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NLMSvariants
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some fixes

This commit is contained in:
gurkenhabicht 2018-06-11 14:27:48 +02:00
parent e3ed31aa0f
commit 486bc83403
1 changed files with 25 additions and 16 deletions
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31
src/ansi_c_implementation/NLMSvariants.c
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@ -194,7 +194,7 @@ 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;
memcpy(localWeights, mlData->weights, sizeof(double) * sizeof(mlData->windowSize) );
char fileName[50];
const unsigned xErrorLength = mlData->samplesCount;
@ -227,7 +227,7 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
xError[xCount] = xActual - xPredicted; // Get error value
xSquared = 0.0;
for (i = 1; i < _arrayLength; i++) { // Get xSquared
xSquared += pow(xSamples[xCount - i] - xMean, 2);
xSquared += (xSamples[xCount - i] - xMean) * (xSamples[xCount - i] - xMean);
}
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
@ -235,9 +235,9 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
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] );
fprintf( fp9, "%lf;", localWeights[i] );
}
fprintf(fp9, "\n");
fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]); // Write to logfile
points[xCount].xVal[1] = xCount; // Save points so graph can be build later on
@ -253,12 +253,14 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
double deviation = 0.0;
for (i = 1; i < xErrorLength; i++) { // Mean square
deviation += pow(xError[i] - mean, 2);
deviation += (xError[i] - mean) * (xError[i] - mean);
}
deviation /= xErrorLength; // Deviation
printf("mean square err: %lf, variance: %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
fclose(fp4);
free(localWeights);
}
/*
@ -273,7 +275,7 @@ substract direct predecessor
*/
void directPredecessor( mldata_t *mlData, point_t points[]) {
double *localWeights = (double * ) malloc ( sizeof(double) * mlData->windowSize + 1 );
localWeights = mlData->weights;
memcpy(localWeights, mlData->weights, sizeof(double) * sizeof(mlData->windowSize));
char fileName[512];
const unsigned xErrorLength = mlData->samplesCount;
@ -303,7 +305,8 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
double xSquared = 0.0;
for (i = 1; i < _arrayLength; i++) {
xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
xSquared += (xSamples[xCount - 1] - xSamples[xCount - i - 1])
* (xSamples[xCount - 1] - xSamples[xCount - i - 1]); // substract direct predecessor
}
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
@ -327,12 +330,14 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
for (i = 1; i < xErrorLength; i++) {
deviation += pow(xError[i] - mean, 2); // Mean square
deviation += (xError[i] - mean) * (xError[i] - mean); // Mean square
}
deviation /= xErrorLength; // Deviation
printf("mean square err: %lf, variance: %lf\t\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);
}
/*
@ -347,7 +352,8 @@ differential predecessor.
*/
void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
double *localWeights = (double *) malloc ( sizeof(double) * mlData->windowSize + 1 );
localWeights = mlData->weights;
memcpy(localWeights, mlData->weights, sizeof(double) * sizeof(mlData->windowSize));
const unsigned xErrorLength = mlData->samplesCount;
char fileName[512];
double xError[xErrorLength];
@ -377,7 +383,8 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
double xSquared = 0.0;
for (i = 1; i < _arrayLength; i++) {
xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // Substract direct predecessor
xSquared += (xSamples[xCount - i] - xSamples[xCount - i - 1])
* (xSamples[xCount - i] - xSamples[xCount - i -1]); // Substract direct predecessor
}
if ( xSquared == 0.0 ) { // Otherwise returns Pred: -1.#IND00 in some occassions
xSquared = 1.0;
@ -404,12 +411,14 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
for (i = 1; i < xErrorLength; i++) { // Mean square
deviation += pow(xError[i] - mean, 2);
deviation += (xError[i] - mean) * (xError[i] - mean);;
}
deviation /= xErrorLength;
printf("mean square err: %lf, variance: %lf\t\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);
}
/*