{
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# IsolationNNE example\n\nAn example using :class:`inne.IsolationNNE` for anomaly\ndetection.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom inne import IsolationNNE\n\nrng = np.random.RandomState(42)\n\n# Generate train data\nX = 0.3 * rng.randn(100, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate some regular novel observations\nX = 0.3 * rng.randn(20, 2)\nX_test = np.r_[X + 2, X - 2]\n# Generate some abnormal novel observations\nX_outliers = rng.uniform(low=-4, high=4, size=(20, 2))\n\n# fit the model\nclf = IsolationNNE()\nclf.fit(X_train)\ny_pred_train = clf.predict(X_train)\ny_pred_test = clf.predict(X_test)\ny_pred_outliers = clf.predict(X_outliers)\n\n# plot the line, the samples, and the nearest vectors to the plane\nxx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\nplt.title(\"IsolationNNE\")\nplt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)\n\nb1 = plt.scatter(X_train[:, 0], X_train[:, 1], c=\"white\", s=20, edgecolor=\"k\")\nb2 = plt.scatter(X_test[:, 0], X_test[:, 1], c=\"green\", s=20, edgecolor=\"k\")\nc = plt.scatter(X_outliers[:, 0], X_outliers[:, 1],\n                c=\"red\", s=20, edgecolor=\"k\")\nplt.axis(\"tight\")\nplt.xlim((-5, 5))\nplt.ylim((-5, 5))\nplt.legend(\n    [b1, b2, c],\n    [\"training observations\", \"new regular observations\", \"new abnormal observations\"],\n    loc=\"upper left\",\n)\nplt.show()"
      ]
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