def test_geometric_sandwich_product_dense_v_v(self):
sta = GeometricAlgebra([1, -1, -1, -1])
geom_tensor = tf.concat(
[tf.zeros([32, 6, 1]),
tf.ones([32, 6, 4]),
tf.zeros([32, 6, 11])],
axis=-1)
vector_blade_indices = [1, 2, 3, 4]
result_indices = tf.concat([
sta.get_kind_blade_indices(BladeKind.VECTOR),
sta.get_kind_blade_indices(BladeKind.TRIVECTOR)
],
axis=0)
geom_prod_layer = GeometricSandwichProductDense(
sta,
8,
blade_indices_kernel=vector_blade_indices,
blade_indices_bias=result_indices,
bias_initializer=tf.keras.initializers.RandomNormal())
result = geom_prod_layer(geom_tensor)
# vector * vector * ~vector + vector -> vector + trivector
self.assertTrue(sta.is_pure(result, result_indices))
def test_geometric_product_dense_v_v(self):
sta = GeometricAlgebra([1, -1, -1, -1])
geom_tensor = tf.concat(
[tf.zeros([32, 6, 1]), tf.ones([32, 6, 4]), tf.zeros([32, 6, 11])],
axis=-1
)
vector_blade_indices = [1, 2, 3, 4]
geom_prod_layer = GeometricProductDense(
sta, 8,
blade_indices_kernel=vector_blade_indices,
blade_indices_bias=vector_blade_indices,
bias_initializer=tf.keras.initializers.RandomNormal()
)
result = geom_prod_layer(geom_tensor)
# vector * vector + vector -> scalar + bivector + vector
expected_result_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertTrue(sta.is_pure(result, expected_result_indices))