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some work on periodics

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This commit is contained in:
James Hensman 2014-02-24 11:23:29 +00:00
parent 88c080eece
commit efd262965e
9 changed files with 308 additions and 754 deletions
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11
GPy/kern/_src/mlp.py
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@ -32,10 +32,10 @@ class MLP(Kern):
"""
def __init__(self, input_dim, variance=1., weight_variance=1., bias_variance=100., name='mlp'):
super(Linear, self).__init__(input_dim, name)
self.variance = Param('variance', variance, Logexp)
self.weight_variance = Param('weight_variance', weight_variance, Logexp)
self.bias_variance = Param('bias_variance', bias_variance, Logexp)
super(MLP, self).__init__(input_dim, name)
self.variance = Param('variance', variance, Logexp())
self.weight_variance = Param('weight_variance', weight_variance, Logexp())
self.bias_variance = Param('bias_variance', bias_variance, Logexp())
self.add_parameters(self.variance, self.weight_variance, self.bias_variance)
@ -109,14 +109,15 @@ class MLP(Kern):
"""Pre-computations for the covariance matrix (used for computing the covariance and its gradients."""
if X2 is None:
self._K_inner_prod = np.dot(X,X.T)
self._K_numer = self._K_inner_prod*self.weight_variance + self.bias_variance
vec = np.diag(self._K_numer) + 1.
self._K_denom = np.sqrt(np.outer(vec,vec))
else:
self._K_inner_prod = np.dot(X,X2.T)
self._K_numer = self._K_inner_prod*self.weight_variance + self.bias_variance
vec1 = (X*X).sum(1)*self.weight_variance + self.bias_variance + 1.
vec2 = (X2*X2).sum(1)*self.weight_variance + self.bias_variance + 1.
self._K_denom = np.sqrt(np.outer(vec1,vec2))
self._K_numer = self._K_inner_prod*self.weight_variance + self.bias_variance
self._K_asin_arg = self._K_numer/self._K_denom
self._K_dvar = four_over_tau*np.arcsin(self._K_asin_arg)