def main(job_id, params): ndims = 256 nbasis = 72 rand_val = rand(ndims,nbasis/2,density=0.25) W = np.concatenate([rand_val.toarray(), -rand_val.toarray()],axis=1) logalpha = np.random.randn(nbasis, 1) print "job id: {}, params: {}".format(job_id, params) return obj_func(MarkovJumpHMC, ProductOfT(nbatch=250,ndims=ndims,nbasis=nbasis, W=W, logalpha=logalpha), job_id, **params)
def main(job_id, params): ndims = 256 nbasis = 72 rand_val = rand(ndims,nbasis/2,density=0.25) W = np.concatenate([rand_val.toarray(), -rand_val.toarray()],axis=1) logalpha = np.random.randn(nbasis, 1) print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, ProductOfT(nbatch=250,ndims=ndims,nbasis=nbasis, W=W, logalpha=logalpha), job_id, **params)
def main(job_id, params): ndims = 100 nbasis = 100 rand_val = rand(ndims, nbasis / 2, density=0.25) print "job id: {}, params: {}".format(job_id, params) return obj_func(MarkovJumpHMC, ProductOfT(nbatch=25, ndims=ndims, nbasis=nbasis), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) # counter-intuitively, benchmarks indicate that this is optimal device_dict = {'grad': '/cpu:0', 'energy': '/gpu:0'} return obj_func( ControlHMC, SparseImageCode(n_patches=1, n_batches=10, device=device_dict, gpu_frac=1), job_id, **params)
def main(job_id, params): ndims = 36 nbasis = 36 print "job id: {}, params: {}".format(job_id, params) weights, lognu = init_weights(ndims, nbasis) return obj_func( ControlHMC, ProductOfT(nbatch=25, ndims=ndims, nbasis=nbasis, lognu=lognu, W=weights), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, Gaussian(ndims=10, nbatch=200), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, Gaussian(ndims=50, nbatch=50, log_conditioning=1.1), job_id, **params)
def main(job_id, params): ndims = 100 nbasis = 100 rand_val = rand(ndims,nbasis/2,density=0.25) print "job id: {}, params: {}".format(job_id, params) return obj_func(MarkovJumpHMC, ProductOfT(nbatch=25,ndims=ndims,nbasis=nbasis), job_id, **params)
def main(job_id, params): params.update({"display": [0]}) print "job id: {}, params: {}".format(job_id, params) return obj_func(LAHMC, MultimodalGaussian(ndims=5, separation=1), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(MarkovJumpHMC, Funnel(nbatch=1000,scale=3), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(MarkovJumpHMC, Funnel(nbatch=1000, scale=3), job_id, **params)
def main(job_id, params): ndims = 36 nbasis = 36 print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, ProductOfT(nbatch=25, ndims=ndims, nbasis=nbasis), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, RoughWell(nbatch=200), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, Funnel(nbatch=1000), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(MarkovJumpHMC, Gaussian(ndims=10, nbatch=200), job_id, **params)
def main(job_id, params): print "job id: {}, params: {}".format(job_id, params) return obj_func(ControlHMC, SparseImageCode(nbatch=10), job_id, **params)
def main(job_id, params): ndims = 36 nbasis = 36 print "job id: {}, params: {}".format(job_id, params) weights, lognu = init_weights(ndims, nbasis) return obj_func(MarkovJumpHMC, ProductOfT(nbatch=25, ndims=ndims, nbasis=nbasis, lognu=lognu, W=weights), job_id, **params)