Description: VC 6.0下的主成分分析代码，包括关系矩阵，协方差矩阵，以及因子分析的功能-VC 6.0 Principal Component Analysis code, including the relationship matrix, covariance matrix, and the function of factor analysis Platform: |
Size: 241664 |
Author:Eniak |
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Description: 进行多元统计分析基类代码及接口的实现
元统计分析 基类 声明
提供矩阵数据格式 及矩阵的 加减乘 运算及多元统计中常用的求 样本均值 协方差 方法-Multivariate statistical analysis for the base class code and the interface element statistical analysis of base class declaration data format and the matrix matrix addition and subtraction, multiplication and Multivariate Statistics used in order to sample mean covariance method Platform: |
Size: 43008 |
Author:胡子春 |
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Description: 本程序编程语言为C，主要用来对遥感训练数据进行处理，得到covariance矩阵。-This program is used to generate 3 files:mean file,covariance matrix of the training set, and inverse covariance matrix for training set. Platform: |
Size: 4096 |
Author:李会利 |
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Description: 经典的最大似然法分类法的C语言实现，有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file.
Note: For constructong variance-covariance matrix must be generic binary file.
Platform: |
Size: 4096 |
Author:李会利 |
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Description: 用来产生多变量高斯过程的MATLAB源程序。-MULTI_GP generates a multivariate Gaussian random process with mean vector m (column vector) and covariance matrix C。 Platform: |
Size: 1024 |
Author:selen32 |
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Description: 计算矩阵的协方差，通过c++实现，可以计算任意向量或矩阵的协方差-Covariance matrix calculated by c++ implementation, we can calculate any vector or matrix of covariance Platform: |
Size: 2118656 |
Author:张增波 |
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Description: 对称矩阵相关元算，主成分分析(PCA)， fisher
discriminant analysis(FDA).，-Introduction
============
This is a class for symmetric matrix related computations. It can be used for
symmetric matrix diagonalization and inversion. If given the covariance matrix,
users can utilize the class for principal component analysis(PCA) and fisher
discriminant analysis(FDA). It can also be used for some elementary matrix and
vector computations.
Usage
=====
It s a C++ program for symmetric matrix diagonalization, inversion and principal
component anlaysis(PCA).
To use it, you need to define an instance of CMatrix class, initialize matrix,
call the public funtions, and finally, free the matrix. For example, for PCA,
CMarix theMat // define CMatrix instance
float** C // define n*n matrix
C = theMat.allocMat( n )
Calculate the matrix (e.g., covariance matrix from data)
float*phi,*lambda // eigenvectors and eigenvalues
int vecNum // number of eigenvectors (<=n)
phi = new float [n*vecNum]
lambda = new float [vecNum]
the Platform: |
Size: 63488 |
Author:朱 |
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Description: Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x.-Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x. Platform: |
Size: 769024 |
Author:bassoum |
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Description: Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x.-Evaluate the multi-variate density with mean vector m and covariance
matrix C for the input vector x. Platform: |
Size: 53248 |
Author:bassoum |
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Description: 水准测量中间接平差以及观测精度的计算，本程序由c++编写，可以得到平差后未知点坐标，协方差阵，观测值改正数等等。-Leveling in indirect adjustment and the calculation of the observational accuracy of the program written by c++ unknown point coordinates can be adjustment, the covariance matrix of observations corrections. Platform: |
Size: 16384 |
Author:李飞 |
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Description: 七单元天线阵MUSIC DOA估计：
d=1 ， 天线阵元的间距；
lma=2， 信号中心波长；
四输入信号；
A=[A1,A2,A3,A4]， 得出A矩；
四信号的频率d=[1.3*cos(v1*n)
1*sin(v2*n)
1*sin(v3*n)
1*sin(v4*n)]
构造输入信号矢量
U=A*d
总的输入信号
总输入信号的协方差矩阵
[s,h]=eig(c)
求协方差的特征矢量及特征值
取出与零特征值对应的特征矢量
求协方差矩阵的逆矩阵
应用Music法估计输出
绘出各波达方向图-Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure Platform: |
Size: 1024 |
Author:xiang |
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Description: 经典的最大似然法分类法的C语言实现，有助于深入了解遥感分类原理。-This program implements the maximum likelihood classification procedure. ouput:1.classified image, and 2. probability file.Note: For constructong variance-covariance matrix must be generic binary file. Platform: |
Size: 3072 |
Author:whicme |
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Description: 两个代码一个是demo, demo是它的小样例子, 另外一个是它的源代码.
- -
This is the matlab implementation of following noise level estimation:
Xinhao Liu, Masayuki Tanaka and Masatoshi Okutomi
Noise Level Estimation Using Weak Textured Patches of a Single Noisy Image
IEEE International Conference on Image Processing (ICIP), 2012.
-
Copyright (C) 2012 Masayuki Tanaka. All rights reserved.
mtanaka@ctrl.titech.ac.jp
-
Contents
-
* NoiseLevel.m
The main code of the noise level estimation.
You can show the description by
> help NoiseLevel
demo.m also includes simple usage.
This algorithm is implemented with only single m file.
* demo.m
Demonstration example.
* lena.png
Sample image.
* README.txt
This file.
-
Note
-
We used the maximum eigenvalue of the gradient covariance matrix Platform: |
Size: 3072 |
Author:马欢 |
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Description: SensorPositons: (Dim x M) matrix, each column is a sensor position and
first column is the reference sensor
the sensors should not lie in one plane or line
r: a (M-1) x 1 vector of TDOA measurements times signal propagation speed
M is the number of sensors and should be at least Dim+2
Q: the covariance matrix of the r vector
SourceLocation: estimated source location
Note: W1 is updated 3 times (RptCnt=3) in Stage-1, however in most
cases updating W1 once (RptCnt=1) is sufficient.
The program can be used for 2D(Dim=2) or 3D(Dim=3) localization
Ming Sun, K. C. Ho 08-01-2009
10-01-2010, revised
Copyright (C) 2009
Computational Intelligence Signal Processing Laboratory
University of Missouri
Columbia, MO 65211, USA.
hod@missouri.edu- SensorPositons: (Dim x M) matrix, each column is a sensor position and
first column is the reference sensor
the sensors should not lie in one plane or line
r: a (M-1) x 1 vector of TDOA measurements times signal propagation speed
M is the number of sensors and should be at least Dim+2
Q: the covariance matrix of the r vector
SourceLocation: estimated source location
Note: W1 is updated 3 times (RptCnt=3) in Stage-1, however in most
cases updating W1 once (RptCnt=1) is sufficient.
The program can be used for 2D(Dim=2) or 3D(Dim=3) localization
Ming Sun, K. C. Ho 08-01-2009
10-01-2010, revised
Copyright (C) 2009
Computational Intelligence Signal Processing Laboratory
University of Missouri
Columbia, MO 65211, USA.
hod@missouri.edu Platform: |
Size: 1024 |
Author:marcelonog29 |
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Description: SensorPositons: (Dim x M) matrix, each column is a sensor position and
first column is the reference sensor
the sensors should not lie in one plane or line
r: a (M-1) x 1 vector of TDOA measurements times signal propagation speed
M is the number of sensors and should be at least Dim+2
Q: the covariance matrix of the r vector
SourceLocation: estimated source location
Note: W1 is updated 3 times (RptCnt=3) in Stage-1, however in most
cases updating W1 once (RptCnt=1) is sufficient.
The program can be used for 2D(Dim=2) or 3D(Dim=3) localization
Ming Sun, K. C. Ho 08-01-2009
10-01-2010, revised
Copyright (C) 2009
Computational Intelligence Signal Processing Laboratory
University of Missouri
Columbia, MO 65211, USA.
hod@missouri.edu- SensorPositons: (Dim x M) matrix, each column is a sensor position and
first column is the reference sensor
the sensors should not lie in one plane or line
r: a (M-1) x 1 vector of TDOA measurements times signal propagation speed
M is the number of sensors and should be at least Dim+2
Q: the covariance matrix of the r vector
SourceLocation: estimated source location
Note: W1 is updated 3 times (RptCnt=3) in Stage-1, however in most
cases updating W1 once (RptCnt=1) is sufficient.
The program can be used for 2D(Dim=2) or 3D(Dim=3) localization
Ming Sun, K. C. Ho 08-01-2009
10-01-2010, revised
Copyright (C) 2009
Computational Intelligence Signal Processing Laboratory
University of Missouri
Columbia, MO 65211, USA.
hod@missouri.edu Platform: |
Size: 1024 |
Author:marcelonog29 |
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Description: 基于C语言编写的一个协方差矩阵工程项目，希望对大家有帮助。(A covariance matrix engineering project based on C language is expected to be helpful to all of you.) Platform: |
Size: 2761728 |
Author:学无ZJ |
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Search - covariance matrix c