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(Path) and Linear.saveModel(Path, Model) to load/save it

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`(package private) double`	`bias`
`(package private) int[]`	`label`	label of each class
`(package private) int`	`nr_class`
`(package private) int`	`nr_feature`
`(package private) double`	`rho`	one-class SVM only
`private static long`	`serialVersionUID`
`(package private) SolverType`	`solverType`
`(package private) double[]`	`w`	feature weight array

Constructor Summary

Constructors
Constructor Description

Model()

Method Summary

All Methods Static Methods Instance Methods Concrete Methods Deprecated Methods
Modifier and Type	Method	Description
`private static boolean`	`arrayEquals(double[] a, double[] a2)`	don't use `Arrays.equals(double[], double[])` here, cause 0.0 and -0.0 should be handled the same
`private static int`	`arrayHashCode(double[] w)`	see `Arrays.hashCode(double[])` but treat 0.0 and -0.0 the same
`boolean`	`equals(java.lang.Object o)`
`private double`	`get_w_value(int idx, int label_idx)`
`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`	`getDecfunRho()`	This function gives rho, the bias term used in one-class SVM only.
`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 modelFile)`	Deprecated. use `load(Path)` instead
`static Model`	`load(java.io.Reader inputReader)`	see `Linear.loadModel(Reader)`
`static Model`	`load(java.nio.file.Path modelPath)`	see `Linear.loadModel(Path)`
`void`	`save(java.io.File modelFile)`	Deprecated. use `save(Path)` instead
`void`	`save(java.io.Writer writer)`	see `Linear.saveModel(Writer, Model)`
`void`	`save(java.nio.file.Path modelPath)`	see `Linear.saveModel(Path, Model)`
`java.lang.String`	`toString()`

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

- Field Detail
  - serialVersionUID
```
private static final long serialVersionUID
```
    See Also:
    
    Constant Field Values
  - bias
```
double bias
```
  - label
```
int[] label
```
    label of each class
  - nr_class
```
int nr_class
```
  - nr_feature
```
int nr_feature
```
  - solverType
```
SolverType solverType
```
  - w
```
double[] w
```
    feature weight array
  - rho
```
double rho
```
    one-class SVM only
- 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()
  - get_w_value
```
private double get_w_value(int idx,
                           int label_idx)
```
  - 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
  - getDecfunRho
```
public double getDecfunRho()
```
    This function gives rho, the bias term used in one-class SVM only. This function can only be called for a one-class SVM model.
    
    Since:
    
    2.40
  - 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 o)
```
    Overrides:
    
    equals in class java.lang.Object
  - arrayEquals
```
private static boolean arrayEquals(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)
  - arrayHashCode
```
private static int arrayHashCode(double[] w)
```
    see Arrays.hashCode(double[]) but treat 0.0 and -0.0 the same
  - save
```
public void save(java.io.File modelFile)
          throws java.io.IOException
```
    Deprecated.
    use save(Path) instead
    
    Throws:
    
    java.io.IOException
  - save
```
public void save(java.nio.file.Path modelPath)
          throws java.io.IOException
```
    see Linear.saveModel(Path, 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 modelFile)
                  throws java.io.IOException
```
    Deprecated.
    use load(Path) instead
    
    Throws:
    
    java.io.IOException
  - load
```
public static Model load(java.nio.file.Path modelPath)
                  throws java.io.IOException
```
    see Linear.loadModel(Path)
    
    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

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

serialVersionUID

bias

label

nr_class

nr_feature

solverType

w

rho

Constructor Detail

Model

Method Detail

getNrClass

getNrFeature

getLabels

getSolverType

getFeatureWeights

isProbabilityModel

getBias

get_w_value

getDecfunCoef

getDecfunBias

getDecfunRho

toString

hashCode

equals

arrayEquals

arrayHashCode

save

save

save

load

load

load