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# -*- coding: utf-8 -*-
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# Time : 2023/8/9 10:20
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# Author : lirunsheng
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# User : l'r's
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# Software: PyCharm
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# File : X3.py
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import mpl_toolkits.axisartist as axisartist
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from scipy.optimize import curve_fit
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from pandas import DataFrame
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import matplotlib.pyplot as plt
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import numpy as np
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import pylab as mpl
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from tkinter import *
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import data as gl_data
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import pandas as pd
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import tkinter
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import sys
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import tkinter as tk
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from tkinter import filedialog
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mpl.rcParams['font.sans-serif'] = ['SimHei'] # 解决matplotlib中文不显示问题
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plt.rcParams['axes.unicode_minus'] = False # 解决matplotlib负数坐标显示问题
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# 创建一个二维数组sampleData，用于存储样本数据
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sampleData = []
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def askfile():# 从本地选择一个文件，并返回文件的路径
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global sampleData
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# 弹出文件选择对话框，选择要打开的文本文件
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file_path = filedialog.askopenfilename(filetypes=[('Text Files', '*.txt')])
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# 如果没有选择文件，直接返回
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if not file_path:
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return
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# 步骤1：打开样本数据集文件并读取每行数据
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with open(file_path, 'r') as file:
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lines = file.readlines()
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# 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
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for line in lines:
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sx, sy = line.strip().split(' ')
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sx = float(sx)
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sy = float(sy)
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sampleData.append([sx, sy])
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# # 步骤3：使用循环遍历样本数据并打印出来
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# for data in sampleData:
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# print(data)
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# if __name__ == '__main__':
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# askfile()
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def draw_axis(low, high, step=250):
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fig = plt.figure(figsize=(4.4, 3.2)) # 设置显示大小
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ax = axisartist.Subplot(fig, 111) # 使用axisartist.Subplot方法创建一个绘图区对象ax
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fig.add_axes(ax) # 将绘图区对象添加到画布中
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ax.axis[:].set_visible(False)# 通过set_visible方法设置绘图区所有坐标轴隐藏
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ax.axis["x"] = ax.new_floating_axis(0, 0) # 添加新的x坐标轴
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ax.axis["x"].set_axisline_style("-|>", size=1.0) # 给x坐标轴加上箭头
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ax.axis["y"] = ax.new_floating_axis(1, 0) # 添加新的y坐标轴
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ax.axis["y"].set_axisline_style("-|>", size=1.0) # y坐标轴加上箭头
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ax.axis["x"].set_axis_direction("bottom") # 设置x、y轴上刻度显示方向
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ax.axis["y"].set_axis_direction("left") # 设置x、y轴上刻度显示方向
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plt.xlim(low, high) # 把x轴的刻度范围设置
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plt.ylim(low, high) # 把y轴的刻度范围设置
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ax.set_xticks(np.arange(low, high + 5, step)) # 把x轴的刻度间隔设置
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ax.set_yticks(np.arange(low, high + 5, step)) # 把y轴的刻度间隔设置
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def generate_and_plot_sample_data(low, high):
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num_samples = 25 #设置样本点数量
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gl_data.X = np.random.randint(low=low, high=high, size=num_samples)#随机生成x值
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print(gl_data.X)
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gl_data.Y = np.random.randint(low=low, high=high, size=num_samples)#随机生成y值
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# 提取样本数据的x坐标和y坐标
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# x_values = [point[0] for point in sampleData]
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# print(x_values)
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# y_values = [point[1] for point in sampleData]
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draw_axis(low, high) #绘制坐标轴
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plt.scatter(gl_data.X,gl_data.Y, color='red') # 绘制样本数据点
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plt.savefig(r"dot2.png", facecolor='w') # 保存到本地
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plt.close() # 清除内存
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# set_phtot(1) #显示到主界面
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# if __name__ == '__main__':
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# askfile()
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# # generate_and_plot_sample_data(gl_data.LOW, gl_data.HIGH)
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def quadratic_function(x, a, b, c):#构造二次函数y = a * X^2 + b * X + C
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return a * x ** 2 + b * x + c
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def draw_dots_and_line(low, high, sx, sy):
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draw_axis(low, high)
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popt, pcov = curve_fit(quadratic_function, sx, sy)# 用curve_fit来对点进行拟合
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positive_mask = sy >= 0.0#给样本点分类
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negative_mask = sy < 0.0#给样本点分类
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positive_colors = ['red' if x >= 0.0 else 'blue' for x in sx]#给样本点分类
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negative_colors = ['green' if x >= 0.0 else 'purple' for x in sx]#给样本点分类
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#根据样本点类型决定样本点颜色
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plt.scatter(gl_data.X[positive_mask], gl_data.Y[positive_mask], color=np.array(positive_colors)[positive_mask], lw=1)
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plt.scatter(gl_data.X[negative_mask], gl_data.Y[negative_mask], color=np.array(negative_colors)[negative_mask], lw=1)
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curve_x = np.arange(low, high)#按照步长生成的一串数字
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curve_y = [quadratic_function (i, * popt) for i in curve_x]# 根据x来计算y1值
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plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve')#绘制拟合曲线
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plt.savefig(r"dot5.png", facecolor='w') # 保存到本地
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plt.legend()
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plt.show()
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return popt
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# if __name__ == '__main__':
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# askfile()
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# gl_data.x = np.array([point[0] for point in sampleData]) # 为二次函数.txt
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# gl_data.y = np.array([point[1] for point in sampleData])
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# popt = draw_dots_and_line(gl_data.low, gl_data.high, gl_data.x, gl_data.y)
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# a,b,c = popt
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# print(a,b,c)
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def input_num(root_tk):
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global top
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top = tk.Toplevel(root_tk)
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top.geometry("300x50")
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top.title('坐标点个数')
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label1 = Label(top, text="坐标点个数")
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label1.grid(row=0) # 这里的side可以赋值为LEFT RTGHT TOP BOTTOM
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num1 = IntVar()
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entry1 = Entry(top, textvariable=num1)
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num1.set(0)
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entry1.grid(row=0, column=1)
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Label(top, text=" ").grid(row=0, column=3)
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Button(top, text="确定", command=lambda: input_data(root_tk, int(entry1.get()))).grid(row=0, column=3)
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top.mainloop()
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def add_sample_data():
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global sample_x, sample_y, sample_data
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global entry_x,entry_y,label_status, numx
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try:
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x = float(entry_x.get())
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y = float(entry_y.get())
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except:
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label_status.config(text="输入不合法")
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return
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entry_x.delete(0, tk.END)
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entry_y.delete(0, tk.END)
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if min(x, y) < gl_data.LOW or max(x, y) > gl_data.HIGH:
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label_status.config(text="输入超过范围")
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return
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elif len(sample_data) < numx:
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label_status.config(text="点对已添加")
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sample_data.append((x, y))
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sample_x.append(x)
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sample_y.append(y)
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else:
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label_status.config(text="已达到最大数量")
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def check_sample_data():
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global label_status,numx,sample_x,sample_y,sample_data
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if len(sample_data) == numx:
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label_status.config(text="已达到最大数量")
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gl_data.X = np.array(sample_x)
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gl_data.Y = np.array(sample_y)
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print('已添加', sample_data)
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sys.exit()
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else:
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label_status.config(text="还需输入{}个点对".format(numx - len(sample_data)))
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print(sample_data)
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def input_data(root_tk, num):
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global top
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global sample_x,sample_y,sample_data
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global numx
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numx = num
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sample_x = []
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sample_y = []
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sample_data = []
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top.destroy()
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top = tk.Toplevel(root_tk)
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top.geometry("300x200")
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top.title('坐标')
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global entry_x, entry_y, label_status
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label_x = tk.Label(top, text="X 值：")
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label_x.pack()
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entry_x = tk.Entry(top)
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entry_x.pack()
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label_y = tk.Label(top, text="Y 值：")
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label_y.pack()
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entry_y = tk.Entry(top)
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entry_y.pack()
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button_add = tk.Button(top, text="添加", command=add_sample_data)
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button_add.pack()
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button_check = tk.Button(top, text="检查", command=check_sample_data)
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button_check.pack()
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label_status = tk.Label(top, text="")
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label_status.pack()
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top.mainloop()
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# if __name__ == '__main__':
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# root = tk.Tk()
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# root.withdraw()
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# input_num(root)
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# 定义误差函数
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def error_func(x_data, y_data, a, b, c):
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y_pred = exponential_function(x_data, a, b, c)
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error = np.sum((y_data - y_pred) ** 2)
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residual = y_data - y_pred
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ss_res = np.sum(residual ** 2)
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ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
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r_squared = 1 - (ss_res / ss_tot)
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return error,r_squared
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# 给出一个二次函数拟合的例子，读者可自己写拟合函数
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def exponential_function(x, a, b, c):
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return a * x ** 2 + b * x + c
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def fit(sample_data): # 随机生成样本数据
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x_data = np.array([data[0] for data in sample_data])
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y_data = np.array([data[1] for data in sample_data]) # 拟合样本数据
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popt, pcov = curve_fit(exponential_function, x_data, y_data) # 计算拟合结果
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# 计算拟合误差
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error,r_squared = error_func(x_data, y_data, *popt)
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fit_function_name = exponential_function.__name__# 打印拟合函数的形式、系数、误差和优度
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print("拟合函数形式：{}".format(fit_function_name))
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print("拟合系数：{}".format(popt))
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print("误差：{:.4f}".format(error))
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print("拟合优度（R^2）：{:.4f}".format(r_squared))
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# if __name__ == '__main__':
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# askfile()
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# fit(sampleData) # 对当前x、y值进行拟合
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# # 给出一个指数函数拟合的例子，读者可自己写拟合函数
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# def fit_function(x,a,b,c):
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# return a * np.exp(b * x) + c
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#
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# def fit(sample_data): # 随机生成样本数据
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# x_data = np.array([data[0] for data in sample_data])
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# y_data = np.array([data[1] for data in sample_data]) # 拟合样本数据
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# popt, pcov = curve_fit(fit_function, x_data, y_data) # 计算拟合结果
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# y_fit = fit_function(x_data, *popt)
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# residuals = y_data - y_fit
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# ss_res = np.sum(residuals**2)
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# ss_tot = np.sum((y_data - np.mean(y_data))**2)
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# r_squared = 1 - (ss_res / ss_tot)
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# fit_function_name = fit_function.__name__# 打印拟合函数的形式、系数、误差和优度
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# print("拟合函数形式：{}".format(fit_function_name))
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# print("拟合系数：{}".format(popt))
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# print("误差：{:.4f}".format(np.sqrt(np.mean(residuals**2))))
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# print("拟合优度（R^2）：{:.4f}".format(r_squared))
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# if __name__ == '__main__':
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# askfile()
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# fit(sampleData) # 对当前x、y值进行拟合
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def change_Q(no):
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gl_data.Quadrant = no #更改全局变量的象限显示
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if no: #若为一象限，则修改显示下限为0
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gl_data.LOW = 0
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else: #若为四象限，则修改显示下限为-gl_data.MAXV
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gl_data.LOW = -gl_data.MAXV
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q_button() #更新象限显示面板
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def q_button():
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r = 7.5
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rr = 2.5
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for widget in q_root.winfo_children():
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widget.destroy()
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q_cv = tk.Canvas(q_root, width=450, height=500)
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q_cv.place(x=0, y=0)
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l = tk.Label(q_root, text='坐标轴', bd=0, font=("微软雅黑", 16) , anchor=W)
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l.place(x=20, y=0, width=80, height=50,)
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# 四象b限按钮
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b1 = tk.Button(q_root, text='四象限', bd=0, font=("微软雅黑", 16)
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, command=lambda: change_Q(0), anchor=W)
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b1.place(x=170, y=0, width=80, height=50,)
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# 一象限按钮
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b2 = tk.Button(q_root, text='一象限', bd=0, font=("微软雅黑", 16)
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, command=lambda: change_Q(1), anchor=W)
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b2.place(x=320, y=0, width=80, height=50,)
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# 绘制标记框
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q_cv.create_oval(140 - r, 25 - r, 140 + r, 25 + r, fill="white", width=1, outline="black")
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q_cv.create_oval(290 - r, 25 - r, 290 + r, 25 + r , fill="white", width=1, outline="black")
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# 根据当前的象限选择值来填充标记框
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if gl_data.Quadrant == 0:
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q_cv.create_oval(140 - rr, 25 - rr, 140 + rr, 25 + rr, fill="black", width=1, outline="black")# 绘制象限
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# Q_cv.create_rectangle(80, 80, 270, 270, fill="white") # 第一象限
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# Q_cv.create_rectangle(270, 80, 500, 270, fill="red") # 第二象限
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# Q_cv.create_rectangle(80, 270, 270, 480, fill="blue") # 第三象限
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# Q_cv.create_rectangle(270, 270, 500, 480, fill="green") # 第四象限
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else:
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q_cv.create_oval(290 - rr, 25 - rr, 290 + rr, 25 + rr, fill="black", width=1, outline="black")
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# if __name__ == '__main__':
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# # 象限选择相关界面
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# q_root = tk.Tk()
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# q_root.geometry("500x550") # 设置窗口的大小和位置
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# q_button()
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# q_root.mainloop()
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# 定义模型为y=ax+b
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def model(x, a, b):
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return a*x + b
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# 定义误差函数error_function()
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def error_function(x,y,a, b):
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y_pred = model(x, a, b)
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error = np.mean((y - y_pred)**2)
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return error
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# 定义梯度函数gradient()
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def gradient(a, b,x,y):
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y_pred = model(x, a, b)
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gradient_a = 2*np.mean((y_pred - y)*x)
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gradient_b = 2*np.mean(y_pred - y)
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return gradient_a, gradient_b
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if __name__ == '__main__':
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# 读取sampledata值
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sampledata = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
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x = np.array(sampledata)
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y = np.array([-2, 1, 0, 1, 2, 3, 4, 5, 6, 8])
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# 初始化参数
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a = 0
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b = 0
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learning_rate = 0.01
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num_iterations = 1000
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# 梯度下降算法
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for i in range(num_iterations):
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grad_a, grad_b = gradient(a, b,x,y)
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a -= learning_rate * grad_a
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b -= learning_rate * grad_b
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# 输出最终拟合结果
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final_error = error_function(x,y,a, b)
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# gl_data.X = x # 为二次函数.txt
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# gl_data.Y = y
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# draw_axis(-100, 100)
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# positive_mask = x >= 0.0 # 给样本点分类
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# negative_mask = y < 0.0 # 给样本点分类
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# positive_colors = ['red' if x >= 0.0 else 'blue' for x in x] # 给样本点分类
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# negative_colors = ['green' if x >= 0.0 else 'purple' for x in x] # 给样本点分类
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# # 根据样本点类型决定样本点颜色
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# plt.scatter(gl_data.X[positive_mask], gl_data.Y[positive_mask], color=np.array(positive_colors)[positive_mask],
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# lw=1)
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# plt.scatter(gl_data.X[negative_mask], gl_data.Y[negative_mask], color=np.array(negative_colors)[negative_mask],
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# lw=1)
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# curve_x = np.arange(-1000, 1000) # 按照步长生成的一串数字
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# curve_y = [model(i, a, b) for i in curve_x] # 根据x来计算y1值
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# plt.plot(curve_x, curve_y, color='blue', label='Fitted Curve') # 绘制拟合曲线
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# plt.legend()
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# plt.show()
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print("拟合函数形式: y = ax + b")
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print("系数 a:{:.2f}".format(a))
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print("系数 b:{:.2f}".format( b))
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print("误差计算方法: 均方误差")
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print("最终误差:{:.2f}".format(final_error)) |
