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[GPU] vardtc_likelihood
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Zhenwen Dai
parent
f1d831c5f1
commit
daf5a877f3
2 changed files with 142 additions and 224 deletions
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@ -260,8 +260,11 @@ class PSICOMP_SSRBF(object):
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self.gpuCache = None
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def _initGPUCache(self, N, M, Q):
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if self.gpuCache and self.gpuCacheAll['mu_gpu'].shape[0]<N:
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self._releaseMemory()
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if self.gpuCache == None:
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self.gpuCache = {
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self.gpuCacheAll = {
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'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'),
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'Z_gpu' :gpuarray.empty((M,Q),np.float64,order='F'),
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'mu_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
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@ -301,6 +304,21 @@ class PSICOMP_SSRBF(object):
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'grad_S_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
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'grad_gamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
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}
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nonN_list = ['l_gpu','Z_gpu','psi2exp2_gpu','grad_l_gpu','grad_Z_gpu']
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self._gpuCache_Nlist = [k for k in self.gpuCacheAll.keys() if k not in nonN_list]
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self.gpuCache = self.gpuCacheAll
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elif self.gpuCacheAll['mu_gpu'].shape[0]>N:
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self.gpuCache = self.gpuCacheAll.copy()
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for k in self._gpuCache_Nlist:
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self.gpuCache[k] = self.gpuCacheAll[k][0:N]
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def _releaseMemory(self):
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if not self.gpuCacheAll:
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for k,v in self.gpuCacheAll:
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v.gpudata.free()
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del v
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self.gpuCacheAll = None
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self.gpuCache = None
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def psicomputations(self, variance, lengthscale, Z, mu, S, gamma):
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"""Compute Psi statitsitcs"""
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@ -492,166 +510,3 @@ class PSICOMP_SSRBF(object):
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linalg_gpu.sum_axis(grad_gamma_gpu, psi2_comb_gpu, N, M*M)
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return grad_mu_gpu.get(), grad_S_gpu.get(), grad_gamma_gpu.get()
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@Cache_this(limit=1)
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def _Z_distances(Z):
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Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
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Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
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return Zhat, Zdist
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def _psicomputations(variance, lengthscale, Z, mu, S, gamma):
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"""
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"""
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@Cache_this(limit=1)
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def _psi1computations(variance, lengthscale, Z, mu, S, gamma):
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"""
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Z - MxQ
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mu - NxQ
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S - NxQ
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gamma - NxQ
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"""
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# here are the "statistics" for psi1 and psi2
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# Produced intermediate results:
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# _psi1 NxM
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# _dpsi1_dvariance NxM
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# _dpsi1_dlengthscale NxMxQ
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# _dpsi1_dZ NxMxQ
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# _dpsi1_dgamma NxMxQ
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# _dpsi1_dmu NxMxQ
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# _dpsi1_dS NxMxQ
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lengthscale2 = np.square(lengthscale)
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# psi1
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_psi1_denom = S[:, None, :] / lengthscale2 + 1. # Nx1xQ
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_psi1_denom_sqrt = np.sqrt(_psi1_denom) #Nx1xQ
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_psi1_dist = Z[None, :, :] - mu[:, None, :] # NxMxQ
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_psi1_dist_sq = np.square(_psi1_dist) / (lengthscale2 * _psi1_denom) # NxMxQ
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_psi1_common = gamma[:,None,:] / (lengthscale2*_psi1_denom*_psi1_denom_sqrt) #Nx1xQ
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_psi1_exponent1 = np.log(gamma[:,None,:]) -0.5 * (_psi1_dist_sq + np.log(_psi1_denom)) # NxMxQ
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_psi1_exponent2 = np.log(1.-gamma[:,None,:]) -0.5 * (np.square(Z[None,:,:])/lengthscale2) # NxMxQ
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_psi1_exponent_max = np.maximum(_psi1_exponent1,_psi1_exponent2)
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_psi1_exponent = _psi1_exponent_max+np.log(np.exp(_psi1_exponent1-_psi1_exponent_max) + np.exp(_psi1_exponent2-_psi1_exponent_max)) #NxMxQ
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_psi1_exp_sum = _psi1_exponent.sum(axis=-1) #NxM
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_psi1_exp_dist_sq = np.exp(-0.5*_psi1_dist_sq) # NxMxQ
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_psi1_exp_Z = np.exp(-0.5*np.square(Z[None,:,:])/lengthscale2) # 1xMxQ
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_psi1_q = variance * np.exp(_psi1_exp_sum[:,:,None] - _psi1_exponent) # NxMxQ
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_psi1 = variance * np.exp(_psi1_exp_sum) # NxM
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_dpsi1_dvariance = _psi1 / variance # NxM
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_dpsi1_dgamma = _psi1_q * (_psi1_exp_dist_sq/_psi1_denom_sqrt-_psi1_exp_Z) # NxMxQ
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_dpsi1_dmu = _psi1_q * (_psi1_exp_dist_sq * _psi1_dist * _psi1_common) # NxMxQ
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_dpsi1_dS = _psi1_q * (_psi1_exp_dist_sq * _psi1_common * 0.5 * (_psi1_dist_sq - 1.)) # NxMxQ
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_dpsi1_dZ = _psi1_q * (- _psi1_common * _psi1_dist * _psi1_exp_dist_sq - (1-gamma[:,None,:])/lengthscale2*Z[None,:,:]*_psi1_exp_Z) # NxMxQ
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_dpsi1_dlengthscale = 2.*lengthscale*_psi1_q * (0.5*_psi1_common*(S[:,None,:]/lengthscale2+_psi1_dist_sq)*_psi1_exp_dist_sq + 0.5*(1-gamma[:,None,:])*np.square(Z[None,:,:]/lengthscale2)*_psi1_exp_Z) # NxMxQ
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N = mu.shape[0]
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M = Z.shape[0]
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Q = mu.shape[1]
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l_gpu = gpuarray.empty((Q,),np.float64, order='F')
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l_gpu.fill(lengthscale2)
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Z_gpu = gpuarray.to_gpu(np.asfortranarray(Z))
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mu_gpu = gpuarray.to_gpu(np.asfortranarray(mu))
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S_gpu = gpuarray.to_gpu(np.asfortranarray(S))
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gamma_gpu = gpuarray.to_gpu(np.asfortranarray(gamma))
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logGamma_gpu = gpuarray.to_gpu(np.asfortranarray(np.log(gamma)))
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log1Gamma_gpu = gpuarray.to_gpu(np.asfortranarray(np.log(1.-gamma)))
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logpsi1denom_gpu = gpuarray.to_gpu(np.asfortranarray(np.log(S/lengthscale2+1.)))
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psi1_gpu = gpuarray.empty((mu.shape[0],Z.shape[0]),np.float64, order='F')
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psi1_neq_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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psi1exp1_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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psi1exp2_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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dpsi1_dvar_gpu = gpuarray.empty((N,M),np.float64, order='F')
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dpsi1_dl_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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dpsi1_dZ_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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dpsi1_dgamma_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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dpsi1_dmu_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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dpsi1_dS_gpu = gpuarray.empty((N,M,Q),np.float64, order='F')
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comp_dpsi1_dvar(dpsi1_dvar_gpu,psi1_neq_gpu,psi1exp1_gpu,psi1exp2_gpu, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi1denom_gpu, N, M, Q)
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comp_psi1_der(dpsi1_dl_gpu,dpsi1_dmu_gpu,dpsi1_dS_gpu,dpsi1_dgamma_gpu, dpsi1_dZ_gpu, psi1_neq_gpu,psi1exp1_gpu,psi1exp2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, gamma_gpu, N, M, Q)
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# print np.abs(dpsi1_dmu_gpu.get()-_dpsi1_dmu).max()
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return _psi1, _dpsi1_dvariance, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _dpsi1_dZ, _dpsi1_dlengthscale
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@Cache_this(limit=1)
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def _psi2computations(variance, lengthscale, Z, mu, S, gamma):
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"""
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Z - MxQ
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mu - NxQ
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S - NxQ
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gamma - NxQ
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"""
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# here are the "statistics" for psi1 and psi2
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# Produced intermediate results:
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# _psi2 NxMxM
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# _psi2_dvariance NxMxM
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# _psi2_dlengthscale NxMxMxQ
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# _psi2_dZ NxMxMxQ
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# _psi2_dgamma NxMxMxQ
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# _psi2_dmu NxMxMxQ
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# _psi2_dS NxMxMxQ
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lengthscale2 = np.square(lengthscale)
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_psi2_Zhat, _psi2_Zdist = _Z_distances(Z)
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_psi2_Zdist_sq = np.square(_psi2_Zdist / lengthscale) # M,M,Q
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_psi2_Z_sq_sum = (np.square(Z[:,None,:])+np.square(Z[None,:,:]))/lengthscale2 # MxMxQ
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# psi2
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_psi2_denom = 2.*S[:, None, None, :] / lengthscale2 + 1. # Nx1x1xQ
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_psi2_denom_sqrt = np.sqrt(_psi2_denom)
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_psi2_mudist = mu[:,None,None,:]-_psi2_Zhat #N,M,M,Q
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_psi2_mudist_sq = np.square(_psi2_mudist)/(lengthscale2*_psi2_denom)
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_psi2_common = gamma[:,None,None,:]/(lengthscale2 * _psi2_denom * _psi2_denom_sqrt) # Nx1x1xQ
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_psi2_exponent1 = -_psi2_Zdist_sq -_psi2_mudist_sq -0.5*np.log(_psi2_denom)+np.log(gamma[:,None,None,:]) #N,M,M,Q
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_psi2_exponent2 = np.log(1.-gamma[:,None,None,:]) - 0.5*(_psi2_Z_sq_sum) # NxMxMxQ
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_psi2_exponent_max = np.maximum(_psi2_exponent1, _psi2_exponent2)
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_psi2_exponent = _psi2_exponent_max+np.log(np.exp(_psi2_exponent1-_psi2_exponent_max) + np.exp(_psi2_exponent2-_psi2_exponent_max))
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_psi2_exp_sum = _psi2_exponent.sum(axis=-1) #NxM
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_psi2_q = np.square(variance) * np.exp(_psi2_exp_sum[:,:,:,None]-_psi2_exponent) # NxMxMxQ
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_psi2_exp_dist_sq = np.exp(-_psi2_Zdist_sq -_psi2_mudist_sq) # NxMxMxQ
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_psi2_exp_Z = np.exp(-0.5*_psi2_Z_sq_sum) # MxMxQ
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_psi2 = np.square(variance) * np.exp(_psi2_exp_sum) # N,M,M
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_dpsi2_dvariance = 2. * _psi2/variance # NxMxM
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_dpsi2_dgamma = _psi2_q * (_psi2_exp_dist_sq/_psi2_denom_sqrt - _psi2_exp_Z) # NxMxMxQ
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_dpsi2_dmu = _psi2_q * (-2.*_psi2_common*_psi2_mudist * _psi2_exp_dist_sq) # NxMxMxQ
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_dpsi2_dS = _psi2_q * (_psi2_common * (2.*_psi2_mudist_sq - 1.) * _psi2_exp_dist_sq) # NxMxMxQ
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_dpsi2_dZ = 2.*_psi2_q * (_psi2_common*(-_psi2_Zdist*_psi2_denom+_psi2_mudist)*_psi2_exp_dist_sq - (1-gamma[:,None,None,:])*Z[:,None,:]/lengthscale2*_psi2_exp_Z) # NxMxMxQ
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_dpsi2_dlengthscale = 2.*lengthscale* _psi2_q * (_psi2_common*(S[:,None,None,:]/lengthscale2+_psi2_Zdist_sq*_psi2_denom+_psi2_mudist_sq)*_psi2_exp_dist_sq+(1-gamma[:,None,None,:])*_psi2_Z_sq_sum*0.5/lengthscale2*_psi2_exp_Z) # NxMxMxQ
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N = mu.shape[0]
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M = Z.shape[0]
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Q = mu.shape[1]
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l_gpu = gpuarray.empty((Q,),np.float64, order='F')
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l_gpu.fill(lengthscale2)
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Z_gpu = gpuarray.to_gpu(np.asfortranarray(Z))
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mu_gpu = gpuarray.to_gpu(np.asfortranarray(mu))
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S_gpu = gpuarray.to_gpu(np.asfortranarray(S))
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gamma_gpu = gpuarray.to_gpu(np.asfortranarray(gamma))
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logGamma_gpu = gpuarray.to_gpu(np.asfortranarray(np.log(gamma)))
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log1Gamma_gpu = gpuarray.to_gpu(np.asfortranarray(np.log(1.-gamma)))
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logpsi2denom_gpu = gpuarray.to_gpu(np.asfortranarray(np.log(2.*S/lengthscale2+1.)))
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psi2_gpu = gpuarray.empty((mu.shape[0],Z.shape[0],Z.shape[0]),np.float64, order='F')
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psi2_neq_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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psi2exp1_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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psi2exp2_gpu = gpuarray.empty((M,M,Q),np.float64, order='F')
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dpsi2_dvar_gpu = gpuarray.empty((N,M,M),np.float64, order='F')
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dpsi2_dl_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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dpsi2_dZ_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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dpsi2_dgamma_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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dpsi2_dmu_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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dpsi2_dS_gpu = gpuarray.empty((N,M,M,Q),np.float64, order='F')
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#comp_psi2(psi2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi2denom_gpu, N, M, Q)
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comp_dpsi2_dvar(dpsi2_dvar_gpu,psi2_neq_gpu,psi2exp1_gpu,psi2exp2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi2denom_gpu, N, M, Q)
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comp_psi2_der(dpsi2_dl_gpu,dpsi2_dmu_gpu,dpsi2_dS_gpu,dpsi2_dgamma_gpu, dpsi2_dZ_gpu, psi2_neq_gpu,psi2exp1_gpu,psi2exp2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, gamma_gpu, N, M, Q)
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# print np.abs(dpsi2_dvar_gpu.get()-_dpsi2_dvariance).max()
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return _psi2, _dpsi2_dvariance, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _dpsi2_dZ, _dpsi2_dlengthscale
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