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Copy and paste observable_array from repository to try and resolve bizzare merge request.
This commit is contained in:
Neil Lawrence
commit
c2d3c82944
30 changed files with 1646 additions and 184 deletions
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@ -13,9 +13,10 @@ class Kern(Parameterized):
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#===========================================================================
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# This adds input slice support. The rather ugly code for slicing can be
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# found in kernel_slice_operations
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__metaclass__ = KernCallsViaSlicerMeta
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#__metaclass__ = KernCallsViaSlicerMeta
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#===========================================================================
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def __init__(self, input_dim, active_dims, name, *a, **kw):
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_support_GPU=False
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def __init__(self, input_dim, active_dims, name, useGPU=False, *a, **kw):
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"""
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The base class for a kernel: a positive definite function
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which forms of a covariance function (kernel).
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@ -39,6 +40,8 @@ class Kern(Parameterized):
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active_dim_size = len(self.active_dims)
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assert active_dim_size == self.input_dim, "input_dim={} does not match len(active_dim)={}, active_dims={}".format(self.input_dim, active_dim_size, self.active_dims)
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self._sliced_X = 0
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self.useGPU = self._support_GPU and useGPU
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@Cache_this(limit=10)
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def _slice_X(self, X):
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535
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
Normal file
535
GPy/kern/_src/psi_comp/ssrbf_psi_gpucomp.py
Normal file
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@ -0,0 +1,535 @@
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# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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"""
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The package for the psi statistics computation on GPU
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"""
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import numpy as np
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from GPy.util.caching import Cache_this
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from ....util import gpu_init
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try:
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import pycuda.gpuarray as gpuarray
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from scikits.cuda import cublas
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from pycuda.reduction import ReductionKernel
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from pycuda.elementwise import ElementwiseKernel
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from ....util import linalg_gpu
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# The kernel form computing psi1 het_noise
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comp_psi1 = ElementwiseKernel(
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"double *psi1, double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi1denom, int N, int M, int Q",
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"psi1[i] = comp_psi1_element(var, l, Z, mu, S, logGamma, log1Gamma, logpsi1denom, N, M, Q, i)",
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"comp_psi1",
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preamble="""
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#define IDX_NMQ(n,m,q) ((q*M+m)*N+n)
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#define IDX_NQ(n,q) (q*N+n)
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#define IDX_MQ(m,q) (q*M+m)
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#define LOGEXPSUM(a,b) (a>=b?a+log(1.0+exp(b-a)):b+log(1.0+exp(a-b)))
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__device__ double comp_psi1_element(double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi1denom, int N, int M, int Q, int idx)
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{
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int n = idx%N;
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int m = idx/N;
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double psi1_exp=0;
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for(int q=0;q<Q;q++){
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double muZ = mu[IDX_NQ(n,q)]-Z[IDX_MQ(m,q)];
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double exp1 = logGamma[IDX_NQ(n,q)] - (logpsi1denom[IDX_NQ(n,q)] + muZ*muZ/(S[IDX_NQ(n,q)]+l[q]) )/2.0;
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double exp2 = log1Gamma[IDX_NQ(n,q)] - Z[IDX_MQ(m,q)]*Z[IDX_MQ(m,q)]/(l[q]*2.0);
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psi1_exp += LOGEXPSUM(exp1,exp2);
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}
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return var*exp(psi1_exp);
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}
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""")
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# The kernel form computing psi2 het_noise
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comp_psi2 = ElementwiseKernel(
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"double *psi2, double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi2denom, int N, int M, int Q",
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"psi2[i] = comp_psi2_element(var, l, Z, mu, S, logGamma, log1Gamma, logpsi2denom, N, M, Q, i)",
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"comp_psi2",
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preamble="""
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#define IDX_NMQ(n,m,q) ((q*M+m)*N+n)
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#define IDX_NQ(n,q) (q*N+n)
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#define IDX_MQ(m,q) (q*M+m)
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#define LOGEXPSUM(a,b) (a>=b?a+log(1.0+exp(b-a)):b+log(1.0+exp(a-b)))
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__device__ double comp_psi2_element(double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi2denom, int N, int M, int Q, int idx)
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{
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// psi2 (n,m1,m2)
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int m2 = idx/(M*N);
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int m1 = (idx%(M*N))/N;
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int n = idx%N;
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double psi2_exp=0;
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for(int q=0;q<Q;q++){
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double dZ = Z[IDX_MQ(m1,q)]-Z[IDX_MQ(m2,q)];
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double muZ = mu[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)])/2.0;
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double exp1 = logGamma[IDX_NQ(n,q)] - (logpsi2denom[IDX_NQ(n,q)])/2.0 - dZ*dZ/(l[q]*4.0) - muZ*muZ/(2*S[IDX_NQ(n,q)]+l[q]);
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double exp2 = log1Gamma[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]*Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)]*Z[IDX_MQ(m2,q)])/(l[q]*2.0);
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psi2_exp += LOGEXPSUM(exp1,exp2);
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}
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return var*var*exp(psi2_exp);
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}
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""")
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# compute psidenom
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comp_logpsidenom = ElementwiseKernel(
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"double *out, double *S, double *l, double scale, int N",
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"out[i] = comp_logpsidenom_element(S, l, scale, N, i)",
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"comp_logpsidenom",
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preamble="""
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__device__ double comp_logpsidenom_element(double *S, double *l, double scale, int N, int idx)
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{
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int q = idx/N;
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return log(scale*S[idx]/l[q]+1.0);
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}
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""")
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# The kernel form computing psi1 het_noise
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comp_dpsi1_dvar = ElementwiseKernel(
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"double *dpsi1_dvar, double *psi1_neq, double *psi1exp1, double *psi1exp2, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi1denom, int N, int M, int Q",
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"dpsi1_dvar[i] = comp_dpsi1_dvar_element(psi1_neq, psi1exp1, psi1exp2, l, Z, mu, S, logGamma, log1Gamma, logpsi1denom, N, M, Q, i)",
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"comp_dpsi1_dvar",
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preamble="""
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#define IDX_NMQ(n,m,q) ((q*M+m)*N+n)
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#define IDX_NQ(n,q) (q*N+n)
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#define IDX_MQ(m,q) (q*M+m)
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#define LOGEXPSUM(a,b) (a>=b?a+log(1.0+exp(b-a)):b+log(1.0+exp(a-b)))
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__device__ double comp_dpsi1_dvar_element(double *psi1_neq, double *psi1exp1, double *psi1exp2, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi1denom, int N, int M, int Q, int idx)
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{
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int n = idx%N;
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int m = idx/N;
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double psi1_sum = 0;
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for(int q=0;q<Q;q++){
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double muZ = mu[IDX_NQ(n,q)]-Z[IDX_MQ(m,q)];
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double exp1_e = -(muZ*muZ/(S[IDX_NQ(n,q)]+l[q]) )/2.0;
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double exp1 = logGamma[IDX_NQ(n,q)] - (logpsi1denom[IDX_NQ(n,q)])/2.0 + exp1_e;
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double exp2_e = - Z[IDX_MQ(m,q)]*Z[IDX_MQ(m,q)]/(l[q]*2.0);
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double exp2 = log1Gamma[IDX_NQ(n,q)] + exp2_e;
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double psi1_q = LOGEXPSUM(exp1,exp2);
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psi1_neq[IDX_NMQ(n,m,q)] = -psi1_q;
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psi1exp1[IDX_NMQ(n,m,q)] = exp(exp1_e);
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psi1exp2[IDX_MQ(m,q)] = exp(exp2_e);
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psi1_sum += psi1_q;
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}
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for(int q=0;q<Q;q++) {
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psi1_neq[IDX_NMQ(n,m,q)] = exp(psi1_neq[IDX_NMQ(n,m,q)]+psi1_sum);
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}
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return exp(psi1_sum);
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}
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""")
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# The kernel form computing psi1 het_noise
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comp_psi1_der = ElementwiseKernel(
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"double *dpsi1_dl, double *dpsi1_dmu, double *dpsi1_dS, double *dpsi1_dgamma, double *dpsi1_dZ, double *psi1_neq, double *psi1exp1, double *psi1exp2, double var, double *l, double *Z, double *mu, double *S, double *gamma, int N, int M, int Q",
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"dpsi1_dl[i] = comp_psi1_der_element(dpsi1_dmu, dpsi1_dS, dpsi1_dgamma, dpsi1_dZ, psi1_neq, psi1exp1, psi1exp2, var, l, Z, mu, S, gamma, N, M, Q, i)",
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"comp_psi1_der",
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preamble="""
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#define IDX_NMQ(n,m,q) ((q*M+m)*N+n)
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#define IDX_NQ(n,q) (q*N+n)
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#define IDX_MQ(m,q) (q*M+m)
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__device__ double comp_psi1_der_element(double *dpsi1_dmu, double *dpsi1_dS, double *dpsi1_dgamma, double *dpsi1_dZ, double *psi1_neq, double *psi1exp1, double *psi1exp2, double var, double *l, double *Z, double *mu, double *S, double *gamma, int N, int M, int Q, int idx)
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{
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int q = idx/(M*N);
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int m = (idx%(M*N))/N;
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int n = idx%N;
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double neq = psi1_neq[IDX_NMQ(n,m,q)];
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double gamma_c = gamma[IDX_NQ(n,q)];
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double Z_c = Z[IDX_MQ(m,q)];
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double S_c = S[IDX_NQ(n,q)];
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double l_c = l[q];
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double l_sqrt_c = sqrt(l[q]);
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double psi1exp1_c = psi1exp1[IDX_NMQ(n,m,q)];
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double psi1exp2_c = psi1exp2[IDX_MQ(m,q)];
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double denom = S_c/l_c+1.0;
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double denom_sqrt = sqrt(denom);
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double Zmu = Z_c-mu[IDX_NQ(n,q)];
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double psi1_common = gamma_c/(denom_sqrt*denom*l_c);
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double gamma1 = 1-gamma_c;
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dpsi1_dgamma[IDX_NMQ(n,m,q)] = var*neq*(psi1exp1_c/denom_sqrt - psi1exp2_c);
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dpsi1_dmu[IDX_NMQ(n,m,q)] = var*neq*(psi1_common*Zmu*psi1exp1_c);
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dpsi1_dS[IDX_NMQ(n,m,q)] = var*neq*(psi1_common*(Zmu*Zmu/(S_c+l_c)-1.0)*psi1exp1_c)/2.0;
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dpsi1_dZ[IDX_NMQ(n,m,q)] = var*neq*(-psi1_common*Zmu*psi1exp1_c-gamma1*Z_c/l_c*psi1exp2_c);
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return var*neq*(psi1_common*(S_c/l_c+Zmu*Zmu/(S_c+l_c))*psi1exp1_c+gamma1*Z_c*Z_c/l_c*psi1exp2_c)*l_sqrt_c;
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}
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""")
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# The kernel form computing psi1 het_noise
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comp_dpsi2_dvar = ElementwiseKernel(
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"double *dpsi2_dvar, double *psi2_neq, double *psi2exp1, double *psi2exp2, double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi2denom, int N, int M, int Q",
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"dpsi2_dvar[i] = comp_dpsi2_dvar_element(psi2_neq, psi2exp1, psi2exp2, var, l, Z, mu, S, logGamma, log1Gamma, logpsi2denom, N, M, Q, i)",
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"comp_dpsi2_dvar",
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preamble="""
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#define IDX_NMMQ(n,m1,m2,q) (((q*M+m2)*M+m1)*N+n)
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#define IDX_MMQ(m1,m2,q) ((q*M+m2)*M+m1)
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#define IDX_NMQ(n,m,q) ((q*M+m)*N+n)
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#define IDX_NQ(n,q) (q*N+n)
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#define IDX_MQ(m,q) (q*M+m)
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#define LOGEXPSUM(a,b) (a>=b?a+log(1.0+exp(b-a)):b+log(1.0+exp(a-b)))
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__device__ double comp_dpsi2_dvar_element(double *psi2_neq, double *psi2exp1, double *psi2exp2, double var, double *l, double *Z, double *mu, double *S, double *logGamma, double *log1Gamma, double *logpsi2denom, int N, int M, int Q, int idx)
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{
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// psi2 (n,m1,m2)
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int m2 = idx/(M*N);
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int m1 = (idx%(M*N))/N;
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int n = idx%N;
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double psi2_sum=0;
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for(int q=0;q<Q;q++){
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double dZ = Z[IDX_MQ(m1,q)]-Z[IDX_MQ(m2,q)];
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double muZ = mu[IDX_NQ(n,q)] - (Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)])/2.0;
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double exp1_e = - dZ*dZ/(l[q]*4.0) - muZ*muZ/(2*S[IDX_NQ(n,q)]+l[q]);
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double exp1 = logGamma[IDX_NQ(n,q)] - (logpsi2denom[IDX_NQ(n,q)])/2.0 +exp1_e;
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double exp2_e = - (Z[IDX_MQ(m1,q)]*Z[IDX_MQ(m1,q)]+Z[IDX_MQ(m2,q)]*Z[IDX_MQ(m2,q)])/(l[q]*2.0);
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double exp2 = log1Gamma[IDX_NQ(n,q)] + exp2_e;
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double psi2_q = LOGEXPSUM(exp1,exp2);
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psi2_neq[IDX_NMMQ(n,m1,m2,q)] = -psi2_q;
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psi2exp1[IDX_NMMQ(n,m1,m2,q)] = exp(exp1_e);
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psi2exp2[IDX_MMQ(m1,m2,q)] = exp(exp2_e);
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psi2_sum += psi2_q;
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}
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for(int q=0;q<Q;q++) {
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psi2_neq[IDX_NMMQ(n,m1,m2,q)] = exp(psi2_neq[IDX_NMMQ(n,m1,m2,q)]+psi2_sum);
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}
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return 2*var*exp(psi2_sum);
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}
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""")
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# The kernel form computing psi1 het_noise
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comp_psi2_der = ElementwiseKernel(
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"double *dpsi2_dl, double *dpsi2_dmu, double *dpsi2_dS, double *dpsi2_dgamma, double *dpsi2_dZ, double *psi2_neq, double *psi2exp1, double *psi2exp2, double var, double *l, double *Z, double *mu, double *S, double *gamma, int N, int M, int Q",
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"dpsi2_dl[i] = comp_psi2_der_element(dpsi2_dmu, dpsi2_dS, dpsi2_dgamma, dpsi2_dZ, psi2_neq, psi2exp1, psi2exp2, var, l, Z, mu, S, gamma, N, M, Q, i)",
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"comp_psi2_der",
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preamble="""
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#define IDX_NMMQ(n,m1,m2,q) (((q*M+m2)*M+m1)*N+n)
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#define IDX_MMQ(m1,m2,q) ((q*M+m2)*M+m1)
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#define IDX_NMQ(n,m,q) ((q*M+m)*N+n)
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#define IDX_NQ(n,q) (q*N+n)
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#define IDX_MQ(m,q) (q*M+m)
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__device__ double comp_psi2_der_element(double *dpsi2_dmu, double *dpsi2_dS, double *dpsi2_dgamma, double *dpsi2_dZ, double *psi2_neq, double *psi2exp1, double *psi2exp2, double var, double *l, double *Z, double *mu, double *S, double *gamma, int N, int M, int Q, int idx)
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{
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// dpsi2 (n,m1,m2,q)
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int q = idx/(M*M*N);
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int m2 = (idx%(M*M*N))/(M*N);
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int m1 = (idx%(M*N))/N;
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int n = idx%N;
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double neq = psi2_neq[IDX_NMMQ(n,m1,m2,q)];
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double gamma_c = gamma[IDX_NQ(n,q)];
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double Z1_c = Z[IDX_MQ(m1,q)];
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double Z2_c = Z[IDX_MQ(m2,q)];
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double S_c = S[IDX_NQ(n,q)];
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double l_c = l[q];
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double l_sqrt_c = sqrt(l[q]);
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double psi2exp1_c = psi2exp1[IDX_NMMQ(n,m1,m2,q)];
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double psi2exp2_c = psi2exp2[IDX_MMQ(m1,m2,q)];
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double dZ = Z2_c - Z1_c;
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double muZ = mu[IDX_NQ(n,q)] - (Z1_c+Z2_c)/2.0;
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double Z2 = Z1_c*Z1_c+Z2_c*Z2_c;
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double denom = 2.0*S_c/l_c+1.0;
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double denom_sqrt = sqrt(denom);
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double psi2_common = gamma_c/(denom_sqrt*denom*l_c);
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double gamma1 = 1-gamma_c;
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double var2 = var*var;
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dpsi2_dgamma[IDX_NMMQ(n,m1,m2,q)] = var2*neq*(psi2exp1_c/denom_sqrt - psi2exp2_c);
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dpsi2_dmu[IDX_NMMQ(n,m1,m2,q)] = var2*neq*(-2.0*psi2_common*muZ*psi2exp1_c);
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dpsi2_dS[IDX_NMMQ(n,m1,m2,q)] = var2*neq*(psi2_common*(2.0*muZ*muZ/(2.0*S_c+l_c)-1.0)*psi2exp1_c);
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dpsi2_dZ[IDX_NMMQ(n,m1,m2,q)] = var2*neq*(psi2_common*(dZ*denom/-2.0+muZ)*psi2exp1_c-gamma1*Z2_c/l_c*psi2exp2_c)*2.0;
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return var2*neq*(psi2_common*(S_c/l_c+dZ*dZ*denom/(4.0*l_c)+muZ*muZ/(2.0*S_c+l_c))*psi2exp1_c+gamma1*Z2/(2.0*l_c)*psi2exp2_c)*l_sqrt_c*2.0;
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}
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""")
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except:
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pass
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class PSICOMP_SSRBF(object):
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def __init__(self):
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assert gpu_init.initSuccess, "GPU initialization failed!"
|
||||
self.cublas_handle = gpu_init.cublas_handle
|
||||
self.gpuCache = None
|
||||
self.gpuCacheAll = None
|
||||
|
||||
def _initGPUCache(self, N, M, Q):
|
||||
if self.gpuCache!=None and self.gpuCache['mu_gpu'].shape[0] == N:
|
||||
return
|
||||
|
||||
if self.gpuCacheAll!=None and self.gpuCacheAll['mu_gpu'].shape[0]<N: # Too small cache -> reallocate
|
||||
self._releaseMemory()
|
||||
|
||||
if self.gpuCacheAll == None:
|
||||
self.gpuCacheAll = {
|
||||
'l_gpu' :gpuarray.empty((Q,),np.float64,order='F'),
|
||||
'Z_gpu' :gpuarray.empty((M,Q),np.float64,order='F'),
|
||||
'mu_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'S_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'gamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'logGamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'log1Gamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'logpsi1denom_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'logpsi2denom_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'psi0_gpu' :gpuarray.empty((N,),np.float64,order='F'),
|
||||
'psi1_gpu' :gpuarray.empty((N,M),np.float64,order='F'),
|
||||
'psi2_gpu' :gpuarray.empty((N,M,M),np.float64,order='F'),
|
||||
# derivatives psi1
|
||||
'psi1_neq_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'psi1exp1_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'psi1exp2_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'dpsi1_dvar_gpu' :gpuarray.empty((N,M),np.float64, order='F'),
|
||||
'dpsi1_dl_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'dpsi1_dZ_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'dpsi1_dgamma_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'dpsi1_dmu_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
'dpsi1_dS_gpu' :gpuarray.empty((N,M,Q),np.float64, order='F'),
|
||||
# derivatives psi2
|
||||
'psi2_neq_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
'psi2exp1_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
'psi2exp2_gpu' :gpuarray.empty((M,M,Q),np.float64, order='F'),
|
||||
'dpsi2_dvar_gpu' :gpuarray.empty((N,M,M),np.float64, order='F'),
|
||||
'dpsi2_dl_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
'dpsi2_dZ_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
'dpsi2_dgamma_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
'dpsi2_dmu_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
'dpsi2_dS_gpu' :gpuarray.empty((N,M,M,Q),np.float64, order='F'),
|
||||
# gradients
|
||||
'grad_l_gpu' :gpuarray.empty((Q,),np.float64,order='F'),
|
||||
'grad_Z_gpu' :gpuarray.empty((M,Q),np.float64,order='F'),
|
||||
'grad_mu_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'grad_S_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
'grad_gamma_gpu' :gpuarray.empty((N,Q),np.float64,order='F'),
|
||||
}
|
||||
self.gpuCache = self.gpuCacheAll
|
||||
elif self.gpuCacheAll['mu_gpu'].shape[0]==N:
|
||||
self.gpuCache = self.gpuCacheAll
|
||||
else:
|
||||
# remap to a smaller cache
|
||||
self.gpuCache = self.gpuCacheAll.copy()
|
||||
Nlist=['mu_gpu','S_gpu','gamma_gpu','logGamma_gpu','log1Gamma_gpu','logpsi1denom_gpu','logpsi2denom_gpu','psi0_gpu','psi1_gpu','psi2_gpu',
|
||||
'psi1_neq_gpu','psi1exp1_gpu','psi1exp2_gpu','dpsi1_dvar_gpu','dpsi1_dl_gpu','dpsi1_dZ_gpu','dpsi1_dgamma_gpu','dpsi1_dmu_gpu',
|
||||
'dpsi1_dS_gpu','psi2_neq_gpu','psi2exp1_gpu','dpsi2_dvar_gpu','dpsi2_dl_gpu','dpsi2_dZ_gpu','dpsi2_dgamma_gpu','dpsi2_dmu_gpu','dpsi2_dS_gpu','grad_mu_gpu','grad_S_gpu','grad_gamma_gpu',]
|
||||
oldN = self.gpuCacheAll['mu_gpu'].shape[0]
|
||||
for v in Nlist:
|
||||
u = self.gpuCacheAll[v]
|
||||
self.gpuCache[v] = u.ravel()[:u.size/oldN*N].reshape(*((N,)+u.shape[1:]))
|
||||
|
||||
def _releaseMemory(self):
|
||||
if self.gpuCacheAll!=None:
|
||||
[v.gpudata.free() for v in self.gpuCacheAll.values()]
|
||||
self.gpuCacheAll = None
|
||||
self.gpuCache = None
|
||||
|
||||
def estimateMemoryOccupation(self, N, M, Q):
|
||||
"""
|
||||
Estimate the best batch size.
|
||||
N - the number of total datapoints
|
||||
M - the number of inducing points
|
||||
Q - the number of hidden (input) dimensions
|
||||
return: the constant memory size, the memory occupation of batchsize=1
|
||||
unit: GB
|
||||
"""
|
||||
return (2.*Q+2.*M*Q+M*M*Q)*8./1024./1024./1024., (1.+2.*M+10.*Q+2.*M*M+8.*M*Q+7.*M*M*Q)*8./1024./1024./1024.
|
||||
|
||||
@Cache_this(limit=1,ignore_args=(0,))
|
||||
def psicomputations(self, variance, lengthscale, Z, mu, S, gamma):
|
||||
"""Compute Psi statitsitcs"""
|
||||
if isinstance(lengthscale, np.ndarray) and len(lengthscale)>1:
|
||||
ARD = True
|
||||
else:
|
||||
ARD = False
|
||||
|
||||
N = mu.shape[0]
|
||||
M = Z.shape[0]
|
||||
Q = mu.shape[1]
|
||||
|
||||
self._initGPUCache(N,M,Q)
|
||||
l_gpu = self.gpuCache['l_gpu']
|
||||
Z_gpu = self.gpuCache['Z_gpu']
|
||||
mu_gpu = self.gpuCache['mu_gpu']
|
||||
S_gpu = self.gpuCache['S_gpu']
|
||||
gamma_gpu = self.gpuCache['gamma_gpu']
|
||||
logGamma_gpu = self.gpuCache['logGamma_gpu']
|
||||
log1Gamma_gpu = self.gpuCache['log1Gamma_gpu']
|
||||
logpsi1denom_gpu = self.gpuCache['logpsi1denom_gpu']
|
||||
logpsi2denom_gpu = self.gpuCache['logpsi2denom_gpu']
|
||||
psi0_gpu = self.gpuCache['psi0_gpu']
|
||||
psi1_gpu = self.gpuCache['psi1_gpu']
|
||||
psi2_gpu = self.gpuCache['psi2_gpu']
|
||||
|
||||
if ARD:
|
||||
l_gpu.set(np.asfortranarray(lengthscale**2))
|
||||
else:
|
||||
l_gpu.fill(lengthscale*lengthscale)
|
||||
Z_gpu.set(np.asfortranarray(Z))
|
||||
mu_gpu.set(np.asfortranarray(mu))
|
||||
S_gpu.set(np.asfortranarray(S))
|
||||
gamma_gpu.set(np.asfortranarray(gamma))
|
||||
linalg_gpu.log(gamma_gpu,logGamma_gpu)
|
||||
linalg_gpu.logOne(gamma_gpu,log1Gamma_gpu)
|
||||
comp_logpsidenom(logpsi1denom_gpu, S_gpu,l_gpu,1.0,N)
|
||||
comp_logpsidenom(logpsi2denom_gpu, S_gpu,l_gpu,2.0,N)
|
||||
|
||||
psi0_gpu.fill(variance)
|
||||
comp_psi1(psi1_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi1denom_gpu, N, M, Q)
|
||||
comp_psi2(psi2_gpu, variance, l_gpu, Z_gpu, mu_gpu, S_gpu, logGamma_gpu, log1Gamma_gpu, logpsi2denom_gpu, N, M, Q)
|
||||
|
||||
return psi0_gpu, psi1_gpu, psi2_gpu
|
||||
|
||||
@Cache_this(limit=1,ignore_args=(0,))
|
||||
def _psiDercomputations(self, variance, lengthscale, Z, mu, S, gamma):
|
||||
"""Compute the derivatives w.r.t. Psi statistics"""
|
||||
N, M, Q = mu.shape[0],Z.shape[0], mu.shape[1]
|
||||
|
||||
self._initGPUCache(N,M,Q)
|
||||
l_gpu = self.gpuCache['l_gpu']
|
||||
Z_gpu = self.gpuCache['Z_gpu']
|
||||
mu_gpu = self.gpuCache['mu_gpu']
|
||||
S_gpu = self.gpuCache['S_gpu']
|
||||
gamma_gpu = self.gpuCache['gamma_gpu']
|
||||
logGamma_gpu = self.gpuCache['logGamma_gpu']
|
||||
log1Gamma_gpu = self.gpuCache['log1Gamma_gpu']
|
||||
logpsi1denom_gpu = self.gpuCache['logpsi1denom_gpu']
|
||||
logpsi2denom_gpu = self.gpuCache['logpsi2denom_gpu']
|
||||
|
||||
psi1_neq_gpu = self.gpuCache['psi1_neq_gpu']
|
||||
psi1exp1_gpu = self.gpuCache['psi1exp1_gpu']
|
||||
psi1exp2_gpu = self.gpuCache['psi1exp2_gpu']
|
||||
dpsi1_dvar_gpu = self.gpuCache['dpsi1_dvar_gpu']
|
||||
dpsi1_dl_gpu = self.gpuCache['dpsi1_dl_gpu']
|
||||
dpsi1_dZ_gpu = self.gpuCache['dpsi1_dZ_gpu']
|
||||
dpsi1_dgamma_gpu = self.gpuCache['dpsi1_dgamma_gpu']
|
||||
dpsi1_dmu_gpu = self.gpuCache['dpsi1_dmu_gpu']
|
||||
dpsi1_dS_gpu = self.gpuCache['dpsi1_dS_gpu']
|
||||
|
||||
psi2_neq_gpu = self.gpuCache['psi2_neq_gpu']
|
||||
psi2exp1_gpu = self.gpuCache['psi2exp1_gpu']
|
||||
psi2exp2_gpu = self.gpuCache['psi2exp2_gpu']
|
||||
dpsi2_dvar_gpu = self.gpuCache['dpsi2_dvar_gpu']
|
||||
dpsi2_dl_gpu = self.gpuCache['dpsi2_dl_gpu']
|
||||
dpsi2_dZ_gpu = self.gpuCache['dpsi2_dZ_gpu']
|
||||
dpsi2_dgamma_gpu = self.gpuCache['dpsi2_dgamma_gpu']
|
||||
dpsi2_dmu_gpu = self.gpuCache['dpsi2_dmu_gpu']
|
||||
dpsi2_dS_gpu = self.gpuCache['dpsi2_dS_gpu']
|
||||
|
||||
#==========================================================================================================
|
||||
# Assuming the l_gpu, Z_gpu, mu_gpu, S_gpu, gamma_gpu, logGamma_gpu, log1Gamma_gpu,
|
||||
# logpsi1denom_gpu, logpsi2denom_gpu has been synchonized.
|
||||
#==========================================================================================================
|
||||
|
||||
# psi1 derivatives
|
||||
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)
|
||||
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)
|
||||
|
||||
# psi2 derivatives
|
||||
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)
|
||||
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)
|
||||
|
||||
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
|
||||
mu = variational_posterior.mean
|
||||
S = variational_posterior.variance
|
||||
gamma = variational_posterior.binary_prob
|
||||
self._psiDercomputations(variance, lengthscale, Z, mu, S, gamma)
|
||||
N, M, Q = mu.shape[0],Z.shape[0], mu.shape[1]
|
||||
|
||||
if isinstance(lengthscale, np.ndarray) and len(lengthscale)>1:
|
||||
ARD = True
|
||||
else:
|
||||
ARD = False
|
||||
|
||||
dpsi1_dvar_gpu = self.gpuCache['dpsi1_dvar_gpu']
|
||||
dpsi2_dvar_gpu = self.gpuCache['dpsi2_dvar_gpu']
|
||||
dpsi1_dl_gpu = self.gpuCache['dpsi1_dl_gpu']
|
||||
dpsi2_dl_gpu = self.gpuCache['dpsi2_dl_gpu']
|
||||
psi1_comb_gpu = self.gpuCache['psi1_neq_gpu']
|
||||
psi2_comb_gpu = self.gpuCache['psi2_neq_gpu']
|
||||
grad_l_gpu = self.gpuCache['grad_l_gpu']
|
||||
|
||||
# variance
|
||||
variance.gradient = gpuarray.sum(dL_dpsi0).get() \
|
||||
+ cublas.cublasDdot(self.cublas_handle, dL_dpsi1.size, dL_dpsi1.gpudata, 1, dpsi1_dvar_gpu.gpudata, 1) \
|
||||
+ cublas.cublasDdot(self.cublas_handle, dL_dpsi2.size, dL_dpsi2.gpudata, 1, dpsi2_dvar_gpu.gpudata, 1)
|
||||
|
||||
# lengscale
|
||||
if ARD:
|
||||
grad_l_gpu.fill(0.)
|
||||
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dl_gpu, dL_dpsi1.size)
|
||||
linalg_gpu.sum_axis(grad_l_gpu, psi1_comb_gpu, 1, N*M)
|
||||
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dl_gpu, dL_dpsi2.size)
|
||||
linalg_gpu.sum_axis(grad_l_gpu, psi2_comb_gpu, 1, N*M*M)
|
||||
lengthscale.gradient = grad_l_gpu.get()
|
||||
else:
|
||||
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dl_gpu, dL_dpsi1.size)
|
||||
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dl_gpu, dL_dpsi2.size)
|
||||
lengthscale.gradient = gpuarray.sum(psi1_comb_gpu).get() + gpuarray.sum(psi2_comb_gpu).get()
|
||||
|
||||
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
|
||||
mu = variational_posterior.mean
|
||||
S = variational_posterior.variance
|
||||
gamma = variational_posterior.binary_prob
|
||||
self._psiDercomputations(variance, lengthscale, Z, mu, S, gamma)
|
||||
N, M, Q = mu.shape[0],Z.shape[0], mu.shape[1]
|
||||
|
||||
dpsi1_dZ_gpu = self.gpuCache['dpsi1_dZ_gpu']
|
||||
dpsi2_dZ_gpu = self.gpuCache['dpsi2_dZ_gpu']
|
||||
psi1_comb_gpu = self.gpuCache['psi1_neq_gpu']
|
||||
psi2_comb_gpu = self.gpuCache['psi2_neq_gpu']
|
||||
grad_Z_gpu = self.gpuCache['grad_Z_gpu']
|
||||
|
||||
grad_Z_gpu.fill(0.)
|
||||
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dZ_gpu, dL_dpsi1.size)
|
||||
linalg_gpu.sum_axis(grad_Z_gpu, psi1_comb_gpu, 1, N)
|
||||
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dZ_gpu, dL_dpsi2.size)
|
||||
linalg_gpu.sum_axis(grad_Z_gpu, psi2_comb_gpu, 1, N*M)
|
||||
return grad_Z_gpu.get()
|
||||
|
||||
def gradients_qX_expectations(self, dL_dpsi1, dL_dpsi2, variance, lengthscale, Z, variational_posterior):
|
||||
mu = variational_posterior.mean
|
||||
S = variational_posterior.variance
|
||||
gamma = variational_posterior.binary_prob
|
||||
self._psiDercomputations(variance, lengthscale, Z, mu, S, gamma)
|
||||
N, M, Q = mu.shape[0],Z.shape[0], mu.shape[1]
|
||||
|
||||
dpsi1_dmu_gpu = self.gpuCache['dpsi1_dmu_gpu']
|
||||
dpsi2_dmu_gpu = self.gpuCache['dpsi2_dmu_gpu']
|
||||
dpsi1_dS_gpu = self.gpuCache['dpsi1_dS_gpu']
|
||||
dpsi2_dS_gpu = self.gpuCache['dpsi2_dS_gpu']
|
||||
dpsi1_dgamma_gpu = self.gpuCache['dpsi1_dgamma_gpu']
|
||||
dpsi2_dgamma_gpu = self.gpuCache['dpsi2_dgamma_gpu']
|
||||
psi1_comb_gpu = self.gpuCache['psi1_neq_gpu']
|
||||
psi2_comb_gpu = self.gpuCache['psi2_neq_gpu']
|
||||
grad_mu_gpu = self.gpuCache['grad_mu_gpu']
|
||||
grad_S_gpu = self.gpuCache['grad_S_gpu']
|
||||
grad_gamma_gpu = self.gpuCache['grad_gamma_gpu']
|
||||
|
||||
# mu gradients
|
||||
grad_mu_gpu.fill(0.)
|
||||
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dmu_gpu, dL_dpsi1.size)
|
||||
linalg_gpu.sum_axis(grad_mu_gpu, psi1_comb_gpu, N, M)
|
||||
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dmu_gpu, dL_dpsi2.size)
|
||||
linalg_gpu.sum_axis(grad_mu_gpu, psi2_comb_gpu, N, M*M)
|
||||
|
||||
# S gradients
|
||||
grad_S_gpu.fill(0.)
|
||||
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dS_gpu, dL_dpsi1.size)
|
||||
linalg_gpu.sum_axis(grad_S_gpu, psi1_comb_gpu, N, M)
|
||||
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dS_gpu, dL_dpsi2.size)
|
||||
linalg_gpu.sum_axis(grad_S_gpu, psi2_comb_gpu, N, M*M)
|
||||
|
||||
# gamma gradients
|
||||
grad_gamma_gpu.fill(0.)
|
||||
linalg_gpu.mul_bcast(psi1_comb_gpu, dL_dpsi1, dpsi1_dgamma_gpu, dL_dpsi1.size)
|
||||
linalg_gpu.sum_axis(grad_gamma_gpu, psi1_comb_gpu, N, M)
|
||||
linalg_gpu.mul_bcast(psi2_comb_gpu, dL_dpsi2, dpsi2_dgamma_gpu, dL_dpsi2.size)
|
||||
linalg_gpu.sum_axis(grad_gamma_gpu, psi2_comb_gpu, N, M*M)
|
||||
|
||||
return grad_mu_gpu.get(), grad_S_gpu.get(), grad_gamma_gpu.get()
|
||||
|
|
@ -9,6 +9,7 @@ from stationary import Stationary
|
|||
from GPy.util.caching import Cache_this
|
||||
from ...core.parameterization import variational
|
||||
from psi_comp import ssrbf_psi_comp
|
||||
from psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF
|
||||
|
||||
class RBF(Stationary):
|
||||
"""
|
||||
|
|
@ -19,9 +20,15 @@ class RBF(Stationary):
|
|||
k(r) = \sigma^2 \exp \\bigg(- \\frac{1}{2} r^2 \\bigg)
|
||||
|
||||
"""
|
||||
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf'):
|
||||
super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name)
|
||||
_support_GPU = True
|
||||
def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='rbf', useGPU=False):
|
||||
super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=useGPU)
|
||||
self.weave_options = {}
|
||||
self.group_spike_prob = False
|
||||
|
||||
if self.useGPU:
|
||||
self.psicomp = PSICOMP_SSRBF()
|
||||
|
||||
|
||||
def K_of_r(self, r):
|
||||
return self.variance * np.exp(-0.5 * r**2)
|
||||
|
|
@ -34,18 +41,28 @@ class RBF(Stationary):
|
|||
#---------------------------------------#
|
||||
|
||||
def psi0(self, Z, variational_posterior):
|
||||
return self.Kdiag(variational_posterior.mean)
|
||||
if self.useGPU:
|
||||
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
|
||||
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)[0]
|
||||
else:
|
||||
return self.Kdiag(variational_posterior.mean)
|
||||
|
||||
def psi1(self, Z, variational_posterior):
|
||||
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
|
||||
psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
if self.useGPU:
|
||||
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)[1]
|
||||
else:
|
||||
psi1, _, _, _, _, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
else:
|
||||
_, _, _, psi1 = self._psi1computations(Z, variational_posterior)
|
||||
return psi1
|
||||
|
||||
def psi2(self, Z, variational_posterior):
|
||||
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
|
||||
psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
if self.useGPU:
|
||||
return self.psicomp.psicomputations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)[2]
|
||||
else:
|
||||
psi2, _, _, _, _, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
else:
|
||||
_, _, _, _, psi2 = self._psi2computations(Z, variational_posterior)
|
||||
return psi2
|
||||
|
|
@ -53,26 +70,30 @@ class RBF(Stationary):
|
|||
def update_gradients_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
|
||||
# Spike-and-Slab GPLVM
|
||||
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
|
||||
_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
_, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
||||
#contributions from psi0:
|
||||
self.variance.gradient = np.sum(dL_dpsi0)
|
||||
|
||||
#from psi1
|
||||
self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
|
||||
if self.ARD:
|
||||
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
|
||||
if self.useGPU:
|
||||
self.psicomp.update_gradients_expectations(dL_dpsi0, dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
|
||||
else:
|
||||
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).sum()
|
||||
|
||||
#from psi2
|
||||
self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
|
||||
if self.ARD:
|
||||
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
|
||||
else:
|
||||
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).sum()
|
||||
|
||||
|
||||
_, _dpsi1_dvariance, _, _, _, _, _dpsi1_dlengthscale = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
_, _dpsi2_dvariance, _, _, _, _, _dpsi2_dlengthscale = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
||||
#contributions from psi0:
|
||||
self.variance.gradient = np.sum(dL_dpsi0)
|
||||
|
||||
#from psi1
|
||||
self.variance.gradient += np.sum(dL_dpsi1 * _dpsi1_dvariance)
|
||||
if self.ARD:
|
||||
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
|
||||
else:
|
||||
self.lengthscale.gradient = (dL_dpsi1[:,:,None]*_dpsi1_dlengthscale).sum()
|
||||
|
||||
#from psi2
|
||||
self.variance.gradient += (dL_dpsi2 * _dpsi2_dvariance).sum()
|
||||
if self.ARD:
|
||||
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).reshape(-1,self.input_dim).sum(axis=0)
|
||||
else:
|
||||
self.lengthscale.gradient += (dL_dpsi2[:,:,:,None] * _dpsi2_dlengthscale).sum()
|
||||
|
||||
elif isinstance(variational_posterior, variational.NormalPosterior):
|
||||
l2 = self.lengthscale**2
|
||||
if l2.size != self.input_dim:
|
||||
|
|
@ -107,16 +128,19 @@ class RBF(Stationary):
|
|||
def gradients_Z_expectations(self, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
|
||||
# Spike-and-Slab GPLVM
|
||||
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
|
||||
_, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
_, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
||||
#psi1
|
||||
grad = (dL_dpsi1[:, :, None] * _dpsi1_dZ).sum(axis=0)
|
||||
|
||||
#psi2
|
||||
grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1)
|
||||
|
||||
return grad
|
||||
if self.useGPU:
|
||||
return self.psicomp.gradients_Z_expectations(dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
|
||||
else:
|
||||
_, _, _, _, _, _dpsi1_dZ, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
_, _, _, _, _, _dpsi2_dZ, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
||||
#psi1
|
||||
grad = (dL_dpsi1[:, :, None] * _dpsi1_dZ).sum(axis=0)
|
||||
|
||||
#psi2
|
||||
grad += (dL_dpsi2[:, :, :, None] * _dpsi2_dZ).sum(axis=0).sum(axis=1)
|
||||
|
||||
return grad
|
||||
|
||||
elif isinstance(variational_posterior, variational.NormalPosterior):
|
||||
l2 = self.lengthscale **2
|
||||
|
|
@ -140,22 +164,28 @@ class RBF(Stationary):
|
|||
def gradients_qX_expectations(self, dL_dpsi0, dL_dpsi1, dL_dpsi2, Z, variational_posterior):
|
||||
# Spike-and-Slab GPLVM
|
||||
if isinstance(variational_posterior, variational.SpikeAndSlabPosterior):
|
||||
ndata = variational_posterior.mean.shape[0]
|
||||
|
||||
_, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
_, _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
||||
#psi1
|
||||
grad_mu = (dL_dpsi1[:, :, None] * _dpsi1_dmu).sum(axis=1)
|
||||
grad_S = (dL_dpsi1[:, :, None] * _dpsi1_dS).sum(axis=1)
|
||||
grad_gamma = (dL_dpsi1[:,:,None] * _dpsi1_dgamma).sum(axis=1)
|
||||
|
||||
#psi2
|
||||
grad_mu += (dL_dpsi2[:, :, :, None] * _dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1)
|
||||
grad_S += (dL_dpsi2[:, :, :, None] * _dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1)
|
||||
grad_gamma += (dL_dpsi2[:,:,:, None] * _dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1)
|
||||
|
||||
return grad_mu, grad_S, grad_gamma
|
||||
if self.useGPU:
|
||||
return self.psicomp.gradients_qX_expectations(dL_dpsi1, dL_dpsi2, self.variance, self.lengthscale, Z, variational_posterior)
|
||||
else:
|
||||
ndata = variational_posterior.mean.shape[0]
|
||||
|
||||
_, _, _dpsi1_dgamma, _dpsi1_dmu, _dpsi1_dS, _, _ = ssrbf_psi_comp._psi1computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
_, _, _dpsi2_dgamma, _dpsi2_dmu, _dpsi2_dS, _, _ = ssrbf_psi_comp._psi2computations(self.variance, self.lengthscale, Z, variational_posterior.mean, variational_posterior.variance, variational_posterior.binary_prob)
|
||||
|
||||
#psi1
|
||||
grad_mu = (dL_dpsi1[:, :, None] * _dpsi1_dmu).sum(axis=1)
|
||||
grad_S = (dL_dpsi1[:, :, None] * _dpsi1_dS).sum(axis=1)
|
||||
grad_gamma = (dL_dpsi1[:,:,None] * _dpsi1_dgamma).sum(axis=1)
|
||||
|
||||
#psi2
|
||||
grad_mu += (dL_dpsi2[:, :, :, None] * _dpsi2_dmu).reshape(ndata,-1,self.input_dim).sum(axis=1)
|
||||
grad_S += (dL_dpsi2[:, :, :, None] * _dpsi2_dS).reshape(ndata,-1,self.input_dim).sum(axis=1)
|
||||
grad_gamma += (dL_dpsi2[:,:,:, None] * _dpsi2_dgamma).reshape(ndata,-1,self.input_dim).sum(axis=1)
|
||||
|
||||
if self.group_spike_prob:
|
||||
grad_gamma[:] = grad_gamma.mean(axis=0)
|
||||
|
||||
return grad_mu, grad_S, grad_gamma
|
||||
|
||||
elif isinstance(variational_posterior, variational.NormalPosterior):
|
||||
|
||||
|
|
|
|||
|
|
@ -41,8 +41,8 @@ class Stationary(Kern):
|
|||
|
||||
"""
|
||||
|
||||
def __init__(self, input_dim, variance, lengthscale, ARD, active_dims, name):
|
||||
super(Stationary, self).__init__(input_dim, active_dims, name)
|
||||
def __init__(self, input_dim, variance, lengthscale, ARD, active_dims, name, useGPU=False):
|
||||
super(Stationary, self).__init__(input_dim, active_dims, name,useGPU=useGPU)
|
||||
self.ARD = ARD
|
||||
if not ARD:
|
||||
if lengthscale is None:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue