本文整理汇总了Python中pystruct.models.LatentGridCRF.loss_augmented_inference方法的典型用法代码示例。如果您正苦于以下问题：Python LatentGridCRF.loss_augmented_inference方法的具体用法？Python LatentGridCRF.loss_augmented_inference怎么用？Python LatentGridCRF.loss_augmented_inference使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pystruct.models.LatentGridCRF的用法示例。
在下文中一共展示了LatentGridCRF.loss_augmented_inference方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_blocks_crf_directional
# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import loss_augmented_inference [as 别名]
def test_blocks_crf_directional():
# test latent directional CRF on blocks
# test that all results are the same as equivalent LatentGridCRF
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0])
unary_weights = np.repeat(np.eye(2), 2, axis=0)
w = np.hstack([unary_weights.ravel(), pairwise_weights])
pw_directional = np.array(
[0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0, 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0]
)
w_directional = np.hstack([unary_weights.ravel(), pw_directional])
crf = LatentGridCRF(n_labels=2, n_states_per_label=2)
directional_crf = LatentDirectionalGridCRF(n_labels=2, n_states_per_label=2)
h_hat = crf.inference(x, w)
h_hat_d = directional_crf.inference(x, w_directional)
assert_array_equal(h_hat, h_hat_d)
h = crf.latent(x, y, w)
h_d = directional_crf.latent(x, y, w_directional)
assert_array_equal(h, h_d)
h_hat = crf.loss_augmented_inference(x, y, w)
h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional)
assert_array_equal(h_hat, h_hat_d)
psi = crf.psi(x, h_hat)
psi_d = directional_crf.psi(x, h_hat)
assert_array_equal(np.dot(psi, w), np.dot(psi_d, w_directional))示例2: test_loss_augmented_inference_exhaustive_grid
# 需要导入模块: from pystruct.models import LatentGridCRF [as 别名]
# 或者: from pystruct.models.LatentGridCRF import loss_augmented_inference [as 别名]
def test_loss_augmented_inference_exhaustive_grid():
crf = LatentGridCRF(n_labels=2, n_features=2, n_states_per_label=2)
for i in range(10):
w = np.random.normal(size=18)
y = np.random.randint(2, size=(2, 2))
x = np.random.normal(size=(2, 2, 2))
h_hat = crf.loss_augmented_inference(x, y * 2, w)
h = exhaustive_loss_augmented_inference(crf, x, y * 2, w)
assert_array_equal(h, h_hat)