Model

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

Methods
Modifier and Type	Method and Description
`boolean`	`equals(java.lang.Object obj)`
`double`	`getBias()`
`double[]`	`getFeatureWeights()` The nr_feature*nr_class array w gives feature weights.
`int[]`	`getLabels()`
`int`	`getNrClass()`
`int`	`getNrFeature()`
`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
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()
```
  - getFeatureWeights
```
public double[] getFeatureWeights()
```
    The nr_feature*nr_class array w gives feature weights. 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()
  - 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
  - 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

getFeatureWeights

isProbabilityModel

getBias

toString

hashCode

equals

save

save

load

load