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
import logging
import random
import matplotlib.pyplot as plt

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

'''
Adaline classifier

Attributes
w: ld-array = weights after training
l: 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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（编辑：李大同）

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