Python rng_mrg.guess_n_streams函数代码示例

def test_normal0():

    steps = 50
    std = 2.
    if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
            config.mode == 'Mode' and config.linker in ['py']):
        sample_size = (25, 30)
        default_rtol = .02
    else:
        sample_size = (999, 50)
        default_rtol = .01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = tensor.matrix()

    for size, const_size, var_input, input, avg, rtol, std_tol in [
        (sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
        (x.shape, sample_size, [x],
         [np.zeros(sample_size, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        # test odd value
        (x.shape, sample_size_odd, [x],
         [np.zeros(sample_size_odd, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        (sample_size, sample_size, [], [],
         np.arange(np.prod(sample_size),
                   dtype='float32').reshape(sample_size),
         10. * std / np.sqrt(steps), default_rtol),
        # test empty size (scalar)
        ((), (), [], [], -5., default_rtol, 0.02),
        # test with few samples at the same time
        ((1,), (1,), [], [], -5., default_rtol, 0.02),
        ((3,), (3,), [], [], -5., default_rtol, 0.02),
            ]:

        R = MRG_RandomStreams(234)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.normal(size=size, avg=avg, std=std,
                     nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, n)
        f(*input)

        # Increase the number of steps if size implies only a few samples
        if np.prod(const_size) < 10:
            steps_ = steps * 50
        else:
            steps_ = steps
        basictest(f, steps_, const_size, target_avg=avg, target_std=std,
                  prefix='mrg ', allow_01=True, inputs=input,
                  mean_rtol=rtol, std_tol=std_tol)

        sys.stdout.flush()

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

        nn = RR.normal(size=size, avg=avg, std=std)
        ff = theano.function(var_input, nn)

        basictest(ff, steps_, const_size, target_avg=avg, target_std=std,
                  prefix='numpy ', allow_01=True, inputs=input, mean_rtol=rtol)

def test_uniform():
    # TODO: test param low, high
    # TODO: test size=None
    # TODO: test ndim!=size.ndim
    # TODO: test bad seed
    # TODO: test size=Var, with shape that change from call to call
    if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
            config.mode == 'Mode' and config.linker in ['py']):
        sample_size = (10, 100)
        steps = 50
    else:
        sample_size = (500, 50)
        steps = int(1e3)

    x = tensor.matrix()
    for size, const_size, var_input, input in [
            (sample_size, sample_size, [], []),
            (x.shape, sample_size, [x],
             [np.zeros(sample_size, dtype=config.floatX)]),
            ((x.shape[0], sample_size[1]), sample_size, [x],
             [np.zeros(sample_size, dtype=config.floatX)]),
            # test empty size (scalar)
            ((), (), [], []),
            ]:

        # TEST CPU IMPLEMENTATION
        # The python and C implementation are tested with DebugMode
        x = tensor.matrix()
        R = MRG_RandomStreams(234)
        # Note: we specify `nstreams` to avoid a warning.
        # TODO Look for all occurrences of `guess_n_streams` and `30 * 256`
        # for such situations: it would be better to instead filter the
        # warning using the warning module.
        u = R.uniform(size=size,
                      nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, u)
        assert any([isinstance(node.op, theano.sandbox.rng_mrg.mrg_uniform)
                    for node in f.maker.fgraph.toposort()])
        f(*input)

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

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

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

def test_normal0():

    steps = 50
    std = 2.
    if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
        mode == 'Mode' and config.linker in ['py']):
        sample_size = (25, 30)
        default_rtol = .02
    else:
        sample_size = (999, 50)
        default_rtol = .01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = tensor.matrix()

    for size, const_size, var_input, input, avg, rtol, std_tol in [
        (sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
        (x.shape, sample_size, [x],
         [numpy.zeros(sample_size, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        ((x.shape[0], sample_size[1]), sample_size, [x],
         [numpy.zeros(sample_size, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        #test odd value
        (sample_size_odd, sample_size_odd, [], [], -5.,
         default_rtol, default_rtol),
        #test odd value
        (x.shape, sample_size_odd, [x],
         [numpy.zeros(sample_size_odd, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        (sample_size, sample_size, [], [],
         numpy.arange(numpy.prod(sample_size),
                      dtype='float32').reshape(sample_size),
         10. * std / numpy.sqrt(steps), default_rtol),
        # test empty size (scalar)
        ((), (), [], [], -5., default_rtol, 0.02),
        # test with few samples at the same time
        ((1,), (1,), [], [], -5., default_rtol, 0.02),
        ((2,), (2,), [], [], -5., default_rtol, 0.02),
        ((3,), (3,), [], [], -5., default_rtol, 0.02),
            ]:
        #print ''
        #print 'ON CPU:'

        R = MRG_RandomStreams(234, use_cuda=False)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.normal(size=size, avg=avg, std=std,
                     nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, n, mode=mode)
        #theano.printing.debugprint(f)
        out = f(*input)
        #print 'random?[:10]\n', out[0, 0:10]

        # Increase the number of steps if size implies only a few samples
        if numpy.prod(const_size) < 10:
            steps_ = steps * 50
        else:
            steps_ = steps
        basictest(f, steps_, const_size, target_avg=avg, target_std=std,
                  prefix='mrg ', allow_01=True, inputs=input,
                  mean_rtol=rtol, std_tol=std_tol)

        sys.stdout.flush()

        if mode != 'FAST_COMPILE' and cuda_available:
            #print ''
            #print 'ON GPU:'
            R = MRG_RandomStreams(234, use_cuda=True)
            n = R.normal(size=size, avg=avg, std=std, dtype='float32',
                         nstreams=rng_mrg.guess_n_streams(size, warn=False))
            #well, it's really that this test w GPU doesn't make sense otw
            assert n.dtype == 'float32'
            f = theano.function(var_input, theano.Out(
                theano.sandbox.cuda.basic_ops.gpu_from_host(n),
                borrow=True), mode=mode_with_gpu)

            #theano.printing.debugprint(f)
            sys.stdout.flush()
            gpu_out = numpy.asarray(f(*input))
            #print 'random?[:10]\n', gpu_out[0, 0:10]
            #print '----'
            sys.stdout.flush()
            basictest(f, steps_, const_size, target_avg=avg, target_std=std,
                      prefix='gpu mrg ', allow_01=True, inputs=input,
                      mean_rtol=rtol, std_tol=std_tol)
            # Need to allow some rounding error as their is float
            # computation that are done on the gpu vs cpu
            assert numpy.allclose(out, gpu_out, rtol=5e-6, atol=5e-6)

        #print ''
        #print 'ON CPU w NUMPY:'
        RR = theano.tensor.shared_randomstreams.RandomStreams(234)

        nn = RR.normal(size=size, avg=avg, std=std)
        ff = theano.function(var_input, nn)

        basictest(ff, steps_, const_size, target_avg=avg, target_std=std,
                  prefix='numpy ', allow_01=True, inputs=input, mean_rtol=rtol)

def test_binomial():
#TODO: test size=None, ndim=X
#TODO: test size=X, ndim!=X.ndim
#TODO: test random seed in legal value(!=0 and other)
#TODO: test sample_size not a multiple of guessed #streams
#TODO: test size=Var, with shape that change from call to call
#we test size in a tuple of int and a tensor.shape.
#we test the param p with int.

    if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
        mode == 'Mode' and config.linker in ['py']):
        sample_size = (10, 50)
        steps = 50
        rtol = 0.02
    else:
        sample_size = (500, 50)
        steps = int(1e3)
        rtol = 0.01

    x = tensor.matrix()
    v = tensor.vector()
    for mean in [0.1, 0.5]:
        for size, const_size, var_input, input in [
                (sample_size, sample_size, [], []),
                (x.shape, sample_size, [x],
                 [numpy.zeros(sample_size, dtype=config.floatX)]),
                ((x.shape[0], sample_size[1]), sample_size, [x],
                 [numpy.zeros(sample_size, dtype=config.floatX)]),
                # test empty size (scalar)
                ((), (), [], []),
                ]:

            #print ''
            #print 'ON CPU with size=(%s) and mean(%d):' % (str(size), mean)
            R = MRG_RandomStreams(234, use_cuda=False)
            # Note: we specify `nstreams` to avoid a warning.
            u = R.binomial(size=size, p=mean,
                           nstreams=rng_mrg.guess_n_streams(size, warn=False))
            f = theano.function(var_input, u, mode=mode)
            #theano.printing.debugprint(f)
            out = f(*input)
            #print 'random?[:10]\n', out[0, 0:10]
            #print 'random?[-1,-10:]\n', out[-1, -10:]

            # 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:
                #print ''
                #print 'ON GPU with size=(%s) and mean(%d):' % (str(size), mean)
                R = MRG_RandomStreams(234, use_cuda=True)
                u = R.binomial(size=size, p=mean, dtype='float32',
                               nstreams=rng_mrg.guess_n_streams(size,
                                                                warn=False))
                #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)
                #theano.printing.debugprint(f)
                gpu_out = numpy.asarray(f(*input))
                #print 'random?[:10]\n', gpu_out[0, 0:10]
                #print 'random?[-1,-10:]\n', gpu_out[-1, -10:]
                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)

            #print ''
            #print 'ON CPU w NUMPY with size=(%s) and mean(%d):' % (str(size),
            #                                                       mean)
            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)

def test_uniform():
#TODO: test param low, high
#TODO: test size=None
#TODO: test ndim!=size.ndim
#TODO: test bad seed
#TODO: test size=Var, with shape that change from call to call
    if (mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
        mode == 'Mode' and config.linker in ['py']):
        sample_size = (10, 100)
        steps = 50
    else:
        sample_size = (500, 50)
        steps = int(1e3)

    x = tensor.matrix()
    for size, const_size, var_input, input in [
            (sample_size, sample_size, [], []),
            (x.shape, sample_size, [x],
             [numpy.zeros(sample_size, dtype=config.floatX)]),
            ((x.shape[0], sample_size[1]), sample_size, [x],
             [numpy.zeros(sample_size, dtype=config.floatX)]),
            # test empty size (scalar)
            ((), (), [], []),
            ]:

        #### TEST CPU IMPLEMENTATION ####
        # The python and C implementation are tested with DebugMode
        #print ''
        #print 'ON CPU with size=(%s):' % str(size)
        x = tensor.matrix()
        R = MRG_RandomStreams(234, use_cuda=False)
        # Note: we specify `nstreams` to avoid a warning.
        # TODO Look for all occurrences of `guess_n_streams` and `30 * 256`
        # for such situations: it would be better to instead filter the
        # warning using the warning module.
        u = R.uniform(size=size,
                      nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, u, mode=mode)
        assert any([isinstance(node.op, theano.sandbox.rng_mrg.mrg_uniform)
                    for node in f.maker.fgraph.toposort()])
        #theano.printing.debugprint(f)
        cpu_out = f(*input)

        #print 'CPU: random?[:10], random?[-10:]'
        #print cpu_out[0, 0:10]
        #print cpu_out[-1, -10:]

        # 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)

        if mode != 'FAST_COMPILE' and cuda_available:
            #print ''
            #print 'ON GPU with size=(%s):' % str(size)
            R = MRG_RandomStreams(234, use_cuda=True)
            u = R.uniform(size=size, dtype='float32',
                          nstreams=rng_mrg.guess_n_streams(size, warn=False))
            # 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)
            assert any([isinstance(node.op,
                                   theano.sandbox.rng_mrg.GPU_mrg_uniform)
                        for node in f.maker.fgraph.toposort()])
            #theano.printing.debugprint(f)
            gpu_out = numpy.asarray(f(*input))

            #print 'GPU: random?[:10], random?[-10:]'
            #print gpu_out[0, 0:10]
            #print gpu_out[-1, -10:]
            basictest(f, steps_, const_size, prefix='mrg  gpu', inputs=input)

            numpy.testing.assert_array_almost_equal(cpu_out, gpu_out,
                                                    decimal=6)

        #print ''
        #print 'ON CPU w Numpy with size=(%s):' % str(size)
        RR = theano.tensor.shared_randomstreams.RandomStreams(234)

        uu = RR.uniform(size=size)
        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)

def test_normal_truncation():
    # just a copy of test_normal0 with extra bound check
    steps = 50
    std = 2.
    # standard deviation is slightly less than for a regular Gaussian
    # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
    target_std = .87962566103423978 * std

    if (config.mode in ['DEBUG_MODE', 'DebugMode', 'FAST_COMPILE'] or
            config.mode == 'Mode' and config.linker in ['py']):
        sample_size = (25, 30)
        default_rtol = .02
    else:
        sample_size = (999, 50)
        default_rtol = .01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = tensor.matrix()

    test_cases = [
        (sample_size, sample_size, [], [], -5., default_rtol, default_rtol),
        (x.shape, sample_size, [x],
         [np.zeros(sample_size, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        # test odd value
        (x.shape, sample_size_odd, [x],
         [np.zeros(sample_size_odd, dtype=config.floatX)],
         -5., default_rtol, default_rtol),
        (sample_size, sample_size, [], [],
         np.arange(np.prod(sample_size),
                   dtype='float32').reshape(sample_size),
         10. * std / np.sqrt(steps), default_rtol),
        # test empty size (scalar)
        ((), (), [], [], -5., default_rtol, 0.02),
        # test with few samples at the same time
        ((1,), (1,), [], [], -5., default_rtol, 0.02),
        ((3,), (3,), [], [], -5., default_rtol, 0.02),
    ]

    for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
        R = MRG_RandomStreams(234)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.normal(size=size, avg=avg, std=std, truncate=True,
                     nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, n)

        # check if truncated at 2*std
        samples = f(*input)
        assert np.all(avg + 2 * std - samples >= 0), \
            ("bad upper bound? %s %s" % (samples, avg + 2 * std))
        assert np.all(samples - (avg - 2 * std) >= 0), \
            ("bad lower bound? %s %s" % (samples, avg - 2 * std))

        # Increase the number of steps if size implies only a few samples
        if np.prod(const_size) < 10:
            steps_ = steps * 50
        else:
            steps_ = steps
        basictest(f, steps_, const_size, target_avg=avg, target_std=target_std,
                  prefix='mrg ', allow_01=True, inputs=input,
                  mean_rtol=rtol, std_tol=std_tol)

        sys.stdout.flush()

def test_normal0():

    steps = 50
    std = 2.0
    if mode in ["DEBUG_MODE", "DebugMode", "FAST_COMPILE"]:
        sample_size = (25, 30)
        default_rtol = 0.02
    else:
        sample_size = (999, 50)
        default_rtol = 0.01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = tensor.matrix()
    for size, const_size, var_input, input, avg, rtol in [
        (sample_size, sample_size, [], [], -5.0, default_rtol),
        (x.shape, sample_size, [x], [numpy.zeros(sample_size, dtype=config.floatX)], -5.0, default_rtol),
        (sample_size_odd, sample_size_odd, [], [], -5.0, default_rtol),  # test odd value
        (
            x.shape,
            sample_size_odd,
            [x],
            [numpy.zeros(sample_size_odd, dtype=config.floatX)],
            -5.0,
            default_rtol,
        ),  # test odd value
        (
            sample_size,
            sample_size,
            [],
            [],
            numpy.arange(numpy.prod(sample_size), dtype="float32").reshape(sample_size),
            10.0 * std / numpy.sqrt(steps),
        ),
    ]:
        print ""
        print "ON CPU:"

        R = MRG_RandomStreams(234, use_cuda=False)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.normal(size=size, avg=avg, std=std, nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, n, mode=mode)
        theano.printing.debugprint(f)
        out = f(*input)
        print "random?[:10]\n", out[0, 0:10]
        basictest(
            f,
            steps,
            const_size,
            target_avg=avg,
            target_std=std,
            prefix="mrg ",
            allow_01=True,
            inputs=input,
            mean_rtol=rtol,
        )

        sys.stdout.flush()

        if mode != "FAST_COMPILE" and cuda_available:
            print ""
            print "ON GPU:"
            R = MRG_RandomStreams(234, use_cuda=True)
            n = R.normal(
                size=size, avg=avg, std=std, dtype="float32", nstreams=rng_mrg.guess_n_streams(size, warn=False)
            )
            assert n.dtype == "float32"  # well, it's really that this test w GPU doesn't make sense otw
            f = theano.function(
                var_input, theano.Out(theano.sandbox.cuda.basic_ops.gpu_from_host(n), borrow=True), mode=mode_with_gpu
            )

            theano.printing.debugprint(f)
            sys.stdout.flush()
            gpu_out = numpy.asarray(f(*input))
            print "random?[:10]\n", gpu_out[0, 0:10]
            print "----"
            sys.stdout.flush()
            basictest(
                f,
                steps,
                const_size,
                target_avg=avg,
                target_std=std,
                prefix="gpu mrg ",
                allow_01=True,
                inputs=input,
                mean_rtol=rtol,
            )
            # Need to allow some rounding error as their is float
            # computation that are done on the gpu vs cpu
            assert numpy.allclose(out, gpu_out, rtol=5e-6, atol=5e-6)

        print ""
        print "ON CPU w NUMPY:"
        RR = theano.tensor.shared_randomstreams.RandomStreams(234)

        nn = RR.normal(size=size, avg=avg, std=std)
        ff = theano.function(var_input, nn)

        basictest(
            ff,
            steps,
#.........这里部分代码省略.........

def test_binomial():
    # TODO: test size=None, ndim=X
    # TODO: test size=X, ndim!=X.ndim
    # TODO: test random seed in legal value(!=0 and other)
    # TODO: test sample_size not a multiple of guessed #streams
    # TODO: test size=Var, with shape that change from call to call
    # we test size in a tuple of int and a tensor.shape.
    # we test the param p with int.

    if mode in ["DEBUG_MODE", "DebugMode", "FAST_COMPILE"]:
        sample_size = (10, 50)
        steps = 50
        rtol = 0.02
    else:
        sample_size = (500, 50)
        steps = int(1e3)
        rtol = 0.01

    x = tensor.matrix()
    v = tensor.vector()
    for mean in [0.1, 0.5]:
        for size, var_input, input in [
            (sample_size, [], []),
            (x.shape, [x], [numpy.zeros(sample_size, dtype=config.floatX)]),
        ]:

            print ""
            print "ON CPU with size=(%s) and mean(%d):" % (str(size), mean)
            R = MRG_RandomStreams(234, use_cuda=False)
            # Note: we specify `nstreams` to avoid a warning.
            u = R.binomial(size=size, p=mean, nstreams=rng_mrg.guess_n_streams(size, warn=False))
            f = theano.function(var_input, u, mode=mode)
            theano.printing.debugprint(f)
            out = f(*input)
            print "random?[:10]\n", out[0, 0:10]
            print "random?[-1,-10:]\n", out[-1, -10:]
            basictest(
                f, steps, sample_size, prefix="mrg  cpu", inputs=input, allow_01=True, target_avg=mean, mean_rtol=rtol
            )

            if mode != "FAST_COMPILE" and cuda_available:
                print ""
                print "ON GPU with size=(%s) and mean(%d):" % (str(size), mean)
                R = MRG_RandomStreams(234, use_cuda=True)
                u = R.binomial(size=size, p=mean, dtype="float32", nstreams=rng_mrg.guess_n_streams(size, warn=False))
                assert u.dtype == "float32"  # well, it's really that this test w GPU doesn't make sense otw
                f = theano.function(
                    var_input,
                    theano.Out(theano.sandbox.cuda.basic_ops.gpu_from_host(u), borrow=True),
                    mode=mode_with_gpu,
                )
                theano.printing.debugprint(f)
                gpu_out = numpy.asarray(f(*input))
                print "random?[:10]\n", gpu_out[0, 0:10]
                print "random?[-1,-10:]\n", gpu_out[-1, -10:]
                basictest(
                    f,
                    steps,
                    sample_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)

            print ""
            print "ON CPU w NUMPY with size=(%s) and mean(%d):" % (str(size), mean)
            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, sample_size, prefix="numpy", allow_01=True, inputs=input, target_avg=mean, mean_rtol=rtol
            )

def test_uniform():
    # TODO: test param low, high
    # TODO: test size=None
    # TODO: test ndim!=size.ndim
    # TODO: test bad seed
    # TODO: test size=Var, with shape that change from call to call
    if mode in ["DEBUG_MODE", "DebugMode", "FAST_COMPILE"]:
        sample_size = (10, 100)
        steps = 50
    else:
        sample_size = (500, 50)
        steps = int(1e3)

    x = tensor.matrix()
    for size, var_input, input in [
        (sample_size, [], []),
        (x.shape, [x], [numpy.zeros(sample_size, dtype=config.floatX)]),
    ]:

        #### TEST CPU IMPLEMENTATION ####
        # The python and C implementation are tested with DebugMode
        print ""
        print "ON CPU with size=(%s):" % str(size)
        x = tensor.matrix()
        R = MRG_RandomStreams(234, use_cuda=False)
        # Note: we specify `nstreams` to avoid a warning.
        # TODO Look for all occurrences of `guess_n_streams` and `30 * 256`
        # for such situations: it would be better to instead filter the
        # warning using the warning module.
        u = R.uniform(size=size, nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = theano.function(var_input, u, mode=mode)
        assert any([isinstance(node.op, theano.sandbox.rng_mrg.mrg_uniform) for node in f.maker.env.toposort()])
        theano.printing.debugprint(f)
        cpu_out = f(*input)

        print "CPU: random?[:10], random?[-10:]"
        print cpu_out[0, 0:10]
        print cpu_out[-1, -10:]
        basictest(f, steps, sample_size, prefix="mrg cpu", inputs=input)

        if mode != "FAST_COMPILE" and cuda_available:
            print ""
            print "ON GPU with size=(%s):" % str(size)
            R = MRG_RandomStreams(234, use_cuda=True)
            u = R.uniform(size=size, dtype="float32", nstreams=rng_mrg.guess_n_streams(size, warn=False))
            assert u.dtype == "float32"  # well, it's really that this test w GPU doesn't make sense otw
            f = theano.function(
                var_input, theano.Out(theano.sandbox.cuda.basic_ops.gpu_from_host(u), borrow=True), mode=mode_with_gpu
            )
            assert any([isinstance(node.op, theano.sandbox.rng_mrg.GPU_mrg_uniform) for node in f.maker.env.toposort()])
            theano.printing.debugprint(f)
            gpu_out = numpy.asarray(f(*input))

            print "GPU: random?[:10], random?[-10:]"
            print gpu_out[0, 0:10]
            print gpu_out[-1, -10:]
            basictest(f, steps, sample_size, prefix="mrg  gpu", inputs=input)

            numpy.testing.assert_array_almost_equal(cpu_out, gpu_out, decimal=6)

        print ""
        print "ON CPU w Numpy with size=(%s):" % str(size)
        RR = theano.tensor.shared_randomstreams.RandomStreams(234)

        uu = RR.uniform(size=size)
        ff = theano.function(var_input, uu, mode=mode)
        # It's not our problem if numpy generates 0 or 1
        basictest(ff, steps, sample_size, prefix="numpy", allow_01=True, inputs=input)