test_rng_mrg.py 文件源码

def test_multinomial():
    steps = 100
    mode_ = mode
    if mode == 'FAST_COMPILE':
        mode_ = 'FAST_RUN'

    if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
            mode == 'Mode' and config.linker in ['py']):
        sample_size = (49, 5)
    else:
        sample_size = (450, 6)
    mode_ = theano.compile.mode.get_mode(mode_)
    # print ''
    # print 'ON CPU:'

    pvals = numpy.asarray(numpy.random.uniform(size=sample_size))
    pvals = numpy.apply_along_axis(lambda row: row / numpy.sum(row), 1, pvals)
    R = MRG_RandomStreams(234, use_cuda=False)
    # Note: we specify `nstreams` to avoid a warning.
    m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
    f = theano.function([], m, mode=mode_)
    # theano.printing.debugprint(f)
    out = f()
    basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
                          prefix='mrg ')

    sys.stdout.flush()

    if mode != 'FAST_COMPILE' and cuda_available:
        # print ''
        # print 'ON GPU:'
        R = MRG_RandomStreams(234, use_cuda=True)
        pvals = numpy.asarray(pvals, dtype='float32')
        # We give the number of streams to avoid a warning.
        n = R.multinomial(pvals=pvals, dtype='float32', nstreams=30 * 256)
        # well, it's really that this test w GPU doesn't make sense otw
        assert n.dtype == 'float32'
        f = theano.function(
            [],
            theano.sandbox.cuda.basic_ops.gpu_from_host(n),
            mode=mode_.including('gpu'))

        # theano.printing.debugprint(f)
        gpu_out = f()
        sys.stdout.flush()
        basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1,
                              prefix='gpu mrg ')
        numpy.testing.assert_array_almost_equal(out, gpu_out, decimal=6)