improved graphical output
This commit is contained in:
Friese
parent
402f8937d6
commit
31c3091b30
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@ -45,7 +45,7 @@ typedef struct {
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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
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/* *ppm read, copy, write* */
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/* *ppm read, copy, write* */
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typedef struct {
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unsigned char red, green, blue;
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}colorChannel_t;
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@ -82,7 +82,7 @@ double windowXMean(int _arraylength, int xCount);
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//int main(int argc, char **argv) {
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int main( void ) {
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double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // = { { 0.0 }, {0.0} };
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double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]; // = { { 0.0 }, {0.0} };
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// double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES];
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char fileName[50];
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int i,k, xLength;
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@ -100,7 +100,6 @@ int main( void ) {
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srand((unsigned int)time(NULL));
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for (i = 0; i < NUMBER_OF_SAMPLES; i++) {
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//_x[i] += ((255.0 / M) * i); // Init test values
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for (int k = 0; k < WINDOWSIZE; k++) {
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weights[k][i] = rndm(); // Init weights
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}
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@ -118,16 +117,10 @@ int main( void ) {
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// math magic
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/* for (i = 0; i < NUMBER_OF_SAMPLES; i++){
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for (k = 0; k < WINDOWSIZE; k++){
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local_weights[k][i] = weights[k][i];
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printf("ALT::%f\n", local_weights[k][i]);
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}
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}*/
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//localMean(weights);
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// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
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// directPredecessor(weights);
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// memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
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localMean(weights);
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//memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
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directPredecessor(weights);
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//memcpy(local_weights, weights, sizeof(double) * WINDOWSIZE * NUMBER_OF_SAMPLES);
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differentialPredecessor(weights);
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mkSvgGraph(points);
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// save test_array after math magic happened
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@ -168,7 +161,7 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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int xCount = 0, i; // runtime var;
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mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
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FILE* fp4 = fopen(fileName, "w");
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fprintf(fp4, "\n=====================================LocalMean=====================================\n");
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fprintf(fp4, "\n=====================================LocalMean=====================================\nNo.\txPredicted\txActual\t\txError\n");
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double xMean = xSamples[0];
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double xSquared = 0.0;
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@ -212,7 +205,7 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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// printf("NEU::%lf\n", local_weights[i][xCount]);
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}
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fprintf(fp4, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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fprintf(fp4, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
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}
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// int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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@ -220,9 +213,10 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
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//printf("%lf", xErrorPtr[499]);
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double xErrorLength = *xErrorPtr; // Watch popNAN()!
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xErrorPtr[0] = 0.0;
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printf("Xerrorl:%lf", xErrorLength);
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/* double mean = sum_array(*xErrorPtr, xErrorLength) / xErrorLength;
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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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// Mean square
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@ -230,17 +224,18 @@ void localMean(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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deviation += pow(xError[i] - mean, 2);
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}
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deviation /= xErrorLength;
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printf("mean:%lf, devitation:%lf", mean, deviation);
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// write in file
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mkFileName(fileName, sizeof(fileName), RESULTS);
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FILE *fp2 = fopen(fileName, "w");
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fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
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fclose(fp2);*/
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fclose(fp2);
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free(local_weights);
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fclose(fp4);
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// weightsLogger( local_weights, USED_WEIGHTS );
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mkSvgGraph(points);
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weightsLogger( local_weights, USED_WEIGHTS );
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//mkSvgGraph(points);
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}
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@ -268,7 +263,7 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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// File handling
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mkFileName(fileName, sizeof(fileName), DIRECT_PREDECESSOR);
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FILE *fp3 = fopen(fileName, "w");
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fprintf(fp3, "\n=====================================DirectPredecessor=====================================\n");
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fprintf(fp3, "\n=====================================DirectPredecessor=====================================\nNo.\txPredicted\txAcutal\t\txError\n");
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
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@ -287,7 +282,7 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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xPredicted += xSamples[xCount - 1];
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xError[xCount] = xActual - xPredicted;
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fprintf(fp3, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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//fprintf(fp3, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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points[xCount].xVal[2] = xCount;
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points[xCount].yVal[2] = xPredicted;
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points[xCount].xVal[5] = xCount;
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@ -301,25 +296,34 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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for (i = 1; i < _arrayLength; i++) {
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local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ( (xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
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}
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fprintf(fp3, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
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}
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fclose(fp3);
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double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
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//printf("%lf", xErrorPtr[499]);
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double xErrorLength = *xErrorPtr; // Watch popNAN()!
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xErrorPtr[0] = 0.0;
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printf("Xerrorl:%lf", xErrorLength);
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int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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printf("vor:%d", xErrorLength);
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popNAN(xError);
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printf("nach:%d", xErrorLength);
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xErrorLength = sizeof(xError) / sizeof(xError[0]);
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double mean = sum_array(xError, xErrorLength) / xErrorLength;
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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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for (i = 0; i < xErrorLength - 1; i++) {
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// Mean square
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for (i = 1; i < xErrorLength; i++) {
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deviation += pow(xError[i] - mean, 2);
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}
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deviation /= xErrorLength;
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printf("mean:%lf, devitation:%lf", mean, deviation);
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// mkSvgGraph(points);
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fprintf(fp3, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
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// write in file
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mkFileName(fileName, sizeof(fileName), RESULTS);
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FILE *fp2 = fopen(fileName, "wa");
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fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
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fclose(fp2);
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free(local_weights);
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fclose(fp3);
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weightsLogger( local_weights, USED_WEIGHTS );
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//mkSvgGraph(points);
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}
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@ -329,7 +333,7 @@ void directPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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differentialPredecessor
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variant (3/3),
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differenital predecessor.
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differential predecessor.
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======================================================================================================
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*/
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@ -347,7 +351,7 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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// File handling
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mkFileName(fileName, sizeof(fileName), DIFFERENTIAL_PREDECESSOR);
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FILE *fp6 = fopen(fileName, "w");
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fprintf(fp6, "\n=====================================DifferentialPredecessor=====================================\n");
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fprintf(fp6, "\n=====================================DifferentialPredecessor=====================================\nNo.\txPredicted\txAcutal\t\txError\n");
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
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@ -361,7 +365,7 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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xPredicted += xSamples[xCount - 1];
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xError[xCount] = xActual - xPredicted;
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fprintf(fp6, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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//fprintf(fp6, "{%d}.\txPredicted{%f}\txActual{%f}\txError{%f}\n", xCount, xPredicted, xActual, xError[xCount]);
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points[xCount].xVal[3] = xCount;
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points[xCount].yVal[3] = xPredicted;
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points[xCount].xVal[6] = xCount;
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@ -374,9 +378,10 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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for (i = 1; i < _arrayLength; i++) {
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local_weights[i][xCount+1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
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}
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fprintf(fp6, "%d\t%f\t%f\t%f\n", xCount, xPredicted, xActual, xError[xCount]);
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}
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/*
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int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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fclose(fp6);
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/* int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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printf("vor:%d", xErrorLength);
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popNAN(xError);
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printf("nach:%d", xErrorLength);
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@ -392,7 +397,32 @@ void differentialPredecessor(double weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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//mkSvgGraph(points);
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fprintf(fp6, "{%d}.\tLeast Mean Squared{%f}\tMean{%f}\n\n", xCount, deviation, mean);
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*/
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fclose(fp6);
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double *xErrorPtr = popNAN(xError); // delete NAN values from xError[]
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//printf("%lf", xErrorPtr[499]);
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double xErrorLength = *xErrorPtr; // Watch popNAN()!
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xErrorPtr[0] = 0.0;
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printf("Xerrorl:%lf", xErrorLength);
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double mean = sum_array(xErrorPtr, xErrorLength) / xErrorLength;
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double deviation = 0.0;
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// Mean square
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for (i = 1; i < xErrorLength; i++) {
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deviation += pow(xError[i] - mean, 2);
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}
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deviation /= xErrorLength;
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printf("mean:%lf, devitation:%lf", mean, deviation);
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// write in file
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mkFileName(fileName, sizeof(fileName), RESULTS);
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FILE *fp2 = fopen(fileName, "wa");
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fprintf(fp2, "quadr. Varianz(x_error): {%f}\nMittelwert:(x_error): {%f}\n\n", deviation, mean);
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fclose(fp2);
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free(local_weights);
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weightsLogger( local_weights, USED_WEIGHTS );
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//mkSvgGraph(points);
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}
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@ -461,7 +491,7 @@ void weightsLogger (double weights[WINDOWSIZE], int val ) {
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// }
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}
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fprintf(fp,"\n\n\n\n=====================NEXT=====================\n");
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fclose(fp);
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//fclose(fp);
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}
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@ -485,7 +515,7 @@ void bufferLogger(char *buffer, point_t points[]) {
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}
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strcat(buffer, "\" fill=\"none\" id=\"svg_3\" stroke=\"blue\" stroke-width=\"0.4px\"/>\n<path d=\"M0 0\n");
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for (i = 0; i < NUMBER_OF_SAMPLES - 1; i++) { //xPredicted from diff Pred
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sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].xVal[3]);
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sprintf(_buffer, "L %f %f\n", points[i].xVal[3], points[i].yVal[3]);
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strcat(buffer, _buffer);
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}
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strcat(buffer, "\" fill=\"none\" id=\"svg_4\" stroke=\"red\" stroke-width=\"0.4px\"/>\n");
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