stefan
/
NLMSvariants
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

updated

This commit is contained in:
gurkenhabicht 2018-05-22 17:03:42 +02:00
parent 71bd11c6e9
commit 0233a4c997
4 changed files with 141 additions and 93 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

99
src/ansi_c_implementation/NLMSvariants.c
View File

@ -20,7 +20,7 @@ Created by Stefan Friese on 26.04.2018
typedef SSIZE_T ssize_t;
#endif
double *xSamples;
double *xSamples; // Input values
mldata_t *mlData = NULL; // Machine learning
point_t *points = NULL; // Graphing
@ -64,13 +64,13 @@ double windowXMean(int _arraylength, int xCount); // Returns mean value of give
int main( int argc, char **argv ) {
char *colorChannel = (char *) malloc(sizeof(char)* 32);
char *inputfile = (char *)malloc(sizeof(char) * 32);
unsigned int *seed = NULL;
unsigned *seed = NULL;
unsigned k, xclude = 0;
unsigned int windowSize = 5;
unsigned int samplesCount = 512;
char *stdcolor = "green";
unsigned windowSize = 5;
unsigned samplesCount = 512;
char *stdcolor = "green", xBuffer[512];
colorChannel = stdcolor;
unsigned int uint_buffer[1];
unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4;
@ -82,7 +82,8 @@ int main( int argc, char **argv ) {
--argc;
break;
case 'w':
sscanf(&argv[1][3], "%u", &windowSize);
sscanf(&argv[1][3], "%u", windowBuffer);
windowSize = windowBuffer[0];
++argv;
--argc;
break;
@ -112,6 +113,7 @@ int main( int argc, char **argv ) {
--argc;
break;
case 'x':
sscanf(&argv[1][3], "%s", xBuffer);
xclude = 1;
++argv;
--argc;
@ -124,12 +126,14 @@ int main( int argc, char **argv ) {
++argv;
--argc;
}
init_mldata_t ( windowSize, samplesCount, learnrate );
xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values
points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points
imagePixel_t *image;
image = rdPPM(inputfile); // Set Pointer on input values
printf("window Size: %d\n", mlData->windowSize);
char fileName[50]; // Logfiles and their names
mkFileName(fileName, sizeof(fileName), TEST_VALUES);
FILE* fp5 = fopen(fileName, "w");
@ -146,20 +150,19 @@ int main( int argc, char **argv ) {
printf("srand depends on time\n"); // Default seed is time(NULL)
}
printf("generated weights:\n");
// for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
for (k = 0; k < mlData->windowSize; k++) {
for (k = 0; k < mlData->windowSize; k++) {
mlData->weights[k] = rndm(); // Init random weights
printf("%lf\n", mlData->weights[k]);
}
// }
printf("[%d] %lf\n", k, mlData->weights[k]);
}
mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS); // Logfile weights
FILE *fp0 = fopen(fileName, "w");
// for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
for (k = 0; k < mlData->windowSize; k++) {
fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
}
// }
for (k = 0; k < mlData->windowSize; k++) {
fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
}
fclose(fp0);
/* *math magic* */
@ -169,7 +172,10 @@ int main( int argc, char **argv ) {
if ( xclude == 0 ) {
mkSvgGraph(points); // Graph building
}
free(image);
free(xSamples);
free(points);
free(mlData);
@ -193,10 +199,15 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1); // Includes e(n)
memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero
unsigned i, xCount = 0; // Runtime vars
mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename
FILE* fp4 = fopen(fileName, "w");
fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
FILE *fp9 = fopen(fileName, "w");
double xMean = xSamples[0];
double xSquared = 0.0;
double xPredicted = 0.0;
@ -221,8 +232,9 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
xSquared = 1.0;
}
for ( i = 1; i < _arrayLength; i++ ) { // Update weights
localWeights[ i + 1 ] = localWeights[i] + mlData->learnrate * xError[xCount] // Substract localMean
localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount] // Substract localMean
* ( (xSamples[xCount - i] - xMean) / xSquared );
fprintf( fp9, "%lf\n", localWeights[i] );
}
fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
@ -234,6 +246,7 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
}
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
@ -246,9 +259,11 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
}
deviation /= xErrorLength; // Deviation
printf("mean:%lf, devitation:%lf\t\tlocal Mean\n", mean, deviation);
fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean); // Write to logfile
free(localWeights);
free(xErrorPtr);
free(xError);
fclose(fp4);
//weightsLogger( local_weights, USED_WEIGHTS );
@ -268,7 +283,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy ( localWeights, mlData->weights, mlData->windowSize );
char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount );
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 );
unsigned xCount = 0, i;
double xActual = 0.0;
double xPredicted = 0.0;
@ -276,9 +291,9 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); // Logfile and name handling
FILE *fp3 = fopen(fileName, "w");
fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS);
FILE *fp9 = fopen(fileName, "w");
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted
unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
@ -300,7 +315,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
xSquared = 1.0;
}
for ( i = 1; i < _arrayLength; i++ ) { // Update weights
localWeights[i + 1] = localWeights[i] + mlData->learnrate * xError[xCount]
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
* ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] );
}
@ -329,11 +344,11 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
}
deviation /= xErrorLength; // Deviation
printf("mean:%lf, devitation:%lf\t\tdirect Predecessor\n", mean, deviation);
fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp3);
free(localWeights);
free(xErrorPtr);
free(xError);
}
/*
@ -350,16 +365,18 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy( localWeights, mlData->weights, mlData->windowSize );
char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount);
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1);
unsigned xCount = 0, i;
double xPredicted = 0.0;
double xActual = 0.0;
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling
FILE *fp6 = fopen(fileName, "w");
fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED);
FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // First value will not get predicted
unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount;
@ -381,7 +398,10 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
}
for (i = 1; i < _arrayLength; i++) {
localWeights[i+1] = localWeights[i] + mlData->learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
* ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] );
}
fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
@ -391,7 +411,7 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
points[xCount].yVal[6] = xError[xCount];
}
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
@ -406,10 +426,12 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
}
deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf\t\tdifferential Predecessor\n", mean, deviation);
fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp6);
free(localWeights);
free(xErrorPtr);
free(xError);
// weightsLogger( localWeights, USED_WEIGHTS );
}
@ -448,12 +470,14 @@ Contains and returns every suffix for all existing filenames
*/
char * fileSuffix ( int id ) {
char * suffix[] = { "_weights_pure.txt",
"_weights_used.txt",
"_weights_used_dir_pred_.txt",
"_direct_predecessor.txt",
"_ergebnisse.txt",
"_localMean.txt",
"_testvalues.txt",
"_differential_predecessor.txt"
"_differential_predecessor.txt",
"_weights_used_local_mean",
"_weights_used_diff_pred",
};
return suffix[id];
}
@ -490,11 +514,8 @@ void weightsLogger (double *weights, int val ) {
mkFileName(fileName, sizeof(fileName), val);
FILE* fp = fopen(fileName, "wa");
for (i = 0; i < mlData->windowSize - 1; i++) {
// for (int k = 0; k < WINDOWSIZE; k++) {
fprintf(fp, "[%d]%lf\n", i, weights[i]);
// }
fprintf(fp, "[%d]%lf\n", i, weights[i]);
}
fprintf(fp,"\n\n\n\n===================== NEXT =====================\n");
fclose(fp);
}
@ -853,10 +874,10 @@ void usage ( char **argv ) {
printf("\t-c <color>\t\tUse this color channel from inputfile.\n");
printf("\t-w <digit>\t\tCount of used weights (windowSize).\n");
printf("\t-l <digit>\t\tLearnrate, 0 < learnrate < 1.\n");
printf("\t-x <none>\t\tLogfiles only, no graph building. Choose for intense amount of input data.\n");
printf("\t-x true\t\t\tLogfiles only, no graph building.\n\t\t\t\tChoose for intense amount of input data.\n");
printf("\t-s <digit>\t\tDigit for random seed generator.\n\t\t\t\tSame Digits produce same random values. Default is srand by time.\n");
printf("\n\n");
printf("lms compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\tlms -i myimage.ppm -w 3 -c green -s 5\n");
printf("%s compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\t%s -i myimage.ppm -w 3 -c green -s 5 -x true\n", &argv[0][0], &argv[0][0]);
exit(8);
}
@ -875,6 +896,6 @@ mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double lear
mlData->windowSize = windowSize;
mlData->samplesCount = samplesCount;
mlData->learnrate = learnrate;
mlData->weights = (double *) malloc ( sizeof(double) * windowSize + 1 );
mlData->weights = (double *) malloc ( sizeof(double) * windowSize + 1 );
return mlData;
}

6
src/ansi_c_implementation/nlms_types.h
View File

@ -26,12 +26,14 @@ typedef struct { // Storage for image data
/** file handling* */
enum fileSuffix_t{ // Used in conjunction with MkFileName()
PURE_WEIGHTS,
USED_WEIGHTS,
USED_WEIGHTS_DIR_PRED,
DIRECT_PREDECESSOR,
RESULTS,
LOCAL_MEAN,
TEST_VALUES,
DIFFERENTIAL_PREDECESSOR
DIFFERENTIAL_PREDECESSOR,
USED_WEIGHTS_LOCAL_MEAN,
USED_WEIGHTS_DIFF_PRED
};
enum fileHeader{ // Used in conjunction with MkFilename()

99
src/cpp_implementation/NLMSvariants.cpp
View File

@ -11,6 +11,7 @@ Created by Stefan Friese on 26.04.2018
#include <time.h>
#include <stdlib.h>
#include <string.h>
//#include <float.h> // DBL_MAX
#include "nlms_types.h" // added types
#define RGB_COLOR 255
@ -19,7 +20,7 @@ Created by Stefan Friese on 26.04.2018
typedef SSIZE_T ssize_t;
#endif
double *xSamples;
double *xSamples; // Input values
mldata_t *mlData = NULL; // Machine learning
point_t *points = NULL; // Graphing
@ -63,13 +64,13 @@ double windowXMean(int _arraylength, int xCount); // Returns mean value of give
int main( int argc, char **argv ) {
char *colorChannel = (char *) malloc(sizeof(char)* 32);
char *inputfile = (char *)malloc(sizeof(char) * 32);
unsigned int *seed = NULL;
unsigned *seed = NULL;
unsigned k, xclude = 0;
unsigned int windowSize = 5;
unsigned int samplesCount = 512;
char *stdcolor = "green";
unsigned windowSize = 5;
unsigned samplesCount = 512;
char *stdcolor = "green", xBuffer[512];
colorChannel = stdcolor;
unsigned int uint_buffer[1];
unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4;
@ -81,7 +82,8 @@ int main( int argc, char **argv ) {
--argc;
break;
case 'w':
sscanf(&argv[1][3], "%u", &windowSize);
sscanf(&argv[1][3], "%u", windowBuffer);
windowSize = windowBuffer[0];
++argv;
--argc;
break;
@ -111,6 +113,7 @@ int main( int argc, char **argv ) {
--argc;
break;
case 'x':
sscanf(&argv[1][3], "%s", xBuffer);
xclude = 1;
++argv;
--argc;
@ -123,12 +126,14 @@ int main( int argc, char **argv ) {
++argv;
--argc;
}
init_mldata_t ( windowSize, samplesCount, learnrate );
xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values
points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points
imagePixel_t *image;
image = rdPPM(inputfile); // Set Pointer on input values
printf("window Size: %d\n", mlData->windowSize);
char fileName[50]; // Logfiles and their names
mkFileName(fileName, sizeof(fileName), TEST_VALUES);
FILE* fp5 = fopen(fileName, "w");
@ -145,20 +150,19 @@ int main( int argc, char **argv ) {
printf("srand depends on time\n"); // Default seed is time(NULL)
}
printf("generated weights:\n");
// for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
for (k = 0; k < mlData->windowSize; k++) {
for (k = 0; k < mlData->windowSize; k++) {
mlData->weights[k] = rndm(); // Init random weights
printf("%lf\n", mlData->weights[k]);
}
// }
printf("[%d] %lf\n", k, mlData->weights[k]);
}
mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS); // Logfile weights
FILE *fp0 = fopen(fileName, "w");
// for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
for (k = 0; k < mlData->windowSize; k++) {
fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
}
// }
for (k = 0; k < mlData->windowSize; k++) {
fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
}
fclose(fp0);
/* *math magic* */
@ -168,7 +172,10 @@ int main( int argc, char **argv ) {
if ( xclude == 0 ) {
mkSvgGraph(points); // Graph building
}
free(image);
free(xSamples);
free(points);
free(mlData);
@ -192,10 +199,15 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1); // Includes e(n)
memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero
unsigned i, xCount = 0; // Runtime vars
mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename
FILE* fp4 = fopen(fileName, "w");
fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
FILE *fp9 = fopen(fileName, "w");
double xMean = xSamples[0];
double xSquared = 0.0;
double xPredicted = 0.0;
@ -220,8 +232,9 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
xSquared = 1.0;
}
for ( i = 1; i < _arrayLength; i++ ) { // Update weights
localWeights[ i + 1 ] = localWeights[i] + mlData->learnrate * xError[xCount] // Substract localMean
localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount] // Substract localMean
* ( (xSamples[xCount - i] - xMean) / xSquared );
fprintf( fp9, "%lf\n", localWeights[i] );
}
fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
@ -233,6 +246,7 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
}
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
@ -245,9 +259,11 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
}
deviation /= xErrorLength; // Deviation
printf("mean:%lf, devitation:%lf\t\tlocal Mean\n", mean, deviation);
fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean); // Write to logfile
free(localWeights);
free(xErrorPtr);
free(xError);
fclose(fp4);
//weightsLogger( local_weights, USED_WEIGHTS );
@ -267,7 +283,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy ( localWeights, mlData->weights, mlData->windowSize );
char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount );
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 );
unsigned xCount = 0, i;
double xActual = 0.0;
double xPredicted = 0.0;
@ -275,9 +291,9 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); // Logfile and name handling
FILE *fp3 = fopen(fileName, "w");
fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS);
FILE *fp9 = fopen(fileName, "w");
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted
unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
@ -299,7 +315,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
xSquared = 1.0;
}
for ( i = 1; i < _arrayLength; i++ ) { // Update weights
localWeights[i + 1] = localWeights[i] + mlData->learnrate * xError[xCount]
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
* ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] );
}
@ -331,7 +347,8 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp3);
free(localWeights);
free(xErrorPtr);
free(xError);
}
/*
@ -348,16 +365,18 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy( localWeights, mlData->weights, mlData->windowSize );
char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount);
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1);
unsigned xCount = 0, i;
double xPredicted = 0.0;
double xActual = 0.0;
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling
FILE *fp6 = fopen(fileName, "w");
fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED);
FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // First value will not get predicted
unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount;
@ -379,7 +398,10 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
}
for (i = 1; i < _arrayLength; i++) {
localWeights[i+1] = localWeights[i] + mlData->learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
* ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] );
}
fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
@ -389,7 +411,7 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
points[xCount].yVal[6] = xError[xCount];
}
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
@ -404,10 +426,12 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
}
deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf\t\tdifferential Predecessor\n", mean, deviation);
fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp6);
free(localWeights);
free(xErrorPtr);
free(xError);
// weightsLogger( localWeights, USED_WEIGHTS );
}
@ -446,12 +470,14 @@ Contains and returns every suffix for all existing filenames
*/
char * fileSuffix ( int id ) {
char * suffix[] = { "_weights_pure.txt",
"_weights_used.txt",
"_weights_used_dir_pred_.txt",
"_direct_predecessor.txt",
"_ergebnisse.txt",
"_localMean.txt",
"_testvalues.txt",
"_differential_predecessor.txt"
"_differential_predecessor.txt",
"_weights_used_local_mean",
"_weights_used_diff_pred",
};
return suffix[id];
}
@ -488,11 +514,8 @@ void weightsLogger (double *weights, int val ) {
mkFileName(fileName, sizeof(fileName), val);
FILE* fp = fopen(fileName, "wa");
for (i = 0; i < mlData->windowSize - 1; i++) {
// for (int k = 0; k < WINDOWSIZE; k++) {
fprintf(fp, "[%d]%lf\n", i, weights[i]);
// }
fprintf(fp, "[%d]%lf\n", i, weights[i]);
}
fprintf(fp,"\n\n\n\n===================== NEXT =====================\n");
fclose(fp);
}
@ -851,10 +874,10 @@ void usage ( char **argv ) {
printf("\t-c <color>\t\tUse this color channel from inputfile.\n");
printf("\t-w <digit>\t\tCount of used weights (windowSize).\n");
printf("\t-l <digit>\t\tLearnrate, 0 < learnrate < 1.\n");
printf("\t-x <none>\t\tLogfiles only, no graph building. Choose for intense amount of input data.\n");
printf("\t-x true\t\t\tLogfiles only, no graph building.\n\t\t\t\tChoose for intense amount of input data.\n");
printf("\t-s <digit>\t\tDigit for random seed generator.\n\t\t\t\tSame Digits produce same random values. Default is srand by time.\n");
printf("\n\n");
printf("lms compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\tlms -i myimage.ppm -w 3 -c green -s 5\n");
printf("%s compares prediction methods of least mean square filters.\nBy default it reads ppm file format and return logfiles as well\nas an svg graphs as an output of said least mean square methods.\n\nExample:\n\t%s -i myimage.ppm -w 3 -c green -s 5 -x true\n", &argv[0][0], &argv[0][0]);
exit(8);
}
@ -873,6 +896,6 @@ mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double lear
mlData->windowSize = windowSize;
mlData->samplesCount = samplesCount;
mlData->learnrate = learnrate;
mlData->weights = (double *) malloc ( sizeof(double) * windowSize + 1 );
mlData->weights = (double *) malloc ( sizeof(double) * windowSize + 1 );
return mlData;
}

6
src/cpp_implementation/nlms_types.h
View File

@ -26,12 +26,14 @@ typedef struct { // Storage for image data
/** file handling* */
enum fileSuffix_t{ // Used in conjunction with MkFileName()
PURE_WEIGHTS,
USED_WEIGHTS,
USED_WEIGHTS_DIR_PRED,
DIRECT_PREDECESSOR,
RESULTS,
LOCAL_MEAN,
TEST_VALUES,
DIFFERENTIAL_PREDECESSOR
DIFFERENTIAL_PREDECESSOR,
USED_WEIGHTS_LOCAL_MEAN,
USED_WEIGHTS_DIFF_PRED
};
enum fileHeader{ // Used in conjunction with MkFilename()