# -*- 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))