sklearn正规化（Normalization或者scale）

from sklearn import preprocessing
import numpy as np

a = np.array([[10,2.7,3.6],[-100,5,-2],[120,20,40]],dtype=np.float64)
print(a)
print(preprocessing.scale(a))

from sklearn import preprocessing
import numpy as np
from sklearn.cross_validation import  train_test_split
from sklearn.datasets.samples_generator import  make_classification
from sklearn.svm import SVC
import matplotlib.pyplot as plt
# a = np.array([[10,2.7,3.6],[-100,5,-2],[120,20,40]],dtype=np.float64)
# print(a)
# print(preprocessing.scale(a))
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)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)
clf = SVC()
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

from sklearn import preprocessing
import numpy as np
from sklearn.cross_validation import  train_test_split
from sklearn.datasets.samples_generator import  make_classification
from sklearn.svm import SVC
import matplotlib.pyplot as plt
# a = np.array([[10,2.7,3.6],[-100,5,-2],[120,20,40]],dtype=np.float64)
# print(a)
# print(preprocessing.scale(a))
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)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3)
clf = SVC()
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))