統計學習方法筆記，第二章感知機的python代碼實現

實現的比較粗糙，代碼如下：

class  Perceptron:
    
    import numpy as np
    
    def __init__(self, w = 0, b = 0, lr = 1, epoch = 100):
        self.weight = w
        self.bias = b
        self.lr = lr  #lr: learning rate
        self.epoch = epoch
        
    def sign(self, x):
        if np.dot( np.array(self.weight), x) + self.bias >= 0:
            return 1
        else:
            return -1

    def train(self,X, y):
        X_train = np.array(X)
        convergent = False
        epoch = 0
        while not convergent and epoch < self.epoch:
            convergent = True
            for i in range(np.array(X_train).shape[0]):
                x_i = X_train[i]
                y_i = y[i]
                if y_i * self.sign(x_i) > 0:
                    pass
                else:
                    convergent = False
                    self.weight += self.lr * y_i*x_i
                    self.bias += self.lr * y_i
                epoch += 1
                
    def test(self,X):
        X_test = np.array(X)
        y_test = np.zeros(X_test.shape[0])
        
        for i in range(X_test.shape[0]):
            y_test[i] = self.sign(X_test[i])
        return y_test
    
    def print_arribute(self):
        print("weight:",self.weight)
        print("bias:",self.bias)

用書上例2.1測試一下結果：

X = [[3,3],[4,3],[1,1]]
y = [1,1,-1]
#init the perceptron with [0,0] as weight, 0 as bias
perceptron = Perceptron([0,0],0)
#train
perceptron.train(X,y)
#check the parameters
perceptron.print_arribute()

結果如下

('weight:', array([1, 1]))
('bias:', -3)

嘗試一下訓練集的結果

perceptron.test(X)

結果如下：

array([ 1.,  1., -1.])