Model (liblinear 2.21 API)

java.lang.Object
- de.bwaldvogel.liblinear.Model

All Implemented Interfaces:

java.io.Serializable
```
public final class Model
extends java.lang.Object
implements java.io.Serializable
```
Model stores the model obtained from the training procedure

use Linear.loadModel(File) and Linear.saveModel(File, Model) to load/save it

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

Model()

Constructors
Constructor and Description
`Model()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`protected static boolean`	`equals(double[] a, double[] a2)` don't use `Arrays.equals(double[], double[])` here, cause 0.0 and -0.0 should be handled the same
`boolean`	`equals(java.lang.Object obj)`
`double`	`getBias()`
`double`	`getDecfunBias(int labelIdx)` This function gives the bias term corresponding to the class with the label_idx.
`double`	`getDecfunCoef(int featIdx, int labelIdx)` This function gives the coefficient for the feature with feature index = feat_idx and the class with label index = label_idx.
`double[]`	`getFeatureWeights()` The array w gives feature weights; its size is nr_feature*nr_class but is nr_feature if nr_class = 2.
`int[]`	`getLabels()`
`int`	`getNrClass()`
`int`	`getNrFeature()`
`SolverType`	`getSolverType()`
`int`	`hashCode()`
`boolean`	`isProbabilityModel()`
`static Model`	`load(java.io.File file)` see `Linear.loadModel(File)`
`static Model`	`load(java.io.Reader inputReader)` see `Linear.loadModel(Reader)`
`void`	`save(java.io.File file)` see `Linear.saveModel(java.io.File, Model)`
`void`	`save(java.io.Writer writer)` see `Linear.saveModel(Writer, Model)`
`java.lang.String`	`toString()`

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - Model
```
public Model()
```
- Method Detail
  - getNrClass
```
public int getNrClass()
```
    Returns:
    
    number of classes
  - getNrFeature
```
public int getNrFeature()
```
    Returns:
    
    number of features
  - getLabels
```
public int[] getLabels()
```
  - getSolverType
```
public SolverType getSolverType()
```
  - getFeatureWeights
```
public double[] getFeatureWeights()
```
    The array w gives feature weights; its size is nr_feature*nr_class but is nr_feature if nr_class = 2. We use one against the rest for multi-class classification, so each feature index corresponds to nr_class weight values. Weights are organized in the following way
```
 +------------------+------------------+------------+
 | nr_class weights | nr_class weights |  ...
 | for 1st feature  | for 2nd feature  |
 +------------------+------------------+------------+
 
```
    If bias >= 0, x becomes [x; bias]. The number of features is increased by one, so w is a (nr_feature+1)*nr_class array. The value of bias is stored in the variable bias.
    Returns:
    
    a copy of the feature weight array as described
    
    See Also:
    
    getBias()
  - isProbabilityModel
```
public boolean isProbabilityModel()
```
    Returns:
    
    true for logistic regression solvers
  - getBias
```
public double getBias()
```
    See Also:
    
    getFeatureWeights()
  - getDecfunCoef
```
public double getDecfunCoef(int featIdx,
                            int labelIdx)
```
    This function gives the coefficient for the feature with feature index = feat_idx and the class with label index = label_idx. Note that feat_idx starts from 1, while label_idx starts from 0. If feat_idx is not in the valid range (1 to nr_feature), then a zero value will be returned. For classification models, if label_idx is not in the valid range (0 to nr_class-1), then a zero value will be returned; for regression models, label_idx is ignored.
    
    Since:
    
    1.95
  - getDecfunBias
```
public double getDecfunBias(int labelIdx)
```
    This function gives the bias term corresponding to the class with the label_idx. For classification models, if label_idx is not in a valid range (0 to nr_class-1), then a zero value will be returned; for regression models, label_idx is ignored.
    
    Since:
    
    1.95
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class java.lang.Object
  - equals
```
public boolean equals(java.lang.Object obj)
```
    Overrides:
    
    equals in class java.lang.Object
  - equals
```
protected static boolean equals(double[] a,
                                double[] a2)
```
    don't use Arrays.equals(double[], double[]) here, cause 0.0 and -0.0 should be handled the same
    
    See Also:
    
    Linear.saveModel(java.io.Writer, Model)
  - save
```
public void save(java.io.File file)
          throws java.io.IOException
```
    see Linear.saveModel(java.io.File, Model)
    
    Throws:
    
    java.io.IOException
  - save
```
public void save(java.io.Writer writer)
          throws java.io.IOException
```
    see Linear.saveModel(Writer, Model)
    
    Throws:
    
    java.io.IOException
  - load
```
public static Model load(java.io.File file)
                  throws java.io.IOException
```
    see Linear.loadModel(File)
    
    Throws:
    
    java.io.IOException
  - load
```
public static Model load(java.io.Reader inputReader)
                  throws java.io.IOException
```
    see Linear.loadModel(Reader)
    
    Throws:
    
    java.io.IOException

Class Model

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Model

Method Detail

getNrClass

getNrFeature

getLabels

getSolverType

getFeatureWeights

isProbabilityModel

getBias

getDecfunCoef

getDecfunBias

toString

hashCode

equals

equals

save

save

load

load