package liblinear;

/**

* Problem describes the problem

*

* <pre>

* For example, if we have the following training data:

*

* LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5

* ----- ----- ----- ----- ----- -----

* 1 0 0.1 0.2 0 0

* 2 0 0.1 0.3 -1.2 0

* 1 0.4 0 0 0 0

* 2 0 0.1 0 1.4 0.5

* 3 -0.1 -0.2 0.1 1.1 0.1

*

* and bias = 1, then the components of problem are:

*

* l = 5

* n = 6

*

* y -&gt; 1 2 1 2 3

*

* x -&gt; [ ] -&gt; (2,0.1) (3,0.2) (6,1) (-1,?)

* [ ] -&gt; (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?)

* [ ] -&gt; (1,0.4) (6,1) (-1,?)

* [ ] -&gt; (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?)

* [ ] -&gt; (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?)

* </pre>

*/

public class Problem {

/** the number of training data */

public int l;

/** the number of features (including the bias feature if bias &gt;= 0) */

public int n;

/** an array containing the target values */

public int[] y;

/** array of sparse feature nodes */

public FeatureNode[][] x;

/**

* If bias &gt;= 0, we assume that one additional feature is added

* to the end of each data instance

*/

public double bias;

}