def test_min(self):
src = np.asarray([1, 1, 1, 2, 2, 5, 5, 10])
Assert.all_eq(spartan.min(spartan.from_numpy(src)).glom(), np.min(src))
src = np.arange(100).reshape(10, 10)
Assert.all_eq(
spartan.min(spartan.from_numpy(src), axis=1).glom(),
np.min(src, axis=1))
def _split_sample_and_label(merged_mb):
[s, l] = np.hsplit(merged_mb, [merged_mb.shape[1] - 1])
# change label to sparse representation
n = merged_mb.shape[0]
ll = np.zeros([n, 10], dtype=np.float)
ll[np.arange(n), l.astype(int).flat] = 1
return (spartan.from_numpy(s).evaluate(),
spartan.from_numpy(ll).evaluate())
def test_min(self):
src = np.asarray([1, 1, 1, 2, 2, 5, 5, 10])
Assert.all_eq(
spartan.min(spartan.from_numpy(src)).glom(),
np.min(src))
src = np.arange(100).reshape(10, 10)
Assert.all_eq(
spartan.min(spartan.from_numpy(src), axis=1).glom(),
np.min(src, axis=1))
def test_diag(self):
import random
dim = random.randint(0, 99)
np_array = np.random.randn(dim, dim)
Assert.all_eq(spartan.diag(spartan.from_numpy(np_array)).glom(), np.diag(np_array))
np_array2 = np.random.randn(dim, dim)
Assert.all_eq(spartan.diag(spartan.diag(spartan.from_numpy(np_array2))).glom(), np.diag(np.diag(np_array2)))
def test_diagonal(self):
np_2d = np.random.randn(2, 2)
Assert.all_eq(spartan.diagonal(spartan.from_numpy(np_2d)).glom(), np.diagonal(np_2d))
np_not_square = np.random.randn(15, 10)
Assert.all_eq(spartan.diagonal(spartan.from_numpy(np_not_square)).glom(), np.diagonal(np_not_square))
np_big = np.random.randn(16, 16)
Assert.all_eq(spartan.diagonal(spartan.from_numpy(np_big)).glom(), np.diagonal(np_big))
def test_diag(self):
import random
dim = random.randint(0, 99)
np_array = np.random.randn(dim, dim)
Assert.all_eq(
spartan.diag(spartan.from_numpy(np_array)).glom(),
np.diag(np_array))
np_array2 = np.random.randn(dim, dim)
Assert.all_eq(
spartan.diag(spartan.diag(spartan.from_numpy(np_array2))).glom(),
np.diag(np.diag(np_array2)))
def _split_sample(merged_mb):
r = merged_mb[0]
c = merged_mb[1]
density = 0.23588
data = merged_mb[2]
length = int(r * c * density) + 1
row = np.random.randint(r, size=length)
col = np.random.randint(c, size=length)
# s = scipy.sparse.rand(r, c, density=0.2358).toarray()
s = scipy.sparse.coo_matrix((data, (row, col)), shape=(r, c)).toarray()
l = np.random.randint(10, size=r)
ll = np.zeros([r, 10])
ll[range(r), l.astype(int).flat] = 1
return (sp.from_numpy(s).evaluate(), sp.from_numpy(ll).evaluate())
def test_maximum(self):
# Test arrays of equal length.
np_a = np.random.randn(10, 10)
np_b = np.random.randn(10, 10)
sp_a = spartan.from_numpy(np_a)
sp_b = spartan.from_numpy(np_b)
Assert.all_eq(
spartan.maximum(sp_a, sp_b).glom(),
np.maximum(np_a, np_b))
# Test broadcasting.
Assert.all_eq(
spartan.maximum(sp_a, 0).glom(),
np.maximum(np_a, 0))
def test_std_no_axis(self):
# 1d array.
np_1d = np.random.randn(10)
Assert.float_close(
spartan.std(spartan.from_numpy(np_1d)).glom(), np.std(np_1d))
# 2d array with auto-flattening.
np_2d = np.random.randn(10, 10)
Assert.float_close(
spartan.std(spartan.from_numpy(np_2d)).glom(), np.std(np_2d))
np_big = np.random.randn(17, 17)
Assert.float_close(
spartan.std(spartan.from_numpy(np_big)).glom(), np.std(np_big))
def test_diagonal(self):
np_2d = np.random.randn(2, 2)
Assert.all_eq(
spartan.diagonal(spartan.from_numpy(np_2d)).glom(),
np.diagonal(np_2d))
np_not_square = np.random.randn(15, 10)
Assert.all_eq(
spartan.diagonal(spartan.from_numpy(np_not_square)).glom(),
np.diagonal(np_not_square))
np_big = np.random.randn(16, 16)
Assert.all_eq(
spartan.diagonal(spartan.from_numpy(np_big)).glom(),
np.diagonal(np_big))
def test_std_no_axis(self):
# 1d array.
np_1d = np.random.randn(10)
Assert.float_close(
spartan.std(spartan.from_numpy(np_1d)).glom(),
np.std(np_1d))
# 2d array with auto-flattening.
np_2d = np.random.randn(10, 10)
Assert.float_close(
spartan.std(spartan.from_numpy(np_2d)).glom(),
np.std(np_2d))
np_big = np.random.randn(17, 17)
Assert.float_close(
spartan.std(spartan.from_numpy(np_big)).glom(),
np.std(np_big))
def test_concatenate(self):
np_1d = np.random.randn(10)
sp_1d = spartan.from_numpy(np_1d)
Assert.all_eq(spartan.concatenate(sp_1d, sp_1d).glom(),
np.concatenate((np_1d, np_1d)))
np_2d = np.arange(1024).reshape(32, 32)
sp_2d = spartan.from_numpy(np_2d)
Assert.all_eq(spartan.concatenate(sp_2d, sp_2d).glom(),
np.concatenate((np_2d, np_2d)))
Assert.all_eq(spartan.concatenate(sp_2d, sp_2d, 1).glom(),
np.concatenate((np_2d, np_2d), 1))
np_15x5 = np.random.randn(15, 5)
np_15x7 = np.random.randn(15, 7)
sp_15x5 = spartan.from_numpy(np_15x5)
sp_15x7 = spartan.from_numpy(np_15x7)
Assert.all_eq(spartan.concatenate(sp_15x5, sp_15x7, 1).glom(),
np.concatenate((np_15x5, np_15x7), 1))
def test_std_with_axis(self):
np_2d = np.random.randn(10, 10)
sp_2d = spartan.from_numpy(np_2d)
Assert.all_close(spartan.std(sp_2d, 0).glom(), np.std(np_2d, 0))
Assert.all_close(spartan.std(sp_2d, 1).glom(), np.std(np_2d, 1))
np_uneven_0 = np.random.randn(15, 13)
sp_uneven_0 = spartan.from_numpy(np_uneven_0)
Assert.all_close(spartan.std(sp_uneven_0, 0).glom(), np.std(np_uneven_0, 0))
Assert.all_close(spartan.std(sp_uneven_0, 1).glom(), np.std(np_uneven_0, 1))
np_uneven_1 = np.random.randn(13, 15)
sp_uneven_1 = spartan.from_numpy(np_uneven_1)
Assert.all_close(spartan.std(sp_uneven_1, 0).glom(), np.std(np_uneven_1, 0))
Assert.all_close(spartan.std(sp_uneven_1, 1).glom(), np.std(np_uneven_1, 1))
np_big = np.random.randn(17, 17)
sp_big = spartan.from_numpy(np_big)
Assert.all_close(spartan.std(sp_big, 0).glom(), np.std(np_big, 0))
Assert.all_close(spartan.std(sp_big, 1).glom(), np.std(np_big, 1))
def test_concatenate(self):
np_1d = np.random.randn(10)
sp_1d = spartan.from_numpy(np_1d)
Assert.all_eq(
spartan.concatenate(sp_1d, sp_1d).glom(),
np.concatenate((np_1d, np_1d)))
np_2d = np.arange(1024).reshape(32, 32)
sp_2d = spartan.from_numpy(np_2d)
Assert.all_eq(
spartan.concatenate(sp_2d, sp_2d).glom(),
np.concatenate((np_2d, np_2d)))
Assert.all_eq(
spartan.concatenate(sp_2d, sp_2d, 1).glom(),
np.concatenate((np_2d, np_2d), 1))
np_15x5 = np.random.randn(15, 5)
np_15x7 = np.random.randn(15, 7)
sp_15x5 = spartan.from_numpy(np_15x5)
sp_15x7 = spartan.from_numpy(np_15x7)
Assert.all_eq(
spartan.concatenate(sp_15x5, sp_15x7, 1).glom(),
np.concatenate((np_15x5, np_15x7), 1))
def test_std_with_axis(self):
np_2d = np.random.randn(10, 10)
sp_2d = spartan.from_numpy(np_2d)
Assert.all_close(spartan.std(sp_2d, 0).glom(), np.std(np_2d, 0))
Assert.all_close(spartan.std(sp_2d, 1).glom(), np.std(np_2d, 1))
np_uneven_0 = np.random.randn(15, 13)
sp_uneven_0 = spartan.from_numpy(np_uneven_0)
Assert.all_close(
spartan.std(sp_uneven_0, 0).glom(), np.std(np_uneven_0, 0))
Assert.all_close(
spartan.std(sp_uneven_0, 1).glom(), np.std(np_uneven_0, 1))
np_uneven_1 = np.random.randn(13, 15)
sp_uneven_1 = spartan.from_numpy(np_uneven_1)
Assert.all_close(
spartan.std(sp_uneven_1, 0).glom(), np.std(np_uneven_1, 0))
Assert.all_close(
spartan.std(sp_uneven_1, 1).glom(), np.std(np_uneven_1, 1))
np_big = np.random.randn(17, 17)
sp_big = spartan.from_numpy(np_big)
Assert.all_close(spartan.std(sp_big, 0).glom(), np.std(np_big, 0))
Assert.all_close(spartan.std(sp_big, 1).glom(), np.std(np_big, 1))
def test_bincount(self):
src = np.asarray([1, 1, 1, 2, 2, 5, 5, 10])
Assert.all_eq(
spartan.bincount(spartan.from_numpy(src)).glom(),
np.bincount(src))
def test_sum_scan(self):
source = np.ones(ARRAY_SIZE, np.float32)
self.all_eq_helper(from_numpy(source, tile_hint), source)
for axis in range(len(ARRAY_SIZE)):
self.all_eq_helper(from_numpy(source, tile_hint), source, axis)
def test_bincount(self):
src = np.asarray([1, 1, 1, 2, 2, 5, 5, 10])
Assert.all_eq(
spartan.bincount(spartan.from_numpy(src)).glom(), np.bincount(src))
def test_sum_scan(self):
source = np.ones(ARRAY_SIZE, np.float32)
self.all_eq_helper(from_numpy(source, tile_hint), source)
for axis in range(len(ARRAY_SIZE)):
self.all_eq_helper(from_numpy(source, tile_hint), source, axis)
