SparseLBFGSwithL2

class SparseLBFGSwithL2 extends LabelEstimator[SparseVector[Double], DenseVector[Double], DenseVector[Double]] with WeightedNode with CostModel

Class used to solve an optimization problem using Limited-memory BFGS. Reference: http://en.wikipedia.org/wiki/Limited-memory_BFGS

Linear Supertypes
CostModel, WeightedNode, LabelEstimator[SparseVector[Double], DenseVector[Double], DenseVector[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
1. SparseLBFGSwithL2
2. CostModel
3. WeightedNode
4. LabelEstimator
5. EstimatorOperator
6. Serializable
7. Serializable
8. Operator
9. AnyRef
10. Any
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Visibility
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Instance Constructors

1. new SparseLBFGSwithL2(gradient: SparseGradient, fitIntercept: Boolean = true, numCorrections: Int = 10, convergenceTol: Double = 1.0E-4, numIterations: Int = 100, regParam: Double = 0.0, sparseOverhead: Double = 8)

fitIntercept

Whether to fit the intercepts or not.

numCorrections

3 < numCorrections < 10 is recommended.

convergenceTol

convergence tolerance for L-BFGS

numIterations

max number of iterations to run

regParam

L2 regularization

The cost model overhead for how much more expensive a sparse operation on dense data is compared to the respective dense operation

Value Members

1. final def !=(arg0: AnyRef): Boolean

Definition Classes
AnyRef
2. final def !=(arg0: Any): Boolean

Definition Classes
Any
3. final def ##(): Int

Definition Classes
AnyRef → Any
4. final def ==(arg0: AnyRef): Boolean

Definition Classes
AnyRef
5. final def ==(arg0: Any): Boolean

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Any
6. final def asInstanceOf[T0]: T0

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Any
7. def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
8. val convergenceTol: Double

convergence tolerance for L-BFGS

9. def cost(n: Long, d: Int, k: Int, sparsity: Double, numMachines: Int, cpuWeight: Double, memWeight: Double, networkWeight: Double): Double

Definition Classes
SparseLBFGSwithL2CostModel
10. final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
11. def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
12. def execute(deps: Seq[Expression]): TransformerExpression

Definition Classes
EstimatorOperator → Operator
13. def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
14. def fit(data: RDD[SparseVector[Double]], labels: RDD[DenseVector[Double]]): SparseLinearMapper

The type-safe method that ML developers need to implement when writing new Estimators.

The type-safe method that ML developers need to implement when writing new Estimators.

data

The estimator's training data.

labels

The estimator's training labels

returns

A new transformer

Definition Classes
SparseLBFGSwithL2LabelEstimator
15. val fitIntercept: Boolean

Whether to fit the intercepts or not.

16. final def getClass(): Class[_]

Definition Classes
AnyRef → Any

18. def hashCode(): Int

Definition Classes
AnyRef → Any
19. final def isInstanceOf[T0]: Boolean

Definition Classes
Any
20. def label: String

Definition Classes
Operator
21. final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
22. final def notify(): Unit

Definition Classes
AnyRef
23. final def notifyAll(): Unit

Definition Classes
AnyRef
24. val numCorrections: Int

3 < numCorrections < 10 is recommended.

25. val numIterations: Int

max number of iterations to run

26. val regParam: Double

L2 regularization

The cost model overhead for how much more expensive a sparse operation on dense data is compared to the respective dense operation

28. final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
29. def toString(): String

Definition Classes
AnyRef → Any
30. final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
31. final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
32. final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
33. val weight: Int

Definition Classes
SparseLBFGSwithL2WeightedNode
34. final def withData(data: PipelineDataset[SparseVector[Double]], labels: PipelineDataset[DenseVector[Double]]): Pipeline[SparseVector[Double], DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

data

The training data

labels

The training labels

returns

A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator
35. final def withData(data: RDD[SparseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[SparseVector[Double], DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

data

The training data

labels

The training labels

returns

A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator
36. final def withData(data: PipelineDataset[SparseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[SparseVector[Double], DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

data

The training data

labels

The training labels

returns

A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator
37. final def withData(data: RDD[SparseVector[Double]], labels: PipelineDataset[DenseVector[Double]]): Pipeline[SparseVector[Double], DenseVector[Double]]

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

data

The training data

labels

The training labels

returns

A pipeline that fits this label estimator and applies the result to inputs.

Definition Classes
LabelEstimator