本文整理汇总了Python中pystruct.learners.OneSlackSSVM.w方法的典型用法代码示例。如果您正苦于以下问题：Python OneSlackSSVM.w方法的具体用法？Python OneSlackSSVM.w怎么用？Python OneSlackSSVM.w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.learners.OneSlackSSVM的用法示例。
在下文中一共展示了OneSlackSSVM.w方法的1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: syntetic_train_score_per_iter
# 需要导入模块: from pystruct.learners import OneSlackSSVM [as 别名]
# 或者: from pystruct.learners.OneSlackSSVM import w [as 别名]
def syntetic_train_score_per_iter(result, only_weak=False, plot=True):
w_history = result.data["w_history"]
meta_data = result.meta
n_full = meta_data["n_full"]
n_train = meta_data["n_train"]
n_inference_iter = meta_data["n_inference_iter"]
n_full = meta_data["n_full"]
n_train = meta_data["n_train"]
dataset = meta_data["dataset"]
C = meta_data["C"]
latent_iter = meta_data["latent_iter"]
max_iter = meta_data["max_iter"]
inner_tol = meta_data["inner_tol"]
outer_tol = meta_data["outer_tol"]
alpha = meta_data["alpha"]
min_changes = meta_data["min_changes"]
initialize = meta_data["initialize"]
crf = HCRF(
n_states=10, n_features=10, n_edge_features=2, alpha=alpha, inference_method="gco", n_iter=n_inference_iter
)
base_clf = OneSlackSSVM(crf, max_iter=max_iter, C=C, verbose=0, tol=inner_tol, n_jobs=4, inference_cache=100)
clf = LatentSSVM(base_clf, latent_iter=latent_iter, verbose=2, tol=outer_tol, min_changes=min_changes, n_jobs=4)
X, Y = load_syntetic(dataset)
Xtrain, Ytrain, Ytrain_full, Xtest, Ytest = split_test_train(X, Y, n_full, n_train)
if only_weak:
Xtrain = [x for (i, x) in enumerate(Xtrain) if not Ytrain[i].full_labeled]
Ytrain_full = [y for (i, y) in enumerate(Ytrain_full) if not Ytrain[i].full_labeled]
base_clf.w = None
clf.w_history_ = w_history
clf.iter_done = w_history.shape[0]
train_scores = []
for score in clf.staged_score(Xtrain, Ytrain_full):
train_scores.append(score)
train_scores = np.array(train_scores)
if plot:
x = np.arange(0, train_scores.size)
pl.rc("text", usetex=True)
pl.rc("font", family="serif")
pl.figure(figsize=(10, 10), dpi=96)
pl.title("score on train set")
pl.plot(x, train_scores)
pl.scatter(x, train_scores)
pl.xlabel("iteration")
pl.xlim([-0.5, train_scores.size + 1])
return train_scores