HingeLoss (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.lossfunctions.HingeLoss

All Implemented Interfaces:

Serializable, LossC, LossFunc, LossMC
```
public class HingeLoss
extends Object
implements LossMC
```
The HingeLoss loss function for classification L(x, y) = max(0, 1-y*x) . This also includes the multi-class version of the hinge loss.
This function is only once differentiable.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

HingeLoss()

Constructors
Constructor and Description
`HingeLoss()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static CategoricalResults`	`classify(double score)`
`HingeLoss`	`clone()`
`static double`	`deriv(double pred, double y)` Computes the first derivative of the HingeLoss loss
`void`	`deriv(Vec processed, Vec derivs, int y)` Computes the derivatives with respect to each output `processed` and `derivs` may be the same object, and will simply have all its values altered if so.
`CategoricalResults`	`getClassification(double score)` Given the score value of a data point, this returns the classification results.
`CategoricalResults`	`getClassification(Vec processed)` Given the `processed` predictions, returns the classification results for said predictions.
`double`	`getDeriv(double pred, double y)` Computes the first derivative of the getLoss function.
`double`	`getDeriv2(double pred, double y)` Computes the second derivative of the getLoss function.
`double`	`getDeriv2Max()` Returns an upper bound on the maximum value of the second derivative.
`double`	`getLoss(double pred, double y)` Computes the getLoss for a classification problem.
`double`	`getLoss(Vec processed, int y)` Computes the scalar loss for on the given example
`static double`	`loss(double pred, double y)` Computes the HingeLoss loss
`void`	`process(Vec pred, Vec processed)` Given the vector of raw outputs for each class, transform it into a new vector.

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

- Constructor Detail
  - HingeLoss
```
public HingeLoss()
```
- Method Detail
  - loss
```
public static double loss(double pred,
                          double y)
```
    Computes the HingeLoss loss
    
    Parameters:
    
    pred - the predicted value
    
    y - the target value
    
    Returns:
    
    the HingeLoss loss
  - deriv
```
public static double deriv(double pred,
                           double y)
```
    Computes the first derivative of the HingeLoss loss
    
    Parameters:
    
    pred - the predicted value
    
    y - the target value
    
    Returns:
    
    the first derivative of the HingeLoss loss
  - classify
```
public static CategoricalResults classify(double score)
```
  - getLoss
```
public double getLoss(double pred,
                      double y)
```
    Description copied from interface: LossC
    
    Computes the getLoss for a classification problem.
    
    Specified by:
    
    getLoss in interface LossC
    
    Specified by:
    
    getLoss in interface LossFunc
    
    Parameters:
    
    pred - the predicted score in (-Infinity, Infinity)
    
    y - the true class label in {-1, 1}
    
    Returns:
    
    the getLoss in [0, Inf)
  - getDeriv
```
public double getDeriv(double pred,
                       double y)
```
    Description copied from interface: LossC
    
    Computes the first derivative of the getLoss function.
    
    Specified by:
    
    getDeriv in interface LossC
    
    Specified by:
    
    getDeriv in interface LossFunc
    
    Parameters:
    
    pred - the predicted score in (-Infinity, Infinity)
    
    y - the true class label in {-1, 1}
    
    Returns:
    
    the first derivative of the getLoss
  - getDeriv2
```
public double getDeriv2(double pred,
                        double y)
```
    Description copied from interface: LossC
    
    Computes the second derivative of the getLoss function.
    
    Specified by:
    
    getDeriv2 in interface LossC
    
    Specified by:
    
    getDeriv2 in interface LossFunc
    
    Parameters:
    
    pred - the predicted score in (-Infinity, Infinity)
    
    y - the true class label in {-1, 1}
    
    Returns:
    
    the second derivative of the getLoss function
  - getDeriv2Max
```
public double getDeriv2Max()
```
    Description copied from interface: LossFunc
    
    Returns an upper bound on the maximum value of the second derivative. If the second derivative does not exist, Double.NaN is a valid result. It is also possible for 0 and Double.POSITIVE_INFINITY to be valid results, and must be checked for.
    
    Specified by:
    
    getDeriv2Max in interface LossFunc
    
    Returns:
    
    the max value of LossFunc.getDeriv2(double, double)
  - clone
```
public HingeLoss clone()
```
    Specified by:
    
    clone in interface LossC
    
    Specified by:
    
    clone in interface LossFunc
    
    Overrides:
    
    clone in class Object
  - getClassification
```
public CategoricalResults getClassification(double score)
```
    Description copied from interface: LossC
    
    Given the score value of a data point, this returns the classification results.
    
    Specified by:
    
    getClassification in interface LossC
    
    Parameters:
    
    score - the score for a data point
    
    Returns:
    
    the categorical results with the correct probability values for this loss function.
  - getLoss
```
public double getLoss(Vec processed,
                      int y)
```
    Description copied from interface: LossMC
    
    Computes the scalar loss for on the given example
    
    Specified by:
    
    getLoss in interface LossMC
    
    Parameters:
    
    processed - the vector of raw predictions.
    
    y - the true class label in [0, k-1] for k classes
    
    Returns:
    
    the loss in [0, Inf)
  - process
```
public void process(Vec pred,
                    Vec processed)
```
    Description copied from interface: LossMC
    
    Given the vector of raw outputs for each class, transform it into a new vector.
    processed and derivs may be the same object, and will simply have all its values altered if so.
    
    Specified by:
    
    process in interface LossMC
    
    Parameters:
    
    pred - the vector of raw predictions
    
    processed - the location to store the processed predictions.
  - deriv
```
public void deriv(Vec processed,
                  Vec derivs,
                  int y)
```
    Description copied from interface: LossMC
    
    Computes the derivatives with respect to each output
    processed and derivs may be the same object, and will simply have all its values altered if so.
    
    Specified by:
    
    deriv in interface LossMC
    
    Parameters:
    
    processed - the processed predictions
    
    derivs - the vector to place the derivative of the loss to.
    
    y - the true class label in [0, k-1] for k classes
  - getClassification
```
public CategoricalResults getClassification(Vec processed)
```
    Description copied from interface: LossMC
    
    Given the processed predictions, returns the classification results for said predictions.
    
    Specified by:
    
    getClassification in interface LossMC
    
    Parameters:
    
    processed - the processed score/prediction vector
    
    Returns:
    
    the classification results

Class HingeLoss

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

HingeLoss

Method Detail

loss

deriv

classify

getLoss

getDeriv

getDeriv2

getDeriv2Max

clone

getClassification

getLoss

process

deriv

getClassification