added stuff
This commit is contained in:
Friese
parent
66177c97da
commit
e73cfda76c
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@ -32,7 +32,8 @@ typedef SSIZE_T ssize_t;
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//double x[] = { 0.0 };
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double xSamples[NUMBER_OF_SAMPLES] = { 0.0 };
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double w[WINDOWSIZE][NUMBER_OF_SAMPLES] = { { 0.0 },{ 0.0 } };
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/* *svg graph building* */
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typedef struct {
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@ -69,14 +70,16 @@ double rndm(void);
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/* *math* */
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double sum_array(double x[], int length);
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void directPredecessor(void);
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void localMean(void);
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void differentialPredecessor(void);
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void directPredecessor(double localweights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
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void localMean(double localweights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
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void differentialPredecessor(double localweights[WINDOWSIZE][NUMBER_OF_SAMPLES]);
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double *popNAN(double *xError, int xErrorLength); //return new array without NAN values
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double windowXMean(int _arraylength, int xCount);
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int main(int argc, char **argv) {
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double w[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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int *colorChannel;
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@ -113,9 +116,26 @@ int main(int argc, char **argv) {
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// math magic
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localMean();
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//directPredecessor();
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//differentialPredecessor();
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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] = w[k][i];
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}
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}
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localMean(local_weights);
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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] = w[k][i];
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}
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}
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//directPredecessor(local_weights);
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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] = w[k][i];
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}
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}
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//differentialPredecessor(local_weights);
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mkSvgGraph(points);
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// save test_array after math magic happened
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// memset( fileName, '\0', sizeof(fileName) );
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mkFileName(fileName, sizeof(fileName), USED_WEIGHTS);
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@ -144,9 +164,10 @@ Variant (1/3), substract local mean.
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======================================================================================================
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*/
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void localMean(void) {
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void localMean(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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char fileName[50];
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double xError[NUMBER_OF_SAMPLES]; // includes e(n)
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double xError[2048]; // includes e(n)
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memset(xError, 0.0, NUMBER_OF_SAMPLES);// initialize xError-array with Zero
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int xCount = 0, i; // runtime var;
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mkFileName(fileName, sizeof(fileName), LOCAL_MEAN);
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@ -173,7 +194,7 @@ void localMean(void) {
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// weightedSum += _x[ xCount-1 ] * w[xCount][0];
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for (i = 1; i < _arrayLength; i++) { //get predicted value
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xPredicted += (w[i][xCount] * (xSamples[xCount - i] - xMean));
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xPredicted += (local_weights[i][xCount] * (xSamples[xCount - i] - xMean));
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}
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xPredicted += xMean;
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@ -195,13 +216,13 @@ void localMean(void) {
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}
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//printf("%f\n", xSquared);
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for (i = 1; i < _arrayLength; i++) { //update weights
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xMean) / xSquared);
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local_weights[i][xCount + 1] = local_weights[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xMean) / xSquared);
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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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}
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int xErrorLength = sizeof(xError) / sizeof(xError[0]);
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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, xErrorLength);
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printf("nach:%d", xErrorLength);
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@ -215,13 +236,14 @@ void localMean(void) {
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}
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deviation /= xErrorLength;
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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(fp4);
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*/
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//mkSvgGraph(points);
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}
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/*
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@ -235,29 +257,30 @@ substract direct predecessor
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======================================================================================================
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*/
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void directPredecessor(void) {
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void directPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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char fileName[512];
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double xError[2048];
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int xCount = 0, i;
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double xActual;
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double xActual = 0.0;
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int xPredicted = 0.0;
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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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for (xCount = 1; xCount < NUMBER_OF_SAMPLES + 1; xCount++) {
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//double xPartArray[xCount]; //includes all values at the size of runtime var
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
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//double xPartArray[1000]; //includes all values at the size of runtime var
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//int _sourceIndex = (xCount > WINDOWSIZE) ? xCount - WINDOWSIZE : xCount;
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int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
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printf("xCount:%d, length:%d\n", xCount, _arrayLength);
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double xMean = (xCount > 0) ? windowXMean(_arrayLength, xCount) : 0;
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printf("%f\n", windowXMean(_arrayLength, xCount));
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//printf("xCount:%d, length:%d\n", xCount, _arrayLength);
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// printf("WINDOWSIZE:%f\n", windowXMean(_arrayLength, xCount));
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xPredicted = 0.0;
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xActual = xSamples[xCount + 1];
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// weightedSum += _x[ xCount-1 ] * w[xCount][0];
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for (i = 1; i < _arrayLength; i++) {
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xPredicted += (w[i][xCount] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
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xPredicted += (local_weights[i][xCount] * (xSamples[xCount - 1] - xSamples[xCount - i - 1]));
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}
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xPredicted += xSamples[xCount - 1];
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xError[xCount] = xActual - xPredicted;
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@ -274,10 +297,10 @@ void directPredecessor(void) {
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xSquared += pow(xSamples[xCount - 1] - xSamples[xCount - i - 1], 2); // substract direct predecessor
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}
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for (i = 1; i < _arrayLength; i++) {
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - 1] - xSamples[xCount - i - 1]) / xSquared);
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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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}
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/*
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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, xErrorLength);
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@ -291,8 +314,9 @@ void directPredecessor(void) {
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}
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deviation /= xErrorLength;
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mkSvgGraph(points);
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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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*/
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fclose(fp3);
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}
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@ -307,24 +331,26 @@ differenital predecessor.
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======================================================================================================
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*/
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void differentialPredecessor(void) {
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void differentialPredecessor(double local_weights[WINDOWSIZE][NUMBER_OF_SAMPLES]) {
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char fileName[512];
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double xError[2048];
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int xCount = 0, i;
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double xActual;
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double xPredicted = 0.0;
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double xActual = 0.0;
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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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for (xCount = 1; xCount < NUMBER_OF_SAMPLES + 1; xCount++) {
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xActual = xSamples[xCount + 1];
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for (xCount = 1; xCount < NUMBER_OF_SAMPLES; xCount++) { // first value will not get predicted
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int _arrayLength = (xCount > WINDOWSIZE) ? WINDOWSIZE + 1 : xCount;
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double xPredicted = 0.0;
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for (i = 1; i < xCount; i++) {
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xPredicted += (w[i][xCount] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
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for (i = 1; i < _arrayLength; i++) {
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xPredicted += (local_weights[i][xCount] * (xSamples[xCount - i] - xSamples[xCount - i - 1]));
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}
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xPredicted += xSamples[xCount - 1];
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xError[xCount] = xActual - xPredicted;
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@ -336,14 +362,19 @@ void differentialPredecessor(void) {
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points[xCount].yVal[6] = xError[xCount];
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double xSquared = 0.0;
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for (i = 1; i < xCount; i++) {
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for (i = 1; i < _arrayLength; i++) {
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xSquared += pow(xSamples[xCount - i] - xSamples[xCount - i - 1], 2); // substract direct predecessor
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}
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for (i = 1; i < xCount; i++) {
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w[i][xCount + 1] = w[i][xCount] + learnrate * xError[xCount] * ((xSamples[xCount - i] - xSamples[xCount - i - 1]) / xSquared);
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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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}
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/*
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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, xErrorLength);
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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 deviation = 0.0;
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@ -352,8 +383,9 @@ void differentialPredecessor(void) {
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}
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deviation /= xErrorLength;
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mkSvgGraph(points);
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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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@ -530,8 +562,8 @@ parses template.svg and writes results in said template
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*/
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void mkSvgGraph(point_t points[]) {
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FILE *input = fopen("template.svg", "r");
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FILE *target = fopen("output.svg", "w");
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FILE *input = fopen("GraphResults_template.html", "r");
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FILE *target = fopen("GraphResults.html", "w");
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char line[512];
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char firstGraph[15] = { "<path d=\"M0 0" };
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