本文整理汇总了Python中sklearn.preprocessing.LabelBinarizer.mean方法的典型用法代码示例。如果您正苦于以下问题：Python LabelBinarizer.mean方法的具体用法？Python LabelBinarizer.mean怎么用？Python LabelBinarizer.mean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sklearn.preprocessing.LabelBinarizer的用法示例。
在下文中一共展示了LabelBinarizer.mean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: chi2_contingency_matrix
# 需要导入模块: from sklearn.preprocessing import LabelBinarizer [as 别名]
# 或者: from sklearn.preprocessing.LabelBinarizer import mean [as 别名]
def chi2_contingency_matrix(X_train, y_train):
X = X_train.copy()
X.data = np.ones_like(X.data)
X = check_array(X, accept_sparse='csr')
if np.any((X.data if issparse(X) else X) < 0):
raise ValueError("Input X must be non-negative.")
Y = LabelBinarizer().fit_transform(y_train)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
observed = safe_sparse_dot(Y.T, X) # n_classes * n_features
# feature_count = check_array(X.sum(axis=0))
# class_prob = check_array(Y.mean(axis=0))
feature_count = X.sum(axis=0).reshape(1, -1)
class_prob = Y.mean(axis=0).reshape(1, -1)
expected = np.dot(class_prob.T, feature_count)
observed = np.asarray(observed, dtype=np.float64)
k = len(observed)
# Reuse observed for chi-squared statistics
contingency_matrix = observed
contingency_matrix -= expected
contingency_matrix **= 2
expected[expected == 0.0] = 1.0
contingency_matrix /= expected
# weights = contingency_matrix.max(axis=0)
return contingency_matrix