ML之MLiR：輸入兩個向量，得出兩個向量之間的相關度

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目錄

輸出結果

實現代碼

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輸出結果

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實現代碼

import numpy as np from astropy.units import Ybarn import math from statsmodels.graphics.tukeyplot import resultsdef computeCorrelation(X, Y): xBar = np.mean(X) yBar = np.mean(Y)SSR = 0 varX = 0varY = 0for i in range(0 , len(X)): diffXXBar = X[i] - xBar diffYYBar = Y[i] - yBarSSR += (diffXXBar * diffYYBar)varX += diffXXBar**2 varY += diffYYBar**2SST = math.sqrt(varX * varY) return SSR / SSTtestX = [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/sstotreturn resultsprint (polyfit(testX, testY, 1)["determination"])

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ML之MLiR：輸入兩個向量，得出兩個向量之間的相關度

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