感知机算法 统计学习方法

from sklearn.linear_model import Perceptron import numpy as np #训练的数据集 X_train = ([[3,3],[4,3],[1,1]]) y = np.array([1,1,-1]) #构建对象 perceptron = Perceptron() #参数 ： penalty:正则化项; alpha:正则化系数;eta0:学习率;max_iter:最大迭代次数（默认5次）;tol:终止条件 perceptron.fit(X_train,y) #用于训练数据集 #coef:权重 w ；intercept：b ；n_iter_迭代次数 print("w:", perceptron.coef_ ,"\n","b:",perceptron.intercept_,"\n","n_iter:",perceptron.n_iter_) res = perceptron.score(X_train,y) print("correct rate:{:.0%}".format(res))