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27 lines
941 B
27 lines
941 B
3 years ago
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import cupy as xp
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from sklearn.metrics.pairwise import rbf_kernel
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def kernel_linear(x1, x2, params):
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return x1.dot(x2.T)
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def kernel_poly(x1, x2, params):
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return (x1.dot(x2.T) + 1)**params['degree']
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def kernel_rbf(x1, x2, params):
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if x2.ndim == 2:
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return xp.exp(-xp.linalg.norm(xp.subtract(x1[:, :, xp.newaxis], x2[:, :, xp.newaxis].T), axis=1)**2/params['sigma']**2)
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else:
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return xp.exp(-xp.linalg.norm(xp.subtract(x1, x2), axis=1) ** 2 / params['sigma'] ** 2)
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def kernel_rbf_sklearn(x1, x2, params):
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return xp.asarray(rbf_kernel(xp.asnumpy(x1), gamma=params['sigma']))
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def kernel_sigmoid(x1, x2, params):
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return xp.tanh(params['alpha'] * (x1.dot(x2.T)) + params['beta'])
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kernel_dict = {'linear': kernel_linear,
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'poly': kernel_poly,
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'rbf': kernel_rbf,
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'rbf_sklearn': kernel_rbf_sklearn,
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'sigmoid': kernel_sigmoid
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}
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