wehub-resource-sync
38 lines
994 B
Python
Executable File
38 lines
994 B
Python
Executable File
import numpy as np
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class GaussianProcessClassifier(object):
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def __init__(self, kernel, noise_level=1e-4):
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"""
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construct gaussian process classifier
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Parameters
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----------
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kernel
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kernel function to be used to compute Gram matrix
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noise_level : float
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parameter to ensure the matrix to be positive
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"""
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self.kernel = kernel
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self.noise_level = noise_level
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def _sigmoid(self, a):
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return np.tanh(a * 0.5) * 0.5 + 0.5
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def fit(self, X, t):
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if X.ndim == 1:
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X = X[:, None]
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self.X = X
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self.t = t
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Gram = self.kernel(X, X)
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self.covariance = Gram + np.eye(len(Gram)) * self.noise_level
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self.precision = np.linalg.inv(self.covariance)
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def predict(self, X):
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if X.ndim == 1:
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X = X[:, None]
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K = self.kernel(X, self.X)
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a_mean = K @ self.precision @ self.t
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return self._sigmoid(a_mean)
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