knn演算法實現
Ⅰ 請簡述為什麼kNN演算法是懶惰的
kNN演算法，對於分類的不同屬性定義距離。對於一個新的待分類樣本點，只取k個與該樣本距離最近的點，然後找這k個點所歸屬的最多的類做為新樣本點的分類。由於只取k個點，不需要全部樣本點來做分類，所以說這個演算法懶惰。
Ⅱ KNN演算法中距離度量方式L1,L2,
L1是一階范數,L2是二階范數,COS是餘弦距離
Ⅲ KNN演算法，k近鄰
K最近鄰(k-Nearest Neighbour，KNN)分類演算法，是一個理論上比較成熟的方法，也是最簡單的機器學習演算法之一。該方法的思路是：如果一個樣本在特徵空間中的k個最相似(即特徵空間中最鄰近)的樣本中的大多數屬於某一個類別，則該樣本也屬於這個類別。
Ⅳ 如何用python實現knn演算法
1. 數據分類：離散型標簽 2. 數據回歸：連續型標簽 近鄰演算法的准則是：尋找接近新數據點的訓練樣本的數目，根據訓練樣本的信息來預測新數據點的某些信息。
Ⅳ 文本分類器（基於KNN演算法），語言最好是Matlab的，有測試數據集。。。。
function [ccr,pgroupt]=knnt(x,group,K,dist,xt,groupt)
%#
%# AIM: to classify test set objects or unknown objects with the
%# K Nearest Neighbour method
%#
%# PRINCIPLE: KNN is a supervised, deterministic, non-parametric
%# classification method. It uses the majority rule to
%# assign new objects to a class.
%# It is assumed that the number of objects in each class
%# is similar.
%# There are no assumptions about the data distribution and
%# the variance-covariance matrices of each class.
%# There is no limitation of the number of variables when
%# the Euclidean distance is used.
%# However, when the correlation coefficient is used, the
%# number of variables must be larger than 1.
%# Ref: Massart D. L., Vandeginste B. G. M., Deming S. N.,
%# Michotte Y. and Kaufman L., Chemometrics: a textbook,
%# Chapter 23, 395-397, Elsevier Science Publishers B. V.,
%# Amsterdam 1988.
%#
%# INPUT: x: (mxn) data matrix with m objects and n variables,
%# containing samples of several classes (training set)
%# group: (mx1) column vector labelling the m objects from the
%# training set
%# K: integer, number of nearest neighbours
%# dist: integer,
%# = 1, Euclidean distance
%# = 2, Correlation coefficient, (No. of variables >1)
%# xt: (mtxn) data matrix with mt objects and n variables
%# (test set or unknowns)
%# groupt: (mtx1) column vector labelling the mt objects from
%# the test set
%# --> if the new objects are unknown, input [].
%#
%# OUTPUT: ccr: scalar, correct classification rate
%# pgroupt:row vector, predicted class label for the test set
%# 0 means that the object is not classified to any
%# class
%#
%# SUBROUTINES: sortlab.m: sorts the group label vector into classes
%#
%# AUTHOR: Wen Wu
%# Copyright(c) 1997 for ChemoAc
%# FABI, Vrije Universiteit Brussel
%# Laarbeeklaan 103 1090 Jette
%#
%# VERSION: 1.1 (28/02/1998)
%#
%# TEST: Andrea Candolfi
%#
function [ccr,pgroupt]=knnt(x,group,K,dist,xt,groupt);
if nargin==5, groupt=[]; end % for unknown objects
distance=dist; clear dist % change variable
if size(group,1)>1,
group=group'; % change column vector into row vector
groupt=groupt'; % change column vector into row vector
end;
[m,n]=size(x); % size of the training set
if distance==2 & n<2, error('Number of variables must > 1'),end % to check the number of variables when using correlation coefficient
[mt,n]=size(xt); % size of the test set
dis=zeros(mt,m); % initial values for the distance (matrix of zeros)
% Calculation of the distance for each test set object
for i=1:mt
for j=1:m % between each training set object and each test set object
if distance==1
dis(i,j)=(xt(i,:)-x(j,:))*(xt(i,:)-x(j,:))'; % Euclidian distance
else
r=corrcoef(xt(i,:)',x(j,:)'); % Correlation coefficient matrix
r=r(1,2); % Correlation coefficient
dis(i,j)=1-r*r; % 1 - the power of correlation coefficient
end
end
end
% Finding of the nearest neighbours
lab=zeros(1,mt); % initial values of lab
for i=1:mt % for each test object
[a,b]=sort(dis(i,:)); % sort distances
b=b(find(a<=a(K))); % to find the nearest neighbours indices
b=group(b); % the nearest neighbours objects
[ng,lgroup]=sortlab(b); % calculate the number of objects from each class in the nearest neighbours
a=find(ng==max(ng)); % find the class with the maximum number of objects
if length(a)==1 % only one class
lab(i)=lgroup(a); % class label
else
lab(i)=0; % more than one class
end
end
% Calculation of the success rate
if ~isempty(groupt)
dif=groupt-lab; % difference between predicted class label and known class label
ccr=sum(dif==0)/mt; % success rate
end
pgroupt=lab; % the output vector
Ⅵ 用CUDA實現KNN演算法加速有哪些方法
- BF方法 Matlab實現 (BF-Matlab) - BF方法 C實現（BF-C） - BF方法 CUDA實現（BF-CUDA） - ANN C++庫
Ⅶ python 問題 實現KNN演算法
你好：
字面意思是：字典對象沒有屬性：iteritems；
你這是要遍歷什麼嗎？
Ⅷ KNN演算法小例子看不懂
你的理解是對的，也就是說，第一張圖錯誤，第二張圖正確。
Ⅸ 推薦演算法和KNN演算法的關系
knn演算法是推薦演算法的一種
整體思想比較簡單：計算一個點A與其他所有點之間的距離，取出與該點最近的k個點，然後統計這k個點裡面所屬分類比例最大的，則點A屬於該分類。