自适应线性神经网络Adaline的python实现详解

概述自适应线性神经网络Adaline的python实现详解 自适应线性神经网络Adaptive linear network, 是神经网络的入门级别网络. 相对于感知器,采用了f(z)=z的激活函数,属于连续函数. 代价函数为LMS函数,最小均方算法,Least mean square. 实现上,采用随机梯度下降,由于更新的随机性,运行多次结果是不同的. ''' Adaline classifier created on 2019.9.14 author: vince ''' import pandas import math import numpy

自适应线性神经网络Adaptive linear network， 是神经网络的入门级别网络。

相对于感知器，采用了f（z）=z的激活函数，属于连续函数。

代价函数为LMS函数，最小均方算法，Least mean square。

实现上，采用随机梯度下降，由于更新的随机性，运行多次结果是不同的。

'''adaline classifIErcreated on 2019.9.14author: vince'''import pandasimport mathimport numpyimport loggingimport randomimport matplotlib.pyplot as pltfrom sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score'''adaline classifIErAttributesw: ld-array = weights after trainingl: List = number of misclassification during each iteration'''class adaline:  def __init__(self,eta = 0.001,iter_num = 500,batch_size = 1):    '''    eta: float = learning rate (between 0.0 and 1.0).    iter_num: int = iteration over the training dataset.    batch_size: int = gradIEnt descent batch number,if batch_size == 1,used SGD;      if batch_size == 0,use BGD;      else MBGD;    '''    self.eta = eta;    self.iter_num = iter_num;    self.batch_size = batch_size;  def train(self,X,Y):    '''    train training data.    X:{array-like},shape=[n_samples,n_features] = Training vectors,where n_samples is the number of training samples and      n_features is the number of features.    Y:{array-like},share=[n_samples] = traget values.    '''    self.w = numpy.zeros(1 + X.shape[1]);    self.l = numpy.zeros(self.iter_num);    for iter_index in range(self.iter_num):      for rand_time in range(X.shape[0]):        sample_index = random.randint(0,X.shape[0] - 1);        if (self.activation(X[sample_index]) == Y[sample_index]):          continue;        output = self.net_input(X[sample_index]);        errors = Y[sample_index] - output;        self.w[0] += self.eta * errors;        self.w[1:] += self.eta * numpy.dot(errors,X[sample_index]);        break;      for sample_index in range(X.shape[0]):        self.l[iter_index] += (Y[sample_index] - self.net_input(X[sample_index])) ** 2 * 0.5;      logging.info("iter %s: w0(%s),w1(%s),w2(%s),l(%s)" %          (iter_index,self.w[0],self.w[1],self.w[2],self.l[iter_index]));      if iter_index > 1 and math.fabs(self.l[iter_index - 1] - self.l[iter_index]) < 0.0001:        break;  def activation(self,x):    return numpy.where(self.net_input(x) >= 0.0,1,-1);  def net_input(self,x):    return numpy.dot(x,self.w[1:]) + self.w[0];  def predict(self,x):    return self.activation(x);def main():  logging.basicConfig(level = logging.INFO,format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',datefmt = '%a,%d %b %Y %H:%M:%s');  iris = load_iris();  features = iris.data[:99,[0,2]];  # normalization  features_std = numpy.copy(features);  for i in range(features.shape[1]):    features_std[:,i] = (features_std[:,i] - features[:,i].mean()) / features[:,i].std();  labels = numpy.where(iris.target[:99] == 0,-1,1);  # 2/3 data from training,1/3 data for testing  train_features,test_features,train_labels,test_labels = train_test_split(      features_std,labels,test_size = 0.33,random_state = 23323);  logging.info("train set shape:%s" % (str(train_features.shape)));  classifIEr = adaline();  classifIEr.train(train_features,train_labels);  test_predict = numpy.array([]);  for feature in test_features:    predict_label = classifIEr.predict(feature);    test_predict = numpy.append(test_predict,predict_label);  score = accuracy_score(test_labels,test_predict);  logging.info("The accruacy score is: %s "% (str(score)));  #plot  x_min,x_max = train_features[:,0].min() - 1,train_features[:,0].max() + 1;  y_min,y_max = train_features[:,1].min() - 1,1].max() + 1;  plt.xlim(x_min,x_max);  plt.ylim(y_min,y_max);  plt.xlabel("wIDth");  plt.ylabel("heigt");  plt.scatter(train_features[:,0],1],c = train_labels,marker = 'o',s = 10);  k = - classifIEr.w[1] / classifIEr.w[2];  d = - classifIEr.w[0] / classifIEr.w[2];  plt.plot([x_min,x_max],[k * x_min + d,k * x_max + d],"go-");  plt.show();if __name__ == "__main__":  main();

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