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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
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85
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; typedef SSIZE_T ssize_t;
#endif #endif
double *xSamples; double *xSamples; // Input values
mldata_t *mlData = NULL; // Machine learning mldata_t *mlData = NULL; // Machine learning
point_t *points = NULL; // Graphing 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 ) { int main( int argc, char **argv ) {
char *colorChannel = (char *) malloc(sizeof(char)* 32); char *colorChannel = (char *) malloc(sizeof(char)* 32);
char *inputfile = (char *)malloc(sizeof(char) * 32); char *inputfile = (char *)malloc(sizeof(char) * 32);
unsigned int *seed = NULL; unsigned *seed = NULL;
unsigned k, xclude = 0; unsigned k, xclude = 0;
unsigned int windowSize = 5; unsigned windowSize = 5;
unsigned int samplesCount = 512; unsigned samplesCount = 512;
char *stdcolor = "green"; char *stdcolor = "green", xBuffer[512];
colorChannel = stdcolor; colorChannel = stdcolor;
unsigned int uint_buffer[1]; unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4; double learnrate = 0.4;
@ -82,7 +82,8 @@ int main( int argc, char **argv ) {
--argc; --argc;
break; break;
case 'w': case 'w':
sscanf(&argv[1][3], "%u", &windowSize); sscanf(&argv[1][3], "%u", windowBuffer);
windowSize = windowBuffer[0];
++argv; ++argv;
--argc; --argc;
break; break;
@ -112,6 +113,7 @@ int main( int argc, char **argv ) {
--argc; --argc;
break; break;
case 'x': case 'x':
sscanf(&argv[1][3], "%s", xBuffer);
xclude = 1; xclude = 1;
++argv; ++argv;
--argc; --argc;
@ -124,12 +126,14 @@ int main( int argc, char **argv ) {
++argv; ++argv;
--argc; --argc;
} }
init_mldata_t ( windowSize, samplesCount, learnrate ); init_mldata_t ( windowSize, samplesCount, learnrate );
xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values
points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points
imagePixel_t *image; imagePixel_t *image;
image = rdPPM(inputfile); // Set Pointer on input values image = rdPPM(inputfile); // Set Pointer on input values
printf("window Size: %d\n", mlData->windowSize);
char fileName[50]; // Logfiles and their names char fileName[50]; // Logfiles and their names
mkFileName(fileName, sizeof(fileName), TEST_VALUES); mkFileName(fileName, sizeof(fileName), TEST_VALUES);
FILE* fp5 = fopen(fileName, "w"); 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("srand depends on time\n"); // Default seed is time(NULL)
} }
printf("generated weights:\n"); 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 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 mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS); // Logfile weights
FILE *fp0 = fopen(fileName, "w"); FILE *fp0 = fopen(fileName, "w");
// for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
for (k = 0; k < mlData->windowSize; k++) { for (k = 0; k < mlData->windowSize; k++) {
fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]); fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
} }
// }
fclose(fp0); fclose(fp0);
/* *math magic* */ /* *math magic* */
@ -169,7 +172,10 @@ int main( int argc, char **argv ) {
if ( xclude == 0 ) { if ( xclude == 0 ) {
mkSvgGraph(points); // Graph building mkSvgGraph(points); // Graph building
} }
free(image);
free(xSamples); free(xSamples);
free(points); free(points);
free(mlData); 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) double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1); // Includes e(n)
memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero
unsigned i, xCount = 0; // Runtime vars unsigned i, xCount = 0; // Runtime vars
mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename
FILE* fp4 = fopen(fileName, "w"); FILE* fp4 = fopen(fileName, "w");
fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) ); fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
FILE *fp9 = fopen(fileName, "w");
double xMean = xSamples[0]; double xMean = xSamples[0];
double xSquared = 0.0; double xSquared = 0.0;
double xPredicted = 0.0; double xPredicted = 0.0;
@ -221,8 +232,9 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
xSquared = 1.0; xSquared = 1.0;
} }
for ( i = 1; i < _arrayLength; i++ ) { // Update weights 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 ); * ( (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 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 *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()! double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0; xErrorPtr[0] = 0.0;
@ -246,9 +259,11 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
} }
deviation /= xErrorLength; // Deviation deviation /= xErrorLength; // Deviation
printf("mean:%lf, devitation:%lf\t\tlocal Mean\n", mean, 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 fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean); // Write to logfile
free(localWeights); free(localWeights);
free(xErrorPtr);
free(xError);
fclose(fp4); fclose(fp4);
//weightsLogger( local_weights, USED_WEIGHTS ); //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 ); double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy ( localWeights, mlData->weights, mlData->windowSize ); memcpy ( localWeights, mlData->weights, mlData->windowSize );
char fileName[512]; char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount ); double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 );
unsigned xCount = 0, i; unsigned xCount = 0, i;
double xActual = 0.0; double xActual = 0.0;
double xPredicted = 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 mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); // Logfile and name handling
FILE *fp3 = fopen(fileName, "w"); FILE *fp3 = fopen(fileName, "w");
fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) ); 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 for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted
unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount; unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
@ -300,7 +315,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
xSquared = 1.0; xSquared = 1.0;
} }
for ( i = 1; i < _arrayLength; i++ ) { // Update weights 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); * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] ); fprintf( fp9, "%lf\n", localWeights[i] );
} }
@ -329,11 +344,11 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
} }
deviation /= xErrorLength; // Deviation deviation /= xErrorLength; // Deviation
printf("mean:%lf, devitation:%lf\t\tdirect Predecessor\n", mean, 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); fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp3); fclose(fp3);
free(localWeights); 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 ); double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy( localWeights, mlData->weights, mlData->windowSize ); memcpy( localWeights, mlData->weights, mlData->windowSize );
char fileName[512]; char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount); double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1);
unsigned xCount = 0, i; unsigned xCount = 0, i;
double xPredicted = 0.0; double xPredicted = 0.0;
double xActual = 0.0; double xActual = 0.0;
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling
FILE *fp6 = fopen(fileName, "w"); FILE *fp6 = fopen(fileName, "w");
fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER) ); 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 for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // First value will not get predicted
unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount; 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++) { 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 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]; points[xCount].yVal[6] = xError[xCount];
} }
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[] double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()! double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0; xErrorPtr[0] = 0.0;
@ -406,10 +426,12 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
} }
deviation /= xErrorLength; deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf\t\tdifferential Predecessor\n", mean, deviation); 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); fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp6); fclose(fp6);
free(localWeights); free(localWeights);
free(xErrorPtr);
free(xError);
// weightsLogger( localWeights, USED_WEIGHTS ); // weightsLogger( localWeights, USED_WEIGHTS );
} }
@ -448,12 +470,14 @@ Contains and returns every suffix for all existing filenames
*/ */
char * fileSuffix ( int id ) { char * fileSuffix ( int id ) {
char * suffix[] = { "_weights_pure.txt", char * suffix[] = { "_weights_pure.txt",
"_weights_used.txt", "_weights_used_dir_pred_.txt",
"_direct_predecessor.txt", "_direct_predecessor.txt",
"_ergebnisse.txt", "_ergebnisse.txt",
"_localMean.txt", "_localMean.txt",
"_testvalues.txt", "_testvalues.txt",
"_differential_predecessor.txt" "_differential_predecessor.txt",
"_weights_used_local_mean",
"_weights_used_diff_pred",
}; };
return suffix[id]; return suffix[id];
} }
@ -490,11 +514,8 @@ void weightsLogger (double *weights, int val ) {
mkFileName(fileName, sizeof(fileName), val); mkFileName(fileName, sizeof(fileName), val);
FILE* fp = fopen(fileName, "wa"); FILE* fp = fopen(fileName, "wa");
for (i = 0; i < mlData->windowSize - 1; i++) { 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); fclose(fp);
} }
@ -853,10 +874,10 @@ void usage ( char **argv ) {
printf("\t-c <color>\t\tUse this color channel from inputfile.\n"); 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-w <digit>\t\tCount of used weights (windowSize).\n");
printf("\t-l <digit>\t\tLearnrate, 0 < learnrate < 1.\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("\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("\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); exit(8);
} }

6
src/ansi_c_implementation/nlms_types.h
View File

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

85
src/cpp_implementation/NLMSvariants.cpp
View File

@ -11,6 +11,7 @@ Created by Stefan Friese on 26.04.2018
#include <time.h> #include <time.h>
#include <stdlib.h> #include <stdlib.h>
#include <string.h> #include <string.h>
//#include <float.h> // DBL_MAX
#include "nlms_types.h" // added types #include "nlms_types.h" // added types
#define RGB_COLOR 255 #define RGB_COLOR 255
@ -19,7 +20,7 @@ Created by Stefan Friese on 26.04.2018
typedef SSIZE_T ssize_t; typedef SSIZE_T ssize_t;
#endif #endif
double *xSamples; double *xSamples; // Input values
mldata_t *mlData = NULL; // Machine learning mldata_t *mlData = NULL; // Machine learning
point_t *points = NULL; // Graphing 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 ) { int main( int argc, char **argv ) {
char *colorChannel = (char *) malloc(sizeof(char)* 32); char *colorChannel = (char *) malloc(sizeof(char)* 32);
char *inputfile = (char *)malloc(sizeof(char) * 32); char *inputfile = (char *)malloc(sizeof(char) * 32);
unsigned int *seed = NULL; unsigned *seed = NULL;
unsigned k, xclude = 0; unsigned k, xclude = 0;
unsigned int windowSize = 5; unsigned windowSize = 5;
unsigned int samplesCount = 512; unsigned samplesCount = 512;
char *stdcolor = "green"; char *stdcolor = "green", xBuffer[512];
colorChannel = stdcolor; colorChannel = stdcolor;
unsigned int uint_buffer[1]; unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4; double learnrate = 0.4;
@ -81,7 +82,8 @@ int main( int argc, char **argv ) {
--argc; --argc;
break; break;
case 'w': case 'w':
sscanf(&argv[1][3], "%u", &windowSize); sscanf(&argv[1][3], "%u", windowBuffer);
windowSize = windowBuffer[0];
++argv; ++argv;
--argc; --argc;
break; break;
@ -111,6 +113,7 @@ int main( int argc, char **argv ) {
--argc; --argc;
break; break;
case 'x': case 'x':
sscanf(&argv[1][3], "%s", xBuffer);
xclude = 1; xclude = 1;
++argv; ++argv;
--argc; --argc;
@ -123,12 +126,14 @@ int main( int argc, char **argv ) {
++argv; ++argv;
--argc; --argc;
} }
init_mldata_t ( windowSize, samplesCount, learnrate ); init_mldata_t ( windowSize, samplesCount, learnrate );
xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values xSamples = (double *) malloc ( sizeof(double) * mlData->samplesCount ); // Resize input values
points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points points = (point_t *) malloc ( sizeof(point_t) * mlData->samplesCount); // Resize points
imagePixel_t *image; imagePixel_t *image;
image = rdPPM(inputfile); // Set Pointer on input values image = rdPPM(inputfile); // Set Pointer on input values
printf("window Size: %d\n", mlData->windowSize);
char fileName[50]; // Logfiles and their names char fileName[50]; // Logfiles and their names
mkFileName(fileName, sizeof(fileName), TEST_VALUES); mkFileName(fileName, sizeof(fileName), TEST_VALUES);
FILE* fp5 = fopen(fileName, "w"); 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("srand depends on time\n"); // Default seed is time(NULL)
} }
printf("generated weights:\n"); 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 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 mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS); // Logfile weights
FILE *fp0 = fopen(fileName, "w"); FILE *fp0 = fopen(fileName, "w");
// for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
for (k = 0; k < mlData->windowSize; k++) { for (k = 0; k < mlData->windowSize; k++) {
fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]); fprintf(fp0, "[%d]%lf\n", k, mlData->weights[k]);
} }
// }
fclose(fp0); fclose(fp0);
/* *math magic* */ /* *math magic* */
@ -168,7 +172,10 @@ int main( int argc, char **argv ) {
if ( xclude == 0 ) { if ( xclude == 0 ) {
mkSvgGraph(points); // Graph building mkSvgGraph(points); // Graph building
} }
free(image);
free(xSamples); free(xSamples);
free(points); free(points);
free(mlData); 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) double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1); // Includes e(n)
memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero memset(xError, 0.0, mlData->samplesCount); // Initialize xError-array with Zero
unsigned i, xCount = 0; // Runtime vars unsigned i, xCount = 0; // Runtime vars
mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); // Create Logfile and its filename
FILE* fp4 = fopen(fileName, "w"); FILE* fp4 = fopen(fileName, "w");
fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) ); fprintf( fp4, fileHeader(LOCAL_MEAN_HEADER) );
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
FILE *fp9 = fopen(fileName, "w");
double xMean = xSamples[0]; double xMean = xSamples[0];
double xSquared = 0.0; double xSquared = 0.0;
double xPredicted = 0.0; double xPredicted = 0.0;
@ -220,8 +232,9 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
xSquared = 1.0; xSquared = 1.0;
} }
for ( i = 1; i < _arrayLength; i++ ) { // Update weights 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 ); * ( (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 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 *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()! double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0; xErrorPtr[0] = 0.0;
@ -245,9 +259,11 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
} }
deviation /= xErrorLength; // Deviation deviation /= xErrorLength; // Deviation
printf("mean:%lf, devitation:%lf\t\tlocal Mean\n", mean, 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 fprintf(fp4, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean); // Write to logfile
free(localWeights); free(localWeights);
free(xErrorPtr);
free(xError);
fclose(fp4); fclose(fp4);
//weightsLogger( local_weights, USED_WEIGHTS ); //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 ); double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy ( localWeights, mlData->weights, mlData->windowSize ); memcpy ( localWeights, mlData->weights, mlData->windowSize );
char fileName[512]; char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount ); double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 );
unsigned xCount = 0, i; unsigned xCount = 0, i;
double xActual = 0.0; double xActual = 0.0;
double xPredicted = 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 mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); // Logfile and name handling
FILE *fp3 = fopen(fileName, "w"); FILE *fp3 = fopen(fileName, "w");
fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) ); 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 for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted
unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount; unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
@ -299,7 +315,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
xSquared = 1.0; xSquared = 1.0;
} }
for ( i = 1; i < _arrayLength; i++ ) { // Update weights 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); * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] ); 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); fprintf(fp3, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp3); fclose(fp3);
free(localWeights); 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 ); double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
memcpy( localWeights, mlData->weights, mlData->windowSize ); memcpy( localWeights, mlData->weights, mlData->windowSize );
char fileName[512]; char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount); double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1);
unsigned xCount = 0, i; unsigned xCount = 0, i;
double xPredicted = 0.0; double xPredicted = 0.0;
double xActual = 0.0; double xActual = 0.0;
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling
FILE *fp6 = fopen(fileName, "w"); FILE *fp6 = fopen(fileName, "w");
fprintf(fp6, fileHeader(DIFFERENTIAL_PREDECESSOR_HEADER) ); 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 for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // First value will not get predicted
unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount; 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++) { 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 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]; points[xCount].yVal[6] = xError[xCount];
} }
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[] double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()! double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0; xErrorPtr[0] = 0.0;
@ -404,10 +426,12 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
} }
deviation /= xErrorLength; deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf\t\tdifferential Predecessor\n", mean, deviation); 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); fprintf(fp6, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
fclose(fp6); fclose(fp6);
free(localWeights); free(localWeights);
free(xErrorPtr);
free(xError);
// weightsLogger( localWeights, USED_WEIGHTS ); // weightsLogger( localWeights, USED_WEIGHTS );
} }
@ -446,12 +470,14 @@ Contains and returns every suffix for all existing filenames
*/ */
char * fileSuffix ( int id ) { char * fileSuffix ( int id ) {
char * suffix[] = { "_weights_pure.txt", char * suffix[] = { "_weights_pure.txt",
"_weights_used.txt", "_weights_used_dir_pred_.txt",
"_direct_predecessor.txt", "_direct_predecessor.txt",
"_ergebnisse.txt", "_ergebnisse.txt",
"_localMean.txt", "_localMean.txt",
"_testvalues.txt", "_testvalues.txt",
"_differential_predecessor.txt" "_differential_predecessor.txt",
"_weights_used_local_mean",
"_weights_used_diff_pred",
}; };
return suffix[id]; return suffix[id];
} }
@ -488,11 +514,8 @@ void weightsLogger (double *weights, int val ) {
mkFileName(fileName, sizeof(fileName), val); mkFileName(fileName, sizeof(fileName), val);
FILE* fp = fopen(fileName, "wa"); FILE* fp = fopen(fileName, "wa");
for (i = 0; i < mlData->windowSize - 1; i++) { 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); fclose(fp);
} }
@ -851,10 +874,10 @@ void usage ( char **argv ) {
printf("\t-c <color>\t\tUse this color channel from inputfile.\n"); 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-w <digit>\t\tCount of used weights (windowSize).\n");
printf("\t-l <digit>\t\tLearnrate, 0 < learnrate < 1.\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("\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("\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); exit(8);
} }

6
src/cpp_implementation/nlms_types.h
View File

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