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https://github.com/SoftFever/OrcaSlicer.git
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2ae2672ee9
Fixing dep build script on Windows and removing some warnings. Use bundled igl by default. Not building with the dependency scripts if not explicitly stated. This way, it will stay in Fix the libigl patch to include C source files in header only mode.
85 lines
2.9 KiB
C++
85 lines
2.9 KiB
C++
// This file is part of libigl, a simple c++ geometry processing library.
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//
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// Copyright (C) 2017 Daniele Panozzo <daniele.panozzo@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla Public License
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// v. 2.0. If a copy of the MPL was not distributed with this file, You can
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// obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef IGL_SPARSE_CACHED_H
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#define IGL_SPARSE_CACHED_H
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#include "igl_inline.h"
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#define EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
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#include <Eigen/Dense>
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#include <Eigen/Sparse>
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namespace igl
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{
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// Build a sparse matrix from list of indices and values (I,J,V), similarly to
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// the sparse function in matlab. Divides the construction in two phases, one
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// for fixing the sparsity pattern, and one to populate it with values. Compared to
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// igl::sparse, this version is slower for the first time (since it requires a
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// precomputation), but faster to the subsequent evaluations.
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//
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// Templates:
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// IndexVector list of indices, value should be non-negative and should
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// expect to be cast to an index. Must implement operator(i) to retrieve
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// ith element
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// ValueVector list of values, value should be expect to be cast to type
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// T. Must implement operator(i) to retrieve ith element
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// T should be a eigen sparse matrix primitive type like int or double
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// Input:
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// I nnz vector of row indices of non zeros entries in X
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// J nnz vector of column indices of non zeros entries in X
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// V nnz vector of non-zeros entries in X
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// Optional:
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// m number of rows
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// n number of cols
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// Outputs:
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// X m by n matrix of type T whose entries are to be found
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//
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// Example:
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// Eigen::SparseMatrix<double> A;
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// std::vector<Eigen::Triplet<double> > IJV;
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// buildA(IJV);
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// if (A.rows() == 0)
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// {
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// A = Eigen::SparseMatrix<double>(rows,cols);
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// igl::sparse_cached_precompute(IJV,A,A_data);
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// }
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// else
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// igl::sparse_cached(IJV,s.A,s.A_data);
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template <typename DerivedI, typename Scalar>
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IGL_INLINE void sparse_cached_precompute(
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const Eigen::MatrixBase<DerivedI> & I,
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const Eigen::MatrixBase<DerivedI> & J,
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Eigen::VectorXi& data,
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Eigen::SparseMatrix<Scalar>& X
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);
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template <typename Scalar>
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IGL_INLINE void sparse_cached_precompute(
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const std::vector<Eigen::Triplet<Scalar> >& triplets,
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Eigen::VectorXi& data,
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Eigen::SparseMatrix<Scalar>& X
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);
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template <typename Scalar>
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IGL_INLINE void sparse_cached(
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const std::vector<Eigen::Triplet<Scalar> >& triplets,
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const Eigen::VectorXi& data,
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Eigen::SparseMatrix<Scalar>& X);
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template <typename DerivedV, typename Scalar>
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IGL_INLINE void sparse_cached(
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const Eigen::MatrixBase<DerivedV>& V,
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const Eigen::VectorXi& data,
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Eigen::SparseMatrix<Scalar>& X
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);
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}
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#ifndef IGL_STATIC_LIBRARY
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# include "sparse_cached.cpp"
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#endif
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#endif
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