Description: 假近邻法(False Nearest Neighbor, FNN)计算嵌入维的Matlab程序
文件夹说明：
Main_FNN.m - 程序主函数，直接运行此文件即可
LorenzData.dll - 产生Lorenz时间序列
PhaSpaRecon.m - 相空间重构
fnn_luzhenbo.dll - 假近邻计算主函数
SearchNN.dll - 近邻点搜索
buffer_SearchNN_1.dll - 近邻点搜索缓存1
buffer_SearchNN_2.dll - 近邻点搜索缓存2
参考文献：
M.B.Kennel, R.Brown, H.D.I.Abarbanel. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Phys. Rev. A 1992,45:3403.
-false neighbors (False Nearest Neighbor, FNN) calculation embedding dimension of the Matlab program folder : Main_FNN.m-procedure main function, Direct operating this document can be LorenzData.dll-time series produced Lorenz PhaS paRecon.m-phase space reconstruction fnn_luzhenbo.dll-calculated at the main function neighbors SearchNN.dll-point search buffer_SearchNN_1.dll neighbor- Search neighbor point a buffer_SearchNN_2.dll Cache-Cache Search neighbors point two reference Literature : M. B. Kennel, R. Brown, H. D. I. Abarbanel. Determining embedding dime nsion for phase-space reconstruction using a g eometrical construction [J]. Phys. Rev. A 1992 , 45:3403. Platform: |
Size: 99328 |
Author:呆雁 |
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Description: 用matlab实现的kdtree，kdtree适合用于数据挖掘中用于寻找最近邻。-Using matlab to achieve kdtree, kdtree suitable for data mining used to find the nearest neighbors. Platform: |
Size: 102400 |
Author:项龙江 |
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Description: knn算法演示代码，可自动学习输入点，并形象表现输入点的最近k个邻居。-KNN algorithm demo code can be automatically input to learn, and the image input of the recent performance of k-neighbors. Platform: |
Size: 5120 |
Author: |
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Description: Nearest neighbors is a semi deconvolution algorithm. I m a newbie in matlab but I hope this helps somebody :) Platform: |
Size: 1024 |
Author:caldeira |
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Description: K-nearest neighbors 搜索 聚类时经常使用的一种方法 国外网站转载- The following utilities are provided:
- Nearest neighbor
- K-Nearest neighbors
- Radius Search
They al supports N-dimensions and work on double, it is possible to choose if return the distances.
Here is a time comparison with a vectrized m-code:
N=1000000 number of reference points
Nq=100 number of query points
dim=3 dimension of points
k=3 number of neighbor
tic
[idc,dist]=BruteSearchMex(p ,qp , k ,k) MEX
toc
tic
[idc,dist]=knnsearch(qp,p,k) VECTORIZED M-CODE
toc
p=rand(N,dim)
qp=rand(Nq,dim)
Output:
Elapsed time is 0.962640 seconds.
Elapsed time is 18.813100 seconds. Platform: |
Size: 4096 |
Author:Ming |
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Description: 数据挖掘导论中的K近邻聚类算法，用C++编写而成。-Introduction to Data Mining of the K neighbors clustering algorithm, using C++ has been prepared by. Platform: |
Size: 440320 |
Author:绍敏 |
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Description: FLANN - Fast Library for Approximate Nearest neighbors
This is a library for fast approximate nearest neighbor matching. -FLANN- Fast Library for Approximate Nearest neighbors This is a library for fast approximate nearest neighbor matching. Platform: |
Size: 277504 |
Author:cui |
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Description: FAST K-NEAREST NEIGHBORS SEARCH
-FAST K-NEAREST NEIGHBORS SEARCH
Description You can find the description at:
http://www.advancedmcode.org/gltree.html
A Pro Version has been published on:
http://www.advancedmcode.org/gltree-pro-version-a-fast-nearest-neighbour-library-for-matlab-and-c.html
Acknowledgements The author wishes to acknowledge the following in the creation of this submission:
K-NEAREST NEIGHBOURS AND RADIUS (RANGE) SEARCH
This submission has inspired the following:
K-NEAREST NEIGHBOURS AND RADIUS (RANGE) SEARCH, FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION
MATLAB release MATLAB 7.5 (R2007b)
Other requirements Need a mex compiler
Zip File Content
Other Files BuildGLTree2DFEX.cpp,
BuildGLTree2DFEX.m,
DeleteGLTree2DFEX.cpp,
DeleteGLTree2DFEX.m,
GLTree2DFEX.cpp,
GLTree2DFEX.h,
license.txt,
NNSearch2DFEX.cpp,
NNSearch2DFEX.m,
TestMexFiles.m
Platform: |
Size: 8192 |
Author:谢冉 |
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Description: In pattern recognition, the k-nearest neighbor algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor. Platform: |
Size: 589824 |
Author:bwindhya |
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Description: FAST K-NEAREST NEIGHBORS SEARCH，
Simple but very fast algorithm for nearest neighbors search in 2D space.
Platform: |
Size: 2048 |
Author:hfy |
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Description: 采用快速K近邻与Kmeans聚类算法来计算前K个近邻，舍弃了一部分不可能成为待测样本的前K个近邻的训练样本，从而减少了计算量，提高了分类速度-Fast K-nearest neighbor Kmeans clustering algorithm to calculate the K nearest neighbors, abandoning the training samples of the part can not become the first K neighbors of the test sample, thereby reducing the amount of calculation and improve the speed of classification Platform: |
Size: 4096 |
Author:houying |
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Search - nearest neighbors