コード例 #1
0
ファイル: eigenembedding.cpp プロジェクト: genba/tapkee
TEST(EigenEmbedding, ArpackSparseSmallestEigenvector) 
{
	const int N = 3;
	tapkee::tapkee_internal::SparseTriplets sparse_triplets;
	for (int i=0; i<N; i++)
		sparse_triplets.push_back(tapkee::tapkee_internal::SparseTriplet(i,i,tapkee::ScalarType(i+1)));

#ifdef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
	Eigen::DynamicSparseMatrix<tapkee::ScalarType> dynamic_weight_matrix(N,N);
	dynamic_weight_matrix.reserve(sparse_triplets.size());
	for (tapkee::tapkee_internal::SparseTriplets::const_iterator it=sparse_triplets.begin(); it!=sparse_triplets.end(); ++it)
		dynamic_weight_matrix.coeffRef(it->col(),it->row()) += it->value();
	tapkee::SparseWeightMatrix mat(dynamic_weight_matrix);
#else
	tapkee::SparseWeightMatrix mat(N,N);
	mat.setFromTriplets(sparse_triplets.begin(),sparse_triplets.end());
#endif

	tapkee::tapkee_internal::EmbeddingResult result = 
		tapkee::tapkee_internal::eigen_embedding<tapkee::SparseWeightMatrix,tapkee::tapkee_internal::SparseInverseMatrixOperation>
		(tapkee::Arpack, mat, 1, 0);

	ASSERT_EQ(1,result.second.size());
	// smallest eigenvalue is 1
	ASSERT_NEAR(1,result.second[0],PRECISION);
	ASSERT_EQ(1,result.first.cols());
	ASSERT_EQ(3,result.first.rows());
	// check if it is an eigenvector
	ASSERT_NEAR(0.0,(mat*result.first - result.second[0]*result.first).norm(),PRECISION);
}
コード例 #2
0
ファイル: sparse.hpp プロジェクト: AmyAmeesha/tapkee
SparseMatrix sparse_matrix_from_triplets(const SparseTriplets& sparse_triplets, IndexType m, IndexType n)
{
#ifdef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
	Eigen::DynamicSparseMatrix<ScalarType> dynamic_weight_matrix(m, n);
	dynamic_weight_matrix.reserve(sparse_triplets.size());
	for (SparseTriplets::const_iterator it=sparse_triplets.begin(); it!=sparse_triplets.end(); ++it)
		dynamic_weight_matrix.coeffRef(it->col(),it->row()) += it->value();
	SparseMatrix matrix(dynamic_weight_matrix);
#else
	SparseMatrix matrix(m, n);
	matrix.setFromTriplets(sparse_triplets.begin(),sparse_triplets.end());
#endif
	return matrix;
}
コード例 #3
0
Laplacian compute_laplacian(RandomAccessIterator begin, 
			RandomAccessIterator end,const Neighbors& neighbors, 
			DistanceCallback callback, ScalarType width)
{
	SparseTriplets sparse_triplets;

	timed_context context("Laplacian computation");
	const IndexType k = neighbors[0].size();
	sparse_triplets.reserve((k+1)*(end-begin));

	DenseVector D = DenseVector::Zero(end-begin);
	for (RandomAccessIterator iter=begin; iter!=end; ++iter)
	{
		const LocalNeighbors& current_neighbors = neighbors[iter-begin];

		for (IndexType i=0; i<k; ++i)
		{
			ScalarType distance = callback(*iter,begin[current_neighbors[i]]);
			ScalarType heat = exp(-distance*distance/width);
			D(iter-begin) += heat;
			D(current_neighbors[i]) += heat;
			sparse_triplets.push_back(SparseTriplet(current_neighbors[i],(iter-begin),-heat));
			sparse_triplets.push_back(SparseTriplet((iter-begin),current_neighbors[i],-heat));
		}
	}
	for (IndexType i=0; i<(end-begin); ++i)
		sparse_triplets.push_back(SparseTriplet(i,i,D(i)));

#ifdef EIGEN_YES_I_KNOW_SPARSE_MODULE_IS_NOT_STABLE_YET
	Eigen::DynamicSparseMatrix<ScalarType> dynamic_weight_matrix(end-begin,end-begin);
	dynamic_weight_matrix.reserve(sparse_triplets.size());
	for (SparseTriplets::const_iterator it=sparse_triplets.begin(); it!=sparse_triplets.end(); ++it)
		dynamic_weight_matrix.coeffRef(it->col(),it->row()) += it->value();
	SparseWeightMatrix weight_matrix(dynamic_weight_matrix);
#else
	SparseWeightMatrix weight_matrix(end-begin,end-begin);
	weight_matrix.setFromTriplets(sparse_triplets.begin(),sparse_triplets.end());
#endif

	return Laplacian(weight_matrix,DenseDiagonalMatrix(D));
}