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

Merge branch 'master' of https://github.com/FBRDNLMS/NLMSvariants

This commit is contained in:
kbecke23 2018-05-24 10:37:41 +02:00
parent 4370075c4d 684dc3c434
commit 25251cd572
3 changed files with 18 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
src/ansi_c_implementation/NLMSvariants.c
View File

@ -11,7 +11,6 @@ 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
@ -193,8 +192,8 @@ Variant (1/3), substract local mean.
*/ */
void localMean ( mldata_t *mlData, point_t points[] ) { void localMean ( 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 ); // Copy weights so they can be changed locally localWeights = mlData->weights;
char fileName[50]; char fileName[50];
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
@ -213,7 +212,7 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
double xPredicted = 0.0; double xPredicted = 0.0;
double xActual = 0.0; double xActual = 0.0;
for ( xCount = 1; xCount < mlData->samplesCount; xCount++ ) { // First value will not get predicted for ( xCount = 1; xCount < mlData->samplesCount-1; xCount++ ) { // First value will not get predicted
unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount; // Ensures corect length at start unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount; // Ensures corect length at start
xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0; xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
xPredicted = 0.0; xPredicted = 0.0;
@ -281,9 +280,11 @@ substract direct predecessor
*/ */
void directPredecessor( mldata_t *mlData, point_t points[]) { 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 ); localWeights = mlData->weights;
char fileName[512]; char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 ); double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1 );
memset(xError, 0.0, mlData->samplesCount);
unsigned xCount = 0, i; unsigned xCount = 0, i;
double xActual = 0.0; double xActual = 0.0;
double xPredicted = 0.0; double xPredicted = 0.0;
@ -295,7 +296,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED); mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
FILE *fp9 = fopen(fileName, "w"); FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // first value will not get predicted for (xCount = 1; xCount < mlData->samplesCount-1; 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;
xPredicted = 0.0; xPredicted = 0.0;
xActual = xSamples[xCount]; xActual = xSamples[xCount];
@ -363,9 +364,12 @@ differential predecessor.
*/ */
void differentialPredecessor ( mldata_t *mlData, point_t points[] ) { 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 ); localWeights = mlData->weights;
char fileName[512]; char fileName[512];
double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1); double *xError = (double *) malloc ( sizeof(double) * mlData->samplesCount + 1);
memset(xError, 0.0, mlData->samplesCount);
unsigned xCount = 0, i; unsigned xCount = 0, i;
double xPredicted = 0.0; double xPredicted = 0.0;
double xActual = 0.0; double xActual = 0.0;
@ -377,7 +381,7 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED); mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED);
FILE *fp9 = fopen(fileName, "w"); FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount; xCount++) { // First value will not get predicted for (xCount = 1; xCount < mlData->samplesCount-1; 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;
xPredicted = 0.0; xPredicted = 0.0;
@ -539,22 +543,22 @@ void bufferLogger(char *buffer, point_t points[]) {
unsigned i; unsigned i;
char _buffer[512] = ""; // TODO: resize buffer and _buffer so greater sampleval can be choosen char _buffer[512] = ""; // TODO: resize buffer and _buffer so greater sampleval can be choosen
// char *_buffer = (char *) malloc ( sizeof(char) * 512 + 1); // char *_buffer = (char *) malloc ( sizeof(char) * 512 + 1);
for (i = 0; i < mlData->samplesCount - 1; i++) { // xActual for (i = 1; i < mlData->samplesCount - 1; i++) { // xActual
sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]); sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
strcat(buffer, _buffer); strcat(buffer, _buffer);
} }
strcat(buffer, "\" fill=\"none\" id=\"svg_1\" stroke=\"black\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n"); strcat(buffer, "\" fill=\"none\" id=\"svg_1\" stroke=\"black\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
for (i = 0; i < mlData->samplesCount - 1; i++) { // xPredicted from localMean for (i = 1; i < mlData->samplesCount - 1; i++) { // xPredicted from localMean
sprintf(_buffer, "L %f %f\n", points[i].xVal[1], points[i].yVal[1]); sprintf(_buffer, "L %f %f\n", points[i].xVal[1], points[i].yVal[1]);
strcat(buffer, _buffer); strcat(buffer, _buffer);
} }
strcat(buffer, "\" fill=\"none\" id=\"svg_2\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n"); strcat(buffer, "\" fill=\"none\" id=\"svg_2\" stroke=\"green\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
for (i = 0; i <= mlData->samplesCount - 1; i++) { //xPredicted from directPredecessor for (i = 1; i <= mlData->samplesCount - 1; i++) { //xPredicted from directPredecessor
sprintf(_buffer, "L %f %f\n", points[i].xVal[2], points[i].yVal[2]); sprintf(_buffer, "L %f %f\n", points[i].xVal[2], points[i].yVal[2]);
strcat(buffer, _buffer); strcat(buffer, _buffer);
} }
strcat(buffer, "\" fill=\"none\" id=\"svg_3\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n"); strcat(buffer, "\" fill=\"none\" id=\"svg_3\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
for (i = 0; i < mlData->samplesCount - 1; i++) { //xPredicted from diff Pred for (i = 1; i < mlData->samplesCount - 1; i++) { //xPredicted from diff Pred
sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].yVal[3]); sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].yVal[3]);
strcat(buffer, _buffer); strcat(buffer, _buffer);
} }

2
src/ansi_c_implementation/README.md
View File

@ -13,7 +13,7 @@ This little piece of code compares 3 different implementations of a least mean s
+ direct predecessor + direct predecessor
+ differential predecessor + differential predecessor
Greyscale PPM files can be used for input at this iteration. Output will be generated as .txt files with predicted value generated by the filter and its error value as well as given actual value from the PPM file. Furthermore there is an output as an svg graph to compare the implementatiosn on a visual level. These graphical results are build from __graphResults_template.html__ which creates the actual output to __graphResults.html__. Open this file to see the results. PPM files can be used for input at this iteration. Output will be generated as .txt files with predicted value generated by the filter and its error value as well as given actual value from the PPM file. Furthermore there is an output as an svg graph to compare the implementatiosn on a visual level. These graphical results are build from __graphResults_template.html__ which creates the actual output to __graphResults.html__. Open this file to see the results.
You can hide graphs by clicking on its name for better visibility. You can hide graphs by clicking on its name for better visibility.
Use `$ ./lms -h` for help. Use `$ ./lms -h` for help.

2
src/cpp_implementation/README.md
View File

@ -12,7 +12,7 @@ This little piece of code compares 3 different implementations of a least mean s
+ direct predecessor + direct predecessor
+ differential predecessor + differential predecessor
Greyscale PPM files can be used for input at this iteration. Output will be generated as .txt files with predicted value generated by the filter and its error value as well as given actual value from the PPM file. Furthermore there is an output as an svg graph to compare the implementatiosn on a visual level. These graphical results are build from __graphResults_template.html__ which creates the actual output to __graphResults.html__. Open this file to see the results. PPM files can be used for input at this iteration. Output will be generated as .txt files with predicted value generated by the filter and its error value as well as given actual value from the PPM file. Furthermore there is an output as an svg graph to compare the implementatiosn on a visual level. These graphical results are build from __graphResults_template.html__ which creates the actual output to __graphResults.html__. Open this file to see the results.
You can hide graphs by clicking on its name for better visibility. You can hide graphs by clicking on its name for better visibility.
Use `$ <binary> -h` for help. Use `$ <binary> -h` for help.