本文整理汇总了Python中sklearn.preprocessing.StandardScaler.std_方法的典型用法代码示例。如果您正苦于以下问题：Python StandardScaler.std_方法的具体用法？Python StandardScaler.std_怎么用？Python StandardScaler.std_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.StandardScaler的用法示例。
在下文中一共展示了StandardScaler.std_方法的3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: standard_scaler
# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import std_ [as 别名]
def standard_scaler(self):
"""Return a sklearn.preprocessing.StandardScaler"""
s = StandardScaler()
s.mean_ = self.mean()
var = self.var()
var[var <= 0] = 1 # ignore variables with zero variance
s.std_ = np.sqrt(var)
return s示例2: normalize
# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import std_ [as 别名]
def normalize(self, means=None, stds=None):
"""
Normalize dataset either from its own statistical properties or from
external one. In the second case, both means and stds must be provided.
"""
scaler = StandardScaler()
assert (means is None) == (stds is None)
if means and stds:
scaler.mean_ = np.array(means)
scaler.std_ = np.array(stds)
else:
scaler.fit(self.data)
self.data = scaler.transform(self.data, copy=False)
return scaler.mean_.tolist(), scaler.std_.tolist()示例3: train_test_split
# 需要导入模块: from sklearn.preprocessing import StandardScaler [as 别名]
# 或者: from sklearn.preprocessing.StandardScaler import std_ [as 别名]
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(
X, y, weights, test_size=0.25, random_state=0)
print("train data shape: %r, train target shape: %r, train weights shape: %r"
% (X_train.shape, y_train.shape, w_train.shape))
print("test data shape: %r, test target shape: %r, test weights shape: %r"
% (X_test.shape, y_test.shape, w_test.shape))
scaler = StandardScaler()
means = np.mean(X_train)
std = np.std(X_train)
print means[0]
scaler.mean_ = np.zeros(len(means))
scaler.std_ = np.ones(len(means))
for i in range(len(means)):
scaler.mean_[i] = means[i]
scaler.std_[i] = std[i]
print scaler.mean_
#scaler.mean_ =
#X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
print scaler.get_params(deep=True)
print scaler.mean_
print scaler.std_
sys.exit()
# Let's retrain a new model on the first subset call the **training set**: