Merge branch 'lightK'

Conflicts:
	python/multired.py

1 files changed, 10 insertions, 8 deletions

diff --git a/python/multired.py b/python/multired.py
index 2b52a4f..06fbc79 100644
--- a/python/multired.py
+++ b/python/multired.py
@@ -35,6 +35,7 @@
 # --------------------------------------------
 #  
 #  -- 2015/04/23 -- release 0.1 
+#  -- 2015/05/11 -- release 0.1.1 -- removed the last full matrices 
 # 
 
 
@@ -122,8 +123,8 @@ class layer:
         elif matrix != None:
             self.adj_matr = copy.copy(matrix)
             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).reshape((1, self.N)).tolist()[0]
+            D = csr_matrix((K, (range(self.N), range(self.N)) ), shape=(self.N, self.N))
             self.laplacian = csr_matrix(D - self.adj_matr)
             K = self.laplacian.diagonal().sum()
             self.resc_laplacian = csr_matrix(self.laplacian / K)
@@ -134,13 +135,13 @@ class layer:
         self.N = N 
         self.adj_matr = csr_matrix((self._ww, (self._ii, self._jj)), shape=(self.N, self.N))
         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).reshape((1, self.N)).tolist()[0]
+        D = csr_matrix((K, (range(self.N), range(self.N)) ), shape=(self.N, self.N))
         self.laplacian = csr_matrix(D - self.adj_matr)
         K = self.laplacian.diagonal().sum()
         self.resc_laplacian = csr_matrix(self.laplacian / K)
         self._matrix_called = True
-    
+
     def dump_info(self):
         N, M = self.adj_matr.shape
         K = self.adj_matr.nnz
@@ -170,14 +171,15 @@ class layer:
             self.adj_matr = self.adj_matr + other_layer.adj_matr
         else:
             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).reshape((1, self.N)).tolist()[0]
+        D = csr_matrix((K, (range(self.N), range(self.N)) ), shape=(self.N, self.N))
         self.laplacian = csr_matrix(D - self.adj_matr)
         K = self.laplacian.diagonal().sum()
         self.resc_laplacian = csr_matrix(self.laplacian / K)
         self._matrix_called = True
 
-        
+    def dump_laplacian(self):
+        print self.laplacian
 
 class multiplex_red: