# Package org.apache.sysds.hops.estim

- Class Summary
  Class | Description
  --- | ---
  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
  Enum | Description
  --- | ---
  SparsityEstimator.OpCode |

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