LDA,Perceptron,SVM三种算法的sklearn简单使用

数据如下

x1 = [1,5,1.5,8,1,9]

x2 = [2,8,1.8,8,0.6,11]

y = [0,1,0,1,0,1]

预测[0.58,0.76]

LDA算法

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import numpy as np
train_x = np.array([[1,2],
         [5,8],
         [1.5,1.8],
         [8,8],
         [1,6.6],
         [9,11],])

train_y = np.array([0,1,0,1,0,1])
clf = LinearDiscriminantAnalysis()
clf.fit(train_x, train_y)
LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,solver='svd', store_covariance=False, tol=0.0001)
print(clf.predict([[0.58,0.76]]))

sklearn官方文档：https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html

Perceptron算法

from sklearn.linear_model import Perceptron
import numpy as np
train_x = np.array([[1,2],
         [5,8],
         [1.5,1.8],
         [8,8],
         [1,6.6],
         [9,11],])

train_y = np.array([0,1,0,1,0,1])
clf = Perceptron(fit_intercept=False,n_iter=30,shuffle=False)
clf.fit(train_x, train_y)
print(clf.predict([[0.58,0.76]]))

sklearn官方文档：https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Perceptron.html#sklearn.linear_model.Perceptron

SVM算法

from sklearn.svm import SVC
import numpy as np
train_x = np.array([[1,2],
         [5,8],
         [1.5,1.8],
         [8,8],
         [1,6.6],
         [9,11],])

train_y = np.array([0,1,0,1,0,1])
clf = SVC(kernel='rbf', class_weight='balanced',)
clf.fit(train_x, train_y)
print(clf.predict([[0.58,0.76]]))

sklearn官方文档：https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC