| Package | Description | 
|---|---|
| org.apache.sysds.hops.estim | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | EstimatorBasicAvgBasic average case estimator for matrix sparsity:
 sp = 1 - Math.pow(1-sp1*sp2, k) | 
| class  | EstimatorBasicWorstBasic average case estimator for matrix sparsity:
 sp = Math.min(1, sp1 * k) * Math.min(1, sp2 * k). | 
| class  | EstimatorBitsetMMThis 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. | 
| class  | EstimatorDensityMapThis 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. | 
| class  | EstimatorLayeredGraphThis estimator implements an approach based on a so-called layered graph,
 introduced in
 Edith Cohen. | 
| class  | EstimatorMatrixHistogramThis estimator implements a remarkably simple yet effective
 approach for incorporating structural properties into sparsity
 estimation. | 
| class  | EstimatorSampleThis estimator implements an approach based on row/column sampling
 Yongyang Yu, MingJie Tang, Walid G. | 
| class  | EstimatorSampleRaThis estimator implements an approach based on row/column sampling
 
 Rasmus Resen Amossen, Andrea Campagna, Rasmus Pagh:
 Better Size Estimation for Sparse Matrix Products. | 
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