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add missing colons and fix indentation

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This commit is contained in:
Alexander Pitchford 2018-10-19 09:11:31 +01:00
parent 4f3047e035
commit e0de2805b6
2 changed files with 19531 additions and 9260 deletions
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28783
GPy/models/state_space_cython.c
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8
GPy/models/state_space_cython.pyx
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@ -484,7 +484,7 @@ cdef class AQcompute_batch_Cython(Q_handling_Cython):
if matrix_index in self.Q_square_root_dict: if matrix_index in self.Q_square_root_dict:
square_root = self.Q_square_root_dict[matrix_index] square_root = self.Q_square_root_dict[matrix_index]
else: else:
if matrix_index not in self.Q_svd_dict if matrix_index not in self.Q_svd_dict:
U,S,Vh = sp.linalg.svd( self.Qs[:,:, matrix_index], U,S,Vh = sp.linalg.svd( self.Qs[:,:, matrix_index],
full_matrices=False, compute_uv=True, full_matrices=False, compute_uv=True,
overwrite_a=False, check_finite=False) overwrite_a=False, check_finite=False)
@ -514,7 +514,7 @@ cdef class AQcompute_batch_Cython(Q_handling_Cython):
if matrix_index in self.Q_inverse_dict: if matrix_index in self.Q_inverse_dict:
Q_inverse = self.Q_inverse_dict[matrix_index] Q_inverse = self.Q_inverse_dict[matrix_index]
else: else:
if matrix_index not in self.Q_svd_dict if matrix_index not in self.Q_svd_dict:
U,S,Vh = sp.linalg.svd( self.Qs[:,:, matrix_index], U,S,Vh = sp.linalg.svd( self.Qs[:,:, matrix_index],
full_matrices=False, compute_uv=True, full_matrices=False, compute_uv=True,
overwrite_a=False, check_finite=False) overwrite_a=False, check_finite=False)
@ -522,7 +522,7 @@ cdef class AQcompute_batch_Cython(Q_handling_Cython):
else: else:
U,S,Vh = self.Q_svd_dict[matrix_index] U,S,Vh = self.Q_svd_dict[matrix_index]
Q_inverse = Q_inverse = np.dot( Vh.T * ( 1.0/(S + jitter)) , U.T ) Q_inverse = Q_inverse = np.dot( Vh.T * ( 1.0/(S + jitter)) , U.T )
self.Q_inverse_dict[matrix_index] = Q_inverse self.Q_inverse_dict[matrix_index] = Q_inverse
return Q_inverse return Q_inverse
@ -998,4 +998,4 @@ def _cont_discr_kalman_filter_raw_Cython(int state_dim, Dynamic_Callables_Cython
M[k+1,:,:] = m_upd # separate mean value for each time series M[k+1,:,:] = m_upd # separate mean value for each time series
P[k+1,:,:] = P_upd[0] P[k+1,:,:] = P_upd[0]
return (M, P, log_likelihood, grad_log_likelihood, p_dynamic_callables.reset(False)) return (M, P, log_likelihood, grad_log_likelihood, p_dynamic_callables.reset(False))