线性回归3

# coding=utf-8
# 线性回归-单特征梯度下降练习
from numpy import *
import matplotlib.pyplot as plt
from matplotlib import animation
import numpy as np
 
 
# 【1】特征缩放 X:=[X-mean(X)]/std(X) || X:=[X-min(X)]/max(X)-min(X) ;
def feature_scaling(data_set):
    # 特征缩放参数
    max_value = data_set.max(0)
    min_value = data_set.min(0)
    # avg_value = (min_value + max_value)/2
    diff_value = max_value - min_value
    norm_data_set = zeros(shape(data_set))  # 初始化与data_set结构一样的零array
    m = data_set.shape[0]
    norm_data_set = data_set - tile(min_value, (m, 1))  # avg_value
    norm_data_set = norm_data_set/tile(diff_value, (m, 1))
    return norm_data_set, diff_value, min_value
 
 
# def hy(theta, x_sample):
#      # hypothsis = theta0 + theta1 * x1 + theta2 * x2 .....
#     temp = [thetai * xi for thetai, xi in zip(theta, x_sample)]
#     result = sum(temp)
#     return result
def create_hy(θ1, θ0):
    return lambda xi: θ1*xi + θ0
 
 
# 【2】梯度下降  hypothsis = theta0 + theta1 * x1 + theta2 * x2 .....
# α=0.001 0.01 0.03 0.1 0.3 1 3 10
def step_gradient(xa, ya, α=0.001, variance=0.00001):
    # init the parameters to zero
    θ0_temp = θ1_temp = 1.
    θ0_last = θ1_last = 100.
    reduction = 1
    # 循环
    while reduction > variance:
        hypothesis = create_hy(θ1_temp, θ0_temp)
        m = len(xa)
        # 计算θ0、θ1
        θ0_temp -= α * (1./2) * sum([hypothesis(xa[i]) - ya[i] for i in range(m)])
        θ1_temp -= α * (1./2) * sum([(hypothesis(xa[i]) - ya[i]) * xa[i] for i in range(m)])
        # 存储梯度下降过程theta0，theta1
        theta.append((θ0_temp, θ1_temp))
        # 画线
        reduction = min(abs(θ0_temp - θ0_last), abs(θ1_temp - θ1_last))
        θ0_last = θ0_temp
        θ1_last = θ1_temp
    return θ0_temp, θ1_temp, theta
 
# 主方法
if __name__ == "__main__":
    # 打开训练集文件
    f = open(r"C:\Users\Administrator\Desktop\data.csv", "r")
    rows = f.readlines()
    # 设训练集为房屋价格
    x1 = []  # 房屋大小 平方米 x∈(25,71) avg=48
    y1 = []  # 房屋价格 万 y∈(30,120) avg=75
    # 转列表存储
    for row in [value.split(",") for value in rows]:
        x1.append(float(row[0]))
        y1.append(float(row[1]))
    # 关闭打开的文件
    f.close()
    # 特征缩放
    # x, y = feature_scaling1(x1, y1)
    x, diff_x, min_x = feature_scaling(np.array(x1))
    y, diff_y, min_y = feature_scaling(np.array(y1))
    theta = []
 
    # 线性回归：单特征梯度下降
    t0, t1, thetaArr = step_gradient(x.tolist()[0], y.tolist()[0])
    print("Final result: theta0_guess:%f theta1_guess:%f" % (t0, t1))
# 绘图
fig = plt.figure()
# ax = plt.axes(xlim=(-0.6, 0.6), ylim=(-0.6, 0.6))
ax = plt.axes(xlim=(-0.5, 1.5), ylim=(-0.5, 1.2))
line, = ax.plot([], [],  color='m', linewidth=2)
label = ax.text([], [], '')

# 初始化动画节点
def init():
    line.set_data([], [])
    plt.plot(x, y, 'bo')
    plt.title('house detail')
    plt.xlabel('size')
    plt.ylabel('price')
    return line

xx = [-0.55, 0.0, 0.34, 0.78]  # 画线点

# 动画循环
def animate(i):
    global ax, line, label
    yy = []
    θ = thetaArr[i]
    for xxi in xx:
        yy.append(θ[0]+xxi*θ[1])
    line.set_data(xx, yy)
    label.set_position([θ[0], θ[1]])
    return line, label

# animation动态记录
anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(theta), interval=1000, repeat=True)
# 要保存gif 调小frames参数
anim.save(r'C:\Users\Administrator\Desktop\123.gif', writer='imagemagick', fps=1)
plt.show()