linalg.matmul now works with any number of operands

pyerrors/linalg.py

@@ -124,18 +124,50 @@ def derived_array(func, data, **kwargs):
     return final_result
 
 
-def matmul(x1, x2):
-    if isinstance(x1[0, 0], CObs) or isinstance(x2[0, 0], CObs):
-        Lr, Li = np.vectorize(lambda x: (np.real(x), np.imag(x)))(x1)
-        Rr, Ri = np.vectorize(lambda x: (np.real(x), np.imag(x)))(x2)
-        Nr = derived_array(lambda x: x[0] @ x[2] - x[1] @ x[3], [Lr, Li, Rr, Ri])
-        Ni = derived_array(lambda x: x[0] @ x[3] + x[1] @ x[2], [Lr, Li, Rr, Ri])
+def matmul(*operands):
+    if any(isinstance(o[0, 0], CObs) for o in operands):
+        extended_operands = []
+        for op in operands:
+            tmp = np.vectorize(lambda x: (np.real(x), np.imag(x)))(op)
+            extended_operands.append(tmp[0])
+            extended_operands.append(tmp[1])
+
+        def multi_dot(operands, part):
+            stack_r = operands[0]
+            stack_i = operands[1]
+            for op_r, op_i in zip(operands[2::2], operands[3::2]):
+                tmp_r = stack_r @ op_r - stack_i @ op_i
+                tmp_i = stack_r @ op_i + stack_i @ op_r
+
+                stack_r = tmp_r
+                stack_i = tmp_i
+
+            if part == 'Real':
+                return stack_r
+            else:
+                return stack_i
+
+        def multi_dot_r(operands):
+            return multi_dot(operands, 'Real')
+
+        def multi_dot_i(operands):
+            return multi_dot(operands, 'Imag')
+
+        Nr = derived_array(multi_dot_r, extended_operands)
+        Ni = derived_array(multi_dot_i, extended_operands)
+
         res = np.empty_like(Nr)
         for (n, m), entry in np.ndenumerate(Nr):
             res[n, m] = CObs(Nr[n, m], Ni[n, m])
+
         return res
     else:
-        return derived_array(lambda x: x[0] @ x[1], [x1, x2])
+        def multi_dot(operands):
+            stack = operands[0]
+            for op in operands[1:]:
+                stack = stack @ op
+            return stack
+        return derived_array(multi_dot, operands)
 
 
 def inv(x):

tests/test_linalg.py

@@ -29,6 +29,28 @@ def test_matmul():
             assert e.is_zero(), t
 
 
+def test_multi_dot():
+    for dim in [4, 8]:
+        my_list = []
+        length = 1000 + np.random.randint(200)
+        for i in range(dim ** 2):
+            my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
+        my_array = np.array(my_list).reshape((dim, dim))
+        tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
+        for t, e in np.ndenumerate(tt):
+            assert e.is_zero(), t
+
+        my_list = []
+        length = 1000 + np.random.randint(200)
+        for i in range(dim ** 2):
+            my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
+                                   pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
+        my_array = np.array(my_list).reshape((dim, dim))
+        tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
+        for t, e in np.ndenumerate(tt):
+            assert e.is_zero(), t
+
+
 def test_matrix_inverse():
     content = []
     for t in range(9):