import numpy as npfrom math import sqrtfrom collections import Counterclass KNNClassifier:  def __init__(self,k):    """初始化KNN分類器"""    assert k >= 1    """斷言判斷k的值是否合法"""    self.k = k    self._X_train = None    self._y_train = None  def fit(self,X_train,y_train):    """根據訓練數據集X_train和Y_train訓練KNN分類器，形成模型"""    assert X_train.shape[0] == y_train.shape[0]    """數據和標簽的大小必須一樣    assert self.k <= X_train.shape[0]    """k的值不能超過數據的大小"""    self._X_train = X_train    self._y_train = y_train    return self  def predict(self,X_predict):    """必須將訓練數據集和標簽擬合為模型才能進行預測的過程"""    assert self._X_train is not None and self._y_train is not None    """訓練數據和標簽不可以是空的"""    assert X_predict.shape[1]== self._X_train.shape[1]    """待預測數據和訓練數據的列(特征個數)必須相同"""    y_predict = [self._predict(x) for x in X_predict]    return np.array(y_predict)  def _predict(self,x):    """給定單個待測數據x,返回x的預測數據結果"""    assert x.shape[0] == self._X_train.shape[1]    """x表示一行數據，即一個數組，那么它的特征數據個數，必須和訓練數據相同    distances = [sqrt(np.sum((x_train - x)**2))for x_train in self._X_train]    nearest = np.argsort(distances)    topk_y = [self._y_train[i] for i in nearest[:self.k]]    votes = Counter(topk_y)    return votes.most_common(1)[0][0]

from KNN.py import KNNClassifierraw_data_x = [[3.393,2.331],       [3.110,1.781],       [1.343,3.368],       [3.582,4.679],       [2.280,2.866],       [7.423,4.696],       [5.745,3.533],       [9.172,2.511],       [7.792,3.424],       [7.939,0.791]]raw_data_y = [0,0,0,0,0,1,1,1,1,1]X_train = np.array(raw_data_x)y_train = np.array(raw_data_y)x = np.array([9.880,3.555])# 要將x這個矩陣轉換成2維的矩陣，一行兩列的矩陣X_predict = x.reshape(1,-1)"""1，創建一個對象，設置K的值為6"""knn_clf = KNNClassifier(6)"""2，將訓練數據和訓練標簽融合"""knn_clf.fit(X_train,y_train)"""3，經過2才能跳到這里，傳入待預測的數據"""y_predict = knn_clf.predict(X_predict)print(y_predict)