Method mfh/getJacobian
GETJACOBIAN calculate Jacobian matrix for a given function.
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GETJACOBIAN calculate Jacobian matrix for a given function. Each function
is assumed to be function only of the parameters resepect to
which the derivative should be calculated. All the other
quantities should be inserted in the 'constants' field.
CALL: J = getJacobian(func,pl)
INPUTS:
- func. The function
PARAMETERS:
- p0. The set of parameters. (double vector).
- DerivStep. The set of derivative steps. (doble vactor).
OUTPUTS:
- J. the Jacobian matrix. n x q where q is numel(p0) and n is
numel(func(p0).y).
ALGORITHM:
For each parameter an incremented parameter is calculated as
pd = p0
pd(i) = DerivStep(i)*p0(i) + p0(i)
if p0(i) = 0 then pd(i) = DerivStep(i) + p0(i).
Then the function is evaluated
f0 = func(p0)
fd = func(pd)
J = (fd - f0)./(pd(i) - p0(i))
Parameters Description
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Method Details |
|
Access |
public |
Defining Class |
mfh |
Sealed |
0 |
Static |
0 |
Parameter Description
Sets for this method … |
Default |
Default |
no description |
Key |
Default Value |
Options |
Description |
getJacobian |
PARS |
[] |
none |
The set of parameter values. A NumParams x 1 array or a pest object. |
DERIVSTEP |
[] |
none |
The set of derivative steps. A NumParams x 1 array |
Example |
plist('PARS', [[]], 'DERIVSTEP', [[]]) |
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Some information of the method mfh/getJacobian are listed below: |
Class name |
mfh |
Method name |
getJacobian |
Category |
Signal Processing |
Package name |
ltpda |
VCS Version |
967b0eec0dece803a81af8ef54ad2f8c784b20b2 |
Min input args |
1 |
Max input args |
-1 |
Min output args |
1 |
Max output args |
-1 |
Can be used as modifier |
1 |
Supported numeric types |
{'double'} |
|
Method: mfh/getHessian |
|
Method: mfh/loglikelihood |
 |
©LTP Team