fit_rayleigh_pdf.zip - fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution.
given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format:result = fit_rayleigh_pdf( x,y,W,hAx )
input: y - vector, samples of the histogram to be fitted
x - vector, position of the samples of the histogram (i.e. y = f(x,a))
W - matrix or scalar, a square weighting matrix of the size NxN where
N = length(y), or 0 to indicate no weighting is needed.
hAx - handle of an axis, on which the fitted distribution is plotted
output: result - structure with the fields
s - fitted parameter
VAR - variance of the estimation
type- weighted LS or not weighted LS
iter- number of iteration for the solution,2011-02-09 19:13:28,下载10次
fit_ML_rayleigh.zip - fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!.
Given the samples of a rayleigh distribution, the PDF parameter is found
fits data to the probability of the form:
p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format: result = fit_ML_rayleigh( x,hAx )
input: x - vector, samples with rayleigh distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
s - fitted parameter
CRB - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type- ML ,2011-02-09 19:10:54,下载61次
fit_ML_normal.zip - fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!.
Given the samples of a normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))
with parameters: u,sig^2
format: result = fit_ML_normal( x,hAx )
input: x - vector, samples with normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
sig^2,u - fitted parameters
CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML ,2011-02-09 19:09:33,下载14次
fit_ML_maxwell.zip - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML ,2011-02-09 19:08:34,下载17次
fit_ML_log_normal.zip - fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
,2011-02-09 19:07:30,下载10次
fit_ML_laplace.zip - fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
,2011-02-09 19:06:32,下载17次
fit_mix_gaussian.zip - fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm
format: [u,sig,t,iter] = fit_mix_gaussian( X,M )
input: X - input samples, Nx1 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian
sig - fitted standard deviation for each gaussian
t - probability of each gaussian in the complete distribution
iter- number of iterations done by the function,2011-02-09 19:05:43,下载11次
fit_mix_2D_gaussian.zip - fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm
format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M )
input: X - input samples, Nx2 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian (each mean is a 2x1 vector)
covar - fitted covariance for each gaussian. this is a 2x2xM matrix.
t - probability of each gaussian in the complete distribution
iter - number of iterations done by the function,2011-02-09 19:04:25,下载21次
fit_maxwell_pdf.zip - fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution.
given the samples of the histogram of the samples, finds the
distribution parameter that fits the histogram samples.
fits data to the probability of the form:
p(r) = sqrt(2/pi)*(a^(-3/2))*(r^2)*exp(-(r^2)/(2*a))
with parameter: a
format: result = fit_maxwell_pdf( x,y,W,hAx )
input: y - vector, samples of the histogram to be fitted
x - vector, position of the samples of the histogram (i.e. y = f(x,a))
W - matrix or scalar, a square weighting matrix of the size NxN where
N = length(y), or 0 to indicate no weighting is needed.
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
a - fitted parameter
VAR - variance of the estimation
type- weighted LS or not weighted LS
iter- number of iteration for the solution,2011-02-09 19:02:55,下载3次
randgen2.zip - randgen(mu,mu1,mu2,cov1,cov2,cov3) = Random generation of Gaussian Samples
in d-dimensions where d = 2
mu, mu1, mu2 = (x,y) coordinates(means) that the gaussian samples are centered around
cov1, cov2, cov3 are the covariance matrices and will vary changing the
shape of the distribution, example: cov = sigma^2*Identity Matrix, where sigma^2 = a scalar
N = the number of gaussian samples used are provided as user input,
A test set of N/2 and a training set of N/2 gaussian samples is also generated
Output is directed to the command window and a plot of the distributions are generated,2011-02-09 18:52:20,下载10次
plot3d_2.zip - This function produces an image of a 3D object defined by matrix a(l,m,n) in terms of voxels the image is a view after rotating the object by angles alfa and beta (in degree) b is the image and d is its ditance to the viewer matrix The first figure depicts the object using only its gray level values
The second image depicts the object using some lighting effect
rotate3d may be used for reorientation but the obtained image is planner,2011-02-09 18:49:45,下载13次
parzenwin2.zip - PARZEN WINDOWN(N) returns the N-point Parzen window in a column vector,2011-02-09 18:44:17,下载18次