def test_power__default_matrices__should_compute_correctly():
A = sp.sparray((2, 2), default=2.0)
B = sp.sparray((2, 2), default=3.0)
C = A**B
assert C[0, 0] == 8.0
assert C[0, 1] == 8.0
assert C[1, 0] == 8.0
assert C[1, 1] == 8.0
def test_add__two_matrices_with_default_values__should_be_sum_of_default_values(
):
A = sp.sparray((3, 3), default=2.52)
B = sp.sparray((3, 3), default=7.33)
C = A + B
assert C[0, 0] == 2.52 + 7.33
assert C[0, 2] == 2.52 + 7.33
assert C[1, 0] == 2.52 + 7.33
assert C[1, 1] == 2.52 + 7.33
assert C[2, 2] == 2.52 + 7.33
def test_frobenius_norm__custom_matrices__should_be_equal_to_result_for_dense_matrices(
):
A = sp.sparray((3, 3), default=0.0)
B = sp.sparray((3, 3), default=7.0)
A[0, 0] = 27
A[1, 0] = 9
A[2, 2] = 45
B[2, 2] = 4500
actual = ((A - B) * (A - B)).sum()
expected = np.linalg.norm(A.dense() - B.dense(), 'fro')**2
assert np.allclose(actual, expected, rtol=1e-13, atol=1e-15)
def test_init__default_arguments__should_set_properties_correctly():
shape = (2, 7, 9)
s = sp.sparray(shape)
assert s.shape == (2, 7, 9)
assert s.ndim == 3
assert s.dtype == float
def test_delitem__indices_within_shape__should_work_correctly():
s = sp.sparray((25, 14))
s[5, 2] = 87.5
del s[5, 2]
assert s[5, 2] == 0.0
def __init__(self, llabel='', rlabel='', lshape=[], rshape=[], lmins=[], rmins=[], lmaxs=[], rmaxs=[], lcircular=[], rcircular=[], default=0.0, dtype=float):
self.LDistrib= Distrib(llabel, lshape, lmins, lmaxs, lcircular)
self.RDistrib=Distrib(rlabel, rshape, rmins, rmaxs, rcircular)
self.probs=sp.sparray(lshape+rshape,default,dtype)
self.shape=self.probs.shape
def test_mod__default_matrices__should_compute_correctly():
A = sp.sparray((3, 3))
B = sp.sparray((3, 3), default=2.0)
A[0, 0] = 5
C = A % B
assert C[0, 0] == 1.0
assert C[0, 1] == 0.0
assert C[0, 2] == 0.0
assert C[1, 0] == 0.0
assert C[1, 1] == 0.0
assert C[1, 2] == 0.0
assert C[2, 0] == 0.0
assert C[2, 1] == 0.0
assert C[2, 2] == 0.0
def test_iadd__default_matrix__should_double_when_add_itself():
A = sp.sparray((2, 2), default=1.3)
A += A
assert A[0, 0] == 2.6
assert A[0, 1] == 2.6
assert A[1, 0] == 2.6
assert A[1, 1] == 2.6
def Build(self):
data = dict()
for x in range(self.X.GetNumElements()*self.U.GetNumElements()):
res = self.StepBuild(x)
data.update(res)
proba=sp.sparray(self.probs.shape,0.0,float,data)
print "Nb of non zero proba: ",len(data)
self.probs.SetData(proba)
def test_dense__custom_matrix__should_be_equiv_to_ndarray():
A = sp.sparray((2, 2), default=1.1)
A[0, 0] = 25.0
A[1, 0] = 7.5
A[1, 1] = 9.2
desired = np.array([[25.0, 1.1], [7.5, 9.2]])
assert np.allclose(A.dense(), desired, rtol=1e-13, atol=1e-15)
def test_mult__two_custom_matrices__should_multiply_elementwise():
A = sp.sparray((3, 3), default=1.1)
B = sp.sparray((3, 3), default=2.5)
A[0, 0] = 3.5
A[1, 0] = 5.7
B[2, 2] = 8.9
B[0, 0] = 2.1
C = A * B
assert C[0, 0] == 3.5 * 2.1
assert C[0, 1] == 1.1 * 2.5
assert C[0, 2] == 1.1 * 2.5
assert C[1, 0] == 5.7 * 2.5
assert C[1, 1] == 1.1 * 2.5
assert C[1, 2] == 1.1 * 2.5
assert C[2, 0] == 1.1 * 2.5
assert C[2, 1] == 1.1 * 2.5
assert C[2, 2] == 1.1 * 8.9
def test_getitem_setitem__indices_within_shape__should_work_correctly():
A = sp.sparray((3, 3))
A[0, 0] = 10.0
assert A[0, 0] == 10.0
A[2, 1] = 47.98
assert A[2, 1] == 47.98
A[0, 2] = -1.32
assert A[0, 2] == -1.32
def test_add__two_custom_matrices__should_be_sum():
A = sp.sparray((3, 3), default=2.52)
B = sp.sparray((3, 3), default=7.33)
A[0, 2] = 1.5
A[1, 1] = 3.0
B[0, 0] = 8.3
B[0, 2] = 0.3
C = A + B
assert C[0, 0] == 2.52 + 8.30
assert C[0, 1] == 2.52 + 7.33
assert C[0, 2] == 1.80
assert C[1, 0] == 2.52 + 7.33
assert C[1, 1] == 3.00 + 7.33
assert C[1, 2] == 2.52 + 7.33
assert C[2, 0] == 2.52 + 7.33
assert C[2, 1] == 2.52 + 7.33
assert C[2, 2] == 2.52 + 7.33
def test_sub__two_custom_matrics__should_subtract_correctly():
A = sp.sparray((3, 3), default=4.32)
B = sp.sparray((3, 3), default=1.10)
A[0, 0] = 7.83
B[0, 0] = 10.32
B[1, 2] = 3.3
C = A - B
assert C[0, 0] == 7.83 - 10.32
assert C[0, 1] == 4.32 - 1.10
assert C[0, 2] == 4.32 - 1.10
assert C[1, 0] == 4.32 - 1.10
assert C[1, 1] == 4.32 - 1.10
assert C[1, 2] == 4.32 - 3.30
assert C[2, 0] == 4.32 - 1.10
assert C[2, 1] == 4.32 - 1.10
assert C[2, 2] == 4.32 - 1.10
def test_div__two_custom_matrics__should_divide_elementwise():
A = sp.sparray((3, 3), default=1.1)
B = sp.sparray((3, 3), default=2.5)
A[0, 0] = 3.5
A[1, 0] = 5.7
B[2, 2] = 8.9
B[0, 0] = 2.1
C = A / B
assert C[0, 0] == 3.5 / 2.1
assert C[0, 1] == 1.1 / 2.5
assert C[0, 2] == 1.1 / 2.5
assert C[1, 0] == 5.7 / 2.5
assert C[1, 1] == 1.1 / 2.5
assert C[1, 2] == 1.1 / 2.5
assert C[2, 0] == 1.1 / 2.5
assert C[2, 1] == 1.1 / 2.5
assert C[2, 2] == 1.1 / 8.9
def test_mult__A_times_A_with_default_value__should_square_default_value():
A = sp.sparray((2, 1), default=7)
C = A * A
assert C[0, 0] == 7**2
assert C[0, 1] == 7**2
assert C[0, 2] == 7**2
assert C[1, 0] == 7**2
assert C[1, 1] == 7**2
assert C[1, 2] == 7**2
assert C[2, 0] == 7**2
assert C[2, 1] == 7**2
assert C[2, 2] == 7**2
def ParallelBuild(self):
if self.StepBuild == None:
print "Please define StepBuild"
return False
print "building distrib"
manager = Manager()
shareddict=manager.dict()
core_worker = cpu_count()
print "Creating %d workers"%(core_worker)
#cut the total range of element into subranges for each worker
split_range=np.array_split(np.array(range(self.X.GetNumElements()*self.U.GetNumElements())),core_worker)
#create workers
workers = [Process(target=self.ProcessWorker, args=(i, shareddict, split_range[i])) for i in range(core_worker)]
for each in workers:
# print "Starting Worker"
each.start()
print "Waiting for process..."
for each in workers:
each.join()
print "\tJoin() done"
proba=sp.sparray(self.probs.shape,0.0,float,shareddict)
# print shareddict
print "Nb of non zero proba: ",len(shareddict)
self.probs.SetData(proba)
edge_types = list(edge_types)
graphs = []
k = 0
for par in sents_par:
k += 1
print(k)
s = sents_par[par]
if any(i >= len(sents) for i in s):
break
graph_sents = [sent_graphs[i] for i in s]
graph_verts = [sent_nodes[i] for i in s]
verts = []
for i in range(len(graph_verts)):
for j in range(len(graph_verts[i])):
verts.append(graph_verts[i][j] + "-" + str(i))
adj = np.zeros((len(verts), len(verts), len(edge_types) + 1))
for i in range(len(graph_sents)):
for (v1, v2, label) in graph_sents[i].edges.data("label"):
label = edge_types.index(label)
v1_ind = sum(map(len, graph_verts[:i])) + graph_verts[i].index(v1)
v2_ind = sum(map(len, graph_verts[:i])) + graph_verts[i].index(v2)
adj[v1_ind, v2_ind, label] = 1.0
for i in range(len(graph_sents) - 1):
adj[sum(map(len, graph_verts[:i])),
sum(map(len, graph_verts[:i + 1])),
len(edge_types)] = 1.0
graphs.append((verts, sparray.sparray(adj.shape, adj)))
pickle.dump(graphs, open("graphs.pcl", "w+"))
def test_init__different_default_value__should_set_for_all_elements():
x = sp.sparray((3, 3), default=7)
assert x[2, 2] == 7
assert x[0, 2] == 7
assert x[1, 1] == 7
def test_sum__default_matrix__should_compute_sum_correctly():
A = sp.sparray((2, 2), default=1.3)
assert A.sum() == 2 * 2 * 1.3
def test_sum__custom_matrix__should_compute_sum_correctly():
A = sp.sparray((2, 2), default=1.3)
A[0, 1] = 99.0
assert A.sum() == (2 * 2 - 1) * 1.3 + 99.0
def test_dense__default_matrix__should_be_equiv_to_ndarray():
A = sp.sparray((2, 2), default=1.1)
desired = np.array([[1.1, 1.1], [1.1, 1.1]])
assert np.allclose(A.dense(), desired, rtol=1e-13, atol=1e-15)