import numpy as npfrom math import sqrtfrom collections import Counterfrom .metrics import accuracy_score

class KNNClassifier:

    def __init__(self, k):        """初始化kNN分类器"""        assert k >= 1, "k must be valid"        self.k = k        self._X_train = None        self._y_train = None

    def fit(self, X_train, y_train):        """根据训练数据集X_train和y_train训练kNN分类器"""        assert X_train.shape[0] == y_train.shape[0], \            "the size of X_train must be equal to the size of y_train"        assert self.k <= X_train.shape[0], \            "the size of X_train must be at least k."

        self._X_train = X_train        self._y_train = y_train        return self

    def predict(self, X_predict):        """给定待预测数据集X_predict，返回表示X_predict的结果向量"""        assert self._X_train is not None and self._y_train is not None, \                "must fit before predict!"        assert X_predict.shape[1] == self._X_train.shape[1], \                "the feature number of X_predict must be equal to X_train"

        y_predict = [self._predict(x) for x in X_predict]        return np.array(y_predict)

    def _predict(self, x):        """给定单个待预测数据x，返回x的预测结果值"""        assert x.shape[0] == self._X_train.shape[1], \            "the feature number of x must be equal to X_train"

        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]

    def score(self, X_test, y_test):        """根据测试数据集 X_test 和 y_test 确定当前模型的准确度"""

        y_predict = self.predict(X_test)        return accuracy_score(y_test, y_predict)

    def __repr__(self):        return "KNN(k=%d)" % self.k