if __name__ == '__main__':
GaussianProcess.set_testing_val = MethodType(set_testing_val, None,
GaussianProcess)
GaussianProcess.predict_benchmark = MethodType(predict_benchmark, None,
GaussianProcess)
print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
print '-----------------------------'
for i in xrange(10):
Npredict = np.int(1e6)
Ntrain = 250
Ninputs = 10
inputs = np.random.random((Ntrain, Ninputs))
testing = np.random.random((Npredict, Ninputs))
gp = GaussianProcess(inputs, [])
gp.set_testing_val(Ninputs, Npredict, Ntrain)
#CPU predict
start = time.time()
[mu_c, var_c, deriv_c] = gp.predict_benchmark(testing)
end = time.time()
cputime = end - start
print 'cputime', cputime
print '=================='
return mu, deriv
if __name__ == '__main__':
GaussianProcess.set_testing_val = MethodType(set_testing_val, None, GaussianProcess)
GaussianProcess.predict_benchmark = MethodType(predict_benchmark, None, GaussianProcess)
print 'Problem_size\tCPU time\tGPU time\tSpeedup\tStatus'
print '-----------------------------'
for i in xrange(10):
Npredict = np.int(1e6)
Ntrain = 250
Ninputs = 10
inputs = np.random.random(( Ntrain, Ninputs))
testing = np.random.random(( Npredict, Ninputs))
gp = GaussianProcess(inputs, [])
gp.set_testing_val(Ninputs, Npredict, Ntrain)
#CPU predict
start = time.time()
[mu_c, var_c, deriv_c] = gp.predict_benchmark(testing )
end = time.time()
cputime = end -start
print 'cputime', cputime
print '=================='
