/* Create the training set, and normalize the Iris data. If you are using a data set other than Iris, you will need to update the normalization. */ static DATA_SET *create_iris_training(NORM_DATA *norm) { char filename[FILENAME_MAX]; DATA_SET *data; LocateFile("iris.csv",filename,FILENAME_MAX); NormDefRange(norm,0,1); NormDefRange(norm,0,1); NormDefRange(norm,0,1); NormDefRange(norm,0,1); NormDefClass(norm,NORM_CLASS_ONEOFN,0,1); NormAnalyze(norm,filename); data = NormProcess(norm,filename,4,1); return data; }
void ExampleAnalyze(int argIndex, int argc, char **argv) { char filename[FILENAME_MAX]; NORM_DATA *norm; NORM_DATA_ITEM *col; NORM_DATA_CLASS *currentClass; LocateFile("iris.csv",filename,FILENAME_MAX); norm = NormCreate(); NormDefRange(norm,0,1); NormDefRange(norm,0,1); NormDefRange(norm,0,1); NormDefRange(norm,0,1); NormDefClass(norm,NORM_CLASS_ONEOFN,0,1); NormAnalyze(norm,filename); col = norm->firstItem; while(col!=NULL) { if( col->type == NORM_TYPE_RANGE ) { printf("Column: \"%s\",actualMin=%.2f,actualHigh=%.2f\n",col->name,col->actualHigh,col->actualLow); } else { printf("Column: \"%s\",classes= ",col->name); currentClass = col->firstClass; while(currentClass!=NULL) { printf("\"%s\";",currentClass->name); currentClass = currentClass->next; } printf("\n"); } col = col->next; } printf("Rows: %i\n",norm->rowCount); NormDelete(norm); }