JR.Gemini's Knowledge Base

Tuesday, March 3, 2009

Pointer in C

A simple example to using pointer in C.

we claim a pointer in main function, and hope to pass it to subroutin to allocate the memory.

How to use it?

Solution 1, return the address back.

=======================

#include

int array_size =10;

int fill_array( float *ptr)

{

int i;

printf("Address %d\n", &ptr);

ptr = (float *) malloc(array_size *sizeof(float));

for(i=0; i<>

{

ptr[i] = (float)i;

}

for(i=0; i

{

printf("In function\n");

printf("%d - %f ", &ptr[i],ptr[i]);

}

printf("\n");

printf("ptr = %d\n", &ptr);

return(ptr);

}

main()

{

float *ptr = NULL;

int i;

printf("Address %d\n", &ptr);

ptr=fill_array(&ptr);

printf("Out function\n");

printf("Address %d\n", &ptr);

for(i=0; i

{

printf("Out function\n");

printf("%d - %f ", &ptr[i], ptr[i]);

}

=======================

The result will be

-------------------------------------

Address 1245052 //ptr in main( )

Address 1244968 //ptr in fill_array( )

In function

4397824 - 0.000000 In function

4397828 - 1.000000 In function

4397832 - 2.000000 In function

4397836 - 3.000000 In function

4397840 - 4.000000 In function

4397844 - 5.000000 In function

4397848 - 6.000000 In function

4397852 - 7.000000 In function

4397856 - 8.000000 In function

4397860 - 9.000000

ptr = 1244968 //ptr in fill_array( )

Out function

Address 1245052

Out function

4397824 - 0.000000 Out function

4397828 - 1.000000 Out function

4397832 - 2.000000 Out function

4397836 - 3.000000 Out function

4397840 - 4.000000 Out function

4397844 - 5.000000 Out function

4397848 - 6.000000 Out function

4397852 - 7.000000 Out function

4397856 - 8.000000 Out function

4397860 - 9.000000 Press any key to continue

-------------------------------------

Solution 2 , call by address.

=======================

#include

void function(float ** ptrf)

{

int i;

printf("address of *ptrf = %d\n", &*ptrf);

*ptrf = (float *) malloc(5*sizeof(float*));

printf("address of *ptrf = %d\n", &*ptrf);

for(i=0; i<5;>

{

(*ptrf)[i] =i;

}

int main()

{

float *ptr=NULL;

int i;

printf("address of *ptr = %d\n", &ptr);

function(&ptr);

for(i=0; i<5;>

{

printf("%f ", ptr[i]);

}

printf("\nAddress ponited to by the ptr out function \nptr=%d, &ptr=%d\n", ptr, &ptr);

}

=======================

The result will be

---------------------------------

address of *ptr = 1245052

address of *ptrf = 1245052

0.000000 1.000000 2.000000 3.000000 4.000000

Address ponited to by the ptr out function

ptr=4397856, &ptr=1245052

Press any key to continue

---------------------------------

Simple Matlab demo of LM algorithm

Special thanks to Pradit Mittrapiyanuruk.

His open article gives us a very good simple demo of Levenberg-Marquardt Algorithm.

The code is almost the same with code provided from http://www.cse.ucsd.edu/classes/fa04/cse252c/vrabaud1.pdf

The red statements are simple plotting graphic. The statements will show the result as iteration=1,2,3,5,10, and 100.

=================================================================

%definition, giving a function f and create jacobian function for function

syms a b x y real;

f=( a * cos(b*x) + b * sin(a*x))

d=y-f;

Jsym=jacobian(d,[a b]);

%Generate the synthetic data from the curve function with some additional

%noise

a=100;

b=102;

x=[0:0.1:2*pi]';

y = a * cos(b*x) + b * sin(a*x);

% add random noise

y_input = y + 5*rand(length(x),1);

%start the LMA

% initial guess for the parameters

a0=100.5; b0=102.5;

y_init = a0 * cos(b0*x) + b0 * sin(a0*x);

Ndata=length(y_input);

Nparams=2; % a and b are the parameters to be estimated

n_iters=100; % set # of iterations for the LM

lamda=0.01; % set an initial value of the damping factor for the LM

updateJ=1;

a_est=a0;

b_est=b0;

for it=1:n_iters

if updateJ==1

% Evaluate the Jacobian matrix at the current parameters (a_est, b_est)

J=zeros(Ndata,Nparams);

for i=1:length(x)

J(i,:)=[-cos(b_est*x(i))-(b_est*cos(a_est*x(i))*x(i)) (a_est*sin(b_est*x(i))*x(i))-sin(a_est*x(i))];

end

% Evaluate the distance error at the current parameters

y_est = a_est * cos(b_est*x) + b_est * sin(a_est*x);

d=y_input-y_est;

% compute the approximated Hessian matrix, J’ is the transpose of J

H=J'*J;

if it==1 % the first iteration : compute the total error

e=dot(d,d);

end

% Apply the damping factor to the Hessian matrix

H_lm=H+(lamda*eye(Nparams,Nparams));

% Compute the updated parameters

dp=-inv(H_lm)*(J'*d(:));

a_lm=a_est+dp(1);

b_lm=b_est+dp(2);

% Evaluate the total distance error at the updated parameters

y_est_lm = a_lm * cos(b_lm*x) + b_lm * sin(a_lm*x);

% plot

if it == 1

h1=plot(y_input, 'b');

hold on

h1=plot(y_est_lm, 'r');

legend(h1,'iteration=1');

hold off

pause

end

if it == 2

h1=plot(y_input, 'b');

hold on

h1=plot(y_est_lm, 'r');

legend(h1,'iteration=2');

hold off

pause

end

if it == 3

h1=plot(y_input, 'b');

hold on

h1=plot(y_est_lm, 'r');

legend(h1,'iteration=3');

hold off

pause

end

if it == 5

h1=plot(y_input, 'b');

hold on

h1=plot(y_est_lm, 'r');

legend(h1,'iteration=5');

hold off

pause

end

if it == 10

h2=plot(y_input, 'b');

hold on

h2=plot(y_est_lm, 'r');

legend(h2,'iteration=10');

hold off

pause

end

if it == 100

h3=plot(y_input, 'b');

hold on

h3=plot(y_est_lm, 'r');

legend(h3,'iteration=100');

hold off

pause

end

d_lm=y_input-y_est_lm;

e_lm=dot(d_lm,d_lm);

% If the total distance error of the updated parameters is less than the previous one

% then makes the updated parameters to be the current parameters

% and decreases the value of the damping factor

if e_lm

lamda=lamda/10;

a_est=a_lm;

b_est=b_lm;

e=e_lm;

disp(e);

updateJ=1;

else % otherwise increases the value of the damping factor

updateJ=0;

lamda=lamda*10;

end

=================================================================

Code download, lmademo.m

You will the blue line is fitting close to red line after few iterations.

Friday, February 20, 2009

HTK Chapter 3 - Section 2 - Step 7

Below paragraphs are belong to

HTKBooks,
苏统华, 哈尔滨工业大学人工智能研究室, 2006年10月30日,
Howard Hung-Ju Chou, Intelligence Information Retrieval Lab., NCKU, Taiwan(R.O.C.).

Environment:

HTK 3.4
Cygwin NT-5.1 1.5.25

Section 2 is Creating Monophone HMMs - 建立單音素模型

Step 6 - Creating Flat Start Monophones
Step 7 - Fixing the Slience Models
Step 8 - Realigning the Training Data

In Step 6, we generate hmm0, hmm1, hmm2, with slience model "sil".

Now the tutorial teaching us to model the model in Fig. 3.9 in Subsection 3.2.2 in HTK Books.

Physical meaning,....

Create a 3 state model for "sp", so we just only one non-emtting state for "sp" model.

How to do that?

Use text editor to...
Use HHEd

The content of "sil" is

=================================================================

~h "sil"

< BEGINHMM >

< NUMSTATES > 5

< STATE > 2

<> 39

-9.389361e-001 -1.287944e+000 8.473723e-002 -4.411200e+000 5.332393e-001 1.843251e-001 2.939802e+000 -2.362492e+000 3.039350e-001 5.898609e-003 -3.105349e+000 -1.462931e+000 5.539479e+001 -2.752953e-002 -2.782337e-002 5.648132e-003 4.534409e-002 1.876847e-002 2.492056e-002 1.361921e-002 -1.723138e-002 1.886967e-002 3.497830e-002 1.276191e-002 2.784961e-002 -3.208526e-002 3.180009e-004 1.971325e-003 -3.830043e-003 -1.048350e-002 -1.810746e-003 -1.773861e-003 -9.375007e-004 3.254613e-004 8.180511e-004 3.765909e-003 1.624564e-003 -3.620259e-004 3.278390e-003

< VARIANCE > 39

4.180664e+001 3.271134e+001 3.581472e+001 6.693031e+001 3.528064e+001 5.052157e+001 2.934049e+001 3.423428e+001 3.710680e+001 3.691701e+001 3.710829e+001 2.969890e+001 6.507053e+001 1.337987e+000 1.072017e+000 1.307518e+000 1.887581e+000 1.697909e+000 1.890241e+000 1.785829e+000 2.181937e+000 1.875866e+000 1.797650e+000 1.730149e+000 1.642454e+000 9.926388e-001 1.710149e-001 1.462792e-001 1.848170e-001 2.626473e-001 2.605441e-001 2.970572e-001 3.222261e-001 3.782587e-001 3.125882e-001 3.063583e-001 2.895371e-001 2.911398e-001 1.187189e-001

< GCONST > 1.071964e+002

< STATE > 3

< MEAN > 39

-1.991913e+000 -4.775551e-002 2.959489e+000 2.209434e+000 2.078557e+000 5.562240e+000 5.464221e+000 -4.776323e+000 1.673594e+000 2.683963e+000 -4.633354e+000 -9.166243e-001 4.628856e+001 -1.207492e-001 -8.760695e-002 -7.070365e-002 7.516075e-002 -4.011013e-003 3.128541e-002 8.115381e-002 -3.286631e-002 1.295639e-001 1.558424e-001 5.380721e-002 1.054287e-001 -1.449030e-001 1.667164e-002 2.022874e-002 1.105829e-003 -2.183086e-002 -7.496935e-003 -4.172942e-002 -3.657551e-002 1.193289e-002 -1.476659e-002 -2.710904e-002 1.349834e-002 9.330045e-004 2.211097e-002

< VARIANCE > 39

5.752877e+000 5.706749e+000 9.791572e+000 1.276698e+001 1.414043e+001 1.682921e+001 1.643664e+001 1.884838e+001 1.942560e+001 2.041147e+001 1.927709e+001 1.510888e+001 1.051241e+001 2.168639e-001 3.732721e-001 6.485465e-001 8.246439e-001 9.308486e-001 1.138545e+000 1.447520e+000 1.688959e+000 1.681041e+000 1.680561e+000 1.580671e+000 1.330634e+000 9.859556e-002 3.477598e-002 6.478215e-002 1.191088e-001 1.600942e-001 1.801341e-001 2.153407e-001 2.852951e-001 3.301157e-001 3.403606e-001 3.369383e-001 3.197604e-001 2.676942e-001 1.323066e-002

7.787578e+001

< STATE > 4

< MEAN > 39

-2.982345e+000 -1.252340e+000 1.087486e+000 7.909203e-001 1.536108e+000 3.573169e+000 5.625374e+000 -3.234990e+000 2.314626e+000 3.188504e+000 -9.258319e-001 1.509047e+000 4.699720e+001 -7.613304e-003 5.702919e-003 -6.563795e-003 -4.346590e-003 -7.446251e-003 -8.997340e-003 -3.822424e-003 -2.726374e-003 -3.682886e-003 -1.174716e-003 1.001520e-002 1.304566e-002 -2.283418e-003 -2.802775e-004 1.980037e-003 1.587337e-003 -6.755204e-004 2.919145e-003 1.646213e-003 -1.079046e-004 1.305768e-003 2.884402e-004 -2.650670e-003 -2.699222e-003 -4.054980e-003 3.949025e-003

< VARIANCE > 39

5.313723e+000 4.299637e+000 5.806711e+000 7.572632e+000 1.195562e+001 1.127259e+001 1.345822e+001 1.842092e+001 1.902783e+001 1.841946e+001 1.679353e+001 1.275744e+001 2.541775e+000 1.125962e-001 2.241242e-001 3.554686e-001 4.804470e-001 7.102868e-001 8.679712e-001 1.053879e+000 1.259253e+000 1.247817e+000 1.199414e+000 1.138910e+000 9.791774e-001 7.236452e-002 2.274701e-002 4.417740e-002 7.068438e-002 9.644291e-002 1.455498e-001 1.809241e-001 2.171511e-001 2.593471e-001 2.625059e-001 2.464305e-001 2.333392e-001 2.013770e-001 1.429966e-002

< GCONST > 6.495581e+001

< TRANSP > 5

0.000000e+000 1.000000e+000 0.000000e+000 0.000000e+000 0.000000e+000

0.000000e+000 9.399074e-001 6.009261e-002 0.000000e+000 0.000000e+000

0.000000e+000 0.000000e+000 8.703428e-001 1.296572e-001 0.000000e+000

0.000000e+000 0.000000e+000 0.000000e+000 9.800954e-001 1.990458e-002

0.000000e+000 0.000000e+000 0.000000e+000 0.000000e+000 0.000000e+000

< ENDHMM >

=================================================================

We copy the red statements to be model of "sp", but remember that only 3 state exist in model "sp" so we have to modify to 2.

=======================================================================

~h "sp"

< BEGINHMM >

< NUMSTATES > 3

< STATE > 2

< MEAN > 39

< VARIANCE > 39

< GCONST > 1.071964e+002

< TRANSP > 3

0.000000e+000 1.000000e+000 0.000000e+000

0.000000e+000 8.703428e-001 1.296572e-001

0.000000e+000 0.000000e+000 0.000000e+000

< ENDHMM >

=======================================================================

Then use HHEd to modefy hmm4/macros and hmm4/hmmdefs accroding the edit commands in sil.hed.

--------------------------------------------------------------------------------------------------------------------

$ HHEd -H ./hmms/hmm4/macros -H ./hmms/hmm4/hmmdefs -M ./hmms/hmm5 sil.hed ./lists/monophones1

--------------------------------------------------------------------------------------------------------------------

The new hmmdefs, will be added the following statements,

======================================================================================

~s "silst"

< MEAN > 39

-9.389361e-01 -1.287944e+00 8.473723e-02 -4.411200e+00 5.332393e-01 1.843251e-01 2.939802e+00 -2.362492e+00 3.039350e-01 5.898609e-03 -3.105349e+00 -1.462931e+00 5.539479e+01 -2.752953e-02 -2.782337e-02 5.648132e-03 4.534409e-02 1.876847e-02 2.492056e-02 1.361921e-02 -1.723138e-02 1.886967e-02 3.497830e-02 1.276191e-02 2.784961e-02 -3.208526e-02 3.180009e-04 1.971325e-03 -3.830043e-03 -1.048350e-02 -1.810746e-03 -1.773861e-03 -9.375007e-04 3.254613e-04 8.180511e-04 3.765909e-03 1.624564e-03 -3.620259e-04 3.278390e-03

4.180664e+01 3.271134e+01 3.581472e+01 6.693031e+01 3.528064e+01 5.052157e+01 2.934049e+01 3.423428e+01 3.710680e+01 3.691701e+01 3.710829e+01 2.969890e+01 6.507053e+01 1.337987e+00 1.072017e+00 1.307518e+00 1.887581e+00 1.697909e+00 1.890241e+00 1.785829e+00 2.181937e+00 1.875866e+00 1.797650e+00 1.730149e+00 1.642454e+00 9.926388e-01 1.710149e-01 1.462792e-01 1.848170e-01 2.626473e-01 2.605441e-01 2.970572e-01 3.222261e-01 3.782587e-01 3.125882e-01 3.063583e-01 2.895371e-01 2.911398e-01 1.187189e-01

< GCONST > 1.071964e+02

======================================================================================

And original ~h "sil" and ~h "sp" become to be like following,

====================================================

~h "sp"

~s "silst"

0.000000e+00 7.000000e-01 3.000000e-01

0.000000e+00 8.703428e-01 1.296572e-01

0.000000e+00 0.000000e+00 0.000000e+00

~h "sil"

< BEGINHMM >

< NUMSTATES > 5

< STATE > 2

< MEAN > 39

< VARIANCE > 39

< GCONST > 1.071964e+02

~s "silst"

< STATE > 4

< MEAN > 39

-2.982345e+00 -1.252340e+00 1.087486e+00 7.909203e-01 1.536108e+00 3.573169e+00 5.625374e+00 -3.234990e+00 2.314626e+00 3.188504e+00 -9.258319e-01 1.509047e+00 4.699720e+01 -7.613304e-03 5.702919e-03 -6.563795e-03 -4.346590e-03 -7.446251e-03 -8.997340e-03 -3.822424e-03 -2.726374e-03 -3.682886e-03 -1.174716e-03 1.001520e-02 1.304566e-02 -2.283418e-03 -2.802775e-04 1.980037e-03 1.587337e-03 -6.755204e-04 2.919145e-03 1.646213e-03 -1.079046e-04 1.305768e-03 2.884402e-04 -2.650670e-03 -2.699222e-03 -4.054980e-03 3.949025e-03

< VARIANCE > 39

5.313723e+00 4.299637e+00 5.806711e+00 7.572632e+00 1.195562e+01 1.127259e+01 1.345822e+01 1.842092e+01 1.902783e+01 1.841946e+01 1.679353e+01 1.275744e+01 2.541775e+00 1.125962e-01 2.241242e-01 3.554686e-01 4.804470e-01 7.102868e-01 8.679712e-01 1.053879e+00 1.259253e+00 1.247817e+00 1.199414e+00 1.138910e+00 9.791774e-01 7.236452e-02 2.274701e-02 4.417740e-02 7.068438e-02 9.644291e-02 1.455498e-01 1.809241e-01 2.171511e-01 2.593471e-01 2.625059e-01 2.464305e-01 2.333392e-01 2.013770e-01 1.429966e-02

< GCONST > 6.495583e+01

< TRANSP > 5

0.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00

0.000000e+00 7.519259e-01 4.807409e-02 2.000000e-01 0.000000e+00

0.000000e+00 0.000000e+00 8.703428e-01 1.296572e-01 0.000000e+00

0.000000e+00 2.000000e-01 0.000000e+00 7.840764e-01 1.592367e-02

0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00

< ENDHMM >

====================================================

Because the commands in sil.hed,

============================

AT 2 4 0.2 { sil. transp }

AT 4 2 0.2 { sil. transp }

AT 1 3 0.3 { sp. transp }

TI slist { sil.state[3], sp.state[2] }

============================

AT i j prob itemList(t) in page 256 in HTKBook 3.4.

The probability will be rescaled so that summation of p is equal to 1.0.

For example in ~h "sp",

We modify it by AT 1 3 0.3 { sp.transp }, so the sp.transp

==================================

< TRANSP > 3

0.000000e+000 1.000000e+000 0.000000e+000

0.000000e+000 8.703428e-001 1.296572e-001

0.000000e+000 0.000000e+000 0.000000e+000

==================================

to be rescaled to

==================================

< TRANSP > 3

0.000000e+00 7.000000e-01 3.000000e-01

0.000000e+00 8.703428e-01 1.296572e-01

0.000000e+00 0.000000e+00 0.000000e+00

==================================

TI means Tie itemlist to be macroname.

-------------------------------------

$ Ti macroname itemlist

-------------------------------------

To know more about TI command, please refer to Chapter 10.3 in HTK Book. (PS: the section 10.3 and section 10.4 should be exchanged.)

To know more about HHed, please refer to Chapter 10 in HTK Book.

Continue...

Wednesday, February 18, 2009

Memo to use CLAMP to generate simulation data

Feel free to use the model save file, named "generate_100_200_0.25.mdl"

Aother one is the same with the tutorial, named "generate_6_200_0.25.mdl"

Download from HERE.

"generate_100_200_0.25.mdl" means

Number of curves: 100
Total time: 200 seconds
Sample interval: 0.25
Concentration is 1, 0.99, 0.98~0.01
Injection time: 100 seconds

Generate simulation file with noise fast,

Selection add noise, choose the value you want
Save the file to be "Save sim"
Click model page
Press Simulation again
You will recover the data with no noise

標點符號的英文

相信一定有人跟我一樣，英文不是很好。

看到一些文件，會看到各式各樣的符號，然後也不太熟悉，所以這些網頁到是整理不少符號。

http://www.eol.cn/article/20060227/3175182.shtml

http://www.hoyo.idv.tw/78/english-comma.htm

http://pub.thit.edu.tw/ThitCC/邱世芬老師/doc/3-1-6/3-1-6-2.htm

http://www.grammarbook.com/

JR.Gemini's Knowledge Base

Tuesday, March 3, 2009

Pointer in C

Simple Matlab demo of LM algorithm

Friday, February 20, 2009

HTK Chapter 3 - Section 2 - Step 7

Wednesday, February 18, 2009

Memo to use CLAMP to generate simulation data

標點符號的英文

Clicky

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