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now it looks more stable but doesn't converge yet

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i will better check it with a smaller matrix
Probably the gradient is wrong
This commit is contained in:
vasiloglou
2008-05-25 04:44:18 +00:00
parent 44ebad5424
commit b5ba2d5d57
1 changed files with 20 additions and 9 deletions
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fastlib2/contrib/nvasil/convex_nmf/sdp_objectives_impl.h
+20 -9
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@@ -84,6 +84,7 @@ void SmallSdpNmf::ComputeGradient(Matrix &coordinates, Matrix *gradient) {
double t2_minus_hh=(t2-h*h);
double wh_minus_v=(w*h-v);
double determinant=t1_minus_ww*t2_minus_hh-math::Pow<2,1>(wh_minus_v);
DEBUG_ERR_MSG_IF(determinant==0.0, "Determinant equal to zero");
double dw=(-2*w*(t2_minus_hh)-2*h*(wh_minus_v))/determinant;
double dh=(-2*h*(t1_minus_ww)-2*w*(wh_minus_v))/determinant;
double dt1=(t2_minus_hh)/determinant;
@@ -122,9 +123,13 @@ double SmallSdpNmf::ComputeLagrangian(Matrix &coordinates) {
for(index_t j=0; j<new_dim_; j++) {
diff+=coordinates.get(j, v_i);
}
diff-values_[i];
DEBUG_ERR_MSG_IF(diff<=0, "LP cone is invalid, you are "
" out of the feasible region");
diff-=values_[i];
if unlikely(diff<0) {
return DBL_MAX;
}
// DEBUG_ERR_MSG_IF(diff<=0, "LP cone is invalid, you are "
// " out of the feasible region, constraint %i, diff %lg",
// i, diff);
lagrangian-=log(diff);
}
// from the SDP cones
@@ -145,8 +150,11 @@ double SmallSdpNmf::ComputeLagrangian(Matrix &coordinates) {
double t2_minus_hh=(t2-h*h);
double wh_minus_v=(w*h-v);
double determinant=t1_minus_ww*t2_minus_hh-math::Pow<2,1>(wh_minus_v);
DEBUG_ERR_MSG_IF(determinant<=0, "SDP cone is invalid, you are "
" out of the feasible region");
if (unlikely(determinant<=0)) {
return DBL_MAX;
}
// DEBUG_ERR_MSG_IF(determinant<=0, "SDP cone is invalid, you are "
// " out of the feasible region");
lagrangian-=log(determinant);
}
}
@@ -182,14 +190,17 @@ void SmallSdpNmf::GiveInitMatrix(Matrix *init_data) {
for(index_t j=0; j<new_dim_; j++) {
double w=init_data->get(j, w_i);
double h=init_data->get(j, h_i);
double v=init_data->get(j, v_i);
// ensure that Sum w_ij*hij > v_ij
init_data->set(j, v_i, std::max(w*h+math::Random(), values_[i]));
init_data->set(j, t1_i, fabs(w*h-v)+w*w+math::Random());
init_data->set(j, t2_i, fabs(w*h-v)+h*h+math::Random());
double v=init_data->get(j, v_i);
init_data->set(j, t1_i, std::max(fabs(w*h-v)+w*w+math::Random(),
init_data->get(j, t1_i)));
init_data->set(j, t2_i, std::max(fabs(w*h-v)+h*h+math::Random(),
init_data->get(j ,t2_i)));
}
}
}
data::Save("init_data.csv", *init_data);
}
bool SmallSdpNmf::IsDiverging(double objective) {