/* 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);
}
