# -*- encoding: utf-8 -*-
"""
 @Author: packy945
 @FileName: data.py
 @DateTime: 2023/5/31 11:42
 @SoftWare: PyCharm
"""
import tkinter as tk
from tkinter import *
import random
import numpy as np

X = []                  # 等待拟合的x值
Y = []                  # 等待拟合的y值
global Canvas2          # 用于显示函数的画布
Img1 = None             # 绘制的dot.png图像
Img2 = None             # 绘制的line.png图像
Xian_index = 0          # 当前选择的函数编号
Quadrant = 0            # 当前选择的象限信息0为四象限，1为一象限
MAXV = 1000             # 最大值
Out = ''                # 拟合输出信息
LOW = -MAXV             # 坐标轴显示上界
HIGH = MAXV             # 坐标轴显示下界


def random_points():
    x=[]
    y=[]
    for i in range(20):
        x_= random.uniform(0, 1000)#生成随机浮点数
        y_= random.uniform(0, 1000)#生成随机浮点数
        x.append(x_) #加入列表
        y.append(y_)
    x=np.array(x) #列表转数组
    y=np.array(y)
    arr = np.array(list(zip(x, y)))# 将两个一维数组拼接成二维数组
    return arr

def compute_curveData(num, step, coefficient):#coefficient代表二次函数系数，数组形式
    def quadratic_function(x):#构造二次函数y = a * X^2 + b * X + C
        return coefficient[0] * x ** 2 + coefficient[1] * x + coefficient[2]
    x_values = np.arange(-num, num, step)
    y_values = quadratic_function(x_values)#调用quadratic_function(x)函数得到y值
    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
    return curve_data


# #################################拟合优度R^2的计算######################################
def __sst(y_no_fitting):
    """
    计算SST(total sum of squares) 总平方和
    :param y_no_predicted: List[int] or array[int] 待拟合的y
    :return: 总平方和SST
    """
    y_mean = sum(y_no_fitting) / len(y_no_fitting)
    s_list =[(y - y_mean)**2 for y in y_no_fitting]
    sst = sum(s_list)
    return sst


def __ssr(y_fitting, y_no_fitting):
    """
    计算SSR(regression sum of squares) 回归平方和
    :param y_fitting: List[int] or array[int]  拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 回归平方和SSR
    """
    y_mean = sum(y_no_fitting) / len(y_no_fitting)
    s_list =[(y - y_mean)**2 for y in y_fitting]
    ssr = sum(s_list)
    return ssr


def __sse(y_fitting, y_no_fitting):
    """
    计算SSE(error sum of squares) 残差平方和
    :param y_fitting: List[int] or array[int] 拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 残差平方和SSE
    """
    s_list = [(y_fitting[i] - y_no_fitting[i])**2 for i in range(len(y_fitting))]
    sse = sum(s_list)
    return sse


def goodness_of_fit(y_fitting, y_no_fitting):
    """
    计算拟合优度R^2
    :param y_fitting: List[int] or array[int] 拟合好的y值
    :param y_no_fitting: List[int] or array[int] 待拟合y值
    :return: 拟合优度R^2
    """
    ssr = __ssr(y_fitting, y_no_fitting)
    sst = __sst(y_no_fitting)
    rr = ssr /sst
    return rr

def least_sqaure(x,y):
    n = len(x)
    sumx, sumy, sumxy, sumxx = 0, 0, 0, 0
    for i in range(0, n):
        sumx += x[i]
        sumy += y[i]
        sumxx += x[i] * x[i]
        sumxy += x[i] * y[i]
    a = (n * sumxy - sumx * sumy) / (n * sumxx - sumx * sumx)
    b = (sumxx * sumy - sumx * sumxy) / (n * sumxx - sumx * sumx)
    return a, b
import  time

def gradient_descent(x, y):
    s = time.time()
    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
    eta = 0.09
    i = 1
    while True:
        y = x ^ 2 / 2 - 2 * x
        x -= eta * 2 * x
        if y <= 0.0001:
            print('x is %.6f\ny is %.6f\nand steps are %d' % (x, y, i))
            break
        i += 1
    e = time.time()
    print('time is %.4f second' % (e - s))


if __name__ == '__main__':
    random_points()
    pass