def test_validation_split_shuffle():
"""Test the validation split operation with shuffling"""
out = PhysicsGuidedNeuralNetwork._get_val_split(X,
Y,
P,
shuffle=True,
validation_split=0.3)
x, y, p, x_val, y_val, p_val = out
assert (x_val == p_val).all()
assert (x == p).all()
assert id(x) != id(X)
assert x.shape[1] == x.shape[1]
assert len(x) == int(0.7 * len(X))
assert len(x_val) == int(0.3 * len(X))
assert id(y) != id(Y)
assert y.shape[1] == y.shape[1]
assert len(y) == int(0.7 * len(Y))
assert len(y_val) == int(0.3 * len(Y))
assert id(p) != id(P)
assert p.shape[1] == p.shape[1]
assert len(p) == int(0.7 * len(P))
assert len(p_val) == int(0.3 * len(P))
for i in range(len(x_val)):
row = x_val[i, :]
assert ~np.any(np.all((row == x), axis=1))
for i in range(len(p_val)):
row = p_val[i, :]
assert ~np.any(np.all((row == p), axis=1))
def test_validation_split_no_shuffle():
"""Test the validation split operation without shuffling"""
out = PhysicsGuidedNeuralNetwork._get_val_split(X,
Y,
P,
shuffle=False,
validation_split=0.3)
x, y, p, x_val, y_val, p_val = out
assert (x_val == p_val).all()
assert (x == p).all()
assert all(np.sqrt(x[:, 0]**2 + x[:, 1]**2).reshape((len(x), 1)) == y)
assert (x_val == X[0:len(x_val)]).all()
assert (y_val == Y[0:len(y_val)]).all()
assert (p_val == P[0:len(p_val)]).all()