package liblinear;

import static liblinear.Linear.copyOf;

import java.io.File;

import java.io.IOException;

import java.io.Reader;

import java.io.Serializable;

import java.io.Writer;

import java.util.Arrays;

/**

* <p>Model stores the model obtained from the training procedure</p>

*

* <p>use {@link Linear#loadModel(String)} and {@link Linear#saveModel(String, Model)} to load/save it</p>

*/

public final class Model implements Serializable {

private static final long serialVersionUID = -6456047576741854834L;

double bias;

/** label of each class */

int[] label;

int nr_class;

int nr_feature;

SolverType solverType;

/** feature weight array */

double[] w;

/**

* @return number of classes

*/

public int getNrClass() {

return nr_class;

}

/**

* @return number of features

*/

public int getNrFeature() {

return nr_feature;

}

public int[] getLabels() {

return copyOf(label, nr_class);

}

/**

* 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

*

* <pre>

* +------------------+------------------+------------+

* | nr_class weights | nr_class weights | ...

* | for 1st feature | for 2nd feature |

* +------------------+------------------+------------+

* </pre>

*

* If bias &gt;= 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.

* @see #getBias()

* @return a <b>copy of</b> the feature weight array as described

*/

public double[] getFeatureWeights() {

return Linear.copyOf(w, w.length);

}

/**

* @return true for logistic regression solvers

*/

public boolean isProbabilityModel() {

return (solverType == SolverType.L2R_LR || solverType == SolverType.L2R_LR_DUAL || solverType == SolverType.L1R_LR);

}

/**

* @see #getFeatureWeights()

*/

public double getBias() {

return bias;

}

@Override

public String toString() {

StringBuilder sb = new StringBuilder("Model");

sb.append(" bias=").append(bias);

sb.append(" nr_class=").append(nr_class);

sb.append(" nr_feature=").append(nr_feature);

sb.append(" solverType=").append(solverType);

return sb.toString();

}

@Override

public int hashCode() {

final int prime = 31;

int result = 1;

long temp;

temp = Double.doubleToLongBits(bias);

result = prime * result + (int)(temp ^ (temp >>> 32));

result = prime * result + Arrays.hashCode(label);

result = prime * result + nr_class;

result = prime * result + nr_feature;

result = prime * result + ((solverType == null) ? 0 : solverType.hashCode());

result = prime * result + Arrays.hashCode(w);

return result;

}

@Override

public boolean equals(Object obj) {

if (this == obj) return true;

if (obj == null) return false;

if (getClass() != obj.getClass()) return false;

Model other = (Model)obj;

if (Double.doubleToLongBits(bias) != Double.doubleToLongBits(other.bias)) return false;

if (!Arrays.equals(label, other.label)) return false;

if (nr_class != other.nr_class) return false;

if (nr_feature != other.nr_feature) return false;

if (solverType == null) {

if (other.solverType != null) return false;

} else if (!solverType.equals(other.solverType)) return false;

if (!equals(w, other.w)) return false;

return true;

}

/**

* don't use {@link Arrays#equals(double[], double[])} here, cause 0.0 and -0.0 should be handled the same

*

* @see Linear#saveModel(java.io.Writer, Model)

*/

protected static boolean equals(double[] a, double[] a2) {

if (a == a2) return true;

if (a == null || a2 == null) return false;

int length = a.length;

if (a2.length != length) return false;

for (int i = 0; i < length; i++)

if (a[i] != a2[i]) return false;

return true;

}

/**

* see {@link Linear#saveModel(java.io.File, Model)}

*/

public void save(File file) throws IOException {

Linear.saveModel(file, this);

}

/**

* see {@link Linear#saveModel(Writer, Model)}

*/

public void save(Writer writer) throws IOException {

Linear.saveModel(writer, this);

}

/**

* see {@link Linear#loadModel(File)}

*/

public static Model load(File file) throws IOException {

return Linear.loadModel(file);

}

/**

* see {@link Linear#loadModel(Reader)}

*/

public static Model load(Reader inputReader) throws IOException {

return Linear.loadModel(inputReader);

}

}