DANN (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.knn.DANN

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized
```
public class DANN
extends Object
implements Classifier, Parameterized
```
DANN is an implementation of Discriminant Adaptive Nearest Neighbor. DANN has a fixed O(n) classification time. At each classification, DANN uses a large set of points to iteratively create and adjust a distance metic that reflects the separability of classes at a localized level. This increases the work considerably over a normal NearestNeighbour classifier. The localized metric is similar to the MahalanobisDistance
Because DANN builds its own metric, it is not possible to provide one. The VectorCollectionFactory allowed in the constructor is to accelerate the first convergence step. In homogeneous areas of the data set, queries can be answered in O(log n) if the vector collection supports it.

See: Hastie, T.,&Tibshirani, R. (1996). Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(6), 607–616. doi:10.1109/34.506411

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static double`	`DEFAULT_EPS` The default regularization used when building a metric is 1.0
`static int`	`DEFAULT_ITERATIONS` The default number of iterations for creating the metric is 1
`static int`	`DEFAULT_K` The default number of neighbors to use when classifying is 1
`static int`	`DEFAULT_KN` The default number of neighbors to use when building a metric is 40.

Constructor Summary

Constructors
Constructor and Description
`DANN()` Creates a new DANN classifier
`DANN(int kn, int k)` Creates a new DANN classifier
`DANN(int kn, int k, double eps)` Creates a new DANN classifier
`DANN(int kn, int k, double eps, int maxIterations, VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)` Creates a new DANN classifier
`DANN(int kn, int k, double eps, VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)` Creates a new DANN classifier

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`Classifier`	`clone()`
`double`	`getEpsilon()` Returns the regularization parameter that is applied to the diagonal of the matrix when creating each new metric.
`int`	`getK()` Returns the number of nearest neighbors to use when predicting
`int`	`getKn()` Returns the number of nearest neighbors to use when adapting the distance metric
`int`	`getMaxIterations()` Returns the number of times the distance metric will be updated.
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`static Distribution`	`guessK(DataSet d)` Guesses the distribution to use for the number of neighbors to consider
`static Distribution`	`guessKn(DataSet d)` Guesses the distribution to use for the number of neighbors to consider
`void`	`setEpsilon(double eps)` Sets the regularization to apply the the diagonal of the scatter matrix when creating each new metric.
`void`	`setK(int k)` Sets the number of nearest neighbors to use when predicting
`void`	`setKn(int kn)` Sets the number of nearest neighbors to use when adapting the distance metric.
`void`	`setMaxIterations(int maxIterations)` Sets the number of times a new distance metric will be created for each query.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

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

- Field Detail
  - DEFAULT_KN
```
public static final int DEFAULT_KN
```
    The default number of neighbors to use when building a metric is 40.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_K
```
public static final int DEFAULT_K
```
    The default number of neighbors to use when classifying is 1
    
    See Also:
    
    Constant Field Values
  - DEFAULT_EPS
```
public static final double DEFAULT_EPS
```
    The default regularization used when building a metric is 1.0
    
    See Also:
    
    Constant Field Values
  - DEFAULT_ITERATIONS
```
public static final int DEFAULT_ITERATIONS
```
    The default number of iterations for creating the metric is 1
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - DANN
```
public DANN()
```
    Creates a new DANN classifier
  - DANN
```
public DANN(int kn,
            int k)
```
    Creates a new DANN classifier
    
    Parameters:
    
    kn - the number of neighbors to use in casting a net to build a new metric
    
    k - the number of neighbors to use with the final metric in classification
  - DANN
```
public DANN(int kn,
            int k,
            double eps)
```
    Creates a new DANN classifier
    
    Parameters:
    
    kn - the number of neighbors to use in casting a net to build a new metric
    
    k - the number of neighbors to use with the final metric in classification
    
    eps - the regularization to add to the metric creation
  - DANN
```
public DANN(int kn,
            int k,
            double eps,
            VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)
```
    Creates a new DANN classifier
    
    Parameters:
    
    kn - the number of neighbors to use in casting a net to build a new metric
    
    k - the number of neighbors to use with the final metric in classification
    
    eps - the regularization to add to the metric creation
    
    vcf - the default vector collection that will be used for initial neighbor search
  - DANN
```
public DANN(int kn,
            int k,
            double eps,
            int maxIterations,
            VectorCollectionFactory<VecPaired<Vec,Integer>> vcf)
```
    Creates a new DANN classifier
    
    Parameters:
    
    kn - the number of neighbors to use in casting a net to build a new metric
    
    k - the number of neighbors to use with the final metric in classification
    
    eps - the regularization to add to the metric creation
    
    maxIterations - the maximum number of times to adjust the metric for each classification
    
    vcf - the default vector collection that will be used for initial neighbor search
- Method Detail
  - setK
```
public void setK(int k)
```
    Sets the number of nearest neighbors to use when predicting
    
    Parameters:
    
    k - the number of neighbors
  - getK
```
public int getK()
```
    Returns the number of nearest neighbors to use when predicting
    
    Returns:
    
    the number of neighbors used for classification
  - setKn
```
public void setKn(int kn)
```
    Sets the number of nearest neighbors to use when adapting the distance metric. At each iteration of the algorithm, a new distance metric will be created. A larger number of neighbors is used to create a net of points, around which the metric will be adapted.
    
    Parameters:
    
    kn - the number of neighbors to use
  - getKn
```
public int getKn()
```
    Returns the number of nearest neighbors to use when adapting the distance metric
    
    Returns:
    
    the number of neighbors used to adapt the metric
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the number of times a new distance metric will be created for each query. The metric should converge quickly. For this reason, and do to a lack of performance difference, it is highly recommended to use the default of 1 iteration.
    
    Parameters:
    
    maxIterations - the maximum number of times the metric will be updated
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the number of times the distance metric will be updated.
    
    Returns:
    
    the number of iterations the metric will be updated
  - setEpsilon
```
public void setEpsilon(double eps)
```
    Sets the regularization to apply the the diagonal of the scatter matrix when creating each new metric.
    
    Parameters:
    
    eps - the regularization value
  - getEpsilon
```
public double getEpsilon()
```
    Returns the regularization parameter that is applied to the diagonal of the matrix when creating each new metric.
    
    Returns:
    
    the regularization used.
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - clone
```
public Classifier clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - guessK
```
public static Distribution guessK(DataSet d)
```
    Guesses the distribution to use for the number of neighbors to consider
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the K parameter
  - guessKn
```
public static Distribution guessKn(DataSet d)
```
    Guesses the distribution to use for the number of neighbors to consider
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the Kn parameter
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class DANN

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_KN

DEFAULT_K

DEFAULT_EPS

DEFAULT_ITERATIONS

Constructor Detail

DANN

DANN

DANN

DANN

DANN

Method Detail

setK

getK

setKn

getKn

setMaxIterations

getMaxIterations

setEpsilon

getEpsilon

classify

trainC

trainC

supportsWeightedData

clone

guessK

guessKn

getParameters

getParameter