Sklearn

标准化数据

from __future__ import print_function

from sklearn import preprocessing

import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.datasets.samples_generator import make_classification

from sklearn.svm import SVC

import matplotlib.pyplot as plt

#每一列是一个属性

a = np.array([[10, 2.7, 3.6],

                     [-100, 5, -2],

                     [120, 20, 40]], dtype=np.float64)

print(a)

#归一化

print(preprocessing.scale(a))

# 生成一堆数据 有两个属性 有两个相关属性

X, y = make_classification(n_samples=300, n_features=2 , n_redundant=0, n_informative=2,

                           random_state=22, n_clusters_per_class=1, scale=100)

plt.scatter(X[:, 0], X[:, 1], c=y)

plt.show()

X = preprocessing.scale(X)    # normalization step

#minmax_scale(X,feature_range=(-1,1))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)

clf = SVC()

clf.fit(X_train, y_train)

print(clf.score(X_test, y_test))
巴特西

sklearn 标准化数据的方法

Sklearn

标准化数据

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