def test_forward_works_with_subtract_combinations(self):
linear = LinearSimilarity(2, 2, combination='x-y')
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5]))
linear._bias = Parameter(torch.FloatTensor([0]))
a_vectors = Variable(torch.FloatTensor([[1, 1], [-1, -1]]))
b_vectors = Variable(torch.FloatTensor([[1, 0], [0, 1]]))
result = linear(a_vectors, b_vectors).data.numpy()
assert result.shape == (2,)
assert_almost_equal(result, [.5, -.7])
def test_forward_does_a_weighted_product(self):
linear = LinearSimilarity(3, 1, combination='x,y')
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5, 2.0, -1.0]))
linear._bias = Parameter(torch.FloatTensor([.1]))
a_vectors = torch.FloatTensor([[[1, 1, 1], [-1, -1, 0]]])
b_vectors = torch.FloatTensor([[[0], [1]]])
result = linear(Variable(a_vectors), Variable(b_vectors)).data.numpy()
assert result.shape == (1, 2,)
assert_almost_equal(result, [[2.3, -1.1]])
def test_forward_works_with_divide_combinations(self):
linear = LinearSimilarity(2, 2, combination='x/y')
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5]))
linear._bias = Parameter(torch.FloatTensor([0]))
a_vectors = torch.FloatTensor([[1, 1], [-1, -1]])
b_vectors = torch.FloatTensor([[1, 2], [2, 1]])
result = linear(a_vectors, b_vectors).data.numpy()
assert result.shape == (2,)
assert_almost_equal(result, [-.05, -.35])
def test_forward_works_with_subtract_combinations(self):
linear = LinearSimilarity(2, 2, combination='x-y')
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5]))
linear._bias = Parameter(torch.FloatTensor([0]))
a_vectors = torch.FloatTensor([[1, 1], [-1, -1]])
b_vectors = torch.FloatTensor([[1, 0], [0, 1]])
result = linear(a_vectors, b_vectors).data.numpy()
assert result.shape == (2, )
assert_almost_equal(result, [.5, -.7])
def test_forward_works_with_divide_combinations(self):
linear = LinearSimilarity(2, 2, combination='x/y')
linear._weight_vector = Parameter(torch.FloatTensor([-.3, .5]))
linear._bias = Parameter(torch.FloatTensor([0]))
a_vectors = Variable(torch.FloatTensor([[1, 1], [-1, -1]]))
b_vectors = Variable(torch.FloatTensor([[1, 2], [2, 1]]))
result = linear(a_vectors, b_vectors).data.numpy()
assert result.shape == (2, )
assert_almost_equal(result, [-.05, -.35])
def test_forward_does_a_weighted_product(self):
linear = LinearSimilarity(3, 1, combination="x,y")
linear._weight_vector = Parameter(
torch.FloatTensor([-0.3, 0.5, 2.0, -1.0]))
linear._bias = Parameter(torch.FloatTensor([0.1]))
a_vectors = torch.FloatTensor([[[1, 1, 1], [-1, -1, 0]]])
b_vectors = torch.FloatTensor([[[0], [1]]])
result = linear(a_vectors, b_vectors).data.numpy()
assert result.shape == (1, 2)
assert_almost_equal(result, [[2.3, -1.1]])
def test_forward_works_with_higher_order_tensors(self):
linear = LinearSimilarity(7, 7, combination='x,y')
weights = numpy.random.rand(14)
linear._weight_vector = Parameter(torch.from_numpy(weights).float())
linear._bias = Parameter(torch.FloatTensor([0.]))
a_vectors = numpy.random.rand(5, 4, 3, 6, 7)
b_vectors = numpy.random.rand(5, 4, 3, 6, 7)
result = linear(Variable(torch.from_numpy(a_vectors).float()),
Variable(torch.from_numpy(b_vectors).float()))
result = result.data.numpy()
assert result.shape == (5, 4, 3, 6)
combined_vectors = numpy.concatenate([a_vectors[3, 2, 1, 3, :], b_vectors[3, 2, 1, 3, :]])
expected_result = numpy.dot(combined_vectors, weights)
assert_almost_equal(result[3, 2, 1, 3], expected_result, decimal=6)
def test_forward_works_with_higher_order_tensors(self):
linear = LinearSimilarity(7, 7, combination='x,y')
weights = numpy.random.rand(14)
linear._weight_vector = Parameter(torch.from_numpy(weights).float())
linear._bias = Parameter(torch.FloatTensor([0.]))
a_vectors = numpy.random.rand(5, 4, 3, 6, 7)
b_vectors = numpy.random.rand(5, 4, 3, 6, 7)
result = linear(Variable(torch.from_numpy(a_vectors).float()),
Variable(torch.from_numpy(b_vectors).float()))
result = result.data.numpy()
assert result.shape == (5, 4, 3, 6)
combined_vectors = numpy.concatenate(
[a_vectors[3, 2, 1, 3, :], b_vectors[3, 2, 1, 3, :]])
expected_result = numpy.dot(combined_vectors, weights)
assert_almost_equal(result[3, 2, 1, 3], expected_result, decimal=6)