test_rng_mrg.py 文件源码

def t_binomial(mean, size, const_size, var_input, input, steps, rtol):
    R = MRG_RandomStreams(234, use_cuda=False)
    u = R.binomial(size=size, p=mean)
    f = theano.function(var_input, u, mode=mode)
    out = f(*input)

    # Increase the number of steps if sizes implies only a few samples
    if numpy.prod(const_size) < 10:
        steps_ = steps * 100
    else:
        steps_ = steps
    basictest(f, steps_, const_size, prefix='mrg  cpu',
              inputs=input, allow_01=True,
              target_avg=mean, mean_rtol=rtol)

    if mode != 'FAST_COMPILE' and cuda_available:
        R = MRG_RandomStreams(234, use_cuda=True)
        u = R.binomial(size=size, p=mean, dtype='float32')
        # well, it's really that this test w GPU doesn't make sense otw
        assert u.dtype == 'float32'
        f = theano.function(var_input, theano.Out(
            theano.sandbox.cuda.basic_ops.gpu_from_host(u),
            borrow=True), mode=mode_with_gpu)
        gpu_out = numpy.asarray(f(*input))

        basictest(f, steps_, const_size, prefix='mrg  gpu',
                  inputs=input, allow_01=True,
                  target_avg=mean, mean_rtol=rtol)
        numpy.testing.assert_array_almost_equal(out, gpu_out,
                                                decimal=6)

    RR = theano.tensor.shared_randomstreams.RandomStreams(234)

    uu = RR.binomial(size=size, p=mean)
    ff = theano.function(var_input, uu, mode=mode)
    # It's not our problem if numpy generates 0 or 1
    basictest(ff, steps_, const_size, prefix='numpy', allow_01=True,
              inputs=input, target_avg=mean, mean_rtol=rtol)