def testNearestNeighbors(self):
x = tf.constant([[0, 0.9, 0], [0.8, 0., 0.]], dtype=tf.float32)
x = tf.reshape(x, [1, 1, 2, 3])
means = tf.constant(
[[1, 0, 0], [0, 1, 0], [0, 0, 1], [9, 9, 9]], dtype=tf.float32)
means = tf.stack([means, means], axis=0)
x_means_hot, _ = discretization.nearest_neighbor(
x, means, block_v_size=4)
x_means_hot_test = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
x_means_hot_test = np.expand_dims(x_means_hot_test, axis=0)
x_means_hot_eval = self.evaluate(x_means_hot)
self.assertEqual(np.shape(x_means_hot_eval), (1, 2, 4))
self.assertTrue(np.all(x_means_hot_eval == x_means_hot_test))
def testNearestNeighbors(self):
x = tf.constant([[0, 0.9, 0], [0.8, 0., 0.]], dtype=tf.float32)
x = tf.reshape(x, [1, 1, 2, 3])
means = tf.constant(
[[1, 0, 0], [0, 1, 0], [0, 0, 1], [9, 9, 9]], dtype=tf.float32)
means = tf.stack([means, means], axis=0)
x_means_hot, _ = discretization.nearest_neighbor(
x, means, block_v_size=4)
x_means_hot_test = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
x_means_hot_test = np.expand_dims(x_means_hot_test, axis=0)
x_means_hot_eval = self.evaluate(x_means_hot)
self.assertEqual(np.shape(x_means_hot_eval), (1, 2, 4))
self.assertTrue(np.all(x_means_hot_eval == x_means_hot_test))
def testNearestNeighbors(self):
x = tf.constant([[0, 0.9, 0], [0.8, 0., 0.]], dtype=tf.float32)
x = tf.expand_dims(x, axis=0)
means = tf.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1], [9, 9, 9]],
dtype=tf.float32)
means = tf.stack([means, means], axis=0)
x_means_hot = discretization.nearest_neighbor(x, means, block_v_size=4)
x_means_hot_test = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
x_means_hot_test = np.expand_dims(x_means_hot_test, axis=0)
with self.test_session() as sess:
tf.global_variables_initializer().run()
x_means_hot_eval = sess.run(x_means_hot)
self.assertEqual(np.shape(x_means_hot_eval), (1, 2, 4))
self.assertTrue(np.all(x_means_hot_eval == x_means_hot_test))
def testNearestNeighbors(self):
x = tf.constant([[0, 0.9, 0], [0.8, 0., 0.]], dtype=tf.float32)
x = tf.expand_dims(x, axis=0)
means = tf.constant(
[[1, 0, 0], [0, 1, 0], [0, 0, 1], [9, 9, 9]], dtype=tf.float32)
means = tf.stack([means, means], axis=0)
x_means_hot = discretization.nearest_neighbor(x, means, block_v_size=4)
x_means_hot_test = np.array([[0, 1, 0, 0], [1, 0, 0, 0]])
x_means_hot_test = np.expand_dims(x_means_hot_test, axis=0)
with self.test_session() as sess:
tf.global_variables_initializer().run()
x_means_hot_eval = sess.run(x_means_hot)
self.assertEqual(np.shape(x_means_hot_eval), (1, 2, 4))
self.assertTrue(np.all(x_means_hot_eval == x_means_hot_test))
