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Searched refs:grad_c (Results 1 – 9 of 9) sorted by relevance

/dports/science/pybrain/pybrain-0.3.3/pybrain/supervised/trainers/
H A Dmixturedensity.py67 grad_c = par[0:nGauss] - gamma
77 self.module.outputerror[time] = -np.r_[grad_c,grad_s,np.array(grad_m).flatten()]
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/OPTPP/src/Constraints/
H A DCompoundConstraint.C366 SerialDenseMatrix<int,double> grad_c; in computeFeasibleInequalities() local
379 grad_c.reshape(xc.length(),v.length()); in computeFeasibleInequalities()
380 grad_c = test.evalGradient(xc); in computeFeasibleInequalities()
387 int alpha = grad_c.numRows(); in computeFeasibleInequalities()
390 { g(i) = grad_c(i,indices[j]);} in computeFeasibleInequalities()
/dports/math/optpp/optpp-2.4/src/Constraints/
H A DCompoundConstraint.C298 Matrix grad_c; in computeFeasibleInequalities() local
308 grad_c = test.evalGradient(xc); in computeFeasibleInequalities()
315 g = grad_c.Column(indices[j-1]); in computeFeasibleInequalities()
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/src/inactive/
H A DNonDUnilevelRBDO.C842 Matrix& grad_c, int& result_mode)
859 grad_c(i+1, 1) = fnGradU(i); // grad_c transposed from DAKOTA grads
902 Matrix& grad_c, int& result_mode)
925 grad_c(i, 1) = 2*u(i); // gradient of constraint is 2u
/dports/math/py-theano/Theano-1.0.5/theano/gpuarray/
H A Ddnn.py2497 grad_c=(dcy is not None))(
2518 def __init__(self, rnn_mode, grad_h, grad_c): argument
2522 self.grad_c = grad_c
2523 if self.grad_c:
2539 if self.grad_c:
2556 if self.grad_c:
/dports/math/libnormaliz/normaliz-3.9.0/PyNormaliz/
H A DNormalizModule.cpp1475 vector< Integer > grad_c; in NmzSetGrading_inner() local
1476 bool result = PyListToNmz(grad_c, grad); in NmzSetGrading_inner()
1482 cone->resetGrading(grad_c); in NmzSetGrading_inner()
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/src/
H A DGaussProcApproximation.cpp950 Teuchos::SerialDenseVector<int, double>& g, Teuchos::SerialDenseMatrix<int, double>& grad_c, in constraint_eval() argument
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/
H A D_sag_fast.pyx141 grad_c = - sw * (p[c] - \delta_{y,c})
241 grad_c = - sw * (p[c] - \delta_{y,c})
H A D_sag_fast.pyx.tp153 grad_c = - sw * (p[c] - \delta_{y,c})