linear_unit.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-

from perceptron import Perceptron
import matplotlib.pyplot as plt


# 定义激活函数f
f = lambda x: x
# def f(x):
#     return x


class LinearUnit(Perceptron):
    def __init__(self, input_num):
        '''初始化线性单元，设置输入参数的个数'''
        Perceptron.__init__(self, input_num, f)


def get_training_dataset():
    '''
    捏造5个人的收入数据
    '''
    # 构建训练数据
    # 输入向量列表，每一项是工作年限
    input_vecs = [[5], [3], [8], [1.4], [10.1]]
    # 期望的输出列表，月薪，注意要与输入一一对应
    labels = [5500, 2300, 7600, 1800, 11400]
    return input_vecs, labels


def train_linear_unit():
    '''
    使用数据训练线性单元
    '''
    # 创建感知器，输入参数的特征数为1（工作年限）
    lu = LinearUnit(1)
    # 训练，迭代10轮, 学习速率为0.01
    input_vecs, labels = get_training_dataset()
    lu.train(input_vecs, labels, 100, 0.01)
    # 返回训练好的线性单元
    return lu


def plot(linear_unit):
    input_vecs, labels = get_training_dataset()
    fig = plt.figure()
    ax = fig.add_subplot(111)
    # map()  Python2.x 返回列表。 Python3.x 返回迭代器,  matplotlib需要转成list
    input_v = list(map(lambda x: x[0], input_vecs))
    ax.scatter(input_v, labels)
    weights = linear_unit.weights
    bias = linear_unit.bias
    x = range(0, 12, 1)
    y = list(map(lambda x: weights[0] * x + bias, x))
    ax.plot(x, y)
    plt.show()


if __name__ == '__main__':
    '''训练线性单元'''
    linear_unit = train_linear_unit()
    # 打印训练获得的权重
    print(linear_unit)
    # 测试
    print('Work 3.4 years, monthly salary = %.2f' % linear_unit.predict([3.4]))
    print('Work 15 years, monthly salary = %.2f' % linear_unit.predict([15]))
    print('Work 1.5 years, monthly salary = %.2f' % linear_unit.predict([1.5]))
    print('Work 6.3 years, monthly salary = %.2f' % linear_unit.predict([6.3]))
    plot(linear_unit)