from numpy import tile 
import operator 
 
def do_knn_classifier(in_array, data_set, labels, k): 
 ''''' 
 classify the in_array according the data set and labels 
 ''' 
 
 #计算距离适量 
 data_set_size = data_set.shape[0] 
 diff_matrix = tile(in_array, (data_set_size, 1)) - data_set 
 sq_diff_matrix = diff_matrix ** 2 
 sq_distance = sq_diff_matrix.sum(axis=1) 
 distances = sq_distance ** 0.5 
 
 #argsort函数返回的是数组值从小到大的索引值， 距离排序 
 sorted_dist_indicies = distances.argsort() 
 
 # 选择K个紧邻 
 class_count = {} 
 for i in range(k): 
 vote_label = labels[sorted_dist_indicies[i]] 
 class_count[vote_label] = class_count.get(vote_label, 0) + 1 
 
 #排序，并返回最相邻的分类 
 sorted_class_count = sorted(class_count.iteritems(), key=operator.itemgetter(1), reverse=True) 
 
 return sorted_class_count[0][0] 

from numpy import tile 
import operator 
 
def auto_normalize_data(data_set): 
 ''''' 
 对数据集进行归一化操作 
 ''' 
 # 参数0使函数可以从列中选取最小值，而不是当前行的最小值 
 min_vals = data_set.min(0) 
 max_vals = data_set.max(0) 
 ranges = max_vals - min_vals 
 
 
 # 归一化处理 
 m = data_set.shape[0] 
 norm_data_set = data_set - tile(min_vals, (m, 1)) 
 norm_data_set = norm_data_set / tile(ranges, (m, 1)) 
 
 return norm_data_set, ranges, min_vals 

from numpy import array 
from knn.knn_classifier import do_knn_classifier 
 
def get_data_set(): 
 ''''' 
 Get data set and labels 
 ''' 
 group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]]) 
 labels = ['A', 'A', 'B', 'B'] 
 
 return group, labels 
 
if __name__ == '__main__': 
 data_set, labels = get_data_set() 
 
 t = do_knn_classifier(array([0.2, 0.1]), data_set, labels, 3) 
 print t