MatlibBilinearSparseOnlineLearner (OpenIMAJ master project 1.3.1 API)

java.lang.Object
- org.openimaj.ml.linear.learner.matlib.MatlibBilinearSparseOnlineLearner

All Implemented Interfaces:

ReadableBinary, ReadWriteableBinary, WriteableBinary, OnlineLearner<Matrix,Matrix>
```
public class MatlibBilinearSparseOnlineLearner
extends Object
implements OnlineLearner<Matrix,Matrix>, ReadWriteableBinary
```
An implementation of a stochastic gradient decent with proximal perameter adjustment (for regularised parameters). Data is dealt with sequentially using a one pass implementation of the online proximal algorithm described in chapter 9 and 10 of: The Geometry of Constrained Structured Prediction: Applications to Inference and Learning of Natural Language Syntax, PhD, Andre T. Martins The implementation does the following: - When an X,Y is recieved: - Update currently held batch - If the batch is full: - While There is a great deal of change in U and W: - Calculate the gradient of W holding U fixed - Proximal update of W - Calculate the gradient of U holding W fixed - Proximal update of U - Calculate the gradient of Bias holding U and W fixed - flush the batch - return current U and W (same as last time is batch isn't filled yet)

Author:

Sina Samangooei (ss@ecs.soton.ac.uk)

Field Summary

Fields
Modifier and Type	Field and Description
`protected Matrix`	`bias`
`protected Boolean`	`biasMode`
`protected Matrix`	`diagX`
`protected Double`	`eta0_u`
`protected Double`	`eta0_w`
`protected Double`	`lambda_u`
`protected Double`	`lambda_w`
`protected LossFunction`	`loss`
`protected BilinearLearnerParameters`	`params`
`protected Regulariser`	`regul`
`protected Matrix`	`u`
`protected Matrix`	`w`

Constructor Summary

Constructors
Constructor and Description

MatlibBilinearSparseOnlineLearner()
The default parameters.

MatlibBilinearSparseOnlineLearner(BilinearLearnerParameters params)

Constructors
Constructor and Description
`MatlibBilinearSparseOnlineLearner()` The default parameters.
`MatlibBilinearSparseOnlineLearner(BilinearLearnerParameters params)`

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`addU(int newUsers)` Expand the U parameters matrix by added a set of rows.
`void`	`addW(int newWords)` Expand the W parameters matrix by added a set of rows.
`byte[]`	`binaryHeader()` Header for binary input.
`MatlibBilinearSparseOnlineLearner`	`clone()`
`static SparseMatrix`	`expandY(Matrix Y)` Given a flat value matrix, makes a diagonal sparse matrix containing the values as the diagonal
`Matrix`	`getBias()`
`BilinearLearnerParameters`	`getParams()`
`Matrix`	`getU()`
`Matrix`	`getW()`
`Matrix`	`predict(Matrix x)`
`void`	`process(Matrix X, Matrix Y)`
`void`	`readBinary(DataInput in)` Read internal state from in.
`void`	`reinitParams()` must be called if any parameters are changed
`void`	`setU(Matrix newu)`
`void`	`setW(Matrix neww)`
`protected Matrix`	`updateBias(Matrix biasGrad, double biasLossWeight)`
`protected Matrix`	`updateU(Matrix currentU, double uLossWeight, double uWeightedLambda)`
`protected Matrix`	`updateW(Matrix currentW, double wLossWeighted, double weightedLambda)`
`void`	`writeBinary(DataOutput out)` Write the content of this as binary to out.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - params
```
protected BilinearLearnerParameters params
```
  - w
```
protected Matrix w
```
  - u
```
protected Matrix u
```
  - loss
```
protected LossFunction loss
```
  - regul
```
protected Regulariser regul
```
  - lambda_w
```
protected Double lambda_w
```
  - lambda_u
```
protected Double lambda_u
```
  - biasMode
```
protected Boolean biasMode
```
  - bias
```
protected Matrix bias
```
  - diagX
```
protected Matrix diagX
```
  - eta0_u
```
protected Double eta0_u
```
  - eta0_w
```
protected Double eta0_w
```
- Constructor Detail
  - MatlibBilinearSparseOnlineLearner
```
public MatlibBilinearSparseOnlineLearner()
```
    The default parameters. These won't work with your dataset, i promise.
  - MatlibBilinearSparseOnlineLearner
```
public MatlibBilinearSparseOnlineLearner(BilinearLearnerParameters params)
```
    Parameters:
    params - the parameters used by this learner
- Method Detail
  - reinitParams
```
public void reinitParams()
```
    must be called if any parameters are changed
  - process
```
public void process(Matrix X,
           Matrix Y)
```
    Specified by:
    
    process in interface OnlineLearner<Matrix,Matrix>
  - updateBias
```
protected Matrix updateBias(Matrix biasGrad,
                double biasLossWeight)
```
  - updateW
```
protected Matrix updateW(Matrix currentW,
             double wLossWeighted,
             double weightedLambda)
```
  - updateU
```
protected Matrix updateU(Matrix currentU,
             double uLossWeight,
             double uWeightedLambda)
```
  - expandY
```
public static SparseMatrix expandY(Matrix Y)
```
    Given a flat value matrix, makes a diagonal sparse matrix containing the values as the diagonal
    
    Parameters:
    Y -
    
    Returns:
    the diagonalised Y
  - getParams
```
public BilinearLearnerParameters getParams()
```
    Returns:
    the current apramters
  - getU
```
public Matrix getU()
```
    Returns:
    the current user matrix
  - getW
```
public Matrix getW()
```
    Returns:
    the current word matrix
  - getBias
```
public Matrix getBias()
```
    Returns:
    the current bias (null if BilinearLearnerParameters.BIAS is false
  - addU
```
public void addU(int newUsers)
```
    Expand the U parameters matrix by added a set of rows. If currently unset, this function does nothing (assuming U will be initialised in the first round) The new U parameters are initialised used BilinearLearnerParameters.EXPANDEDUINITSTRAT
    
    Parameters:
    newUsers - the number of new users to add
  - addW
```
public void addW(int newWords)
```
    Expand the W parameters matrix by added a set of rows. If currently unset, this function does nothing (assuming W will be initialised in the first round) The new W parameters are initialised used BilinearLearnerParameters.EXPANDEDWINITSTRAT
    
    Parameters:
    newWords - the number of new words to add
  - clone
```
public MatlibBilinearSparseOnlineLearner clone()
```
    Overrides:
    
    clone in class Object
  - setU
```
public void setU(Matrix newu)
```
    Parameters:
    newu - set the model's U
  - setW
```
public void setW(Matrix neww)
```
    Parameters:
    neww - set the model's W
  - readBinary
```
public void readBinary(DataInput in)
                throws IOException
```
    Description copied from interface: ReadableBinary
    
    Read internal state from in.
    
    Specified by:
    
    readBinary in interface ReadableBinary
    
    Parameters:
    in - source to read from.
    
    Throws:
    
    IOException - an error reading input
  - binaryHeader
```
public byte[] binaryHeader()
```
    Description copied from interface: ReadableBinary
    
    Header for binary input. Will be automatically read by IOUtils when using readBinary().
    
    Specified by:
    
    binaryHeader in interface ReadableBinary
    
    Specified by:
    
    binaryHeader in interface WriteableBinary
    
    Returns:
    header
  - writeBinary
```
public void writeBinary(DataOutput out)
                 throws IOException
```
    Description copied from interface: WriteableBinary
    
    Write the content of this as binary to out.
    
    Specified by:
    
    writeBinary in interface WriteableBinary
    
    Parameters:
    out - sink to write to
    
    Throws:
    
    IOException - an error writing to out
  - predict
```
public Matrix predict(Matrix x)
```
    Specified by:
    
    predict in interface OnlineLearner<Matrix,Matrix>

Class MatlibBilinearSparseOnlineLearner

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

params

w

u

loss

regul

lambda_w

lambda_u

biasMode

bias

diagX

eta0_u

eta0_w

Constructor Detail

MatlibBilinearSparseOnlineLearner

MatlibBilinearSparseOnlineLearner

Method Detail

reinitParams

process

updateBias

updateW

updateU

expandY

getParams

getU

getW

getBias

addU

addW

clone

setU

setW

readBinary

binaryHeader

writeBinary

predict