first implemntation sparse loss

1 files changed, 78 insertions, 0 deletions

diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp
index 9be2cdc..28e4af2 100644
--- a/ot/lp/EMD_wrapper.cpp
+++ b/ot/lp/EMD_wrapper.cpp
@@ -210,3 +210,81 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D,
     return ret;
 }
 
+int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, 
+                    long *iD, long *jD, double *D, long  nD,
+                    long *iG, long *jG, double *G, long * nG,
+                    double* alpha, double* beta, double *cost, int maxIter)  {
+    // beware M and C anre strored in row major C style!!!
+
+    // Get the number of non zero coordinates for r and c and vectors
+    int n, m, cur;
+
+    typedef FullBipartiteDigraph Digraph;
+    DIGRAPH_TYPEDEFS(FullBipartiteDigraph);
+
+    n=n1;
+    m=n2;
+
+
+    // Define the graph
+
+
+    std::vector<double>  weights2(m);
+    Digraph di(n, m);
+    NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m, maxIter);
+
+    // Set supply and demand, don't account for 0 values (faster)
+
+
+    // Demand is actually negative supply...
+
+    cur=0;
+    for (int i=0; i<n2; i++) {
+        double val=*(Y+i);
+        if (val>0) {
+            weights2[ cur ] = -val;
+        }
+    }
+
+    // Define the graph
+    net.supplyMap(X, n, &weights2[0], m);
+
+    // Set the cost of each edge
+    for (int k=0; k<nD; k++) {
+            int i = iD[k];
+            int j = jD[k];
+            net.setCost(di.arcFromId(i*m+j), D[k]);
+        
+    }
+
+
+    // Solve the problem with the network simplex algorithm
+
+    int ret=net.run();
+    if (ret==(int)net.OPTIMAL || ret==(int)net.MAX_ITER_REACHED) {
+        *cost = net.totalCost();
+        Arc a; di.first(a);
+        cur=0;
+        for (; a != INVALID; di.next(a)) {
+            int i = di.source(a);
+            int j = di.target(a);
+            double flow = net.flow(a);
+            if (flow>0)
+            {
+                
+                *(G+cur) = flow;
+                *(iG+cur) = i;
+                *(jG+cur) = j-n;
+                *(alpha + i) = -net.potential(i);
+                *(beta + j-n) = net.potential(j);
+                cur++;
+            }
+        }
+        *nG=cur; // nb of value +1 for numpy indexing
+
+    }
+
+
+    return ret;
+}
+