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updated .exe and src file

This commit is contained in:
Kevin Becker 2018-05-31 10:02:59 +02:00
parent f48178dba2
commit aa84dad655
2 changed files with 243 additions and 233 deletions
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bin/CPP_NLMS.exe
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src/cpp_implementation/NLMSvariants.cpp
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@ -11,7 +11,7 @@ Created by Stefan Friese on 26.04.2018
#include <time.h>
#include <stdlib.h>
#include <string.h>
#include "nlms_types.h" // added types
#include "nlms_types.h"
#define RGB_COLOR 255
#if defined(_MSC_VER)
@ -23,118 +23,120 @@ double *xSamples; // Input color values from PPM
mldata_t *mlData = NULL; // Machine learning realted data
point_t *points = NULL; // Graphing
/* *Graph building* */
/* *Graph building* */
static imagePixel_t * rdPPM(char *fileName); // Read PPM file format
void mkPpmFile(char *fileName, imagePixel_t *image); // Writes PPM file
int ppmColorChannel(FILE* fp, imagePixel_t *image, // Writes colorChannel from PPM file to log file
char *colorChannel, mldata_t *mlData);
char *colorChannel, mldata_t *mlData);
void colorSamples(FILE* fp, mldata_t *mlData); // Stores color channel values in xSamples
/* *File handling* */
char * mkFileName ( char* buffer, // Date+suffix as filename
size_t max_len, int suffixId );
char *fileSuffix ( int id ); // Filename ending of logs
char *fileHeader ( int id ); // Header inside the logfiles
/* *File handling* */
char * mkFileName(char* buffer, // Date+suffix as filename
size_t max_len, int suffixId);
char *fileSuffix(int id); // Filename ending of logs
char *fileHeader(int id); // Header inside the logfiles
void bufferLogger(char *buffer, point_t points[]); // Writes points to graph template
void mkSvgGraph ( point_t points[], char *templatePath); // Parses graph template and calls bufferLogger()
void weightsLogger ( double *weights, int suffix ); // Writes updated weights to a file
void mkSvgGraph(point_t points[], char *templatePath); // Parses graph template and calls bufferLogger()
void weightsLogger(double *weights, int suffix); // Writes updated weights to a file
/* *rand seed* */
double r2 ( void ); // Random val between 0 and 1
double rndm ( void );
/* *rand seed* */
double r2(void); // Random val between 0 and 1
double rndm(void);
/* *args parser* */
void usage ( char **argv ); // Help text called by args parser
void usage(char **argv); // Help text called by args parser
/* *math* */
/* *math* */
mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double learnrate);
double sum_array(double x[], int length);
void localMean ( mldata_t *mlData,point_t points[] ); // First,
void directPredecessor ( mldata_t *mlData, point_t points[] ); // Second,
void differentialPredecessor ( mldata_t *mlData, point_t points[] ); // Third filter implementation
void localMean(mldata_t *mlData, point_t points[]); // First,
void directPredecessor(mldata_t *mlData, point_t points[]); // Second,
void differentialPredecessor(mldata_t *mlData, point_t points[]); // Third filter implementation
double windowXMean(int _arraylength, int xCount); // Returns mean value of given window
int main( int argc, char **argv ) {
char *colorChannel = (char *) malloc(sizeof(char)* 32);
int main(int argc, char **argv) {
char *colorChannel = (char *)malloc(sizeof(char) * 32);
char *inputfile = (char *)malloc(sizeof(char) * 32);
unsigned *seed = NULL;
unsigned k, include = 0;
unsigned windowSize = 5;
unsigned samplesCount = 512;
char *stdcolor = "green", xBuffer[512];
char *stdcolor = (char*)"green", xBuffer[512];
colorChannel = stdcolor;
unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4;
char *istrue = "true";
char *istrue = (char*)"true";
char *templatePath = NULL;
while( (argc > 1) && (argv[1][0] == '-') ) { // Parses parameters from stdin
switch( argv[1][1] ) {
case 'i':
inputfile = &argv[1][3];
++argv;
--argc;
break;
case 'w':
sscanf(&argv[1][3], "%u", windowBuffer);
windowSize = windowBuffer[0];
++argv;
--argc;
break;
case 'c':
colorChannel = &argv[1][3];
++argv;
--argc;
break;
case 's':
sscanf(&argv[1][3], "%u", uint_buffer);
seed = &uint_buffer[0];
++argv;
--argc;
break;
case 'n':
sscanf(&argv[1][3], "%u", &samplesCount);
++argv;
--argc;
break;
case'h':
printf("Program name: %s\n", argv[0]);
usage(argv);
break;
case 'l':
sscanf(&argv[1][3], "%lf", &learnrate);
++argv;
--argc;
break;
case 'g':
sscanf(&argv[1][3], "%s", xBuffer);
if ( strstr(xBuffer, istrue) ) {
include = 1;
} else if ( xBuffer && !strstr(xBuffer, istrue) ) {
templatePath = xBuffer;
include = 1;
} else {
printf( "Wrong Argruments: %s\n", argv[1]);
usage(argv);
}
++argv;
--argc;
break;
default:
printf("Wrong Arguments: %s\n", argv[1]);
usage(argv);
while ((argc > 1) && (argv[1][0] == '-')) { // Parses parameters from stdin
switch (argv[1][1]) {
case 'i':
inputfile = &argv[1][3];
++argv;
--argc;
break;
case 'w':
sscanf(&argv[1][3], "%u", windowBuffer);
windowSize = windowBuffer[0];
++argv;
--argc;
break;
case 'c':
colorChannel = &argv[1][3];
++argv;
--argc;
break;
case 's':
sscanf(&argv[1][3], "%u", uint_buffer);
seed = &uint_buffer[0];
++argv;
--argc;
break;
case 'n':
sscanf(&argv[1][3], "%u", &samplesCount);
++argv;
--argc;
break;
case'h':
printf("Program name: %s\n", argv[0]);
usage(argv);
break;
case 'l':
sscanf(&argv[1][3], "%lf", &learnrate);
++argv;
--argc;
break;
case 'g':
sscanf(&argv[1][3], "%s", xBuffer);
if (strstr(xBuffer, istrue)) {
include = 1;
}
else if (xBuffer && !strstr(xBuffer, istrue)) {
templatePath = xBuffer;
include = 1;
}
else {
printf("Wrong Argruments: %s\n", argv[1]);
usage(argv);
}
++argv;
--argc;
break;
default:
printf("Wrong Arguments: %s\n", argv[1]);
usage(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
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
@ -146,11 +148,12 @@ int main( int argc, char **argv ) {
FILE* fp6 = fopen(fileName, "r");
colorSamples(fp6, mlData);
if ( (seed != NULL) ){
srand( *seed ); // Seed for random number generating
if ((seed != NULL)) {
srand(*seed); // Seed for random number generating
printf("srand is reproducable\n");
} else {
srand( (unsigned int)time(NULL) );
}
else {
srand((unsigned int)time(NULL));
printf("srand depends on time\n"); // Default seed is time(NULL)
}
printf("generated weights:\n");
@ -167,11 +170,11 @@ int main( int argc, char **argv ) {
}
fclose(fp0);
localMean ( mlData, points ); // math magic functions
directPredecessor ( mlData, points );
differentialPredecessor( mlData, points );
localMean(mlData, points); // math magic functions
directPredecessor(mlData, points);
differentialPredecessor(mlData, points);
if ( include == 1 ) {
if (include == 1) {
mkSvgGraph(points, templatePath); // Graph building
}
@ -192,20 +195,22 @@ Variant (1/3), substract local mean.
======================================================================================================
*/
void localMean ( mldata_t *mlData, point_t points[] ) {
double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1);
void localMean(mldata_t *mlData, point_t points[]) {
double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
localWeights = mlData->weights;
char fileName[50];
const unsigned xErrorLength = mlData->samplesCount;
double xError[xErrorLength];
unsigned i, xCount = 0; // Runtime vars
unsigned xErrorLength = mlData->samplesCount;
double *xError = (double *)malloc(sizeof(double) * xErrorLength+1);
memset(xError, 0.0, sizeof(double) * xErrorLength);
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) );
fprintf(fp4, fileHeader(LOCAL_MEAN_HEADER));
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
mkFileName(fileName, sizeof(fileName), USED_WEIGHTS_LOCAL_MEAN);
FILE *fp9 = fopen(fileName, "w");
@ -214,14 +219,14 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
double xPredicted = 0.0;
double xActual = 0.0;
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
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
xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
xPredicted = 0.0;
xActual = xSamples[xCount];
for ( i = 1; i < _arrayLength; i++ ) { // Get predicted value
xPredicted += ( localWeights[i - 1] * (xSamples[xCount - i] - xMean) );
for (i = 1; i < _arrayLength; i++) { // Get predicted value
xPredicted += (localWeights[i - 1] * (xSamples[xCount - i] - xMean));
}
xPredicted += xMean;
xError[xCount] = xActual - xPredicted; // Get error value
@ -229,13 +234,13 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
for (i = 1; i < _arrayLength; i++) { // Get xSquared
xSquared += pow(xSamples[xCount - i] - xMean, 2);
}
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
}
for ( i = 1; i < _arrayLength; i++ ) { // Update weights
for (i = 1; i < _arrayLength; i++) { // Update weights
localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount] // Substract localMean
* ( (xSamples[xCount - i] - xMean) / xSquared );
fprintf( fp9, "%lf\n", localWeights[i] );
* ((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
@ -271,31 +276,32 @@ substract direct predecessor
======================================================================================================
*/
void directPredecessor( mldata_t *mlData, point_t points[]) {
double *localWeights = ( double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
void directPredecessor(mldata_t *mlData, point_t points[]) {
double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
localWeights = mlData->weights;
char fileName[512];
const unsigned xErrorLength = mlData->samplesCount;
double xError[xErrorLength];
unsigned xCount = 0, i;
const unsigned xErrorLength = mlData->samplesCount;
double *xError = (double *)malloc(sizeof(double) * xErrorLength);
memset(xError, 0.0, sizeof(double) * xErrorLength);
unsigned xCount = 0, i;
double xActual = 0.0;
double xPredicted = 0.0;
mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); // Logfile and name handling
FILE *fp3 = fopen(fileName, "w");
fprintf( fp3, fileHeader(DIRECT_PREDECESSOR_HEADER) );
fprintf(fp3, fileHeader(DIRECT_PREDECESSOR_HEADER));
mkFileName ( fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
mkFileName(fileName, sizeof(fileName), USED_WEIGHTS_DIR_PRED);
FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount-1; xCount++) { // first value will not get predicted
unsigned _arrayLength = ( xCount > mlData->windowSize ) ? mlData->windowSize + 1 : xCount;
for (xCount = 1; xCount < mlData->samplesCount - 1; xCount++) { // first value will not get predicted
unsigned _arrayLength = (xCount > mlData->windowSize) ? mlData->windowSize + 1 : xCount;
xPredicted = 0.0;
xActual = xSamples[xCount];
for (i = 1; i < _arrayLength; i++) {
xPredicted += ( localWeights[i - 1] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
xPredicted += (localWeights[i - 1] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
}
xPredicted += xSamples[xCount - 1];
@ -305,16 +311,16 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
for (i = 1; i < _arrayLength; i++) {
xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
}
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
}
for ( i = 1; i < _arrayLength; i++ ) { // Update weights
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
* ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] );
for (i = 1; i < _arrayLength; i++) { // Update weights
localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount]
* ((xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
fprintf(fp9, "%lf\n", localWeights[i]);
}
fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
points[xCount].xVal[2] = xCount; // Fill point_t array for graph building
points[xCount].yVal[2] = xPredicted;
points[xCount].xVal[5] = xCount;
@ -345,12 +351,13 @@ differential predecessor.
======================================================================================================
*/
void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
void differentialPredecessor(mldata_t *mlData, point_t points[]) {
double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
localWeights = mlData->weights;
const unsigned xErrorLength = mlData->samplesCount;
const unsigned xErrorLength = mlData->samplesCount;
char fileName[512];
double xError[xErrorLength];
double *xError = (double *)malloc(sizeof(double) * xErrorLength);
memset(xError, 0.0, sizeof(double) * xErrorLength);
unsigned xCount = 0, i;
double xPredicted = 0.0;
@ -358,19 +365,19 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); // File handling
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);
mkFileName(fileName, sizeof(fileName), USED_WEIGHTS_DIFF_PRED);
FILE *fp9 = fopen(fileName, "w");
for (xCount = 1; xCount < mlData->samplesCount-1; 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;
xPredicted = 0.0;
xActual = xSamples[xCount];
for (i = 1; i < _arrayLength; i++) {
xPredicted += ( localWeights[i - 1] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
xPredicted += (localWeights[i - 1] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
}
xPredicted += xSamples[xCount - 1];
xError[xCount] = xActual - xPredicted;
@ -379,17 +386,17 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
for (i = 1; i < _arrayLength; i++) {
xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // Substract direct predecessor
}
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
if (xSquared == 0.0) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
}
for (i = 1; i < _arrayLength; i++) {
localWeights[i] = localWeights[i-1] + mlData->learnrate * xError[xCount]
localWeights[i] = localWeights[i - 1] + mlData->learnrate * xError[xCount]
* ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
fprintf( fp9, "%lf\n", localWeights[i] );
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
points[xCount].xVal[3] = xCount;
points[xCount].yVal[3] = xPredicted;
@ -402,7 +409,6 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
double mean = sum_array(xError, xErrorLength) / xErrorLength;
double deviation = 0.0;
for (i = 1; i < xErrorLength; i++) { // Mean square
deviation += pow(xError[i] - mean, 2);
}
@ -444,16 +450,16 @@ Contains and returns every suffix for all existing filenames
======================================================================================================
*/
char * fileSuffix ( int id ) {
char * suffix[] = { "_weights_pure.txt",
"_weights_used_dir_pred_.txt",
"_direct_predecessor.txt",
"_ergebnisse.txt",
"_localMean.txt",
"_testvalues.txt",
"_differential_predecessor.txt",
"_weights_used_local_mean.txt",
"_weights_used_diff_pred.txt",
char * fileSuffix(int id) {
char * suffix[] = { (char*)"_weights_pure.txt",
(char*)"_weights_used_dir_pred_.txt",
(char*)"_direct_predecessor.txt",
(char*)"_ergebnisse.txt",
(char*)"_localMean.txt",
(char*)"_testvalues.txt",
(char*)"_differential_predecessor.txt",
(char*)"_weights_used_local_mean.txt",
(char*)"_weights_used_diff_pred.txt",
};
return suffix[id];
}
@ -467,10 +473,10 @@ Contains and returns header from logfiles
======================================================================================================
*/
char * fileHeader ( int id ) {
char * header[] = { "\n=========================== Local Mean ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
"\n=========================== Direct Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
"\n=========================== Differential Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n"
char * fileHeader(int id) {
char * header[] = { (char*)"\n=========================== Local Mean ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
(char*)"\n=========================== Direct Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n",
(char*)"\n=========================== Differential Predecessor ===========================\nNo.\txPredicted\txAcutal\t\txError\n"
};
return header[id];
}
@ -484,7 +490,7 @@ Logs used weights to logfile - not used right now
======================================================================================================
*/
void weightsLogger (double *weights, int val ) {
void weightsLogger(double *weights, int val) {
char fileName[512];
unsigned i;
mkFileName(fileName, sizeof(fileName), val);
@ -501,13 +507,13 @@ void weightsLogger (double *weights, int val ) {
bufferLogger
formats output of mkSvgGraph -- Please open graphResults.html to see the output--
[0] = xActual,
[1] = xPredicted from localMean,
[2] = xPredicted from directPredecessor,
[3] = xPredicted from differentialpredecessor,
[4] = xError from localMean,
[5] = xError from directPredecessor,
[6] = xError from differentialPredecessor
[0] = xActual,
[1] = xPredicted from localMean,
[2] = xPredicted from directPredecessor,
[3] = xPredicted from differentialpredecessor,
[4] = xError from localMean,
[5] = xError from directPredecessor,
[6] = xError from differentialPredecessor
======================================================================================================
*/
@ -594,14 +600,15 @@ parses template.svg and writes results in said template
======================================================================================================
*/
void mkSvgGraph(point_t points[], char *templatePath) {
FILE* input = NULL;
FILE *target = fopen("graphResults.html", "w");
if (templatePath){
printf("\ngraph template src at: %s\n", templatePath);
input = fopen(templatePath, "r");
} else {
input = fopen("graphResults_template.html", "r");
}
FILE* input = NULL;
FILE *target = fopen("graphResults.html", "w");
if (templatePath) {
printf("\ngraph template src at: %s\n", templatePath);
input = fopen(templatePath, "r");
}
else {
input = fopen("graphResults_template.html", "r");
}
char line[512];
char firstGraph[15] = { "<path d=\"M0 0" }; // Position where points will be written after
@ -663,30 +670,30 @@ static imagePixel_t *rdPPM(char *fileName) {
c = getc(fp);
}
ungetc(c, fp);
if ( fscanf(fp, "%d %d", &image->x, &image->y) != 2 ) {
if (fscanf(fp, "%d %d", &image->x, &image->y) != 2) {
fprintf(stderr, "Invalid image size in %s\n", fileName);
exit(EXIT_FAILURE);
}
if ( fscanf(fp, "%d", &rgbColor) != 1 ) {
if (fscanf(fp, "%d", &rgbColor) != 1) {
fprintf(stderr, "Invalid rgb component in %s\n", fileName);
}
if ( rgbColor != RGB_COLOR ) {
if (rgbColor != RGB_COLOR) {
fprintf(stderr, "Invalid image color range in %s\n", fileName);
exit(EXIT_FAILURE);
}
while ( fgetc(fp) != '\n' );
while (fgetc(fp) != '\n');
image->data = (colorChannel_t *)malloc(image->x * image->y * sizeof(imagePixel_t));
if (!image) {
fprintf(stderr, "malloc() on image->data failed");
exit(EXIT_FAILURE);
}
if ( (image->x * image->y) < mlData->samplesCount) {
printf("Changing \"-n\" to %d, image max data size\n", ( image->x * image->y ) );
tmp = (double *) realloc ( xSamples, sizeof(double) * (image->x * image->y) );
if ((image->x * image->y) < mlData->samplesCount) {
printf("Changing \"-n\" to %d, image max data size\n", (image->x * image->y));
tmp = (double *)realloc(xSamples, sizeof(double) * (image->x * image->y));
xSamples = tmp;
mlData->samplesCount = (image->x * image->y );
mlData->samplesCount = (image->x * image->y);
}
if ( fread( image->data, 3 * image->x, image->y, fp) != image->y) {
if (fread(image->data, 3 * image->x, image->y, fp) != image->y) {
fprintf(stderr, "Loading image failed");
exit(EXIT_FAILURE);
}
@ -730,23 +737,26 @@ int ppmColorChannel(FILE* fp, imagePixel_t *image, char *colorChannel, mldata_t
unsigned i = 0;
printf("colorChannel : %s\n", colorChannel);
if ( image ) { // RGB channel can be set through args from cli
if ( strcmp(colorChannel, "green") == 0 ){
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
fprintf ( fp, "%d\n", image->data[i].green );
if (image) { // RGB channel can be set through args from cli
if (strcmp(colorChannel, "green") == 0) {
for (i = 0; i < mlData->samplesCount - 1; i++) {
fprintf(fp, "%d\n", image->data[i].green);
}
} else if ( strcmp(colorChannel, "red") == 0 ){
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
fprintf ( fp, "%d\n", image->data[i].red );
}
else if (strcmp(colorChannel, "red") == 0) {
for (i = 0; i < mlData->samplesCount - 1; i++) {
fprintf(fp, "%d\n", image->data[i].red);
}
} else if ( strcmp(colorChannel, "blue") == 0 ) {
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
fprintf ( fp, "%d\n", image->data[i].blue );
}
else if (strcmp(colorChannel, "blue") == 0) {
for (i = 0; i < mlData->samplesCount - 1; i++) {
fprintf(fp, "%d\n", image->data[i].blue);
}
} else {
}
else {
printf("Colorchannels are red, green and blue. Pick one of them!");
exit( EXIT_FAILURE );
exit(EXIT_FAILURE);
}
}
fclose(fp);
@ -763,9 +773,9 @@ creating the SVG graph
======================================================================================================
*/
void colorSamples ( FILE* fp, mldata_t *mlData ) {
void colorSamples(FILE* fp, mldata_t *mlData) {
int i = 0;
char *buffer = (char *) malloc(sizeof(char) * mlData->samplesCount + 1);
char *buffer = (char *)malloc(sizeof(char) * mlData->samplesCount + 1);
while (!feof(fp)) {
if (fgets(buffer, mlData->samplesCount, fp) != NULL) {
@ -792,7 +802,7 @@ double windowXMean(int _arraylength, int xCount) {
double *ptr;
for (ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { // Set ptr to beginning of window and iterate through array
sum += *ptr;
sum += *ptr;
}
return sum / (double)_arraylength;
}
@ -800,13 +810,13 @@ double windowXMean(int _arraylength, int xCount) {
/*
======================================================================================================
usage
usage
used in conjunction with the args parser. Returns help section of "-h"
used in conjunction with the args parser. Returns help section of "-h"
======================================================================================================
*/
void usage ( char **argv ) {
void usage(char **argv) {
printf("Usage: %s [POSIX style options] -i file ...\n", &argv[0][0]);
printf("POSIX options:\n");
printf("\t-h\t\t\tDisplay this information.\n");
@ -825,18 +835,18 @@ void usage ( char **argv ) {
/*
======================================================================================================
init_mldata_t
init_mldata_t
Init meachine learning data
Init meachine learning data
======================================================================================================
*/
mldata_t * init_mldata_t(unsigned windowSize, unsigned samplesCount, double learnrate) {
mlData = (mldata_t *) malloc( sizeof(mldata_t) );
mlData = (mldata_t *)malloc(sizeof(mldata_t));
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;
}