def test_KernelExpFiniteGaussian_fit_equals_update_fit():
sigma = 1.
lmbda = 2.
m = 2
N = 1
D = 2
rng_state = np.random.get_state()
np.random.seed(0)
est_batch = KernelExpFiniteGaussian(sigma, lmbda, m, D)
np.random.seed(0)
est_update = KernelExpFiniteGaussian(sigma, lmbda, m, D)
np.random.set_state(rng_state)
assert_equal(est_batch.b, None)
assert_equal(est_update.b, None)
assert_equal(est_batch.L_C, None)
assert_allclose(est_batch.n, est_update.n)
assert_allclose(est_batch.theta, np.zeros(m))
assert_allclose(est_update.theta, np.zeros(m))
X = np.random.randn(N, D)
est_batch.fit(X)
est_update.update_fit(X)
assert_allclose(est_batch.b, est_update.b)
assert_allclose(est_batch.L_C, est_update.L_C)
assert_allclose(est_batch.n, est_update.n)
assert_allclose(est_batch.theta, est_update.theta)
plt.title("Gradient norm, ground truth")
# plot initial fit
fig_count += 1
plt.subplot(3, 3, fig_count)
D, G = pdf_grid(Xs, Ys, est)
visualise_array(Xs, Ys, G)
plt.title("Gradient norm, no data")
plt.tight_layout()
# online updates of the model
for i in range(7):
X = np.random.randn(3 * (i + 1), est.D)
# only for plotting
all_data.append(X)
# API for updating estimator
est.update_fit(X)
# visualise current fit
fig_count += 1
plt.subplot(3, 3, fig_count)
D, G = pdf_grid(Xs, Ys, est)
visualise_array(Xs, Ys, G, np.vstack(all_data))
plt.title("Gradient norm, N=%d" % est.n)
plt.tight_layout()
plt.show()
