tensor: scalar as rank==0 special case

t.cc

@@ -3362,7 +3362,7 @@ for(int i=0; i<n; ++i)
 
 
 
-if(1)
+if(0)
 {
 int n=5;
 INDEXGROUP ag;
@@ -3959,6 +3959,41 @@ for(int k=0; k<n; ++k)
 cout<<t2;
 }
 
+if(0)
+{
+//scalar as trivial tensor rank 0 case
+Tensor<double> t(2.),u(3.);
+cout <<t<<endl;
+SUPERINDEX I;
+FLATINDEX J;
+cout <<t(I)<<endl;
+cout <<u(J)<<endl;
+cout <<t+u<<endl;
+cout <<t*u<<endl;
+cout <<t.dot(u)<<endl;
+}
+
+
+if(1)
+{
+int r,n;
+cin>>r>>n;
+INDEXGROUP shape;
+        {
+        shape.number=r;
+        //shape.symmetry= 0;
+        shape.symmetry= -1;
+        shape.range=n;
+        shape.offset=0;
+        }
+Tensor<double> x(shape);
+x.randomize(1.);
+cout<<x;
+Tensor<double> xf=x.flatten();
+cout <<xf;
+
+cout <<x.dot(x) <<" "<< xf.dot(xf)<<endl;
+}
 
 
 }//main

tensor.cc

@@ -34,7 +34,7 @@ int r=0;
 for(int i=0; i<shape.size(); ++i)
         {
 	const indexgroup *sh = &(* const_cast<const NRVec<indexgroup> *>(&shape))[i];
-        if(sh->number<=0) laerror("empty index group"); //we do not support scalar as a trivial case
+        if(sh->number<=0) laerror("empty index group"); //scalar must have shape.size()==0, not empty index group
         r+=sh->number;
         }
 myrank=r;
@@ -46,7 +46,7 @@ return r;
 template<typename T>
 LA_largeindex Tensor<T>::calcsize()
 {
-if(shape.size()==0) laerror("tensor must have rank at least 1");
+if(shape.size()==0) return 1; //scalar
 groupsizes.resize(shape.size()); 
 cumsizes.resize(shape.size());
 LA_largeindex s=1;
@@ -578,6 +578,7 @@ std::ostream & operator<<(std::ostream &s, const Tensor<T> &x)
 {
 s<<x.shape;
 s<<x.names;
+if(x.rank()==0) {s<<x.data[0]; return s;}
 sout= &s;
 x.constloopover(&outputcallback<T>);
 return s;
@@ -1383,25 +1384,26 @@ loopover(permutationalgebra_callback2);
 template<typename T>
 void Tensor<T>::split_index_group(int group)
 {
+const indexgroup *sh = &(* const_cast<const NRVec<indexgroup> *>(&shape))[0];
 if(group<0||group >= shape.size()) laerror("illegal index group number");
-if(shape[group].number==1) return; //nothing to split
+if(sh[group].number==1) return; //nothing to split
-if(shape[group].symmetry!=0) laerror("only non-symmetric index group can be splitted, use flatten instead");
+if(sh[group].symmetry!=0) laerror("only non-symmetric index group can be splitted, use flatten instead");
 
-NRVec<indexgroup> newshape(shape.size()+shape[group].number-1);
+NRVec<indexgroup> newshape(shape.size()+sh[group].number-1);
 int gg=0;
 for(int g=0; g<shape.size(); ++g)
 	{
 	if(g==group)
 		{
-		for(int i=0; i<shape[group].number; ++i)
+		for(int i=0; i<sh[group].number; ++i)
 			{
-			newshape[gg] = shape[group]; 
+			newshape[gg] = sh[group]; 
 			newshape[gg].number = 1;
 			gg++;
 			}
 		}
 	else
-		newshape[gg++] = shape[g];
+		newshape[gg++] = sh[g];
 	}
 
 shape=newshape;

tensor.h

@@ -40,7 +40,6 @@
 #include "miscfunc.h"
 
 //TODO:
-//@@@@@@how to handle contractions yielding a scalar - special treatment, support special case of rank=0 tensor?
 //@@@contraction inside one tensor - compute resulting shape, loopover the shape, create index into the original tensor + loop over the contr. index, do the summation, store result
 //@@@ will need to store vector of INDEX to the original tensor for the result's flatindex
 //@@@ will not be particularly efficient
@@ -187,7 +186,8 @@ public:
 	SUPERINDEX inverse_index(LA_largeindex s) const; //inefficient, but possible if needed
 
 	//constructors
-	Tensor() : myrank(0) {};
+	Tensor() : myrank(-1) {};
+	explicit Tensor(const T &x) : myrank(0), data(1) {data[0]=x;}; //scalar
 	Tensor(const NRVec<indexgroup> &s) : shape(s) { data.resize(calcsize()); calcrank();}; //general tensor
 	Tensor(const NRVec<indexgroup> &s, const NRVec<INDEXNAME> &newnames) : shape(s), names(newnames) { data.resize(calcsize());  calcrank(); if(names.size()!=myrank && names.size()!=0) laerror("bad number of index names");}; //general tensor
 	Tensor(const NRVec<indexgroup> &s, const NRVec<T> &mydata) : shape(s) { LA_largeindex dsize=calcsize(); calcrank(); if(mydata.size()!=dsize) laerror("inconsistent data size with shape"); data=mydata;}