more efficient costruction of the Laplacian

python/multired.py

@@ -125,9 +125,11 @@ class layer:
             self.N, _x = matrix.shape 
             #K = np.multiply(self.adj_matr.sum(0), np.ones((self.N,self.N)))
             #D = np.diag(np.diag(K))
-            K = self.adj_matr.sum(0)
+            K = self.adj_matr.sum(0).reshape((1, self.N)).tolist()[0]
-            D = csr_matrix((self.N, self.N))
+            D = csr_matrix((K, (range(self.N), range(self.N)) ), shape=(self.N, self.N))
-            D.setdiag(eye(self.N) * K.transpose())
+            #K = self.adj_matr.sum(0)
+            #D = csr_matrix((self.N, self.N))
+            #D.setdiag(eye(self.N) * K.transpose())
             self.laplacian = csr_matrix(D - self.adj_matr)
             K = self.laplacian.diagonal().sum()
             self.resc_laplacian = csr_matrix(self.laplacian / K)
@@ -140,9 +142,9 @@ class layer:
         self.adj_matr = self.adj_matr + self.adj_matr.transpose()
         #K = np.multiply(self.adj_matr.sum(0), np.ones((self.N,self.N)))
         #D = np.diag(np.diag(K))
-        K = self.adj_matr.sum(0)
+        K = self.adj_matr.sum(0).reshape((1, self.N)).tolist()[0]
-        D = csr_matrix((self.N, self.N))
+        D = csr_matrix((K, (range(self.N), range(self.N)) ), shape=(self.N, self.N))
-        D.setdiag(eye(self.N) * K.transpose())
+        #D.setdiag(eye(self.N) * K.transpose())
         self.laplacian = csr_matrix(D - self.adj_matr)
         K = self.laplacian.diagonal().sum()
         self.resc_laplacian = csr_matrix(self.laplacian / K)
@@ -179,9 +181,11 @@ class layer:
             self.adj_matr = copy.copy(other_layer.adj_matr)
         #K = np.multiply(self.adj_matr.sum(0), np.ones((self.N,self.N)))
         #D = np.diag(np.diag(K))
-        K = self.adj_matr.sum(0)
+        K = self.adj_matr.sum(0).reshape((1, self.N)).tolist()[0]
-        D = csr_matrix((self.N, self.N))
+        D = csr_matrix((K, (range(self.N), range(self.N)) ), shape=(self.N, self.N))
-        D.setdiag(eye(self.N) * K. transpose())
+        #K = self.adj_matr.sum(0)
+        #D = csr_matrix((self.N, self.N))
+        #D.setdiag(eye(self.N) * K. transpose())
         self.laplacian = csr_matrix(D - self.adj_matr)
         K = self.laplacian.diagonal().sum()
         self.resc_laplacian = csr_matrix(self.laplacian / K)