sklearn 機器學習（二）——K-近鄰（KNN）算法使用

import numpy as np
from sklearn import neighbors

def create_datasets():
    datasets = np.array([[8,4,2],[7,1,1,],[1,4,4],[3,0,5],[3,0,4],[5,2,1],[5,3,2]]) # 數據集
    labels = [0,0,1,1,0,0,1] #['非常熱','非常熱','一般熱','一般熱','一般熱']                     # 類標籤
    return datasets,labels

def knn_sklearn_predict():
    # 調用機器學習庫knn分類器算法
    knn = neighbors.KNeighborsClassifier()
    datasets, labels = create_datasets()
    # 傳入參數，特徵數據和分類標籤
    print(datasets)
    knn.fit(datasets, labels)
    # knn預測
    predictRes = knn.predict([[2, 4, 0]])
    print("天氣：\t", "非常熱" if predictRes[0] == 0 else '一般熱')
    return predictRes

if __name__ == '__main__':
    knn_sklearn_predict()