double ConfusionMatrix::accuracy() const {
double total_sum = totalSum();
double diag_sum = diagSum();
if (total_sum == 0) {
return 0;
} else {
return diag_sum / total_sum;
}
}
void ConfusionMatrix::printNormalized(const char *header) const {
double total = totalSum();
if (header == NULL) {
LOG(INFO) << "--- confusion matrix: (actual, predicted) ---";
} else {
LOG(INFO) << header;
}
for (size_t i = 0; i < _matrix.size(); i++) {
LOG(INFO) << ROW_BEGIN;
for (size_t j = 0; j < _matrix[i].size(); j++) {
if (j > 0) LOG(INFO) << COL_SEP;
LOG(INFO) << ((double)_matrix[i][j] / total);
}
LOG(INFO) << ROW_END;
}
}
int main(int argc,char* argv[])
{
if(argc==1 || argc%2==0)
{
return -1;
}
int i=0;
struct data* myDta = NULL;
struct tree* myT = createTree();
for(i=1;i<argc;i+=2)
{
assert(i+1<argc);
myDta = createData(atof(argv[i]),atof(argv[i+1]));
insertBst(myT,myDta);
}
printf("\ninOrderBst()\n");
inOrderBst(myT);
printf("\npreOrderBst()\n");
preOrderBst(myT);
printf("\npostOrderBst()\n");
postOrderBst(myT);
printf("\ntotalSum()\n");
printf("%f\n",totalSum(myT));
printf("\ngetMaxData()\n");
printData(getMaxData(myT));
printf("\ngetMinData()\n");
printData(getMinData(myT));
printf("\ngetBstHeight()\n");
printf("%d\n",getBstHeight(myT));
printf("\nprintInRange() (range 6.0-14.0)\n");
printInRange(myT,6.0,14.0);
printf("\nisCompleteBst()\n");
printf("%d\n",isCompleteBst(myT));
printf("\nisFullBst()\n");
printf("%d\n",isFullBst(myT));
printf("\nsumLeave()\n");
printf("%f\n",sumLeave(myT));
printf("\nprintDepthFirstSearch()\n");
printDepthFirstSearch(myT);
printf("\nprintBreadthFirstSearch()\n");
printBreadthFirstSearch(myT);
printf("\nreverseBST()\n");
reverseBST(myT);
printf("\ninOrderBst()\n");
inOrderBst(myT);
cleanBST(myT);
return 0;
}
