create_dendogram_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  2.   5. 138.   2.]\n",
      " [  3.   4. 219.   2.]\n",
      " [  0.   7. 255.   3.]\n",
      " [  1.   8. 268.   4.]\n",
      " [  6.   9. 295.   6.]]\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": [
    "ytdist = np.array([662., 877., 255., 412., 996., 295., 468., 268.,400., 754., 564., 138., 219., 869., 669.])\n",
    "Z = linkage(ytdist, 'single')\n",
    "print(Z)\n",
    "plt.figure()\n",
    "dn = dendrogram(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.19652929 0.16334576 0.38843775 0.02342275 0.41947032]\n",
      " [0.46099041 0.8154944  0.25152776 0.93525466 0.04724373]\n",
      " [0.09058823 0.67178529 0.30884184 0.31164468 0.33094653]\n",
      " [0.76453008 0.02029573 0.80985877 0.3226707  0.59332842]\n",
      " [0.29934123 0.72158468 0.61707547 0.37864598 0.89589112]]\n",
      "[[0.         2.         0.60578719 2.        ]\n",
      " [4.         5.         0.68170179 3.        ]\n",
      " [1.         6.         0.79404765 4.        ]\n",
      " [3.         7.         0.80028736 5.        ]]\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": [
    "dist = np.random.rand(5,5)\n",
    "print(dist)\n",
    "Z = hierarchy.linkage(dist, 'single')\n",
    "print(Z)\n",
    "plt.figure()\n",
    "dn = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(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"
    }
   ],
   "source": [
    "# generate two clusters: a with 100 points, b with 50:\n",
    "np.random.seed(4711)  # for repeatability of this tutorial\n",
    "a = np.random.multivariate_normal([10, 0], [[3, 1], [1, 4]], size=[100,])\n",
    "b = np.random.multivariate_normal([0, 20], [[3, 1], [1, 4]], size=[50,])\n",
    "X = np.concatenate((a, b),)\n",
    "print(X.shape)  # 150 samples with 2 dimensions\n",
    "plt.scatter(X[:,0], X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(149, 4)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate the linkage matrix\n",
    "Z = linkage(X, 'ward')\n",
    "Z.shape"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}