Updated ranks in tensorkrylov.py

examples/tensorkrylov.py

@@ -6,6 +6,7 @@
 from numpy.random import seed, rand
 from scikit_tt.tensor_train import TT, build_core, residual_error, uniform, residual_error 
 from scikit_tt.solvers.sle import als
+from time import time
 
 class MatrixCollection(object):
 
@@ -167,9 +168,13 @@ def _update_approximation(x_TT: "TT", V: "MatrixCollection", y_TT: "TT"):
     for s in range(x_TT.order):
 
         x_TT.cores[s] = np.sum(V[s][None, :, :, None, None] @ y_TT.cores[s][:, None, :, :, :], axis = 2)
+        x_TT.ranks[s]     = x_TT.cores[s].shape[0]
+        x_TT.ranks[s + 1] = x_TT.cores[s].shape[3]
+
 
     return
 
+#def _residual_norm()
 
 def symmetric_tensorkrylov(A: "MatrixCollection", b: List[np.ndarray], rank: int, nmax: int, tol = 1e-9):
 
@@ -213,11 +218,9 @@ def symmetric_tensorkrylov(A: "MatrixCollection", b: List[np.ndarray], rank: int
 
         y_TT = als(TT_operator, TT_guess, TT_rhs)
         _update_approximation(x_TT, V_minors, y_TT)
-        print(A_TT)
-        print(x_TT)
-        print(b_TT)
-        r_norm = residual_error(A_TT, x_TT, b_TT)
+        #r_norm = residual_error(A_TT, x_TT, b_TT)
 
+        #print(r_norm)
         if r_norm <= tol:
 
             return x_TT
@@ -239,8 +242,23 @@ def random_rhs(n: int):
 
 A = MatrixCollection([ As for _ in range(d) ])
 b = [ bs for _ in range(d) ]
-rank = 5
+rank = 8
 ranks = [1] + ([rank] * (d - 1)) + [1]
 
+row_dims = [n for _ in range(d)]
+col_dims = [1 for _ in range(d)]
+
+x_TT = scikit_tt.tensor_train.rand(row_dims, col_dims, ranks)
+A_TT = _TT_operator(A, n - 1)
+b_TT = _TT_rhs(b)
+
+
+start = time()
+x_TT = als(A_TT, x_TT, b_TT)
+end = time()
+print("Done", end - start)
+
+print(residual_error(A_TT, x_TT, b_TT))
+
 
 print(symmetric_tensorkrylov(A, b, rank, n, tol = 1e-9))