# Package org.apache.sysds.hops.estim

Class Summary
EstimationUtils
EstimatorBasicAvg: Basic average case estimator for matrix sparsity: sp = 1 - Math.pow(1-sp1*sp2, k)
EstimatorBasicWorst: Basic average case estimator for matrix sparsity: sp = Math.min(1, sp1 * k) * Math.min(1, sp2 * k).
EstimatorBitsetMM: This estimator implements a naive but rather common approach of boolean matrix multiplies which allows to infer the exact non-zero structure and thus is also useful for sparse result preallocation.
EstimatorBitsetMM.BitsetMatrix
EstimatorBitsetMM.BitsetMatrix1: This class represents a boolean matrix and provides key operations.
EstimatorBitsetMM.BitsetMatrix2
EstimatorDensityMap: This estimator implements an approach called density maps, as introduced in David Kernert, Frank Köhler, Wolfgang Lehner: SpMacho - Optimizing Sparse Linear Algebra Expressions with Probabilistic Density Estimation.
EstimatorDensityMap.DensityMap
EstimatorLayeredGraph: This estimator implements an approach based on a so-called layered graph, introduced in Edith Cohen.
EstimatorLayeredGraph.LayeredGraph
EstimatorMatrixHistogram: This estimator implements a remarkably simple yet effective approach for incorporating structural properties into sparsity estimation.
EstimatorMatrixHistogram.MatrixHistogram
EstimatorSample: This estimator implements an approach based on row/column sampling Yongyang Yu, MingJie Tang, Walid G.
EstimatorSampleRa: This estimator implements an approach based on row/column sampling Rasmus Resen Amossen, Andrea Campagna, Rasmus Pagh: Better Size Estimation for Sparse Matrix Products.
MMNode: Helper class to represent matrix multiply operators in a DAG along with references to its abstract data handles.
SparsityEstimator

Enum Summary
SparsityEstimator.OpCode