trying to understand GP

ClassifierAccuracy/gaussian_process.py

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+from sklearn.gaussian_process import GaussianProcessRegressor
+import numpy as np
+from sklearn.gaussian_process.kernels import RBF, GenericKernelMixin, Kernel
+from sklearn.metrics.pairwise import pairwise_distances
+
+np.random.seed(0)
+
+
+class MinL2Kernel(GenericKernelMixin, Kernel):
+    """
+    A minimal (but valid) convolutional kernel for sequences of variable
+    lengths."""
+
+    def __init__(self):
+        pass
+
+    def _f(self, sample1, sample2):
+        """
+        kernel value between a pair of sequences
+        """
+        sample1 = sample1.reshape(-1,3)
+        sample2 = sample2.reshape(-1, 3)
+        dist = pairwise_distances(sample1, sample2)
+        return dist.min(axis=1).mean()
+
+    def __call__(self, X, Y=None, eval_gradient=False):
+        if Y is None:
+            Y = X
+
+        if eval_gradient:
+            raise NotImplementedError()
+        else:
+            return np.array([[self._f(x, y) for y in Y] for x in X])
+
+    def diag(self, X):
+        return np.array([self._f(x, x) for x in X])
+
+    def is_stationary(self):
+        return True
+
+
+def f(X):
+    X = X.reshape(-1,3)
+    return X[:,0]**3 + 2.1*X[:,1]**2 + X[:,0] + 0.1
+
+
+X_train = [np.random.rand(10*3) for _ in range(11)]
+y_train = [f(X).mean() for X in X_train]
+
+X_test = [np.random.rand(10*3) for _ in range(11)]
+y_test = [f(X).mean() for X in X_test]
+
+
+#kernel = 1 * RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e2))
+kernel = MinL2Kernel()
+gaussian_process = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
+gaussian_process.fit(X_train, y_train)
+
+print(gaussian_process.kernel_)
+
+y_pred = gaussian_process.predict(X_test)
+
+mse = np.mean((y_test - y_pred)**2)
+
+print(mse)