How to realize the knn algorithm of Machine Learning by pandas
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The details are as follows
# coding: gbkimport pandas as pdimport numpy as npdef getdata (path): data = pd.read_csv (path, header=None, sep='\ t') character = data.iloc [:,:-1] label = data.iloc [:,-1] chara_max = character.max () chara_min = character.min () chara_range = chara_max-chara_min normal_chara = (character-chara_min) / chara_range return normal_chara, label # obtains normalized eigenvalues and tags def knn (inX, normal_chara Label, k): data_sub = normal_chara-inX data_square = data_sub.applymap (np.square) data_sum = data_square.sum (axis=1) data_sqrt = data_sum.map (np.sqrt) dis_sort = data_sqrt.argsort () k_label = label [dis _ sort [: K]] label_sort = k_label.value_counts () res_label = label_sort.index [0] return res_label # knn algorithm classification
Machine Learning-- the basic implementation of KNN
# _ * _ coding _ * _ import numpy as npimport mathimport operator def get_data (dataset): X = dataset [:,:-1] .astype (np.float) y = dataset [:,-1] return XMagi y # def cal_dis (aMague b): # x1Magna y1 = a [:] # x2Magi y2 = b [:] # dist = math.sqrt (math.pow (2Magnex2)-math.pow (2Query x1)) def knnclassifer (dataset,predict,k=3): X Y = get_data (dataset) dic = {} distince = np.sum ((predict-x) * * 2 countLabel axisym1) * * 0.5 sorted_dict = np.argsort (distince) # [2 034] countLabel = {} for i in range (k): label = y [sorted _ ordered [I]] # print (icountLabel sorted [I], label) countLabel [label] = countLabel.get (label,0) + 1 new_dic = sorted (countLabel,key=operator.itemgetter (0) Reverse=True) return new_dic [0] [0] if _ _ name__ ='_ _ main__': dataset = np.loadtxt ("dataset.txt", dtype=np.str,delimiter= ",") predict = [2Maine 2] label = knnclassifer (dataset,predict,3) print (label) these are all the contents of the article "how pandas implements the knn algorithm for Machine Learning" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!
