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它是 tiff 格式 imagery1 和 Imagery2[![][1]] 2

我想绘制回归线并计算回归系数。随之而来的是 R 平方/拟合优度。

我已经确定了曲线上的回归线并计算了该回归线的系数，但无法计算出它们的拟合优度。

请帮我解决这个问题。

代码计算回归系数并在图上绘制回归线。但我无法计算 R 平方/拟合优度。

还编写了拟合优度代码，但给出错误“TypeError：无法将类型'ndarray'转换为分子/分母”

该错误与“def确定系数部分”有关

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from statistics import mean
from matplotlib import style
style.use('ggplot')

#loading of imageries
im1 = Image.open('D:\\code\\test\\subset\\image1.tif')
im2 = Image.open('D:\\code\\test\\subset\\image2.tif')
#im.show()

#Conversion into array
img1 = np.array(im1)
img2 = np.array(im2)

def estimate_coef(x, y): 
    # number of observations/points 
    n = np.size(x) 

    # mean of x and y vector 
    m_x, m_y = np.mean(x), np.mean(y) 

    # calculating cross-deviation and deviation about x 
    SS_xy = np.sum(y*x) - n*m_y*m_x 
    SS_xx = np.sum(x*x) - n*m_x*m_x 

    # calculating regression coefficients 
    b_1 = SS_xy / SS_xx 
    b_0 = m_y - b_1*m_x 

    return(b_0, b_1) 

def squared_error(ys_orig,ys_line):
    return sum((ys_line - ys_orig) * (ys_line - ys_orig))

def coefficient_of_determination(ys_orig,ys_line):
    y_mean_line = [mean(ys_orig) for y in ys_orig]
    squared_error_regr = squared_error(ys_orig, ys_line)
    squared_error_y_mean = squared_error(ys_orig, ys_mean_line)
    return 1 - (squared_error_regr/squared_error_y_mean)


def plot_regression_line(x, y, b): 
    # plotting the actual points as scatter plot 
    plt.scatter(x, y, color = "m", 
            marker = "o", s = 30)
    # predicted response vector 
    y_pred = b[0] + b[1]*x 

    # plotting the regression line 
    plt.plot(x, y_pred, color = "g") 

    # putting labels 
    plt.xlabel('x') 
    plt.ylabel('y') 

    # function to show plot 
    plt.show() 

b_0, b_1 = estimate_coef(img1, img2)
regression_line = [(b_0*x)+b_1 for x in img1]

r_squared = coefficient_of_determination(img2,regression_line)
print(r_squared)


def main(): 
    # observations 
    x = img1 
    y = img2 

    # estimating coefficients 
    b = estimate_coef(x, y) 
    print("Estimated coefficients:\nb_0 = {} \ \nb_1 = {}".format(b[0], b[1])) 

    # plotting regression line 
    plot_regression_line(x, y, b) 

if __name__ == "__main__": 
    main()
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