#! /usr/bin/env python3
# -*- coding: utf-8 -*-

from sklearn import datasets
from collections import Counter  # 为了做投票
from sklearn.model_selection import train_test_split
import numpy as np

# 导入iris数据
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2003)


def euc_dis(instance1, instance2):
    """
    计算两个样本instance1和instance2之间的欧式距离
    instance1: 第一个样本， array型
    instance2: 第二个样本， array型
    """
    # dist = np.sqrt(np.sum(np.square(np.array(instance1) - np.array(instance2))))
    dist = np.sqrt(sum((instance1 - instance2) ** 2))
    return dist


def knn_classify(X, y, testInstance, k):
    """
    给定一个测试数据testInstance, 通过KNN算法来预测它的标签。
    X: 训练数据的特征
    y: 训练数据的标签
    testInstance: 测试数据，这里假定一个测试数据 array型
    k: 选择多少个neighbors?
    """
    # distances = []
    # for i in range(0, len(y)):
    #     distances.append((euc_dis(X[i], testInstance), y[i]))
    # distances.sort(key=lambda x: x[0])
    # counter = Counter()
    # for top in distances[0:k]:
    #     counter[top[1]] += 1
    distances = [euc_dis(x, testInstance) for x in X]
    k_neighbors = np.argsort(distances)[:k]
    counter = Counter(y[k_neighbors])
    return counter.most_common()[0][0]


# 预测结果。
predictions = [knn_classify(X_train, y_train, data, 3) for data in X_test]
correct = np.count_nonzero(predictions == y_test)
print("Accuracy is: %.3f" % (correct / len(X_test)))
