Package gov.nih.mipav.view.renderer.WildMagic.ProstateFramework.liblinearsvm

Class Problem

  • public class Problem
extends java.lang.Object
    Copyright (c) 2007-2014 The LIBLINEAR Project. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither name of copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

    Problem describes the problem

      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 -> 1 2 1 2 3

  x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?)
       [ ] -> (2,0.1) (3,0.3) (4,-1.2) (6,1) (-1,?)
       [ ] -> (1,0.4) (6,1) (-1,?)
       [ ] -> (2,0.1) (4,1.4) (5,0.5) (6,1) (-1,?)
       [ ] -> (1,-0.1) (2,-0.2) (3,0.1) (4,1.1) (5,0.1) (6,1) (-1,?)

    • Field Summary

      Fields 
      Modifier and Type Field Description
      double bias
      If bias >= 0, we assume that one additional feature is added to the end of each data instance
      int l
      the number of training data
      int n
      the number of features (including the bias feature if bias >= 0)
      FeatureNode[][] x
      array of sparse feature nodes
      int[] y
      an array containing the target values

    • Constructor Summary

      Constructors 
      Constructor Description
      Problem()  

    • Field Detail

      • l

        public int l
        the number of training data

      • n

        public int n
        the number of features (including the bias feature if bias >= 0)

      • y

        public int[] y
        an array containing the target values

      • x

        public FeatureNode[][] x
        array of sparse feature nodes

      • bias

        public double bias
        If bias >= 0, we assume that one additional feature is added to the end of each data instance

    • Constructor Detail

      • Problem

        public Problem()