def test_draw_samples_w_mean(self):
D, X, Y, noise_var, lengthscale, variance = self.gen_data()
dtype = 'float64'
rand_gen = MockMXNetRandomGenerator(
mx.nd.array(np.random.rand(20 * D), dtype=dtype))
m, net = self.gen_mxfusion_model_w_mean(dtype, D, noise_var,
lengthscale, variance,
rand_gen)
observed = [m.X]
infr = Inference(ForwardSamplingAlgorithm(m,
observed,
num_samples=2,
target_variables=[m.Y]),
dtype=dtype)
samples = infr.run(X=mx.nd.array(X, dtype=dtype),
Y=mx.nd.array(Y, dtype=dtype))[0].asnumpy()
kern = RBF(3, True, name='rbf', dtype=dtype) + White(3, dtype=dtype)
X_var = Variable(shape=(10, 3))
mean_func = MXFusionGluonFunction(net,
num_outputs=1,
broadcastable=True)
mean_var = mean_func(X_var)
gp = GaussianProcess.define_variable(X=X_var,
kernel=kern,
mean=mean_var,
shape=(10, D),
dtype=dtype,
rand_gen=rand_gen).factor
variables = {
gp.X.uuid:
mx.nd.expand_dims(mx.nd.array(X, dtype=dtype), axis=0),
gp.add_rbf_lengthscale.uuid:
mx.nd.expand_dims(mx.nd.array(lengthscale, dtype=dtype), axis=0),
gp.add_rbf_variance.uuid:
mx.nd.expand_dims(mx.nd.array(variance, dtype=dtype), axis=0),
gp.add_white_variance.uuid:
mx.nd.expand_dims(mx.nd.array(noise_var, dtype=dtype), axis=0),
mean_var.uuid:
mx.nd.expand_dims(net(mx.nd.array(X, dtype=dtype)), axis=0)
}
samples_2 = gp.draw_samples(F=mx.nd,
variables=variables,
num_samples=2).asnumpy()
assert np.allclose(samples, samples_2), (samples, samples_2)
def create_white():
return White(input_dim, 1., 'bias', None, dtype)
def test_draw_samples(self):
np.random.seed(0)
X = np.random.rand(10, 3)
Y = np.random.rand(10, 1)
noise_var = np.random.rand(1)
lengthscale = np.random.rand(3)
variance = np.random.rand(1)
dtype = 'float64'
rand_gen = MockMXNetRandomGenerator(
mx.nd.array(np.random.rand(20), dtype=dtype))
m = Model()
m.N = Variable()
m.X = Variable(shape=(m.N, 3))
m.noise_var = Variable(transformation=PositiveTransformation(),
initial_value=mx.nd.array(noise_var,
dtype=dtype))
kernel = RBF(input_dim=3,
ARD=True,
variance=mx.nd.array(variance, dtype=dtype),
lengthscale=mx.nd.array(lengthscale, dtype=dtype),
dtype=dtype)
m.Y = GPRegression.define_variable(X=m.X,
kernel=kernel,
noise_var=m.noise_var,
shape=(m.N, 1),
dtype=dtype,
rand_gen=rand_gen)
observed = [m.X]
infr = Inference(ForwardSamplingAlgorithm(m,
observed,
num_samples=2,
target_variables=[m.Y]),
dtype=dtype)
samples = infr.run(X=mx.nd.array(X, dtype=dtype),
Y=mx.nd.array(Y, dtype=dtype))[0].asnumpy()
kern = RBF(3, True, name='rbf', dtype=dtype) + White(3, dtype=dtype)
X_var = Variable(shape=(10, 3))
gp = GaussianProcess.define_variable(X=X_var,
kernel=kern,
shape=(10, 1),
dtype=dtype,
rand_gen=rand_gen).factor
variables = {
gp.X.uuid:
mx.nd.expand_dims(mx.nd.array(X, dtype=dtype), axis=0),
gp.add_rbf_lengthscale.uuid:
mx.nd.expand_dims(mx.nd.array(lengthscale, dtype=dtype), axis=0),
gp.add_rbf_variance.uuid:
mx.nd.expand_dims(mx.nd.array(variance, dtype=dtype), axis=0),
gp.add_white_variance.uuid:
mx.nd.expand_dims(mx.nd.array(noise_var, dtype=dtype), axis=0)
}
samples_2 = gp.draw_samples(F=mx.nd,
variables=variables,
num_samples=2).asnumpy()
assert np.allclose(samples, samples_2), (samples, samples_2)