def test_blobs_batch():
# make two gaussian blobs
X, Y = make_blobs(n_samples=80, centers=2, random_state=1)
Y = 2 * Y - 1
pbl = BinaryClf(n_features=2)
# test psi
psi_mean = pbl.batch_psi(X, Y)
psi_mean2 = np.sum([pbl.psi(x, y) for x, y in zip(X, Y)], axis=0)
assert_array_equal(psi_mean, psi_mean2)
# test inference
w = np.random.uniform(-1, 1, size=pbl.size_psi)
Y_hat = pbl.batch_inference(X, w)
for i, (x, y_hat) in enumerate(zip(X, Y_hat)):
assert_array_equal(Y_hat[i], pbl.inference(x, w))
# test inference
Y_hat = pbl.batch_loss_augmented_inference(X, Y, w)
for i, (x, y, y_hat) in enumerate(zip(X, Y, Y_hat)):
assert_array_equal(Y_hat[i], pbl.loss_augmented_inference(x, y, w))
def test_blobs_batch():
# make two gaussian blobs
X, Y = make_blobs(n_samples=80, centers=2, random_state=1)
Y = 2 * Y - 1
pbl = BinaryClf(n_features=2)
# test psi
psi_mean = pbl.batch_psi(X, Y)
psi_mean2 = np.sum([pbl.psi(x, y) for x, y in zip(X, Y)], axis=0)
assert_array_equal(psi_mean, psi_mean2)
# test inference
w = np.random.uniform(-1, 1, size=pbl.size_psi)
Y_hat = pbl.batch_inference(X, w)
for i, (x, y_hat) in enumerate(zip(X, Y_hat)):
assert_array_equal(Y_hat[i], pbl.inference(x, w))
# test inference
Y_hat = pbl.batch_loss_augmented_inference(X, Y, w)
for i, (x, y, y_hat) in enumerate(zip(X, Y, Y_hat)):
assert_array_equal(Y_hat[i], pbl.loss_augmented_inference(x, y, w))
def test_break_ties():
pbl = BinaryClf(n_features=2)
X = np.array([[-1., -1.], [-1., 1.], [1., 1.]])
w = np.array([1., 1.])
assert_array_equal(pbl.batch_inference(X, w), np.array([-1, 1, 1]))
def test_break_ties():
pbl = BinaryClf(n_features=2)
X = np.array([[-1., -1.], [-1., 1.], [1., 1.]])
w = np.array([1., 1.])
assert_array_equal(pbl.batch_inference(X, w), np.array([-1, 1, 1]))
