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NLMSvariants
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improved graphical output

This commit is contained in:
Friese 2018-05-14 16:06:03 +02:00
parent 402f8937d6
commit 31c3091b30
1 changed files with 70 additions and 40 deletions
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bin/NLMSvariants.c
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@ -45,7 +45,7 @@ typedef struct {
point_t points[NUMBER_OF_SAMPLES]; // [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 point_t points[NUMBER_OF_SAMPLES]; // [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
/* *ppm read, copy, write* */ /* *ppm read, copy, write* */
typedef struct { typedef struct {
unsigned char red, green, blue; unsigned char red, green, blue;
}colorChannel_t; }colorChannel_t;
@ -82,7 +82,7 @@ double windowXMean(int _arraylength, int xCount);
//int main(int argc, char **argv) { //int main(int argc, char **argv) {
int main( void ) { int main( void ) {
double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // = { { 0.0 }, {0.0} }; double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // = { { 0.0 }, {0.0} };
// double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
char fileName[50]; char fileName[50];
int i,k, xLength; int i,k, xLength;
@ -100,7 +100,6 @@ int main( void ) {
srand((unsigned int)time(NULL)); srand((unsigned int)time(NULL));
for (i = 0; i < NUMBER_OF_SAMPLES; i++) { for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
//_x[i] += ((255.0 / M) * i); // Init test values
for (int k = 0; k < WINDOWSIZE; k++) { for (int k = 0; k < WINDOWSIZE; k++) {
weights[k][i] = rndm(); // Init weights weights[k][i] = rndm(); // Init weights
} }
@ -118,16 +117,10 @@ int main( void ) {
// math magic // math magic
/* for (i = 0; i < NUMBER_OF_SAMPLES; i++){ localMean(weights);
for (k = 0; k < WINDOWSIZE; k++){ //memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
local_weights[k][i] = weights[k][i]; directPredecessor(weights);
printf("ALT::%f\n", local_weights[k][i]); //memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
}
}*/
//localMean(weights);
// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
// directPredecessor(weights);
// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
differentialPredecessor(weights); differentialPredecessor(weights);
mkSvgGraph(points); mkSvgGraph(points);
// save test_array after math magic happened // save test_array after math magic happened
@ -168,7 +161,7 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
int xCount = 0, i; // runtime var; int xCount = 0, i; // runtime var;
mkFileName(fileName, sizeof(fileName), LOCAL_MEAN); mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
FILE* fp4 = fopen(fileName, "w"); FILE* fp4 = fopen(fileName, "w");
fprintf(fp4, "\n=====================================LocalMean=====================================\n"); fprintf(fp4, "\n=====================================LocalMean=====================================\nNo.\txPredicted\txActual\t\txError\n");
double xMean = xSamples[0]; double xMean = xSamples[0];
double xSquared = 0.0; double xSquared = 0.0;
@ -212,7 +205,7 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
// printf("NEU::%lf\n", local_weights[i][xCount]); // printf("NEU::%lf\n", local_weights[i][xCount]);
} }
fprintf(fp4, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]); fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
} }
// int xErrorLength = sizeof(xError) / sizeof(xError[0]); // int xErrorLength = sizeof(xError) / sizeof(xError[0]);
@ -220,9 +213,10 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[] double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
//printf("%lf", xErrorPtr[499]); //printf("%lf", xErrorPtr[499]);
double xErrorLength = *xErrorPtr; // Watch popNAN()! double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
printf("Xerrorl:%lf", xErrorLength); printf("Xerrorl:%lf", xErrorLength);
/* double mean = sum_array(*xErrorPtr, xErrorLength) / xErrorLength;
double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
double deviation = 0.0; double deviation = 0.0;
// Mean square // Mean square
@ -230,17 +224,18 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
deviation += pow(xError[i] - mean, 2); deviation += pow(xError[i] - mean, 2);
} }
deviation /= xErrorLength; deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf", mean, deviation);
// write in file // write in file
mkFileName(fileName, sizeof(fileName), RESULTS); mkFileName(fileName, sizeof(fileName), RESULTS);
FILE *fp2 = fopen(fileName, "w"); FILE *fp2 = fopen(fileName, "w");
fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean); fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
fclose(fp2);*/ fclose(fp2);
free(local_weights); free(local_weights);
fclose(fp4); fclose(fp4);
// weightsLogger( local_weights, USED_WEIGHTS ); weightsLogger( local_weights, USED_WEIGHTS );
mkSvgGraph(points); //mkSvgGraph(points);
} }
@ -268,7 +263,7 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
// File handling // File handling
mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR); mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
FILE *fp3 = fopen(fileName, "w"); FILE *fp3 = fopen(fileName, "w");
fprintf(fp3, "\n=====================================DirectPredecessor=====================================\n"); fprintf(fp3, "\n=====================================DirectPredecessor=====================================\nNo.\txPredicted\txAcutal\t\txError\n");
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
@ -287,7 +282,7 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
xPredicted += xSamples[xCount - 1]; xPredicted += xSamples[xCount - 1];
xError[xCount] = xActual - xPredicted; xError[xCount] = xActual - xPredicted;
fprintf(fp3, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]); //fprintf(fp3, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
points[xCount].xVal[2] = xCount; points[xCount].xVal[2] = xCount;
points[xCount].yVal[2] = xPredicted; points[xCount].yVal[2] = xPredicted;
points[xCount].xVal[5] = xCount; points[xCount].xVal[5] = xCount;
@ -301,25 +296,34 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
for (i = 1; i < _arrayLength; i++) { for (i = 1; i < _arrayLength; i++) {
local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared); local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
} }
fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
} }
fclose(fp3);
double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
//printf("%lf", xErrorPtr[499]);
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
printf("Xerrorl:%lf", xErrorLength);
int xErrorLength = sizeof(xError) / sizeof(xError[0]); double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
printf("vor:%d", xErrorLength);
popNAN(xError);
printf("nach:%d", xErrorLength);
xErrorLength = sizeof(xError) / sizeof(xError[0]);
double mean = sum_array(xError, xErrorLength) / xErrorLength;
double deviation = 0.0; double deviation = 0.0;
for (i = 0; i < xErrorLength - 1; i++) { // Mean square
for (i = 1; i < xErrorLength; i++) {
deviation += pow(xError[i] - mean, 2); deviation += pow(xError[i] - mean, 2);
} }
deviation /= xErrorLength; deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf", mean, deviation);
// mkSvgGraph(points); // write in file
fprintf(fp3, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean); mkFileName(fileName, sizeof(fileName), RESULTS);
FILE *fp2 = fopen(fileName, "wa");
fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
fclose(fp2);
free(local_weights);
fclose(fp3); weightsLogger( local_weights, USED_WEIGHTS );
//mkSvgGraph(points);
} }
@ -329,7 +333,7 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
differentialPredecessor differentialPredecessor
variant (3/3), variant (3/3),
differenital predecessor. differential predecessor.
====================================================================================================== ======================================================================================================
*/ */
@ -347,7 +351,7 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
// File handling // File handling
mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR); mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR);
FILE *fp6 = fopen(fileName, "w"); FILE *fp6 = fopen(fileName, "w");
fprintf(fp6, "\n=====================================DifferentialPredecessor=====================================\n"); fprintf(fp6, "\n=====================================DifferentialPredecessor=====================================\nNo.\txPredicted\txAcutal\t\txError\n");
for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
@ -361,7 +365,7 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
xPredicted += xSamples[xCount - 1]; xPredicted += xSamples[xCount - 1];
xError[xCount] = xActual - xPredicted; xError[xCount] = xActual - xPredicted;
fprintf(fp6, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]); //fprintf(fp6, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
points[xCount].xVal[3] = xCount; points[xCount].xVal[3] = xCount;
points[xCount].yVal[3] = xPredicted; points[xCount].yVal[3] = xPredicted;
points[xCount].xVal[6] = xCount; points[xCount].xVal[6] = xCount;
@ -374,9 +378,10 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
for (i = 1; i < _arrayLength; i++) { for (i = 1; i < _arrayLength; i++) {
local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared); local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
} }
fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
} }
/* fclose(fp6);
int xErrorLength = sizeof(xError) / sizeof(xError[0]); /* int xErrorLength = sizeof(xError) / sizeof(xError[0]);
printf("vor:%d", xErrorLength); printf("vor:%d", xErrorLength);
popNAN(xError); popNAN(xError);
printf("nach:%d", xErrorLength); printf("nach:%d", xErrorLength);
@ -392,7 +397,32 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
//mkSvgGraph(points); //mkSvgGraph(points);
fprintf(fp6, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean); fprintf(fp6, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
*/ */
fclose(fp6); double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
//printf("%lf", xErrorPtr[499]);
double xErrorLength = *xErrorPtr; // Watch popNAN()!
xErrorPtr[0] = 0.0;
printf("Xerrorl:%lf", xErrorLength);
double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
double deviation = 0.0;
// Mean square
for (i = 1; i < xErrorLength; i++) {
deviation += pow(xError[i] - mean, 2);
}
deviation /= xErrorLength;
printf("mean:%lf, devitation:%lf", mean, deviation);
// write in file
mkFileName(fileName, sizeof(fileName), RESULTS);
FILE *fp2 = fopen(fileName, "wa");
fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
fclose(fp2);
free(local_weights);
weightsLogger( local_weights, USED_WEIGHTS );
//mkSvgGraph(points);
} }
@ -461,7 +491,7 @@ void weightsLogger (double weights[WINDOWSIZE], int val ) {
// } // }
} }
fprintf(fp,"\n\n\n\n=====================NEXT=====================\n"); fprintf(fp,"\n\n\n\n=====================NEXT=====================\n");
fclose(fp); //fclose(fp);
} }
@ -485,7 +515,7 @@ void bufferLogger(char *buffer, point_t points[]) {
} }
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 < NUMBER_OF_SAMPLES - 1; i++) { //xPredicted from diff Pred for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { //xPredicted from diff Pred
sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].xVal[3]); sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].yVal[3]);
strcat(buffer, _buffer); strcat(buffer, _buffer);
} }
strcat(buffer, "\" fill=\"none\" id=\"svg_4\" stroke=\"red\" stroke-width=\"0.4px\"/>\n"); strcat(buffer, "\" fill=\"none\" id=\"svg_4\" stroke=\"red\" stroke-width=\"0.4px\"/>\n");