C++ VectorI::end方法代码示例

ALGEB minpoly(MKernelVector kv, ALGEB* args)
  {
    int i;
    ALGEB retlist, blank;
    char err[] = "ERROR!  Associated blackbox object does not exist!";
    int key = MapleToInteger32(kv,args[1]), flag;
    std::map<int,int>::iterator f_i;
    std::map<int,void*>::iterator h_i;

    // Get the data from the hash table
    f_i = typeTable.find(key);
    if( f_i == typeTable.end() )
      MapleRaiseError(kv,err);
    else
      flag = f_i->second;


    h_i = hashTable.find(key);
    if(h_i != hashTable.end() ) { // We've got data
      switch( flag ) {
	// Getting the minimal polynomial is rather complicated, so both instances of this code were
	// wrapped up inside a block, to cut down at the clutter at the top of this function.
	// First declares a vector of the proper type and casts the pointer.  Then computes the minimal
	// polynomial.  It then builds the proper Maple list structure for this application.


         case BlackBoxi: {
	   Vectorl mpreturn;
	    Vectorl::iterator mp_i;
	    TriplesBBi* BB = (TriplesBBi*) h_i->second;
	    LinBox::minpoly( mpreturn, *BB, BB->field() );
	    retlist = MapleListAlloc(kv, mpreturn.size() );
	    for(i = 1, mp_i = mpreturn.begin(); mp_i != mpreturn.end(); ++mp_i, ++i)
	      MapleListAssign(kv, retlist, i, ToMapleInteger(kv, *mp_i));
	 }
	 break;

         case BlackBoxI: {
	   VectorI mpreturn;
	   VectorI::iterator mp_i;
	   TriplesBBI* BB = (TriplesBBI*) h_i->second;
	   LinBox::minpoly( mpreturn, *BB, BB->field() );
	   retlist = MapleListAlloc(kv, mpreturn.size());
	   for(i = 1, mp_i = mpreturn.begin(); mp_i != mpreturn.end(); ++mp_i, ++i)
	     MapleListAssign(kv, retlist, i, LiToM(kv, *mp_i, blank));

	 }
	 break;
      }
    }
    else
      MapleRaiseError(kv,err);

    return retlist;

  }

  ALGEB getVector(MKernelVector kv, ALGEB* args)
  {
    // Get the key, declare variables
    int key = MapleToInteger32(kv,args[1]), flag;
    char err[] = "ERROR!  The associated Vector object does not exist!";
    M_INT index, bound[2];
    RTableData d;
    RTableSettings s;
    ALGEB rtable, blank;
    char MapleStatement[100] = "rtable(1..";


    // Check to see if the object pointed to by key is in the type table.  If not, panic
    std::map<int,int>::iterator f_i = typeTable.find(key);
    if(f_i == typeTable.end() ) {
      MapleRaiseError(kv, err);
    }

    // Otherwise, we have our object
    flag = f_i->second;

    // Get a pointer to the actual data
    std::map<int,void*>::iterator h_i = hashTable.find(key);
    if(h_i != hashTable.end() ) {

      // Diverge over whether we are using maple 7 or 8 ( and 5 & 6)
      // in Maple, arg 3 is a flag indicating which method to use
      switch( MapleToInteger32(kv, args[3])) {

	// In this case, Maple 7 is being used, we have to construct a call using "EvalMapleStatement()"
	// to call the RTable constructor
         case 1:

	   switch(flag) {
	   case SmallV:{
	     // Get the vector
	     Vectorl* V = (Vectorl*) h_i->second;
	     Vectorl::const_iterator V_i;

	     // Create the Maple object
	     sprintf(MapleStatement + strlen(MapleStatement), "%d", V->size() );
	     strcat(MapleStatement, ", subtype=Vector[column], storage=sparse)");
	     rtable = kv->evalMapleStatement(MapleStatement);

	     // populate the Maple vector w/ the entries from V above
	     for(index = 1, V_i = V->begin(); V_i != V->end(); ++V_i, ++index) {
	       d.dag = ToMapleInteger(kv, *V_i); // d is a union, dag is the
	                                         // ALGEB union field
	       RTableAssign(kv, rtable, &index, d);
	     }
	   }
	   break;

	   case LargeV: {
	     // This part works the same way as above
	     VectorI* V = (VectorI*) h_i->second;
	     VectorI::const_iterator V_i;
	     sprintf(MapleStatement + strlen(MapleStatement), "%d", V->size() );
	     strcat(MapleStatement, ",subtype=Vector[column], storage=sparse)");
	     rtable = kv->evalMapleStatement(MapleStatement);

	     // Use maple callback to call the procedure from Maple that translates a gmp integer
	     // into a large maple integer.  Then put this into the Maple vector
	     for(index = 1, V_i = V->begin(); V_i != V->end(); ++V_i, ++index) {

	       /* Okay, here's how this line works.  Basically,
		* in order to set the entries of this RTable to
		* multi-precision integers, I have to first use my own conversion
		* method, LiToM, to convert the integer entry to a ALGEB structure,
		* then do a callback into Maple that calls the ExToM procedure,
		* which converts the results of LiToM into a Maple multi-precision
		* integer. At the moment, this is the best idea I've got as to
		* how to convert a GMP integer into a Maple representation in one shot.
		*/

	       d.dag = EvalMapleProc(kv,args[2],1,LiToM(kv, *V_i, blank));
	       RTableAssign(kv, rtable, &index, d);
	     }
	   }
	   break;

	   default:
	     MapleRaiseError(kv, err);
	     break;
	   }
	   break;

	   // In this case, use the simpler RTableCreate function, rather than building a string
	   // that must be parsed by maple

	   case 2:

	     kv->rtableGetDefaults(&s); // Get default settings - set datatype to Maple,
                               // DAGTAG to anything
	     s.subtype = 2; // Subtype set to column vector
	     s.storage = 4; // Storage set to rectangular
	     s.num_dimensions = 1; // What do you think this means :-)
	     bound[0] = 1; // Set the lower bounds of each dimension to 0

	     switch(flag) {// Switch on data type of vector
//.........这里部分代码省略.........