model = MCGSM(
dim_in=args.dim_in,
dim_out=args.dim_out,
num_components=12,
num_features=40,
num_scales=6)
###
print 'model.loglikelihood'
t = time()
for r in range(args.repetitions):
model.loglikelihood(*data)
print '{0:12.8f} seconds'.format((time() - t) / float(args.repetitions))
print
###
print 'model._check_performance'
for batch_size in [1000, 2000, 5000]:
t = model._check_performance(*data, repetitions=args.repetitions, parameters={'batch_size': batch_size})
print '{0:12.8f} seconds ({1})'.format(t, batch_size)
print
###
print 'model.posterior'
t = time()
for r in range(args.repetitions):
model.posterior(*data)
print '{0:12.8f} seconds'.format((time() - t) / float(args.repetitions))
print
model = MCGSM(dim_in=args.dim_in,
dim_out=args.dim_out,
num_components=12,
num_features=40,
num_scales=6)
###
print 'model.loglikelihood'
t = time()
for r in range(args.repetitions):
model.loglikelihood(*data)
print '{0:12.8f} seconds'.format((time() - t) / float(args.repetitions))
print
###
print 'model._check_performance'
for batch_size in [1000, 2000, 5000]:
t = model._check_performance(*data,
repetitions=args.repetitions,
parameters={'batch_size': batch_size})
print '{0:12.8f} seconds ({1})'.format(t, batch_size)
print
###
print 'model.posterior'
t = time()
for r in range(args.repetitions):
model.posterior(*data)
print '{0:12.8f} seconds'.format((time() - t) / float(args.repetitions))
print
