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graphing is opt in now.

This commit is contained in:
gurkenhabicht 2018-05-24 11:02:00 +02:00
parent 25251cd572
commit 8ce167ff0d
4 changed files with 268 additions and 257 deletions
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18
src/ansi_c_implementation/NLMSvariants.c
View File

@ -64,13 +64,14 @@ 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, xclude = 0;
unsigned k, include = 0;
unsigned windowSize = 5;
unsigned samplesCount = 512;
char *stdcolor = "green", xBuffer[512];
colorChannel = stdcolor;
unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4;
char *istrue = "true";
while( (argc > 1) && (argv[1][0] == '-') ) { // Parses parameters from stdin
@ -111,9 +112,14 @@ int main( int argc, char **argv ) {
++argv;
--argc;
break;
case 'x':
case 'g':
sscanf(&argv[1][3], "%s", xBuffer);
xclude = 1;
if ( strstr(xBuffer, istrue) ) {
include = 1;
} else {
printf( "Wrong Argruments: %s\n", argv[1]);
usage(argv);
}
++argv;
--argc;
break;
@ -169,7 +175,7 @@ int main( int argc, char **argv ) {
directPredecessor ( mlData, points);
differentialPredecessor( mlData, points );
if ( xclude == 0 ) {
if ( include == 1 ) {
mkSvgGraph(points); // Graph building
}
@ -480,8 +486,8 @@ char * fileSuffix ( int id ) {
"_localMean.txt",
"_testvalues.txt",
"_differential_predecessor.txt",
"_weights_used_local_mean",
"_weights_used_diff_pred",
"_weights_used_local_mean.txt",
"_weights_used_diff_pred.txt",
};
return suffix[id];
}

10
src/ansi_c_implementation/README.md
View File

@ -24,12 +24,12 @@ There are a bunch of options you can predefine but do not have to. The only para
| Parameter | Description | StdVal |
|:----------|:-----------------------------:|:-------|
| -i | The inputfile, has to be PPM | none |
| -n | Amount of input data used | 500 |
| -w | Size of M (window) | 5 |
| -i | The inputfile, has to be PPM. | none |
| -n | Amount of input data used. | 500 |
| -w | Size of M (window). | 5 |
| -c | Choose RGB color channel, green has least noise. | green |
| -l | Learnrate of machine learning | 0.4 |
| -x | Exclude graph building. Logfiles only, choose for insane amount of input data. 10Mio. Pixels tested so far.| none|
| -l | Learnrate of machine learning.| 0.4 |
| -g true | include graph building. Choose for amount of input data lower than 1200.| none|
| -s | Seed randomizing weights. Choose for repoducability. | time(NULL)|
This code is ANSI compatible no POSIX, C99, C11 or GNU libs, because it had to be VS compatible . There are way easier methods like getline() or getopt(), I know ...

405
src/cpp_implementation/NLMSvariants.cpp
View File

@ -23,112 +23,118 @@ double *xSamples; // Input values
mldata_t *mlData = NULL; // Machine learning
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,
size_t max_len, int suffixId);
char *fileSuffix(int id);
char *fileHeader(int id); // Header inside the logfiles
//void myLogger ( FILE* fp, point_t points[] );
/* *file handling* */
char * mkFileName ( char* buffer,
size_t max_len, int suffixId );
char *fileSuffix ( int id );
char *fileHeader ( int id ); // Header inside the logfiles
//void myLogger ( FILE* fp, point_t points[] );
void bufferLogger(char *buffer, point_t points[]); // Writes points to graph template
void mkSvgGraph(point_t points[]); // Parses graph template and calls bufferLogger()
void weightsLogger(double *weights, int suffix); // Writes updated weights to a file
void mkSvgGraph ( point_t points[] ); // 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 *popNAN(double *xError); // Returns array without NAN values, if any exist
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, xclude = 0;
unsigned k, include = 0;
unsigned windowSize = 5;
unsigned samplesCount = 512;
char *stdcolor = "green", xBuffer[512];
colorChannel = stdcolor;
unsigned int uint_buffer[1], windowBuffer[1];
double learnrate = 0.4;
char *istrue = "true";
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 'x':
sscanf(&argv[1][3], "%s", xBuffer);
xclude = 1;
++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 {
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
@ -141,12 +147,11 @@ 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");
@ -160,17 +165,17 @@ int main(int argc, char **argv) {
mkFileName(fileName, sizeof(fileName), PURE_WEIGHTS); // Logfile weights
FILE *fp0 = fopen(fileName, "w");
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);
/* *math magic* */
localMean(mlData, points);
directPredecessor(mlData, points);
differentialPredecessor(mlData, points);
localMean ( mlData, points );
directPredecessor ( mlData, points);
differentialPredecessor( mlData, points );
if (xclude == 0) {
if ( include == 1 ) {
mkSvgGraph(points); // Graph building
}
@ -191,20 +196,20 @@ Variant (1/3), substract local mean.
======================================================================================================
*/
void localMean(mldata_t *mlData, point_t points[]) {
double *localWeights = (double *)malloc(sizeof(double) * mlData->windowSize + 1);
localWeights = mlData->weights; // Copy weights so they can be changed locally
void localMean ( mldata_t *mlData, point_t points[] ) {
double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1);
localWeights = mlData->weights;
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
unsigned i, xCount = 0; // Runtime vars
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");
@ -213,14 +218,14 @@ 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
@ -228,13 +233,13 @@ 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
@ -249,8 +254,8 @@ 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;
// printf("Xerrorl:%lf", xErrorLength);
xErrorPtr[0] = 0.0;
// printf("Xerrorl:%lf", xErrorLength);
double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength; // Mean
double deviation = 0.0;
@ -260,7 +265,7 @@ 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(localWeights);
free(xErrorPtr);
free(xError);
@ -279,11 +284,12 @@ 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];
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;
double xActual = 0.0;
@ -291,18 +297,18 @@ 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));
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;
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];
@ -312,29 +318,29 @@ 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;
points[xCount].yVal[5] = xError[xCount];
// weightsLogger( fp, localWeights, USED_WEIGHTS );
// weightsLogger( fp, localWeights, USED_WEIGHTS );
}
fclose(fp9);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0; // Stored length in [0] , won't be used anyway. Bit dirty
//printf("Xerrorl:%lf", xErrorLength);
xErrorPtr[0] = 0.0; // Stored length in [0] , won't be used anyway. Bit dirty
//printf("Xerrorl:%lf", xErrorLength);
double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength; // Mean
double deviation = 0.0;
@ -347,7 +353,7 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
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(localWeights);
free(xErrorPtr);
free(xError);
}
@ -362,31 +368,33 @@ 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;
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;
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));
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;
@ -395,17 +403,17 @@ 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;
@ -416,8 +424,8 @@ void differentialPredecessor(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;
// printf("Xerrorl:%lf", xErrorLength);
xErrorPtr[0] = 0.0;
// printf("Xerrorl:%lf", xErrorLength);
double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
double deviation = 0.0;
@ -430,12 +438,12 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
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(localWeights);
free(xErrorPtr);
free(xError);
// weightsLogger( localWeights, USED_WEIGHTS );
// weightsLogger( localWeights, USED_WEIGHTS );
}
/*
@ -470,16 +478,16 @@ 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",
"_weights_used_diff_pred",
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",
};
return suffix[id];
}
@ -493,10 +501,10 @@ 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[] = { "\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"
};
return header[id];
}
@ -510,7 +518,7 @@ Logs used weights to logfile
======================================================================================================
*/
void weightsLogger(double *weights, int val) {
void weightsLogger (double *weights, int val ) {
char fileName[512];
unsigned i;
mkFileName(fileName, sizeof(fileName), val);
@ -527,20 +535,20 @@ 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
======================================================================================================
*/
void bufferLogger(char *buffer, point_t points[]) {
unsigned i;
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 = 1; i < mlData->samplesCount - 1; i++) { // xActual
sprintf(_buffer, "L %f %f\n", points[i].xVal[0], points[i].yVal[0]);
strcat(buffer, _buffer);
@ -599,18 +607,18 @@ double *popNAN(double *xError) {
double *tmp = NULL;
double *more_tmp = NULL;
for (i = 0; i < mlData->samplesCount - 1; i++) {
counter++;
more_tmp = (double *)realloc(tmp, counter*(sizeof(double)));
if (!isnan(xError[i])) {
tmp = more_tmp;
tmp[counter - 1] = xError[i];
//printf("xERROR:%lf\n", tmp[counter - 1]);
tmpLength++;
}
for ( i = 0; i < mlData->samplesCount - 1; i++ ) {
counter ++;
more_tmp = (double *) realloc ( tmp, counter*(sizeof(double) ));
if ( !isnan(xError[i]) ) {
tmp = more_tmp;
tmp[counter - 1] = xError[i];
//printf("xERROR:%lf\n", tmp[counter - 1]);
tmpLength++;
}
}
counter += 1;
more_tmp = (double *)realloc(tmp, counter * sizeof(double));
more_tmp = (double *) realloc ( tmp, counter * sizeof(double) );
tmp = more_tmp;
*tmp = tmpLength; // Length of array is stored inside tmp[0]. tmp[0] is never used anyways
@ -671,7 +679,7 @@ void mkSvgGraph(point_t points[]) {
char buffer[131072] = ""; // Bit dirty
// char *buffer = (char *) malloc ( sizeof(char) * ( ( 3 * mlData->samplesCount ) + fpLength + 1 ) );
// char *buffer = (char *) malloc ( sizeof(char) * ( ( 3 * mlData->samplesCount ) + fpLength + 1 ) );
memset(buffer, '\0', sizeof(buffer));
while (!feof(input)) { // parses file until "firstGraph" has been found
@ -723,30 +731,30 @@ 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) / sizeof(double);
mlData->samplesCount = (image->x * image->y ) / sizeof(double);
}
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);
}
@ -790,26 +798,23 @@ 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);
@ -826,9 +831,9 @@ 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) {
@ -856,7 +861,7 @@ double windowXMean(int _arraylength, int xCount) {
double *ptr;
for (ptr = &xSamples[xCount - _arraylength]; ptr != &xSamples[xCount]; ptr++) { // Set ptr to beginning of window
sum += *ptr;
sum += *ptr;
}
return sum / (double)_arraylength;
}
@ -864,13 +869,13 @@ 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");
@ -889,18 +894,18 @@ void usage(char **argv) {
/*
======================================================================================================
init_mldata_t
init_mldata_t
Contains meachine learning data
Contains 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;
}

10
src/cpp_implementation/README.md
View File

@ -23,11 +23,11 @@ There are a bunch of options you can predefine but do not have to. The only para
| Parameter | Description | StdVal |
|:----------|:-----------------------------:|:-------|
| -i | The inputfile, has to be PPM | none |
| -n | Amount of input data used | 500 |
| -w | Size of M (window) | 5 |
| -i | The inputfile, has to be PPM. | none |
| -n | Amount of input data used. | 500 |
| -w | Size of M (window). | 5 |
| -c | Choose RGB color channel, green has least noise. | green |
| -l | Learnrate of machine learning | 0.4 |
| -x | Exclude graph building. Logfiles only, choose for insane amount of input data. 10Mio. Pixels tested so far.| none|
| -l | Learnrate of machine learning. | 0.4 |
| -g true | include graph building. Choose for amount of input data lower than 1200.| none|
| -s | Seed randomizing weights. Choose for repoducability. | time(NULL)|