Package javax.constraints.impl.constraint

Source Code of javax.constraints.impl.constraint.Linear

//================================================================
// J A V A  C O M M U N I T Y  P R O C E S S
//
// J S R  3 3 1
//
// CONSTRAINER-BASED REFERENCE IMPLEMENTATION
//
// Copyright (c) Cork Constraint Computation Centre, 2010
// University College Cork, Cork, Ireland, www.4c.ucc.ie
// Constrainer is copyrighted by Exigen Group, USA.
//
//================================================================
package javax.constraints.impl.constraint;


import javax.constraints.Var;
import javax.constraints.impl.Constraint;
import javax.constraints.impl.Problem;

import com.exigen.ie.constrainer.Constrainer;
import com.exigen.ie.constrainer.IntExp;
import com.exigen.ie.constrainer.IntExpArray;

public class Linear extends Constraint {

  static final String name = "Linear";


  public Linear(Var var1, String oper, Var var2) {
    super(var1.getProblem(), name);
    IntExp cVar1 = (IntExp) var1.getImpl();
    Problem problem = (Problem) var1.getProblem();
    problem.defineConstraintImpl(this, cVar1, oper, var2);
  }
 
  public Linear(Var var, String oper, int value) {
    super(var.getProblem(), name);
    IntExp cVar = (IntExp) var.getImpl();
    Problem problem = (Problem) var.getProblem();
    problem.defineConstraintImpl(this, cVar, oper, value);
  }

  public Linear(int[] values, Var[] vars, String oper, int value) {
    super(vars[0].getProblem(), name);
    Problem problem = (Problem) vars[0].getProblem();
    Constrainer constrainer = problem.getConstrainer();
    IntExpArray intvars = new IntExpArray(constrainer, vars.length);
    for (int i = 0; i < vars.length; i++) {
      IntExp cvar = (IntExp) vars[i].getImpl();
      intvars.set(cvar, i);
    }
    IntExp scalProd = constrainer.scalarProduct(intvars, values);
    problem.defineConstraintImpl(this, scalProd, oper, value);
  }

  public Linear(Var[] vars, String oper, int value) {
    super(vars[0].getProblem(), name);
    Problem problem = (Problem) vars[0].getProblem();
    Constrainer constrainer = problem.getConstrainer();
    IntExpArray intvars = new IntExpArray(constrainer, vars.length);
    for (int i = 0; i < vars.length; i++) {
      IntExp cvar = (IntExp) vars[i].getImpl();
      intvars.set(cvar, i);
    }
    IntExp sum = constrainer.sum(intvars);
    problem.defineConstraintImpl(this, sum, oper, value);
  }

  public Linear(int[] values, Var[] vars, String oper, Var var) {
    super(vars[0].getProblem(), name);
    Problem problem = (Problem) vars[0].getProblem();
    Constrainer constrainer = problem.getConstrainer();
    IntExpArray intvars = new IntExpArray(constrainer, vars.length);
    for (int i = 0; i < vars.length; i++) {
      IntExp cvar = (IntExp) vars[i].getImpl();
      intvars.set(cvar, i);
    }
    IntExp scalProd = constrainer.scalarProduct(intvars, values);
    problem.defineConstraintImpl(this, scalProd, oper, var);
  }

  public Linear(Var[] vars, String oper, Var var) {
    super(vars[0].getProblem(), name);
    Problem problem = (Problem) vars[0].getProblem();
    Constrainer constrainer = problem.getConstrainer();
    IntExpArray intvars = new IntExpArray(constrainer, vars.length);
    for (int i = 0; i < vars.length; i++) {
      IntExp cvar = (IntExp) vars[i].getImpl();
      intvars.set(cvar, i);
    }
    IntExp sum = constrainer.sum(intvars);
    problem.defineConstraintImpl(this, sum, oper, var);
  }
}
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