感知器基础原理及python实现

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();