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 |
Hits:
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 |
Hits:
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 |
Hits:
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 |
Hits:
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 |
Hits:
Search - c program for covariance matrix