ML之MLiR：输入两个向量，得出两个向量之间的相关度

目录

输出结果

实现代码

输出结果

实现代码

import numpy as np
from astropy.units import Ybarn
import math  
from statsmodels.graphics.tukeyplot import results
 
 
def computeCorrelation(X, Y): 
    xBar = np.mean(X) 
    yBar = np.mean(Y)
    SSR = 0          
    varX = 0
    varY = 0
    for i in range(0 , len(X)):  
        diffXXBar = X[i] - xBar  
        diffYYBar = Y[i] - yBar
        SSR += (diffXXBar * diffYYBar)
        varX +=  diffXXBar**2    
        varY += diffYYBar**2
    
    SST = math.sqrt(varX * varY) 
    return SSR / SST
 
testX = [1, 3, 8, 7, 9]
testY = [10, 12, 24, 21, 34]
 
print ("r:",computeCorrelation(testX, testY))     
 
def polyfit(x,y,degree):
    results={}
    
    coeffs =np.polyfit(x,y,degree)  
    results['polynomial'] = coeffs.tolist()  
    p=np.poly1d(coeffs)                    
    yhat=p(x)                             
    ybar=np.sum(y)/len(y)                 
    ssreg=np.sum((yhat-ybar)**2)       
    sstot=np.sum((y-ybar)**2)
    results['determination']=ssreg/sstot
    
    return results
 
print (polyfit(testX, testY, 1)["determination"])     

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ML之MLiR：输入两个向量，得出两个向量之间的相关度