def test_soft_conv_10(self):
# Difference between two arrays equals tolerance,
# (would not be enough to reach convergence by itself)
# but amount of observations must also be taken into account!
labels = [0.99, 0.99, 0.1, 0.1, 0.005]
pre_labels = [0.99, 0.99, 0.1, 0.1, 0.01]
tol = 5e-3
converged = corrmut.has_converged(labels, pre_labels, "soft", tol=tol)
assert converged
def test_hard_conv_2(self):
labels = [0] * 10
pre_labels = [0] * 10
converged = corrmut.has_converged(labels, pre_labels, "hard")
assert converged
def test_soft_conv_9(self):
labels = [0.99, 0.95, 0.90, 0.95, 0.1, 0.1, 0.001, 1e-3]
pre_labels = [[0.99, 0.95, 0.90, 0.95, 0.1, 0.1, 0.001, 1e-32]]
tol = 5e-3
converged = corrmut.has_converged(labels, pre_labels, "soft", tol=tol)
assert converged
def test_soft_conv_7(self):
labels = [1, 1, 1, 0, 0, 0]
pre_labels = [1, 1, 1, 0, 0, 0]
converged = corrmut.has_converged(labels, pre_labels, "soft")
assert converged
def test_soft_conv_4(self):
labels = [0] * 10
pre_labels = [1] * 10
converged = corrmut.has_converged(labels, pre_labels, "soft")
assert not converged
def test_hard_conv_7(self):
labels = [1, 1, 1, 0, 0, 0]
pre_labels = [1, 1, 1, 0, 0, 0]
converged = corrmut.has_converged(labels, pre_labels, "hard")
assert converged
