def test_jitter_synthetic_gp(jitter_method, float_type, ctx) -> None:
# TODO: Enable GPU tests on Jenkins
if ctx == mx.Context("gpu") and not check_gpu_support():
return
# Initialize problem parameters
batch_size = 1
prediction_length = 50
context_length = 5
num_samples = 3
# Initialize test data to generate Gaussian Process from
lb = -5
ub = 5
dx = (ub - lb) / (prediction_length - 1)
x_test = nd.arange(lb, ub + dx, dx, ctx=ctx,
dtype=float_type).reshape(-1, 1)
x_test = nd.tile(x_test, reps=(batch_size, 1, 1))
# Define the GP hyper parameters
amplitude = nd.ones((batch_size, 1, 1), ctx=ctx, dtype=float_type)
length_scale = math.sqrt(0.4) * nd.ones_like(amplitude)
sigma = math.sqrt(1e-5) * nd.ones_like(amplitude)
# Instantiate desired kernel object and compute kernel matrix
rbf_kernel = RBFKernel(amplitude, length_scale)
# Generate samples from 0 mean Gaussian process with RBF Kernel and plot it
gp = GaussianProcess(
sigma=sigma,
kernel=rbf_kernel,
prediction_length=prediction_length,
context_length=context_length,
num_samples=num_samples,
ctx=ctx,
float_type=float_type,
jitter_method=jitter_method,
sample_noise=False, # Returns sample without noise
)
# Generate training set on subset of interval using the sine function
x_train = nd.array([-4, -3, -2, -1, 1], ctx=ctx,
dtype=float_type).reshape(context_length, 1)
x_train = nd.tile(x_train, reps=(batch_size, 1, 1))
y_train = nd.sin(x_train.squeeze(axis=2))
# Predict exact GP using the GP predictive mean and covariance using the same fixed hyper-parameters
samples, predictive_mean, predictive_std = gp.exact_inference(
x_train, y_train, x_test)
assert (np.sum(np.isnan(
samples.asnumpy())) == 0), "NaNs in predictive samples!"
def test_jitter_unit(jitter_method, float_type, ctx) -> None:
# TODO: Enable GPU tests on Jenkins
if ctx == mx.Context("gpu") and not check_gpu_support():
return
matrix = nd.array([[[1, 2], [3, 4]], [[10, 100], [-21.5, 41]]],
ctx=ctx,
dtype=float_type)
F = mx.nd
num_data_points = matrix.shape[1]
if jitter_method == "eig":
L = jitter_cholesky_eig(F, matrix, num_data_points, ctx, float_type)
elif jitter_method == "iter":
L = jitter_cholesky(F, matrix, num_data_points, ctx, float_type)
assert np.sum(np.isnan(L.asnumpy())) == 0, "NaNs in Cholesky factor!"