└── Em algorithm.ipynb


/Em algorithm.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "id": "03234774",
  7 |    "metadata": {},
  8 |    "outputs": [
  9 |     {
 10 |      "name": "stdout",
 11 |      "output_type": "stream",
 12 |      "text": [
 13 |       "Means: [[6.5        8.        ]\n",
 14 |       " [1.16666667 1.13333333]]\n",
 15 |       "Covariances: [[[2.25000100e+00 6.31088724e-29]\n",
 16 |       "  [6.31088724e-29 1.00000000e-06]]\n",
 17 |       "\n",
 18 |       " [[5.55565556e-02 1.11111111e-01]\n",
 19 |       "  [1.11111111e-01 2.48889889e-01]]]\n",
 20 |       "Mixing probabilities: [0.4 0.6]\n",
 21 |       "Predicted labels for new data: [1 1]\n"
 22 |      ]
 23 |     }
 24 |    ],
 25 |    "source": [
 26 |     "import numpy as np\n",
 27 |     "from sklearn.mixture import GaussianMixture\n",
 28 |     "# Define data (replace with your actual data)\n",
 29 |     "X = np.array([[1, 1], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6]])\n",
 30 |     "# Define the number of Gaussian components (clusters)\n",
 31 |     "n_components = 2\n",
 32 |     "# Create and fit the GMM model\n",
 33 |     "gmm = GaussianMixture(n_components=n_components)\n",
 34 |     "gmm.fit(X)\n",
 35 |     "# Print the fitted parameters\n",
 36 |     "print(\"Means:\", gmm.means_)\n",
 37 |     "print(\"Covariances:\", gmm.covariances_)\n",
 38 |     "print(\"Mixing probabilities:\", gmm.weights_)\n",
 39 |     "# Predict cluster labels for new data points (optional)\n",
 40 |     "new_data = np.array([[0.5, 0.8], [6, 9]])\n",
 41 |     "labels = gmm.predict(new_data)\n",
 42 |     "print(\"Predicted labels for new data:\", labels)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 4,
 48 |    "id": "10504059",
 49 |    "metadata": {},
 50 |    "outputs": [],
 51 |    "source": [
 52 |     "import matplotlib.pyplot as plt"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 13,
 58 |    "id": "7dc5bca7",
 59 |    "metadata": {},
 60 |    "outputs": [
 61 |     {
 62 |      "data": {
 63 |       "text/plain": [
 64 |        "(array([[0.54054054, 0.27027027, 0.        , 0.        , 0.        ,\n",
 65 |        "         0.27027027, 0.        , 0.        , 0.        , 0.27027027],\n",
 66 |        "        [0.54054054, 0.27027027, 0.        , 0.        , 0.        ,\n",
 67 |        "         0.        , 0.        , 0.        , 0.        , 0.54054054]]),\n",
 68 |        " array([0.6 , 1.34, 2.08, 2.82, 3.56, 4.3 , 5.04, 5.78, 6.52, 7.26, 8.  ]),\n",
 69 |        " <a list of 2 BarContainer objects>)"
 70 |       ]
 71 |      },
 72 |      "execution_count": 13,
 73 |      "metadata": {},
 74 |      "output_type": "execute_result"
 75 |     },
 76 |     {
 77 |      "data": {
 78 |       "image/png": 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G/C/SfZHu5CI50gAAAABJRU5ErkJggg==\n",
 79 |       "text/plain": [
 80 |        "<Figure size 432x288 with 1 Axes>"
 81 |       ]
 82 |      },
 83 |      "metadata": {
 84 |       "needs_background": "light"
 85 |      },
 86 |      "output_type": "display_data"
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "plt.hist(X,density=True)"
 91 |    ]
 92 |   }
 93 |  ],
 94 |  "metadata": {
 95 |   "kernelspec": {
 96 |    "display_name": "Python 3 (ipykernel)",
 97 |    "language": "python",
 98 |    "name": "python3"
 99 |   },
100 |   "language_info": {
101 |    "codemirror_mode": {
102 |     "name": "ipython",
103 |     "version": 3
104 |    },
105 |    "file_extension": ".py",
106 |    "mimetype": "text/x-python",
107 |    "name": "python",
108 |    "nbconvert_exporter": "python",
109 |    "pygments_lexer": "ipython3",
110 |    "version": "3.9.12"
111 |   }
112 |  },
113 |  "nbformat": 4,
114 |  "nbformat_minor": 5
115 | }
116 | 


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