def test_define_posterior_predictive_2d_vector_lengthscale(self):
with self.test_session():
N, D = 3, 2
sigma_signal = 3.3
sigma_noise = 1.5
lengthscale = np.array([0.8, 2.3], np.float32)
X_ = tf.placeholder(tf.float32, [N, D])
K_ = tf.placeholder(tf.float32, [N, N])
f_ = tf.placeholder(tf.float32, [N])
X = np.arange(N * D).reshape(N, D)
K = compute_rbf(X,
sigma_signal=sigma_signal,
sigma_noise=sigma_noise,
lengthscale=lengthscale)
f = np.zeros(N)
x_, mu, var = define_posterior_predictive(X_, K_, f_, sigma_signal,
sigma_noise, lengthscale)
base_dict = {
X_: X,
K_: K,
f_: f,
}
# single query point
x = np.array([[0.5, 2]])
mu_true, var_true = compute_posterior_predictive(
X,
x=x,
f=f,
sigma_signal=sigma_signal,
sigma_noise=sigma_noise,
lengthscale=lengthscale)
feed_dict = {x_: x}
feed_dict.update(base_dict)
self.assertAllClose(var.eval(feed_dict=feed_dict), var_true)
self.assertAllClose(mu.eval(feed_dict=feed_dict), mu_true)
# multiple query points
x = np.array([[0, 1.5], [9.2, -3], [17, 2]])
mu_true, var_true = compute_posterior_predictive(
X,
x=x,
f=f,
sigma_signal=sigma_signal,
sigma_noise=sigma_noise,
lengthscale=lengthscale)
feed_dict = {x_: x}
feed_dict.update(base_dict)
self.assertAllClose(var.eval(feed_dict=feed_dict), var_true)
self.assertAllClose(mu.eval(feed_dict=feed_dict), mu_true)
def test_define_posterior_predictive_1d_simple(self):
with self.test_session():
N, D = 3, 1
sigma_signal = 0.7
sigma_noise = 1.5
lengthscale = 1.2
X_ = tf.placeholder(tf.float32, [N, D])
K_ = tf.placeholder(tf.float32, [N, N])
f_ = tf.placeholder(tf.float32, [N])
X = np.arange(N)[:, np.newaxis]
K = compute_rbf(X,
sigma_signal=sigma_signal,
sigma_noise=sigma_noise,
lengthscale=lengthscale)
f = np.zeros(N)
x_, mu, var = define_posterior_predictive(X_, K_, f_, sigma_signal,
sigma_noise, lengthscale)
base_dict = {
X_: X,
K_: K,
f_: f,
}
# single query point
x = np.array([0.5])[:, np.newaxis]
mu_true, var_true = compute_posterior_predictive(
X,
x=x,
f=f,
sigma_signal=sigma_signal,
sigma_noise=sigma_noise,
lengthscale=lengthscale)
feed_dict = {x_: x}
feed_dict.update(base_dict)
self.assertAllClose(var.eval(feed_dict=feed_dict), var_true)
self.assertAllClose(mu.eval(feed_dict=feed_dict), mu_true)
# multiple query points
x = np.array([0, 1.5, 9])[:, np.newaxis]
mu_true, var_true = compute_posterior_predictive(
X,
x=x,
f=f,
sigma_signal=sigma_signal,
sigma_noise=sigma_noise,
lengthscale=lengthscale)
feed_dict = {x_: x}
feed_dict.update(base_dict)
self.assertAllClose(var.eval(feed_dict=feed_dict), var_true)
self.assertAllClose(mu.eval(feed_dict=feed_dict), mu_true)