void deriveEquiFreqAndGtrParamsForReversibleRM(matrix_t const & Q, vector_t & equiFreq, vector_t & gtrParams) { equiFreq = deriveEquiFreqForReversibleRM(Q); unsigned n = equiFreq.size(); // check for (unsigned i = 0; i < n; i++) assert(equiFreq[i] > 0); matrix_t pm = Q; // parameter matrix for (unsigned i = 0; i < n; i++) for (unsigned j = 0; j < n; j++) pm(i, j) = Q(i, j) / equiFreq[j]; // check grtParams size unsigned paramCount = (n * (n - 1) / 2); if (gtrParams.size() != paramCount) gtrParams.resize(paramCount); // reverse of gtrRateParametersToMatrix unsigned idx = 0; for (unsigned i = 0; i < n; i++) for (unsigned j = i + 1; j < n; j++) { gtrParams[idx] = pm(i, j); idx++; } }
void lfit(vector_t &x, vector_t &y, vector_t &sig, vector_t &a, vector<bool> &ia, matrix_t &covar, double &chisq, matrix_t & X) { int i,j,k,l,m,mfit=0; double ym,wt,sum,sig2i; int ndat=x.size(); int ma=a.size(); vector_t afunc(ma); matrix_t beta; sizeMatrix(beta,ma,1); for (j=0;j<ma;j++) if (ia[j]) mfit++; if (mfit == 0) error("lfit: no parameters to be fitted"); for (j=0;j<mfit;j++) { for (k=0;k<mfit;k++) covar[j][k]=0.0; beta[j][0]=0.0; } for (i=0;i<ndat;i++) { afunc = X[i]; ym=y[i]; if (mfit < ma) { for (j=0;j<ma;j++) if (!ia[j]) ym -= a[j]*afunc[j]; } sig2i=1.0/SQR(sig[i]); for (j=0,l=0;l<ma;l++) { if (ia[l]) { wt=afunc[l]*sig2i; for (k=0,m=0;m<=l;m++) if (ia[m]) covar[j][k++] += wt*afunc[m]; beta[j++][0] += ym*wt; } } } for (j=1;j<mfit;j++) for (k=0;k<j;k++) covar[k][j]=covar[j][k]; vector<vector<double> > temp; sizeMatrix(temp,mfit,mfit); for (j=0;j<mfit;j++) for (k=0;k<mfit;k++) temp[j][k]=covar[j][k]; gaussj(temp,beta); for (j=0;j<mfit;j++) for (k=0;k<mfit;k++) covar[j][k]=temp[j][k]; for (j=0,l=0;l<ma;l++) if (ia[l]) a[l]=beta[j++][0]; chisq=0.0; for (i=0;i<ndat;i++) { afunc = X[i]; sum=0.0; for (j=0;j<ma;j++) sum += a[j]*afunc[j]; chisq += SQR((y[i]-sum)/sig[i]); } covsrt(covar,ia,mfit); }
vector_t next_vec( const vector_t ¤t, const vector_t &grad, const vector_t &lapl ) { vector_t next( current.size() ); // std::cout << "next_vec" << std::endl; // std::cout << current.size() << " " << grad.size() << " " // << lapl.size() << std::endl; for ( unsigned int i = 0; i < current.size(); ++i ) { next[i] = current[i] - grad[i] / lapl[i]; } return next; }
bool ModelUtil::EarlyStop(vector_t val_losses, size_t patience, float delta) { // Check for edge cases PELOTON_ASSERT(patience > 1); PELOTON_ASSERT(delta > 0); if (val_losses.size() < patience) return false; float cur_loss = val_losses[val_losses.size() - 1]; float pat_loss = val_losses[val_losses.size() - patience]; // Loss should have at least dropped by delta at this point return (pat_loss - cur_loss) < delta; }
static std::uint32_t to(state_t &state, const vector_t &val) { ::lua_createtable(state, (int)val.size(), (int)val.size()); std::uint32_t i = 1; std::for_each(val.cbegin(), val.cend(), [&state, &i](const T &t) { convertion_t<T>::to(state, t); ::lua_rawseti(state, -2, i++); }); return 1; }
void pvt_base::check_gas_common (const vector_t &pressure, const vector_t &fvf, const vector_t &visc) { for (t_long i = 1, cnt = (t_long)pressure.size (); i < cnt; ++i) { if (pressure[i] - pressure[i - 1] < EPS_DIFF) { throw bs_exception ("", "pressure curve should be monotonically increasing function"); } if (fvf[i] - fvf[i - 1] >= 0) { BOSOUT (section::pvt, level::critical) << "gas: fvf" << bs_end; for (t_long j = 0; j < cnt; ++j) { BOSOUT (section::pvt, level::critical) << fvf[j] << bs_end; } throw bs_exception ("", "FVF curve should be monotonically decreasing function"); } if (visc[i] - visc[i - 1] <= 0) { BOSOUT (section::pvt, level::critical) << "gas: visc" << bs_end; for (t_long j = 0; j < cnt; ++j) { BOSOUT (section::pvt, level::critical) << visc[j] << bs_end; } throw bs_exception ("", "Viscosity curve should be monotonically increasing function"); } } }
void pvt_base::check_oil_common (const vector_t &pressure, const vector_t &fvf, const vector_t &visc) { for (t_long i = 0, cnt = (t_long)pressure.size (); i < cnt; ++i) { if (pressure[i] < 0) { // TODO: LOG BS_ASSERT (false) (pressure[i]); throw bs_exception ("", "pressure should be greater than 0"); } if (fvf[i] < 0) { // TODO:LOG BS_ASSERT (false) (fvf[i]); throw bs_exception ("", "fvf should be greater than 0"); } if (visc[i] < 0) { // TODO: LOG BS_ASSERT (false) (visc[i]); throw bs_exception ("", "viscosity should be greater than 0"); } } }
unsigned countNonZeroEntries(vector_t const & v) { unsigned n = 0; for (unsigned i = 0; i < v.size(); i++) if ( v(i) ) n++; return n; }
static void CheckVector( const vector_t& cv, size_t expected_size, size_t old_size ) { ASSERT( cv.capacity()>=expected_size, NULL ); ASSERT( cv.size()==expected_size, NULL ); ASSERT( cv.empty()==(expected_size==0), NULL ); for( int j=0; j<int(expected_size); ++j ) { if( cv[j].bar()!=~j ) REPORT("ERROR on line %d for old_size=%ld expected_size=%ld j=%d\n",__LINE__,long(old_size),long(expected_size),j); } }
boost::shared_ptr<BaseTRRateMatrix> TRRateMatrixDispatcher(string const & substModel, vector_t const & rateParameters, vector_t const & equiFreq, StateMap const & alphabet) { boost::shared_ptr<BaseTRRateMatrix> rmPtr; if (substModel == "GTR") rmPtr = boost::shared_ptr<GTRRateMatrix>( new GTRRateMatrix(alphabet) ); // define other substitution models here ... else errorAbort("substitution model '" + substModel + "' not defined. If newly implemented, add to TRRateMatrixDispatcher."); if (equiFreq.size() > 0) // not defined, stick with default rmPtr->resetEquiFreqs(equiFreq); if (rateParameters.size() > 0) // not defined, stick with default rmPtr->resetRateParameters(rateParameters); return rmPtr; }
scalar_t vgrad(const vector_t& x, vector_t* gx) const override { m_idata = map_tensor(x.data(), m_idata.dims()); m_op.output(m_idata, m_wdata, m_bdata, m_odata); if (gx) { gx->resize(x.size()); auto idata = map_tensor(gx->data(), m_idata.dims()); m_op.ginput(idata, m_wdata, m_bdata, m_odata); } return m_odata.array().square().sum() / 2; }
scalar_t vgrad(const vector_t& x, vector_t* gx) const override { m_wdata = map_matrix(x.data(), m_wdata.rows(), m_wdata.cols()); m_bdata = map_vector(x.data() + m_wdata.size(), m_bdata.size()); m_op.output(m_idata, m_wdata, m_bdata, m_odata); if (gx) { gx->resize(x.size()); auto wdata = map_matrix(gx->data(), m_wdata.rows(), m_wdata.cols()); auto bdata = map_vector(gx->data() + m_wdata.size(), m_bdata.size()); m_op.gparam(m_idata, wdata, bdata, m_odata); } return m_odata.array().square().sum() / 2; }
// QR Factorization of a MxN General Matrix A. // a (IN/OUT - matrix(M,N)) On entry, the coefficient matrix A. On exit , the upper triangle and diagonal is the min(M,N) by N upper triangular matrix R. The lower triangle, together with the tau vector, is the orthogonal matrix Q as a product of min(M,N) elementary reflectors. // tau (OUT - vector (min(M,N))) Vector of the same numerical type as A. The scalar factors of the elementary reflectors. // info (OUT - int) // 0 : function completed normally // < 0 : The ith argument, where i = abs(return value) had an illegal value. int geqrf (matrix_t& a, vector_t& tau) { int _m = int(a.size1()); int _n = int(a.size2()); int _lda = int(a.size1()); int _info; // make_sure tau's size is greater than or equal to min(m,n) if (int(tau.size()) < (_n<_m ? _n : _m) ) return -104; int ldwork = _n*_n; vector_t dwork(ldwork); rawLAPACK::geqrf (_m, _n, a.data().begin(), _lda, tau.data().begin(), dwork.data().begin(), ldwork, _info); return _info; }
BaseTRRateMatrix::BaseTRRateMatrix (vector_t const & rateParameters, vector_t const & equiFreqs, string const & name, bool equiFreqsFixed) : BaseRateMatrix(name), rateParameterCount_(rateParameters.size() ), equiFreqCount_(equiFreqs.size() ), rateParameters_(rateParameters), equiFreqs_(equiFreqs), equiFreqsFixed_(equiFreqsFixed) {}
void build_morton(vector_t<PrimRef>& prims, isa::PrimInfo& pinfo) { size_t N = pinfo.size(); /* array for morton builder */ vector_t<isa::MortonID32Bit> morton_src(N); vector_t<isa::MortonID32Bit> morton_tmp(N); for (size_t i=0; i<N; i++) morton_src[i].index = i; /* fast allocator that supports thread local operation */ FastAllocator allocator; for (size_t i=0; i<2; i++) { std::cout << "iteration " << i << ": building BVH over " << N << " primitives, " << std::flush; double t0 = getSeconds(); allocator.reset(); std::pair<Node*,BBox3fa> node_bounds = isa::bvh_builder_morton<Node*>( /* thread local allocator for fast allocations */ [&] () -> FastAllocator::ThreadLocal* { return allocator.threadLocal(); }, BBox3fa(empty), /* lambda function that allocates BVH nodes */ [&] ( isa::MortonBuildRecord<Node*>& current, isa::MortonBuildRecord<Node*>* children, size_t N, FastAllocator::ThreadLocal* alloc ) -> InnerNode* { assert(N <= 2); InnerNode* node = new (alloc->malloc(sizeof(InnerNode))) InnerNode; *current.parent = node; for (size_t i=0; i<N; i++) children[i].parent = &node->children[i]; return node; }, /* lambda function that sets bounds */ [&] (InnerNode* node, const BBox3fa* bounds, size_t N) -> BBox3fa { BBox3fa res = empty; for (size_t i=0; i<N; i++) { const BBox3fa b = bounds[i]; res.extend(b); node->bounds[i] = b; } return res; }, /* lambda function that creates BVH leaves */ [&]( isa::MortonBuildRecord<Node*>& current, FastAllocator::ThreadLocal* alloc, BBox3fa& box_o) -> Node* { assert(current.size() == 1); const size_t id = morton_src[current.begin].index; const BBox3fa bounds = prims[id].bounds(); // FIXME: dont use morton_src, should be input Node* node = new (alloc->malloc(sizeof(LeafNode))) LeafNode(id,bounds); *current.parent = node; box_o = bounds; return node; }, /* lambda that calculates the bounds for some primitive */ [&] (const isa::MortonID32Bit& morton) -> BBox3fa { return prims[morton.index].bounds(); }, /* progress monitor function */ [&] (size_t dn) { // throw an exception here to cancel the build operation }, morton_src.data(),morton_tmp.data(),prims.size(),2,1024,1,1); Node* root = node_bounds.first; double t1 = getSeconds(); std::cout << 1000.0f*(t1-t0) << "ms, " << 1E-6*double(N)/(t1-t0) << " Mprims/s, sah = " << root->sah() << " [DONE]" << std::endl; } }
vector_eig EigenUtil::ToEigenVec(const vector_t &mat) { return vector_eig::Map(mat.data(), mat.size()); }
void BaseTRRateMatrix::resetRateParameters(vector_t const & rateParameters) { assert(rateParameters.size() == rateParameterCount_); rateParameters_ = rateParameters; }
// additional functionality: size_type size() const { return stack.size(); }
vector_t cauchy_loss_t::vgrad(const vector_t& targets, const vector_t& scores) const { assert(targets.size() == scores.size()); return 2.0 * (scores - targets).array() / (1.0 + (scores - targets).array().square()); }
void BaseTRRateMatrix::resetEquiFreqs(vector_t const & equiFreqs) { assert(not equiFreqsFixed() ); assert(equiFreqs.size() == equiFreqCount_); equiFreqs_ = equiFreqs; }
scalar_t cauchy_loss_t::error(const vector_t& targets, const vector_t& scores) const { assert(targets.size() == scores.size()); return (targets - scores).array().abs().sum(); }
size_type size() const { return data_.size(); }
scalar_t cauchy_loss_t::value(const vector_t& targets, const vector_t& scores) const { assert(targets.size() == scores.size()); return ((targets - scores).array().square() + 1.0).log().sum(); }