def test_scalar_product_structured():
def kernel(x, y):
return 1
structured_field0 = np.array([[0, 0], [0, 0], [1, 1], [0, 0]])
structured_field1 = np.array([[0, 0], [0, 0], [0, 0], [1, 1]])
assert pytest.approx(
structured_vector_fields.scalar_product_structured(
structured_field0, structured_field1, kernel)) == 0
assert pytest.approx(
structured_vector_fields.scalar_product_structured(
structured_field0, structured_field1, kernel) == 4)
def product(vect0, vect1):
return struct.scalar_product_structured(vect0, vect1, kernel)
#%%% Visualize results
def kernel_np(x, y):
#si = tf.shape(x)[0]
return np.exp(-sum([(x[i] - y[i])**2 for i in range(dim)]) / (sigma**2))
get_unstructured_op = struct.get_from_structured_to_unstructured(
space, kernel_np)
structured_computed = []
unstructured_computed = []
fac = []
for i in range(nbdatamax):
structured_temp = struct.create_structured(points_list[i],
np.dot(vectors_list[i], alpha))
prod = struct.scalar_product_structured(structured_temp,
structured_list[i], kernel_np)
squared_norm = struct.scalar_product_structured(structured_temp,
structured_temp, kernel_np)
fac.append(prod / squared_norm)
structured_computed.append(structured_temp.copy())
unstructured_computed.append(get_unstructured_op(structured_temp).copy())
#
for i in range(nbdatamax):
get_unstructured_op(structured_list[i]).show(
'unstructured data {}'.format(i))
(fac[i] * unstructured_computed[i]).show(
'unstructured computed {}'.format(i))
#
for i in range(nbdatamax):
def product(f, g):
return struct.scalar_product_structured(f, g, kernel)
