Triang tests work at rc1.0.
import unittest
-from gpflow.tf_wraps import vec_to_tri
import tensorflow as tf
+
import numpy as np
+from numpy.testing import assert_array_almost_equal
+
+from gpflow import misc
from gpflow.test_util import GPflowTestCase
-from gpflow.tf_wraps import vec_to_tri
class TestVecToTri(GPflowTestCase):
- def referenceInverse(self, matrices):
- #this is the inverse operation of the vec_to_tri
- #op being tested.
+ def reference_inverse(self, matrices):
+ # This is the inverse operation of the vec_to_tri op being tested.
D, N, _ = matrices.shape
M = (N * (N + 1)) // 2
tril_indices = np.tril_indices(N)
@@ -20,7 +21,7 @@ class TestVecToTri(GPflowTestCase):
output[vector_index, :] = matrix[tril_indices]
return output
- def getExampleMatrices(self, D, N ):
+ def get_example_matrices(self, D, N ):
rng = np.random.RandomState(1)
random_matrices = rng.randn(D, N, N)
for matrix_index in range(D):
@@ -30,31 +31,32 @@ class TestVecToTri(GPflowTestCase):
random_matrices[matrix_index, row_index, col_index] = 0.
return random_matrices
- def testBasicFunctionality(self):
+ def test_basic_functionality(self):
with self.test_context() as sess:
N = 3
D = 3
- reference_matrices = self.getExampleMatrices(D, N)
- input_vector_tensor = tf.constant(self.referenceInverse(reference_matrices))
+ reference_matrices = self.get_example_matrices(D, N)
+ input_vector_tensor = tf.constant(self.reference_inverse(reference_matrices))
- test_matrices_tensor = vec_to_tri(input_vector_tensor, N)
+ test_matrices_tensor = misc.vec_to_tri(input_vector_tensor, N)
test_matrices = sess.run(test_matrices_tensor)
- np.testing.assert_array_almost_equal(reference_matrices, test_matrices)
+ assert_array_almost_equal(reference_matrices, test_matrices)
- def testDifferentiable(self):
+ def test_differentiable(self):
with self.test_context() as sess:
N = 3
D = 3
- reference_matrices = self.getExampleMatrices(D, N)
- input_vector_array = self.referenceInverse(reference_matrices)
+ reference_matrices = self.get_example_matrices(D, N)
+ input_vector_array = self.reference_inverse(reference_matrices)
input_vector_tensor = tf.constant(input_vector_array)
- test_matrices_tensor = vec_to_tri(input_vector_tensor, N)
+ test_matrices_tensor = misc.vec_to_tri(input_vector_tensor, N)
reduced_sum = tf.reduce_sum(test_matrices_tensor)
gradient = tf.gradients(reduced_sum, input_vector_tensor)[0]
reference_gradient = np.ones_like(input_vector_array)
test_gradient = sess.run(gradient)
- np.testing.assert_array_almost_equal(reference_gradient, test_gradient)
+ assert_array_almost_equal(reference_gradient, test_gradient)
+
if __name__ == "__main__":
unittest.main()
