generic-library/opencv
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made everything compile and even run somehow

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This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
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30
modules/ml/src/boost.cpp
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@@ -54,8 +54,6 @@ log_ratio( double val )
}
Boost::~Boost() {}
Boost::Params::Params()
{
boostType = Boost::REAL;
@@ -106,6 +104,7 @@ public:
void startTraining( const Ptr<TrainData>& trainData, int flags )
{
DTreesImpl::startTraining(trainData, flags);
sumResult.assign(w->sidx.size(), 0.);
if( bparams.boostType != Boost::DISCRETE )
{
@@ -114,14 +113,10 @@ public:
w->ord_responses.resize(n);
double a = -1, b = 1;
if( bparams.boostType == Boost::REAL )
a = 0;
else if( bparams.boostType == Boost::LOGIT )
if( bparams.boostType == Boost::LOGIT )
{
sumResult.assign(w->sidx.size(), 0.);
a = -2, b = 2;
}
for( i = 0; i < n; i++ )
w->ord_responses[i] = w->cat_responses[i] > 0 ? b : a;
}
@@ -197,7 +192,7 @@ public:
}
else if( bparams.boostType == Boost::REAL )
{
double p = node->value;
double p = (node->value+1)*0.5;
node->value = 0.5*log_ratio(p);
}
}
@@ -227,7 +222,7 @@ public:
{
int i, n = (int)w->sidx.size();
int nvars = (int)varIdx.size();
double sumw = 0.;
double sumw = 0., C = 1.;
cv::AutoBuffer<double> buf(n*3 + nvars);
double* result = buf;
float* sbuf = (float*)(result + n*3);
@@ -261,7 +256,7 @@ public:
if( sumw != 0 )
err /= sumw;
double C = -log_ratio( err );
C = -log_ratio( err );
double scale = std::exp(C);
sumw = 0;
@@ -289,6 +284,7 @@ public:
for( i = 0; i < n; i++ )
{
int si = w->sidx[i];
CV_Assert( std::abs(w->ord_responses[si]) == 1 );
double wval = w->sample_weights[si]*std::exp(-result[i]*w->ord_responses[si]);
sumw += wval;
w->sample_weights[si] = wval;
@@ -330,6 +326,20 @@ public:
}
else
CV_Error(CV_StsNotImplemented, "Unknown boosting type");
/*if( bparams.boostType != Boost::LOGIT )
{
double err = 0;
for( i = 0; i < n; i++ )
{
sumResult[i] += result[i]*C;
if( bparams.boostType != Boost::DISCRETE )
err += sumResult[i]*w->ord_responses[w->sidx[i]] < 0;
else
err += sumResult[i]*w->cat_responses[w->sidx[i]] < 0;
}
printf("%d trees. C=%.2f, training error=%.1f%%, working set size=%d (out of %d)\n", (int)roots.size(), C, err*100./n, (int)sidx.size(), n);
}*/
// renormalize weights
if( sumw > FLT_EPSILON )