int main() {
Star s;
union u {
float f[4];
vector float v;
} data;
data.v = fTest(s);
for (int i=0 ; i < 4; i++)
if (data.f[i] != (float)(i+1))
abort();
return 0;
}
void SPxSolver::updateFtest()
{
METHOD( "SPxSolver::updateFtest()" );
const IdxSet& idx = theFvec->idx();
Vector& ftest = theCoTest; // |== fTest()|
assert(&ftest == &fTest());
assert(type() == LEAVE);
for (int j = idx.size() - 1; j >= 0; --j)
{
int i = idx.index(j);
ftest[i] = ((*theFvec)[i] > theUBbound[i])
? theUBbound[i] - (*theFvec)[i]
: (*theFvec)[i] - theLBbound[i];
}
}
void SPxSolver::qualRedCostViolation(Real& maxviol, Real& sumviol) const
{
maxviol = 0.0;
sumviol = 0.0;
int i;
// TODO: y = c_B * B^-1 => coSolve(y, c_B)
// redcost = c_N - yA_N
// solve system "x = e_i^T * B^-1" to get i'th row of B^-1
// DVector y( nRows() );
// basis().coSolve( x, spx->unitVector( i ) );
// DVector rdcost( nCols() );
#if 0 // un-const
if (lastUpdate() > 0)
factorize();
computePvec();
if (type() == ENTER)
computeTest();
#endif
if (type() == ENTER)
{
for(i = 0; i < dim(); ++i)
{
Real x = coTest()[i];
if (x < 0.0)
{
sumviol -= x;
if (x < maxviol)
maxviol = x;
}
}
for(i = 0; i < coDim(); ++i)
{
Real x = test()[i];
if (x < 0.0)
{
sumviol -= x;
if (x < maxviol)
maxviol = x;
}
}
}
else
{
assert(type() == LEAVE);
for(i = 0; i < dim(); ++i)
{
Real x = fTest()[i];
if (x < 0.0)
{
sumviol -= x;
if (x < maxviol)
maxviol = x;
}
}
}
maxviol *= -1;
}
void RandomForest::build() {
srand(9167543);
vector<pair<double,double> > oob_predict_acum(pXD_->trn_rows,pair<double,double>(0,0));
vector<pair<double,double> > validate_predict_acum(pXD_->trn_rows,pair<double,double>(0,0));
vector<pair<double,double> > tst_predict_acum(pXD_->tst_rows,pair<double,double>(0,0));
vector<double> tst_predict(pXD_->tst_rows,0);
vector<pair<int,int> > tst;
for(int tst_idx=0; tst_idx < pXD_->tst_rows; tst_idx++) {
tst.push_back(pair<int,int>(tst_idx,1));
}
int good_tree_cnt = 0;
// the below is setup for the key press checking
char c;
int n, tem;
int cnt = 0;
tem = fcntl(0, F_GETFL, 0);
fcntl (0, F_SETFL, (tem | O_NDELAY));
// end key press setup
vector<int> train_idx;
vector<pair<int,int> > validate;
for(int i=0; i<pXD_->trn_labl.size(); i++) {
if((rand() % folds_) == fold_) validate.push_back(pair<int,int>(i,1));
else train_idx.push_back(i);
}
int tree = 0;
while(1) {
//base_feature_sample_cnt_ = (int)(pow((double)(rand() % 1000)/1000.0,2) * 400); //(int)((double)train_idx.size()/3.0);
vector<pair<int,int> > bag, oob;
vector<int> w(train_idx.size(),0);
for(int idx=0; idx<train_idx.size(); idx++) {
//w[idx]=(int)(338 / pXD_->trn_attr[train_idx[idx]][(pXD_->trn_cols)-1]);
w[idx]=1;
//cerr << pXD_->trn_attr[train_idx[idx]][(pXD_->trn_cols)-1] << "," << w[idx] << endl;
}
create_bag(bag,oob,train_idx,w);
//Node* root = new Node;
Node root;
int node_cnt=0;
double tree_gini=0;
recAssignNode(&root, bag, "", 0, node_cnt, tree_gini);
/*
vector<double> v_predict(pXD_->trn_rows,0);
recPredict(&root, validate, &(pXD_->trn_attr), v_predict, 0, 0);
double d2 = 0;
for(int i=0; i < validate.size(); i++) {
d2+=pow(pXD_->trn_labl[validate[i].first] - v_predict[validate[i].first],2);
}
d2 /= (double)validate.size();
*/
cerr << tree+1 << "/" << trees_ << "; M: " << base_feature_sample_cnt_ << "; bag_size: " << bag.size() \
<< "; oob_size: " << oob.size() /*<< "; d2: " << d2 */<< "; leaf_node_cnt: " << node_cnt \
<< "; tree_gini: " << tree_gini << endl;
if(/*d2 < 0.3 || */1) {
//double weight = 1 / pow((double)node_cnt,20);
double weight = 1;
predict(&root, &(pXD_->trn_attr), oob, oob_predict_acum, weight);
predict(&root, &(pXD_->trn_attr), validate, validate_predict_acum, weight);
predict(&root, &(pXD_->tst_attr), tst, tst_predict_acum, weight);
good_tree_cnt++;
}
cerr << "good_tree_cnt: " << good_tree_cnt << endl;
eval(oob_predict_acum,good_tree_cnt,"oob");
eval(validate_predict_acum,good_tree_cnt,"validate");
tree++;
n=0;
//n = read(0, &c, 1);
if (n > 0 || good_tree_cnt == trees_) {
cerr << "Key pressed: " << c << endl;
break;
}
}
cerr << endl;
eval(oob_predict_acum,good_tree_cnt,"oob");
eval(validate_predict_acum,good_tree_cnt,"validate");
cerr << "writing tst set predictions..." << endl;
char tstFile[100];
sprintf(tstFile, "workspace/tst_predict_%d_%d_%d_%d.csv", fold_, folds_, base_feature_sample_cnt_, trees_);
ofstream fTest(tstFile);
if(!fTest.is_open()) {
cerr << "Cannot open " << tstFile << " for writing" << endl;
return;
}
for(int tst_idx=0; tst_idx < pXD_->tst_rows; tst_idx++) {
double p = tst_predict_acum[tst_idx].first/(double)tst_predict_acum[tst_idx].second;
p = (p*(double)good_tree_cnt + (double)ls1_)/(double)(good_tree_cnt+ls2_);
fTest << p << endl;
}
fTest.close();
char oobFile[100];
sprintf(oobFile, "workspace/oob_predict_%d_%d_%d_%d.csv", fold_, folds_, base_feature_sample_cnt_, trees_);
ofstream fOob(oobFile);
if(!fOob.is_open()) {
cerr << "Cannot open " << oobFile << " for writing" << endl;
return;
}
for(int trn_idx=0;trn_idx < pXD_->trn_rows; trn_idx++) {
double p = oob_predict_acum[trn_idx].first/(double)oob_predict_acum[trn_idx].second;
fOob << pXD_->trn_labl[trn_idx] << "," << p << endl;
}
fOob.close();
}
