predictor.py 文件源码

def predictKnn(data, priceToPredict):
    corelationCoefficiantDictionary = {}
    corelationCoefficiantArray = []
    openingPriceTrain, openingPriceTest, closingPriceTrain, closingPriceTest = \
        data["openingPriceTrain"], data["openingPriceTest"], data["closingPriceTrain"], data["closingPriceTest"]

    for k in range( 1 , 100 , 1):
        neigh = KNeighborsRegressor(n_neighbors=k)
        #n = 7 best fits
        neigh.fit(openingPriceTrain, closingPriceTrain)

        closingPriceTestArray = np.reshape(closingPriceTest,-1)
        knnpr = neigh.predict(openingPriceTest)
        predictedArray = np.reshape(knnpr,-1)

        corelationCoefficient = pearsonr(closingPriceTestArray,predictedArray)
        corelationCoefficiantDictionary[k] = corelationCoefficient[0]
        corelationCoefficiantArray.append(corelationCoefficient[0])
    plotter.plot(corelationCoefficiantArray)
    # plotter.show()

    bestK = max(corelationCoefficiantDictionary, key=corelationCoefficiantDictionary.get)
    neighBest = KNeighborsRegressor(n_neighbors=bestK)
    neighBest.fit(openingPriceTrain, closingPriceTrain)
    openingPriceToPredict = np.array([priceToPredict])
    print("K = ")
    print(bestK)
    print(neighBest.predict(openingPriceToPredict))