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# -*- encoding: utf-8 -*-
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"""
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@Author: packy945
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@FileName: data.py
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@DateTime: 2023/5/31 11:42
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@SoftWare: PyCharm
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"""
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import tkinter as tk
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from tkinter import *
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import random
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import numpy as np
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FITT_SAVE = {}
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FITT_LIST = []
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X = [] # 等待拟合的x值
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Y = [] # 等待拟合的y值
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global Canvas2 # 用于显示函数的画布
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Img1 = None # 绘制的dot.png图像
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Img2 = None # 绘制的line.png图像
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Xian_index = 0 # 当前选择的函数编号
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Quadrant = 0 # 当前选择的象限信息0为四象限，1为一象限
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MAXV = 1000 # 最大值
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INDEX = 5 # 拟合函数索引
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Out = '' # 拟合输出信息
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LOW = -MAXV # 坐标轴显示上界
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HIGH = MAXV # 坐标轴显示下界
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MAGNIDICATION = 1.0 # 放大倍数
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def random_points():
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x=[]
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y=[]
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for i in range(20):
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x_= random.uniform(0, 1000)#生成随机浮点数
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y_= random.uniform(0, 1000)#生成随机浮点数
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x.append(x_) #加入列表
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y.append(y_)
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x=np.array(x) #列表转数组
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y=np.array(y)
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arr = np.array(list(zip(x, y)))# 将两个一维数组拼接成二维数组
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return arr
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def compute_curveData(num, step, coefficient):#coefficient代表二次函数系数，数组形式
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def quadratic_function(x):#构造二次函数y = a * X^2 + b * X + C
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return coefficient[0] * x ** 2 + coefficient[1] * x + coefficient[2]
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x_values = np.arange(-num, num, step)
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y_values = quadratic_function(x_values)#调用quadratic_function(x)函数得到y值
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curve_data = np.column_stack((x_values, y_values))#将两个一维数组堆叠成二维数组，形成(x,y)的形式
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return curve_data
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# #################################拟合优度R^2的计算######################################
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def __sst(y_no_fitting):
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"""
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计算SST(total sum of squares) 总平方和
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:param y_no_predicted: List[int] or array[int] 待拟合的y
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:return: 总平方和SST
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"""
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y_mean = sum(y_no_fitting) / len(y_no_fitting)
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s_list =[(y - y_mean)**2 for y in y_no_fitting]
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sst = sum(s_list)
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return sst
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def __ssr(y_fitting, y_no_fitting):
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"""
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计算SSR(regression sum of squares) 回归平方和
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:param y_fitting: List[int] or array[int] 拟合好的y值
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:param y_no_fitting: List[int] or array[int] 待拟合y值
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:return: 回归平方和SSR
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"""
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y_mean = sum(y_no_fitting) / len(y_no_fitting)
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s_list =[(y - y_mean)**2 for y in y_fitting]
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ssr = sum(s_list)
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return ssr
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def __sse(y_fitting, y_no_fitting):
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"""
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计算SSE(error sum of squares) 残差平方和
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:param y_fitting: List[int] or array[int] 拟合好的y值
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:param y_no_fitting: List[int] or array[int] 待拟合y值
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:return: 残差平方和SSE
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"""
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s_list = [(y_fitting[i] - y_no_fitting[i])**2 for i in range(len(y_fitting))]
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sse = sum(s_list)
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return sse
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def goodness_of_fit(y_fitting, y_no_fitting):
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"""
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计算拟合优度R^2
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:param y_fitting: List[int] or array[int] 拟合好的y值
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:param y_no_fitting: List[int] or array[int] 待拟合y值
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:return: 拟合优度R^2
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"""
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ssr = __ssr(y_fitting, y_no_fitting)
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sst = __sst(y_no_fitting)
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rr = ssr /sst
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return rr
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def least_sqaure(x,y):
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n = len(x)
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sumx, sumy, sumxy, sumxx = 0, 0, 0, 0
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for i in range(0, n):
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sumx += x[i]
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sumy += y[i]
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sumxx += x[i] * x[i]
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sumxy += x[i] * y[i]
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a = (n * sumxy - sumx * sumy) / (n * sumxx - sumx * sumx)
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b = (sumxx * sumy - sumx * sumxy) / (n * sumxx - sumx * sumx)
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return a, b
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import time
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def gradient_descent(x, y):
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s = time.time()
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x = 10000 * np.random.randn(1) # 产生服从正太分布的一个数(均值=0，方差=1)，扩大10000倍
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eta = 0.09
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i = 1
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while True:
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y = x ^ 2 / 2 - 2 * x
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x -= eta * 2 * x
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if y <= 0.0001:
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print('x is %.6f\ny is %.6f\nand steps are %d' % (x, y, i))
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break
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i += 1
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e = time.time()
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print('time is %.4f second' % (e - s))
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if __name__ == '__main__':
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random_points()
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pass
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