自适应线性神经网络Adaline的python实现详解

更新时间：2020-08-01 21:12:01 作者：startmvc

自适应线性神经网络Adaptivelinearnetwork，是神经网络的入门级别网络。相对于感知器，采用了

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, train_features[:, 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], train_features[:, 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();