Update MetropolisHastings.py

hive/app/domain/MetropolisHastings.py

@@ -8,9 +8,9 @@
 
 
 # region module public functions
-def metropolis_algorithm(adj_matrix, ddv, column_major_in=False, column_major_out=True):
+def metropolis_algorithm(adj, ddv, column_major_in=False, column_major_out=True):
     """
-    :param adj_matrix: any adjacency matrix (list of lits) provided by the user in row major form
+    :param adj: any adjacency matrix (list of lits) provided by the user in row major form
     :type list<list<float>>
     :param ddv: a stochastic desired distribution vector
     :type list<float>
@@ -23,15 +23,21 @@ def metropolis_algorithm(adj_matrix, ddv, column_major_in=False, column_major_ou
     """
 
     ddv = np.asarray(ddv)
-    adj_matrix = np.asarray(adj_matrix)
+    adj = np.asarray(adj)
+
+    # Input checking
+    if ddv.shape[0] != adj.shape[1]:
+        raise DistributionShapeError("distribution shape: {}, proposal matrix shape: {}".format(ddv.shape, adj.shape))
+    if adj.shape[0] != adj.shape[1]:
+        raise MatrixNotSquareError("rows: {}, columns: {}, expected square matrix".format(adj.shape[0], adj.shape[1]))
 
     if column_major_in:
-        adj_matrix = adj_matrix.transpose()
+        adj = adj.transpose()
 
-    shape = adj_matrix.shape
-    size = adj_matrix.shape[0]
+    shape = adj.shape
+    size = adj.shape[0]
 
-    rw = _construct_random_walk_matrix(adj_matrix, shape, size)
+    rw = _construct_random_walk_matrix(adj, shape, size)
     r = _construct_rejection_matrix(ddv, rw, shape, size)
 
     transition_matrix = np.zeros(shape=shape)