對測試數(shù)據(jù)集的歸一化

注：測試數(shù)據(jù)集用的方差與均值應(yīng)是訓(xùn)練數(shù)據(jù)集的方差與均值。

scikit-learn中的Scaler

import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_splitiris = datasets.load_iris()X = iris.data y = iris.targetX_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size = 0.2, random_state = 666)

scikit-learn中的StandardScaler

from sklearn.preprocessing import StandardScalerstandardScaler = StandardScaler() standardScaler.fit(X_train)

之后我們可以看一下他的均值和標(biāo)準(zhǔn)差：

standardScaler.mean_ standardScaler.scale_

然后我們就可以進行歸一化：

X_train = standardScaler.transform(X_train) X_test_standard = standardScaler.transform(X_test)

最后我們可以看一下效果：

封裝

import numpy as npclass StandardScaler:def __init__(self):self.mean_ = Noneself.scale_ = Nonedef fit(self, X):assert X.ndim == 2, "The dimension of X must be 2"self.mean_ = np.array([np.mean(X[:,i]) for i in range(X.shape[1])])self.scale_ = np.array([np.std(X[:, i]) for i in range(X.shape[1])])return selfdef tranform(self, X):assert X.ndim == 2, "The dimension of X must be 2"assert self.mean_ is not None and self.scale_ is not None, \"must fit before transform!"assert X.shape[1] == len(self.mean_), \"the feature number of X must be equal to mean_ and std_"resX = np.empty(shape=X.shape, dtype=float)for col in range(X.shape[1]):resX[:,col] = (X[:, col] - self.mean_[col]) / self.scale_[col]return resX

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