GaussianMixture.ipynb

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        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "import plotly.express as px\n",
        "from numpy import array, unique, where\n",
        "from pandas import DataFrame\n",
        "from sklearn.mixture import GaussianMixture"
      ],
      "execution_count": null,
      "outputs": []
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        "# Create data\n",
        "x_arr = array([11.10, 11.15,\n",
        "               5, 8, 1, 9,])\n",
        "      \n",
        "y_arr =  array([2, 8, 1.8,\n",
        "                8, 0.6, 11,])\n",
        "\n",
        "x = DataFrame(x_arr)\n",
        "y = DataFrame(y_arr) \n",
        "\n",
        "fig = px.scatter(x_arr, y_arr,)\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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      "source": [
        "X=array([[1038,660],\n",
        "         [1045,680],\n",
        "         [1038,750],\n",
        "         [897,750],\n",
        "         [807,780],\n",
        "         [805,850],])"
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      "execution_count": null,
      "outputs": []
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          "height": 84
        },
        "outputId": "07257e3b-d77c-42e9-ab3d-c7d429f8d060"
      },
      "source": [
        "# Fitting GaussianMixture model to the dataset\n",
        "model = GaussianMixture(n_components=2)\n",
        "model.fit(X)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
              "                means_init=None, n_components=2, n_init=1, precisions_init=None,\n",
              "                random_state=None, reg_covar=1e-06, tol=0.001, verbose=0,\n",
              "                verbose_interval=10, warm_start=False, weights_init=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
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        "colab": {
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          "height": 34
        },
        "outputId": "8ed38648-e722-45d0-ce77-712dfcc146cf"
      },
      "source": [
        "# See the division of data in clusters 0 and 1\n",
        "y = model.predict(X)\n",
        "y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 1, 1])"
            ]
          },
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          "execution_count": 71
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      "source": [
        "clusters = unique(y)"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "for cluster in clusters:\n",
        "\trow_ix = where(y == cluster)\n",
        "\tplt.scatter(X[row_ix, 0], \n",
        "              X[row_ix, 1],)\n",
        "plt.show()"
      ],
      "execution_count": null,
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}