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Polynomial kernel gradients wrt X working.

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This commit is contained in:
Neil Lawrence 2013-08-28 00:20:12 +02:00
parent a61feb17a5
commit b127c96bf2
8 changed files with 79 additions and 26 deletions
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21
GPy/kern/parts/mlp.py
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@ -107,12 +107,22 @@ class MLP(Kernpart):
def dK_dX(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_asin_arg
post_div = np.sqrt(1-arg*arg)
numer = self._K_numer
denom = self._K_denom
vec2 = (X2*X2).sum(1)*self.weight_variance + self.bias_variance + 1.
denom3 = denom*denom*denom
target += (((X2[None,:, :]/denom[None, :, None]+vec2[None, None, :]*X[:, :, None]*numer/denom)/post_div[:, :, None]) * dL_dK[:, :, None]).sum(1)
target *= four_over_tau*self.weight_variance*self.variance
raise NotImplementedError
# self._K_computations(X, X2)
# gX = np.zeros((X2.shape[0], X.shape[1], X.shape[0]))
gX = np.zeros((X2.shape[0], X.shape[1], X.shape[0]))
# for i in range(X.shape[0]):
# gX[:, :, i] = self._dK_dX_point(dL_dK, X, X2, target, i)
for i in range(X.shape[0]):
gX[:, :, i] = self._dK_dX_point(dL_dK, X, X2, target, i)
def _dK_dX_point(self, dL_dK, X, X2, target, i):
@ -130,9 +140,8 @@ class MLP(Kernpart):
denom3 = denom*denom*denom
gX = np.zeros((X2.shape[0], X2.shape[1]))
for j in range(X2.shape[1]):
gX[:, j] =X2[:, j]/denom - vec2*X[i, j]*numer/denom3
gX[:, j] = X2[:, j]/denom - vec2*X[i, j]*numer/denom3
gX[:, j] = four_over_tau*self.weight_variance*self.variance*gX[:, j]/np.sqrt(1-arg*arg)
target[i, :]
def _K_computations(self, X, X2):