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      "source": [
        "\n# Iso-probability lines for Gaussian Processes classification (GPC)\n\nA two-dimensional classification example showing iso-probability lines for\nthe predicted probabilities.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport numpy as np\nfrom matplotlib import cm\nfrom matplotlib import pyplot as plt\n\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.gaussian_process.kernels import ConstantKernel as C\nfrom sklearn.gaussian_process.kernels import DotProduct\n\n# A few constants\nlim = 8\n\n\ndef g(x):\n    \"\"\"The function to predict (classification will then consist in predicting\n    whether g(x) <= 0 or not)\"\"\"\n    return 5.0 - x[:, 1] - 0.5 * x[:, 0] ** 2.0\n\n\n# Design of experiments\nX = np.array(\n    [\n        [-4.61611719, -6.00099547],\n        [4.10469096, 5.32782448],\n        [0.00000000, -0.50000000],\n        [-6.17289014, -4.6984743],\n        [1.3109306, -6.93271427],\n        [-5.03823144, 3.10584743],\n        [-2.87600388, 6.74310541],\n        [5.21301203, 4.26386883],\n    ]\n)\n\n# Observations\ny = np.array(g(X) > 0, dtype=int)\n\n# Instantiate and fit Gaussian Process Model\nkernel = C(0.1, (1e-5, np.inf)) * DotProduct(sigma_0=0.1) ** 2\ngp = GaussianProcessClassifier(kernel=kernel)\ngp.fit(X, y)\nprint(\"Learned kernel: %s \" % gp.kernel_)\n\n# Evaluate real function and the predicted probability\nres = 50\nx1, x2 = np.meshgrid(np.linspace(-lim, lim, res), np.linspace(-lim, lim, res))\nxx = np.vstack([x1.reshape(x1.size), x2.reshape(x2.size)]).T\n\ny_true = g(xx)\ny_prob = gp.predict_proba(xx)[:, 1]\ny_true = y_true.reshape((res, res))\ny_prob = y_prob.reshape((res, res))\n\n# Plot the probabilistic classification iso-values\nfig = plt.figure(1)\nax = fig.gca()\nax.axes.set_aspect(\"equal\")\nplt.xticks([])\nplt.yticks([])\nax.set_xticklabels([])\nax.set_yticklabels([])\nplt.xlabel(\"$x_1$\")\nplt.ylabel(\"$x_2$\")\n\ncax = plt.imshow(y_prob, cmap=cm.gray_r, alpha=0.8, extent=(-lim, lim, -lim, lim))\nnorm = plt.matplotlib.colors.Normalize(vmin=0.0, vmax=0.9)\ncb = plt.colorbar(cax, ticks=[0.0, 0.2, 0.4, 0.6, 0.8, 1.0], norm=norm)\ncb.set_label(r\"${\\rm \\mathbb{P}}\\left[\\widehat{G}(\\mathbf{x}) \\leq 0\\right]$\")\nplt.clim(0, 1)\n\nplt.plot(X[y <= 0, 0], X[y <= 0, 1], \"r.\", markersize=12)\n\nplt.plot(X[y > 0, 0], X[y > 0, 1], \"b.\", markersize=12)\n\nplt.contour(x1, x2, y_true, [0.0], colors=\"k\", linestyles=\"dashdot\")\n\ncs = plt.contour(x1, x2, y_prob, [0.666], colors=\"b\", linestyles=\"solid\")\nplt.clabel(cs, fontsize=11)\n\ncs = plt.contour(x1, x2, y_prob, [0.5], colors=\"k\", linestyles=\"dashed\")\nplt.clabel(cs, fontsize=11)\n\ncs = plt.contour(x1, x2, y_prob, [0.334], colors=\"r\", linestyles=\"solid\")\nplt.clabel(cs, fontsize=11)\n\nplt.show()"
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