感知器基础原理及python实现过程详解

简单版本，按照李航的《统计学习方法》的思路编写

数据采用了著名的sklearn自带的iries数据，最优化求解采用了SGD算法。

预处理增加了标准化操作。

'''
perceptron classifier

created on 2019.9.14
author: vince
'''
import pandas
import numpy
import logging
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

'''
perceptron classifier

Attributes
w: ld-array = weights after training
l: list = number of misclassification during each iteration
'''
class Perceptron:
 def __init__(self, eta = 0.01, iter_num = 50, 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 sample_index in range(X.shape[0]):
       if (self.activation(X[sample_index]) != Y[sample_index]):
         logging.debug("％s: pred(％s), label(％s), ％s, ％s" ％ (sample_index,
           self.net_input(X[sample_index]) , Y[sample_index],
           X[sample_index, 0], X[sample_index, 1]));
         self.l[iter_index] += 1;
     for sample_index in range(X.shape[0]):
       if (self.activation(X[sample_index]) != Y[sample_index]):
         self.w[0] += self.eta * Y[sample_index];
         self.w[1:] += self.eta * numpy.dot(X[sample_index], Y[sample_index]);
         break;
     logging.info("iter ％s: ％s, ％s, ％s, ％s" ％
         (iter_index, self.w[0], self.w[1], self.w[2], self.l[iter_index]));

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)));

p = Perceptron();

p.train(train_features, train_labels);

test_predict = numpy.array([]);
 for feature in test_features:
   predict_label = p.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 = - p.w[1] / p.w[2];
 d = - p.w[0] / p.w[2];

plt.plot([x_min, x_max], [k * x_min + d, k * x_max + d], "go-");

plt.show();

if __name__ == "__main__":
 main();

来源：https://www.cnblogs.com/thsss/p/11519846.html