name confl

emlp/solver/product_sum_reps.py

@@ -7,7 +7,7 @@
 from jax import device_put
 import collections,itertools
 from functools import lru_cache as cache
-from .utils import prod
+from .utils import prod as product
 import scipy as sp
 import scipy.linalg
 import functools
@@ -215,7 +215,7 @@ def distribute_product(reps,extra_perm=None):
 
     axis_sizes = [len(perm) for perm in perms]
 
-    order = np.arange(prod(axis_sizes)).reshape(tuple(len(perm) for perm in perms))
+    order = np.arange(product(axis_sizes)).reshape(tuple(len(perm) for perm in perms))
     for i,perm in enumerate(perms):
         order = np.swapaxes(np.swapaxes(order,0,i)[perm,...],0,i)
     order = order.reshape(-1)
@@ -236,7 +236,7 @@ def distribute_product(reps,extra_perm=None):
     for prod in itertools.product(*[rep.reps.items() for rep in reps]):
         rs,cs = zip(*prod)
         #import pdb; pdb.set_trace()
-        prod_rep,canonicalizing_perm = (prod(cs)*reduce(lambda a,b: a*b,rs)).canonicalize()
+        prod_rep,canonicalizing_perm = (product(cs)*reduce(lambda a,b: a*b,rs)).canonicalize()
         #print(f"{rs}:{cs} in distribute yield prod_rep {prod_rep}")
         ordered_reps.append(prod_rep)
         shape = []
@@ -350,7 +350,7 @@ def __hash__(self):
     def __eq__(self, other): #TODO: worry about non canonical?
         return isinstance(other,ProductRep) and self.reps==other.reps# and self.perm == other.perm
     def size(self):
-        return prod([rep.size()**count for rep,count in self.reps.items()])
+        return product([rep.size()**count for rep,count in self.reps.items()])
     @property
     def T(self): #TODO: reavaluate if this needs to change the order (it does not)
         """ only swaps to adjoint representation, does not reorder elems"""
@@ -365,7 +365,7 @@ def compute_canonical(rep_cnters,rep_perms):
         """ given that rep1_perm and rep2_perm are the canonical orderings for
             rep1 and rep2 (ie v[rep1_perm] is in canonical order) computes
             the canonical order for rep1 * rep2"""
-        order = np.arange(prod(len(perm) for perm in rep_perms))
+        order = np.arange(product(len(perm) for perm in rep_perms))
         # First: merge counters
         unique_reps = sorted(reduce(lambda a,b: a|b,[cnter.keys() for cnter in rep_cnters]))
         merged_cnt = defaultdict(int)
@@ -552,7 +552,7 @@ class lazy_kron(LinearOperator):
 
     def __init__(self,Ms):
         self.Ms = Ms
-        self.shape = prod([Mi.shape[0] for Mi in Ms]), prod([Mi.shape[1] for Mi in Ms])
+        self.shape = product([Mi.shape[0] for Mi in Ms]), product([Mi.shape[1] for Mi in Ms])
         #self.dtype=Ms[0].dtype
         self.dtype=jnp.dtype('float32')
 
@@ -577,7 +577,7 @@ class lazy_kronsum(LinearOperator):
 
     def __init__(self,Ms):
         self.Ms = Ms
-        self.shape = prod([Mi.shape[0] for Mi in Ms]), prod([Mi.shape[1] for Mi in Ms])
+        self.shape = product([Mi.shape[0] for Mi in Ms]), product([Mi.shape[1] for Mi in Ms])
         #self.dtype=Ms[0].dtype
         self.dtype=jnp.dtype('float32')