[人工智能]knn算法核心代码

    def predict_labels(self, dists, k=1):
        print("predict_labels")
        """
        Given a matrix of distances between test points and training points,
        predict a label for each test point.

        Inputs:
        - dists: A numpy array of shape (num_test, num_train) where dists[i, j]
          gives the distance betwen the ith test point and the jth training point.

        Returns:
        - y: A numpy array of shape (num_test,) containing predicted labels for the
          test data, where y[i] is the predicted label for the test point X[i].
        """
        num_test = dists.shape[0]
        #获取测试数据的个数
        print(num_test)
        y_pred = np.zeros(num_test)
        #利用测试集的个数创建一个0的向量

        for i in range(num_test):
            # A list of length k storing the labels of the k nearest neighbors to
            # the ith test point.
            closest_y = []
            #这个数组保存与该测试集相似的图片所属的类别
            #########################################################################
            # TODO:                                                                 #
            # Use the distance matrix to find the k nearest neighbors of the ith    #
            # testing point, and use self.y_train to find the labels of these       #
            # neighbors. Store these labels in closest_y.                           #
            # Hint: Look up the function numpy.argsort.                             #
            #########################################################################
            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****

            idx = np.argsort(dists[i])[:k]#idx是比较最小的下标
            #找到与该测试图片相似的最小的k个位置
            closest_y = self.y_train[idx]
            #通过位置找到与该测试图片相似的训练图片所属的类别

            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
            #########################################################################
            # TODO:                                                                 #
            # Now that you have found the labels of the k nearest neighbors, you    #
            # need to find the most common label in the list closest_y of labels.   #
            # Store this label in y_pred[i]. Break ties by choosing the smaller     #
            # label.                                                                #
            #########################################################################
            # *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
            #self.y_train的值是属于哪个分类
            counter = np.zeros(np.max(self.y_train) + 1)  # (C,)
            '''for j in closest_y:
              counter[j] += 1'''
            np.add.at(counter, closest_y, 1)
            y_pred[i] = np.argmax(counter)
			#看哪个类别多，就预测哪个类别 投票机制
            # *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****

        return y_pred```

开始感觉很难 其实知道后 很简单