【Python-ML】自适应线性神经网络(Adaline)

最新推荐文章于 2020-07-05 15:26:06 发布

fjssharpsword

最新推荐文章于 2020-07-05 15:26:06 发布

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分类专栏： Algorithm 算法导论专栏

本文链接：https://blog.youkuaiyun.com/fjssharpsword/article/details/78866316

# -*- coding: utf-8 -*-
'''
Created on 2017年12月21日
@author: Jason.F
@summary: 自适应线性神经网络学习算法
'''
import numpy as np
import time
import matplotlib.pyplot  as plt
import pandas as pd

class AdalineGD(object):
    '''
    Adaptive Linear Neuron classifier.
    
    hyper-Parameters
    eta:float=Learning rate (between 0.0 and 1.0)
    n_iter:int=Passes over the training dataset.
    
    Attributes
    w_:ld-array=weights after fitting.
    costs_:list=Number of misclassification in every epoch.
    '''
    def __init__(self,eta=0.01,n_iter=50):
        self.eta=eta
        self.n_iter=n_iter
    
    def fit(self,X,y):
        '''
        Fit training data.
        Parameters
        X:{array-like},shape=[n_samples,n_features]=Training vectors,where n_samples is the number of samples and n_features is the number of features.
        y:array-like,shape=[n_samples]=Target values.
        Returns
        self:object
        '''
        self.w_=np.zeros(1+X.shape[1])
        self.costs_=[]
        
        for i in range(self.n_iter):
            output=self.net_input(X)
            errors=(y-output)
            self.w_[1:] += self.eta * X.T.dot(errors)
            self.w_[0]  += self.eta * errors.sum()
            cost=(errors ** 2).sum() /2.0
            self.costs_.append(cost)
        return self
    
    def net_input(self,X):
        #calculate net input
        return np.dot(X,self.w_[1:])+self.w_[0]
        
    def activation(self,X):
        #computer linear activation
        return self.net_input(X)
    
    def predict(self,X):
        #return clas