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syntax fix

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alessandratosi 2016-04-21 15:45:37 +01:00
parent 5c0c1d4c3d
commit 0e109cd3da
5 changed files with 11 additions and 12 deletions
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5
GPy/kern/src/add.py
View file

@ -86,7 +86,7 @@ class Add(CombinationKernel):
return target return target
def gradients_XX(self, dL_dK, X, X2, cov=True): def gradients_XX(self, dL_dK, X, X2, cov=True):
if cov==True: # full covarance if cov: # full covarance
if X2 is None: if X2 is None:
target = np.zeros((X.shape[0], X.shape[0], X.shape[1], X.shape[1])) target = np.zeros((X.shape[0], X.shape[0], X.shape[1], X.shape[1]))
else: else:
@ -96,8 +96,7 @@ class Add(CombinationKernel):
target = np.zeros((X.shape[0], X.shape[0], X.shape[1])) target = np.zeros((X.shape[0], X.shape[0], X.shape[1]))
else: else:
target = np.zeros((X.shape[0], X2.shape[0], X.shape[1])) target = np.zeros((X.shape[0], X2.shape[0], X.shape[1]))
[target.__iadd__(p.gradients_XX(dL_dK, X, X2)) for p in self.parts]
[target.__iadd__(p.gradients_XX(dL_dK, X, X2, cov)) for p in self.parts]
return target return target
def gradients_XX_diag(self, dL_dKdiag, X): def gradients_XX_diag(self, dL_dKdiag, X):

6
GPy/kern/src/kernel_slice_operations.py
View file

@ -119,14 +119,14 @@ def _slice_gradients_XX(f):
N, M = X.shape[0], X.shape[0] N, M = X.shape[0], X.shape[0]
else: else:
N, M = X.shape[0], X2.shape[0] N, M = X.shape[0], X2.shape[0]
if cov==True: # full covariance if cov: # full covariance
with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1], X.shape[1])) as s: with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1], X.shape[1])) as s:
#with _Slice_wrap(self, X, X2, ret_shape=None) as s: #with _Slice_wrap(self, X, X2, ret_shape=None) as s:
ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov=True)) ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov))
else: # diagonal covariance else: # diagonal covariance
with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1])) as s: with _Slice_wrap(self, X, X2, ret_shape=(N, M, X.shape[1])) as s:
#with _Slice_wrap(self, X, X2, ret_shape=None) as s: #with _Slice_wrap(self, X, X2, ret_shape=None) as s:
ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov=True)) ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2, cov))
return ret return ret
return wrap return wrap

2
GPy/kern/src/linear.py
View file

@ -101,7 +101,7 @@ class Linear(Kern):
#return (((X2[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1) #return (((X2[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
return dL_dK.dot(X2)*self.variances #np.einsum('jq,q,ij->iq', X2, self.variances, dL_dK) return dL_dK.dot(X2)*self.variances #np.einsum('jq,q,ij->iq', X2, self.variances, dL_dK)
def gradients_XX(self, dL_dK, X, X2=None): def gradients_XX(self, dL_dK, X, X2=None, cov=True):
if X2 is None: dL_dK = (dL_dK+dL_dK.T)/2 if X2 is None: dL_dK = (dL_dK+dL_dK.T)/2
if X2 is None: if X2 is None:
return 2*np.ones(X.shape)*self.variances return 2*np.ones(X.shape)*self.variances

2
GPy/kern/src/static.py
View file

@ -24,7 +24,7 @@ class Static(Kern):
def gradients_X_diag(self, dL_dKdiag, X): def gradients_X_diag(self, dL_dKdiag, X):
return np.zeros(X.shape) return np.zeros(X.shape)
def gradients_XX(self, dL_dK, X, X2): def gradients_XX(self, dL_dK, X, X2=None, cov=True):
if X2 is None: if X2 is None:
X2 = X X2 = X
return np.zeros((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64) return np.zeros((X.shape[0], X2.shape[0], X.shape[1]), dtype=np.float64)

8
GPy/kern/src/stationary.py
View file

@ -222,14 +222,14 @@ class Stationary(Kern):
""" """
Given the derivative of the objective K(dL_dK), compute the second derivative of K wrt X and X2: Given the derivative of the objective K(dL_dK), compute the second derivative of K wrt X and X2:
cov = Full: returns the full covariance matrix [QxQ] of the input dimensionfor each pair or vectors cov = True: returns the full covariance matrix [QxQ] of the input dimensionfor each pair or vectors
cov = Diag: returns the diagonal of the covariance matrix [QxQ] of the input dimensionfor each pair cov = False: returns the diagonal of the covariance matrix [QxQ] of the input dimensionfor each pair
or vectors (computationally more efficient if the full covariance matrix is not needed) or vectors (computationally more efficient if the full covariance matrix is not needed)
..math: ..math:
\frac{\partial^2 K}{\partial X2 ^2} = - \frac{\partial^2 K}{\partial X\partial X2} \frac{\partial^2 K}{\partial X2 ^2} = - \frac{\partial^2 K}{\partial X\partial X2}
..returns: ..returns:
dL2_dXdX2: [NxMxQ] in the cov=Diag case, or [NxMxQxQ] in the cov=full case, dL2_dXdX2: [NxMxQxQ] in the cov=True case, or [NxMxQ] in the cov=False case,
for X [NxQ] and X2[MxQ] (X2 is X if, X2 is None) for X [NxQ] and X2[MxQ] (X2 is X if, X2 is None)
Thus, we return the second derivative in X2. Thus, we return the second derivative in X2.
""" """
@ -251,7 +251,7 @@ class Stationary(Kern):
# (seems to have a bug: it is subtracted to the first X1 anyway) # (seems to have a bug: it is subtracted to the first X1 anyway)
tmp1[invdist2==0.] -= self.variance tmp1[invdist2==0.] -= self.variance
if cov==True: # full covariance if cov: # full covariance
grad = np.empty((X.shape[0], X2.shape[0], X2.shape[1], X.shape[1]), dtype=np.float64) grad = np.empty((X.shape[0], X2.shape[0], X2.shape[1], X.shape[1]), dtype=np.float64)
for q in range(self.input_dim): for q in range(self.input_dim):
for r in range(self.input_dim): for r in range(self.input_dim):