fitt/X3.py

# -*- coding: utf-8 -*-
# Time : 2023/8/9 10:20
# Author : lirunsheng
# User : l'r's
# Software: PyCharm
# File : X3.py

import mpl_toolkits.axisartist as axisartist
from scipy.optimize import curve_fit
from pandas import DataFrame
import matplotlib.pyplot as plt
import numpy as np
import pylab as mpl
from tkinter import *
import data as gl_data
import pandas as pd
import tkinter
import sys
import tkinter as tk
from tkinter import filedialog

mpl.rcParams['font.sans-serif'] = ['SimHei']  		# 解决matplotlib中文不显示问题
plt.rcParams['axes.unicode_minus'] = False # 解决matplotlib负数坐标显示问题

# 创建一个二维数组sampleData，用于存储样本数据
sampleData = []

def askfile():# 从本地选择一个文件，并返回文件的路径
    global sampleData
    # 弹出文件选择对话框，选择要打开的文本文件
    file_path = filedialog.askopenfilename(filetypes=[('Text Files', '*.txt')])

    # 如果没有选择文件，直接返回
    if not file_path:
        return
    # 步骤1：打开样本数据集文件并读取每行数据
    with open(file_path, 'r') as file:
        lines = file.readlines()

    # 步骤2：遍历所有行，将每行数据解析为sx和sy值，转换为浮点数并存储到sampleData中
    for line in lines:
        sx, sy = line.strip().split(' ')
        sx = float(sx)
        sy = float(sy)
        sampleData.append([sx, sy])

    # # 步骤3：使用循环遍历样本数据并打印出来
    # for data in sampleData:
    #     print(data)
# if __name__ == '__main__':
#     askfile()
def draw_axis(low, high, step=250):
    fig = plt.figure(figsize=(4.4, 3.2)) # 设置显示大小
    ax = axisartist.Subplot(fig, 111)	# 使用axisartist.Subplot方法创建一个绘图区对象ax
    fig.add_axes(ax)   # 将绘图区对象添加到画布中
    ax.axis[:].set_visible(False)# 通过set_visible方法设置绘图区所有坐标轴隐藏
    ax.axis["x"] = ax.new_floating_axis(0, 0)	# 添加新的x坐标轴
    ax.axis["x"].set_axisline_style("-|>", size=1.0) # 给x坐标轴加上箭头
    ax.axis["y"] = ax.new_floating_axis(1, 0)	# 添加新的y坐标轴
    ax.axis["y"].set_axisline_style("-|>", size=1.0) # y坐标轴加上箭头
    ax.axis["x"].set_axis_direction("bottom")	# 设置x、y轴上刻度显示方向
    ax.axis["y"].set_axis_direction("left")	# 设置x、y轴上刻度显示方向
    plt.xlim(low, high)  # 把x轴的刻度范围设置
    plt.ylim(low, high)  # 把y轴的刻度范围设置
    ax.set_xticks(np.arange(low, high + 5, step))  # 把x轴的刻度间隔设置
    ax.set_yticks(np.arange(low, high + 5, step))  # 把y轴的刻度间隔设置

def generate_and_plot_sample_data(low, high):
    num_samples = 25		#设置样本点数量
    gl_data.X = np.random.randint(low=low, high=high, size=num_samples)#随机生成x值
    print(gl_data.X)
    gl_data.Y = np.random.randint(low=low, high=high, size=num_samples)#随机生成y值
    # 提取样本数据的x坐标和y坐标
    # x_values = [point[0] for point in sampleData]
    # print(x_values)
    # y_values = [point[1] for point in sampleData]
    draw_axis(low, high)	#绘制坐标轴
    plt.scatter(gl_data.X,gl_data.Y, color='red')  # 绘制样本数据点
    plt.savefig(r"dot2.png", facecolor='w')	# 保存到本地
    plt.close()	# 清除内存
    # set_phtot(1)	#显示到主界面
# if __name__ == '__main__':
#     askfile()
#     # generate_and_plot_sample_data(gl_data.LOW, gl_data.HIGH)
def quadratic_function(x, a, b, c):#构造二次函数y = a * X^2 + b * X + C
    return a * x ** 2 + b * x + c
def draw_dots_and_line(low, high, sx, sy):
    draw_axis(low, high)
    popt, pcov = curve_fit(quadratic_function, sx, sy)# 用curve_fit来对点进行拟合
    positive_mask = sy >= 0.0#给样本点分类
    negative_mask = sy < 0.0#给样本点分类
    positive_colors = ['red' if x >= 0.0 else 'blue' for x in sx]#给样本点分类
    negative_colors = ['green' if x >= 0.0 else 'purple' for x in sx]#给样本点分类
    #根据样本点类型决定样本点颜色
    plt.scatter(gl_data.X[positive_mask], gl_data.Y[positive_mask], color=np.array(positive_colors)[positive_mask], lw=1)
    plt.scatter(gl_data.X[negative_mask], gl_data.Y[negative_mask], color=np.array(negative_colors)[negative_mask], lw=1)
    curve_x = np.arange(low, high)#按照步长生成的一串数字
    curve_y = [quadratic_function (i, * popt) for i in curve_x]# 根据x来计算y1值
    plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve')#绘制拟合曲线
    plt.savefig(r"dot5.png", facecolor='w')  # 保存到本地
    plt.legend()
    plt.show()
    return popt
# if __name__ == '__main__':
#     askfile()
#     gl_data.x = np.array([point[0] for point in sampleData]) # 为二次函数.txt
#     gl_data.y = np.array([point[1] for point in sampleData])
#     popt = draw_dots_and_line(gl_data.low, gl_data.high, gl_data.x, gl_data.y)
#     a,b,c = popt
#     print(a,b,c)

def input_num(root_tk):
    global top
    top = tk.Toplevel(root_tk)
    top.geometry("300x50")
    top.title('坐标点个数')
    label1 = Label(top, text="坐标点个数")
    label1.grid(row=0)  # 这里的side可以赋值为LEFT  RTGHT TOP  BOTTOM
    num1 = IntVar()
    entry1 = Entry(top, textvariable=num1)
    num1.set(0)
    entry1.grid(row=0, column=1)
    Label(top, text=" ").grid(row=0, column=3)
    Button(top, text="确定", command=lambda: input_data(root_tk, int(entry1.get()))).grid(row=0, column=3)
    top.mainloop()

def add_sample_data():
    global sample_x, sample_y, sample_data
    global entry_x,entry_y,label_status, numx
    try:
        x = float(entry_x.get())
        y = float(entry_y.get())
    except:
        label_status.config(text="输入不合法")
        return
    entry_x.delete(0, tk.END)
    entry_y.delete(0, tk.END)
    if min(x, y) < gl_data.LOW or max(x, y) > gl_data.HIGH:
        label_status.config(text="输入超过范围")
        return
    elif len(sample_data) < numx:
        label_status.config(text="点对已添加")
        sample_data.append((x, y))
        sample_x.append(x)
        sample_y.append(y)
    else:
        label_status.config(text="已达到最大数量")
def check_sample_data():
    global label_status,numx,sample_x,sample_y,sample_data
    if len(sample_data) == numx:
        label_status.config(text="已达到最大数量")
        gl_data.X = np.array(sample_x)
        gl_data.Y = np.array(sample_y)
        print('已添加', sample_data)
        sys.exit()
    else:
        label_status.config(text="还需输入{}个点对".format(numx - len(sample_data)))
        print(sample_data)

def input_data(root_tk, num):
    global top
    global sample_x,sample_y,sample_data
    global numx
    numx = num
    sample_x = []
    sample_y = []
    sample_data = []
    top.destroy()
    top = tk.Toplevel(root_tk)
    top.geometry("300x200")
    top.title('坐标')
    global entry_x, entry_y, label_status
    label_x = tk.Label(top, text="X 值：")
    label_x.pack()
    entry_x = tk.Entry(top)
    entry_x.pack()
    label_y = tk.Label(top, text="Y 值：")
    label_y.pack()
    entry_y = tk.Entry(top)
    entry_y.pack()
    button_add = tk.Button(top, text="添加", command=add_sample_data)
    button_add.pack()
    button_check = tk.Button(top, text="检查", command=check_sample_data)
    button_check.pack()
    label_status = tk.Label(top, text="")
    label_status.pack()
    top.mainloop()

# if __name__ == '__main__':
#     root = tk.Tk()
#     root.withdraw()
#     input_num(root)

# 定义误差函数
def error_func(x_data, y_data, a, b, c):
    y_pred = exponential_function(x_data, a, b, c)
    error = np.sum((y_data - y_pred) ** 2)
    residual = y_data - y_pred
    ss_res = np.sum(residual ** 2)
    ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
    r_squared = 1 - (ss_res / ss_tot)
    return error,r_squared

# 给出一个二次函数拟合的例子，读者可自己写拟合函数
def exponential_function(x, a, b, c):
    return a * x ** 2 + b * x + c

def fit(sample_data): # 随机生成样本数据
    x_data = np.array([data[0] for data in sample_data])
    y_data = np.array([data[1] for data in sample_data]) # 拟合样本数据
    popt, pcov = curve_fit(exponential_function, x_data, y_data) # 计算拟合结果
    # 计算拟合误差
    error,r_squared = error_func(x_data, y_data, *popt)
    fit_function_name = exponential_function.__name__# 打印拟合函数的形式、系数、误差和优度
    print("拟合函数形式：{}".format(fit_function_name))
    print("拟合系数：{}".format(popt))
    print("误差：{:.4f}".format(error))
    print("拟合优度（R^2）：{:.4f}".format(r_squared))
# if __name__ == '__main__':
#     askfile()
#     fit(sampleData) # 对当前x、y值进行拟合
# # 给出一个指数函数拟合的例子，读者可自己写拟合函数
# def fit_function(x,a,b,c):
#     return a * np.exp(b * x) + c
#
# def fit(sample_data): # 随机生成样本数据
#     x_data = np.array([data[0] for data in sample_data])
#     y_data = np.array([data[1] for data in sample_data]) # 拟合样本数据
#     popt, pcov = curve_fit(fit_function, x_data, y_data) # 计算拟合结果
#     y_fit = fit_function(x_data, *popt)
#     residuals = y_data - y_fit
#     ss_res = np.sum(residuals**2)
#     ss_tot = np.sum((y_data - np.mean(y_data))**2)
#     r_squared = 1 - (ss_res / ss_tot)
#     fit_function_name = fit_function.__name__# 打印拟合函数的形式、系数、误差和优度
#     print("拟合函数形式：{}".format(fit_function_name))
#     print("拟合系数：{}".format(popt))
#     print("误差：{:.4f}".format(np.sqrt(np.mean(residuals**2))))
#     print("拟合优度（R^2）：{:.4f}".format(r_squared))
# if __name__ == '__main__':
#     askfile()
#     fit(sampleData) # 对当前x、y值进行拟合

def change_Q(no):
    gl_data.Quadrant = no 	#更改全局变量的象限显示
    if no:		#若为一象限，则修改显示下限为0
        gl_data.LOW = 0
    else:		#若为四象限，则修改显示下限为-gl_data.MAXV
        gl_data.LOW = -gl_data.MAXV
    q_button()	#更新象限显示面板

def q_button():
    r = 7.5
    rr = 2.5
    for widget in q_root.winfo_children():
        widget.destroy()
    q_cv = tk.Canvas(q_root, width=450, height=500)
    q_cv.place(x=0, y=0)
    l = tk.Label(q_root, text='坐标轴', bd=0, font=("微软雅黑", 16) , anchor=W)
    l.place(x=20, y=0, width=80, height=50,)
    # 四象b限按钮
    b1 = tk.Button(q_root, text='四象限', bd=0,  font=("微软雅黑", 16)
                               , command=lambda: change_Q(0), anchor=W)
    b1.place(x=170, y=0, width=80, height=50,)
    # 一象限按钮
    b2 = tk.Button(q_root, text='一象限', bd=0, font=("微软雅黑", 16)
                   , command=lambda: change_Q(1), anchor=W)
    b2.place(x=320, y=0, width=80, height=50,)
    # 绘制标记框
    q_cv.create_oval(140 - r, 25 - r, 140 + r, 25 + r, fill="white", width=1, outline="black")
    q_cv.create_oval(290 - r, 25 - r, 290 + r, 25 + r , fill="white", width=1, outline="black")
    # 根据当前的象限选择值来填充标记框
    if gl_data.Quadrant == 0:
        q_cv.create_oval(140 - rr, 25 - rr, 140 + rr, 25 + rr, fill="black", width=1, outline="black")# 绘制象限
        # Q_cv.create_rectangle(80, 80, 270, 270, fill="white")  # 第一象限
        # Q_cv.create_rectangle(270, 80, 500, 270, fill="red")  # 第二象限
        # Q_cv.create_rectangle(80, 270, 270, 480, fill="blue")  # 第三象限
        # Q_cv.create_rectangle(270, 270, 500, 480, fill="green")  # 第四象限
    else:
        q_cv.create_oval(290 - rr, 25 - rr, 290 + rr, 25 + rr, fill="black", width=1, outline="black")
# if __name__ == '__main__':
#     # 象限选择相关界面
#     q_root = tk.Tk()
#     q_root.geometry("500x550")  # 设置窗口的大小和位置
#     q_button()
#     q_root.mainloop()

# 定义模型为y=ax+b
def model(x, a, b):
    return a*x + b

# 定义误差函数error_function()
def error_function(x,y,a, b):
    y_pred = model(x, a, b)
    error = np.mean((y - y_pred)**2)
    return error

# 定义梯度函数gradient()
def gradient(a, b,x,y):
    y_pred = model(x, a, b)
    gradient_a = 2*np.mean((y_pred - y)*x)
    gradient_b = 2*np.mean(y_pred - y)
    return gradient_a, gradient_b

if __name__ == '__main__':
    # 读取sampledata值
    sampledata = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    x = np.array(sampledata)
    y = np.array([-2, 1, 0, 1, 2, 3, 4, 5, 6, 8])
    # 初始化参数
    a = 0
    b = 0
    learning_rate = 0.01
    num_iterations = 1000
    # 梯度下降算法
    for i in range(num_iterations):
        grad_a, grad_b = gradient(a, b,x,y)
        a -= learning_rate * grad_a
        b -= learning_rate * grad_b
    # 输出最终拟合结果
    final_error = error_function(x,y,a, b)
    # gl_data.X = x  # 为二次函数.txt
    # gl_data.Y = y
    # draw_axis(-100, 100)
    # positive_mask = x >= 0.0  # 给样本点分类
    # negative_mask = y < 0.0  # 给样本点分类
    # positive_colors = ['red' if x >= 0.0 else 'blue' for x in x]  # 给样本点分类
    # negative_colors = ['green' if x >= 0.0 else 'purple' for x in x]  # 给样本点分类
    # # 根据样本点类型决定样本点颜色
    # plt.scatter(gl_data.X[positive_mask], gl_data.Y[positive_mask], color=np.array(positive_colors)[positive_mask],
    #             lw=1)
    # plt.scatter(gl_data.X[negative_mask], gl_data.Y[negative_mask], color=np.array(negative_colors)[negative_mask],
    #             lw=1)
    # curve_x = np.arange(-1000, 1000)  # 按照步长生成的一串数字
    # curve_y = [model(i, a, b) for i in curve_x]  # 根据x来计算y1值
    # plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve')  # 绘制拟合曲线
    # plt.legend()
    # plt.show()
    print("拟合函数形式: y = ax + b")
    print("系数 a:{:.2f}".format(a))
    print("系数 b:{:.2f}".format( b))
    print("误差计算方法: 均方误差")
    print("最终误差:{:.2f}".format(final_error))