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Added fixes to repo + rebased

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This commit is contained in:
kcutajar 2016-05-09 15:40:35 +02:00
parent 46b467ae44
commit bcbc58e065
2 changed files with 9 additions and 8 deletions
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4
GPy/core/gp_grid.py
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@ -69,10 +69,10 @@ class GpGrid(GP):
x = b x = b
N = 1 N = 1
G = np.zeros(D) G = np.zeros(D)
for d in xrange(D): for d in range(D):
G[d] = len(A[d]) G[d] = len(A[d])
N = np.prod(G) N = np.prod(G)
for d in xrange(D-1, -1, -1): for d in range(D-1, -1, -1):
X = np.reshape(x, (G[d], round(N/G[d])), order='F') X = np.reshape(x, (G[d], round(N/G[d])), order='F')
Z = np.dot(A[d], X) Z = np.dot(A[d], X)
Z = Z.T Z = Z.T

13
GPy/inference/latent_function_inference/gaussian_grid_inference.py
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@ -37,10 +37,10 @@ class GaussianGridInference(LatentFunctionInference):
N = 1 N = 1
D = len(A) D = len(A)
G = np.zeros((D,1)) G = np.zeros((D,1))
for d in xrange(0, D): for d in range(0, D):
G[d] = len(A[d]) G[d] = len(A[d])
N = np.prod(G) N = np.prod(G)
for d in xrange(D-1, -1, -1): for d in range(D-1, -1, -1):
X = np.reshape(x, (G[d], round(N/G[d])), order='F') X = np.reshape(x, (G[d], round(N/G[d])), order='F')
Z = np.dot(A[d], X) Z = np.dot(A[d], X)
Z = Z.T Z = Z.T
@ -63,7 +63,7 @@ class GaussianGridInference(LatentFunctionInference):
# retrieve the one-dimensional variation of the designated kernel # retrieve the one-dimensional variation of the designated kernel
oneDkernel = kern.get_one_dimensional_kernel(D) oneDkernel = kern.get_one_dimensional_kernel(D)
for d in xrange(D): for d in range(D):
xg = list(set(X[:,d])) #extract unique values for a dimension xg = list(set(X[:,d])) #extract unique values for a dimension
xg = np.reshape(xg, (len(xg), 1)) xg = np.reshape(xg, (len(xg), 1))
oneDkernel.lengthscale = kern.lengthscale[d] oneDkernel.lengthscale = kern.lengthscale[d]
@ -84,11 +84,11 @@ class GaussianGridInference(LatentFunctionInference):
# compute derivatives wrt parameters Thete # compute derivatives wrt parameters Thete
derivs = np.zeros(D+2, dtype='object') derivs = np.zeros(D+2, dtype='object')
for t in xrange(len(derivs)): for t in range(len(derivs)):
dKd_dTheta = np.zeros(D, dtype='object') dKd_dTheta = np.zeros(D, dtype='object')
gamma = np.zeros(D, dtype='object') gamma = np.zeros(D, dtype='object')
gam = 1 gam = 1
for d in xrange(D): for d in range(D):
xg = list(set(X[:,d])) xg = list(set(X[:,d]))
xg = np.reshape(xg, (len(xg), 1)) xg = np.reshape(xg, (len(xg), 1))
oneDkernel.lengthscale = kern.lengthscale[d] oneDkernel.lengthscale = kern.lengthscale[d]
@ -110,4 +110,5 @@ class GaussianGridInference(LatentFunctionInference):
dL_dVar = derivs[D] dL_dVar = derivs[D]
dL_dThetaL = derivs[D+1] dL_dThetaL = derivs[D+1]
return GridPosterior(alpha_kron=alpha_kron, QTs=QTs, Qs=Qs, V_kron=V_kron), log_likelihood, {'dL_dLen':dL_dLen, 'dL_dVar':dL_dVar, 'dL_dthetaL':dL_dThetaL} return GridPosterior(alpha_kron=alpha_kron, QTs=QTs, Qs=Qs, V_kron=V_kron), \
log_likelihood, {'dL_dLen':dL_dLen, 'dL_dVar':dL_dVar, 'dL_dthetaL':dL_dThetaL}