sigmaBxy = 0.0\n",
    "# covariance matrix\n",
    "covarianceB = [[sigmaBx, sigmaBxy], [sigmaBxy, sigmaBy]]\n",
    "\n",
    "sigmaCx = 2.0\n",
    "sigmaCy = 2.0\n",
    "# covariance \n",
    "sigmaCxy = 0.0\n",
    "# covariance matrix\n",
    "covarianceC = [[sigmaCx, sigmaCxy], [sigmaCxy, sigmaCy]]\n",
    "\n",
    "sizeA = 30\n",
    "sizeB = 30\n",
    "sizeC = 30\n",
    "\n",
    "datasetA = np.random.multivariate_normal(muA, covarianceA, size= sizeA)\n",
    "datasetB = np.random.multivariate_normal(muB, covarianceB, size= sizeB)\n",
    "datasetC = np.random.multivariate_normal(muC, covarianceC, size= sizeC)\n",
    "\n",
    "labelsA = [\"A\" for i in range(sizeA)]\n",
    "labelsB = [\"B\" for i in range(sizeB)]\n",
    "labelsC = [\"C\" for i in range(sizeC)]\n",
    "labelsABC = labelsA + labelsB + labelsC\n",
    "\n",
    "dataT = np.concatenate((datasetA, datasetB, datasetC),)\n",
    "\n",
    "plt.scatter(dataT[:,0], dataT[:,1])\n",
    "plt.axis('equal')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute linkage matrix\n",
    "# Read https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html\n",
    "# to learn other options\n",
    "\n",
    "LinkageSE = linkage(dataT, method='single', metric='euclidean', optimal_ordering=False)\n",
    "LinkageAE = linkage(dataT, method='average', metric='euclidean', optimal_ordering=False)\n",
    "LinkageCE = linkage(dataT, method='complete', metric='euclidean', optimal_ordering=False)\n",
    "LinkageWE = linkage(dataT, method='ward', metric='euclidean', optimal_ordering=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dendrogram\n",
    "# Read https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.cluster.hierarchy.dendrogram.html\n",
    "# to learn other options\n",
    "\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram - Euclidean, single')\n",
    "plt.xlabel('Index of the samples')\n",
    "plt.ylabel('Distance')\n",
    "SE = dendrogram(LinkageSE,labels=labelsABC,leaf_rotation=0.0,leaf_font_size=9.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20.         22.          0.52690358  2.        ]\n",
      " [23.         27.          0.62582487  2.        ]\n",
      " [24.         28.          0.66934039  2.        ]\n",
      " [31.         32.          0.69827531  4.        ]\n",
      " [10.         18.          0.80901676  2.        ]\n",
      " [30.         33.          0.84743627  6.        ]\n",
      " [12.         17.          0.99055721  2.        ]\n",
      " [11.         15.          1.09029376  2.        ]\n",
      " [14.         37.          1.09179069  3.        ]\n",
      " [25.         35.          1.14273913  7.        ]\n",
      " [26.         39.          1.20893849  8.        ]\n",
      " [34.         36.          1.32635877  4.        ]\n",
      " [21.         40.          1.44475772  9.        ]\n",
      " [38.         41.          1.45839164  7.        ]\n",
      " [16.         43.          1.70775302  8.        ]\n",
      " [ 6.          9.          1.79842212  2.        ]\n",
      " [29.         42.          1.90048152 10.        ]\n",
      " [ 0.          3.          1.95815464  2.        ]\n",
      " [19.         44.          2.35678676  9.        ]\n",
      " [ 2.          7.          2.40026917  2.        ]\n",
      " [ 1.         47.          2.45856886  3.        ]\n",
      " [13.         48.          2.52148252 10.        ]\n",
      " [45.         50.          2.53981756  5.        ]\n",
      " [ 8.         52.          2.65214919  6.        ]\n",
      " [ 4.         53.          2.84249773  7.        ]\n",
      " [ 5.         54.          2.88707369  8.        ]\n",
      " [49.         55.          3.11231001 10.        ]\n",
      " [46.         56.          6.51129585 20.        ]\n",
      " [51.         57.          8.46053238 30.        ]]\n"
     ]
    }
   ],
   "source": [
    "# How is this matrix?\n",
    "\n",
    "#print(LinkageSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9071760443823862\n"
     ]
    }
   ],
   "source": [
    "# Compute cophenet distance\n",
    "from scipy.cluster.hierarchy import cophenet\n",
    "from scipy.spatial.distance import pdist\n",
    "\n",
    "SEc, SEc_dists = cophenet(LinkageSE, pdist(dataT))\n",
    "print(SEc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15  5 18 10  1  4  8 11 12 19 16 13  5  6 17 11  4  9 14  2 14  1 14 11\n",
      "  9  7 20 11 12  3 31 31 30 33 31 31 31 35 32 31 34 31 31 31 31 31 31 31\n",
      " 31 31 31 31 31 31 29 29 31 31 29 31 21 21 24 21 21 21 26 21 23 22 21 21\n",
      " 21 21 27 21 21 21 25 21 21 21 21 21 21 21 21 21 28 22]\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"
    }
   ],
   "source": [
    "# show the clusters\n",
    "from scipy.cluster.hierarchy import fcluster\n",
    "\n",
    "SEclusters = fcluster(LinkageSE, t=1.0, criterion='distance')\n",
    "\n",
    "print(SEclusters)\n",
    "\n",
    "plt.scatter(dataT[:,0], dataT[:,1],c=SEclusters)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dendrogram\n",
    "# Read https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.cluster.hierarchy.dendrogram.html\n",
    "# to learn other options\n",
    "\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram - Euclidean, average')\n",
    "plt.xlabel('Index of the samples')\n",
    "plt.ylabel('Distance')\n",
    "AE = dendrogram(LinkageAE,labels=labelsABC,leaf_rotation=0.0,leaf_font_size=9.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dendrogram\n",
    "# Read https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.cluster.hierarchy.dendrogram.html\n",
    "# to learn other options\n",
    "\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram - Euclidean, complete')\n",
    "plt.xlabel('Index of the samples')\n",
    "plt.ylabel('Distance')\n",
    "CE = dendrogram(LinkageCE,labels=labelsABC,leaf_rotation=0.0,leaf_font_size=9.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dendrogram\n",
    "# Read https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.cluster.hierarchy.dendrogram.html\n",
    "# to learn other options\n",
    "\n",
    "plt.figure(figsize=(25, 10))\n",
    "plt.title('Hierarchical Clustering Dendrogram - Euclidean, ward')\n",
    "plt.xlabel('Index of the samples')\n",
    "plt.ylabel('Distance')\n",
    "WE = dendrogram(LinkageWE,labels=labelsABC,leaf_rotation=0.0,leaf_font_size=9.0)\n",
    "plt.show()\n"
   ]
  }
 ],
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   "pygments_lexer": "ipython3",
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   "hotkeys": {
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  "toc": {
   "colors": {
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   "moveMenuLeft": true,
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