def setUp(self):
self.nullT = dc.array(0)
self.zeros = dc.zeros(2, 3).asTypeInt()
self.ones = dc.ones(2, 3).asTypeInt()
self.f0_4 = dc.arange(5)
self.f5_9 = dc.arange(10, 5)
self.i0_4 = self.f0_4.asTypeInt()
self.i5_9 = self.f5_9.asTypeInt()
self.b0_4 = self.f0_4.asTypeBool()
self.b5_9 = self.f5_9.asTypeBool()
def setUp(self):
self.np_bool_0_4 = np.arange(5).astype(np.bool)
self.np_bool_5_9 = np.arange(5, 10).astype(np.bool)
self.np_int_0_4 = np.arange(5).astype(np.int)
self.np_int_5_9 = np.arange(5, 10).astype(np.int)
self.np_float_0_4 = np.arange(5).astype(np.float)
self.np_float_5_9 = np.arange(5, 10).astype(np.float)
self.np_double_0_4 = np.arange(5).astype(np.double)
self.np_double_5_9 = np.arange(5, 10).astype(np.double)
self.dc_bool_0_4 = dc.arange(5).asTypeBool()
self.dc_bool_5_9 = dc.arange(5, 10).asTypeBool()
self.dc_int_0_4 = dc.arange(5).asTypeInt()
self.dc_int_5_9 = dc.arange(5, 10).asTypeInt()
self.dc_float_0_4 = dc.arange(5).asTypeFloat()
self.dc_float_5_9 = dc.arange(5, 10).asTypeFloat()
self.dc_double_0_4 = dc.arange(5).asTypeDouble()
self.dc_double_5_9 = dc.arange(5, 10).asTypeDouble()
def test_data(self):
# confirm type as class tuple.
a = dc.zeros(2, 3).astype('int')
adata = a.data()
assert type(adata) == type((1, ))
# load new data
new_data_list = [10, 11, 12, 13, 14, 15]
a.load(dc.ivec(new_data_list))
assert a[0] == 10
# load one element with flat index
a[0] = 777
assert a[0] == 777
# reshape, fetch and load with multi indices
a = dc.arange(12).astype('int')
a.reshape(dc.lvec([2, 2, 3]))
assert a[0, 1, 1] == 4
a[1, 1, 1] = 200
assert a[1, 1, 1] == 200
# negative test
try:
# This throws ValueError
print(a[0, 0, 9, 9, 9])
except ValueError as e:
assert e
def test_create(self):
# null tensor test
a = dc.array(0)
assert a.isnull() == True
assert a.empty() == True
# test assignment is shallow copy of memory
b = a
assert a.sameas(b) == True
assert a.identifier() == b.identifier()
# tensor without initiliaztion
a = dc.array(2, 3, 4, 5)
assert a.length() == 120
# tensor random initiliaztion
a = dc.random(2, 3, 4, 5)
assert a.length() == 120
# tensor without initiliaztion
a = dc.empty(2, 3, 4, 5)
assert a.length() == 120
# zero tensor
a = dc.zeros(2, 3, 4, 5)
assert np.array(list(a.data())).sum().astype(np.int) == 0
# one tensor
a = dc.ones(2, 3, 4, 5)
assert np.array(list(a.data())).sum().astype(np.int) == 120
# tensor from python list
l1D = [1, 3, 5]
a = dc.array(l1D).astype('int')
np.testing.assert_equal(np.array(l1D), np.array(list(a.data())))
# tensor from python list of lists
l2D = [[1, 3, 5], [2, 4, 6]]
a = dc.array(l2D).astype('int')
assert a.rank() == 2
assert a.shape() == (2, 3)
np.testing.assert_equal(np.array(l2D).flatten(), \
np.array(list(a.data())))
# copy tensor
b = a.copy()
assert a.sameas(b) == False
assert a.identifier() != b.identifier()
# arange
a = dc.arange(10)
assert a.length() == 10
# add start and step
a = dc.arange(10, 5, 3).astype('int')
assert a.data() == (5, 8)
# swap start and stop.
a = dc.arange(5, 10, 3).astype('int')
assert a.data() == (5, 8)
t3.load(data)
#print(t3.to_string())
arr = dc.array([1, 2])
#print(arr)
arr2D = dc.array([[1, 2], [10, 20]])
#print(arr2D)
arrRand = dc.random(2, 3);
#print(arrRand)
empty = dc.empty(3, 2);
#print(empty)
zeros = dc.zeros(2, 2);
#print(zeros);
ones = dc.ones(2, 2);
#print(ones)
ranges = dc.arange(15, 3, 2)
#print(ranges)
def test_multiply(a,b):
c = dc.multiply(a, b)
#print(c)
#3D MatMul Test1
a = dc.make_tensor(2, 2, 2)
b = dc.make_tensor(2, 2, 2)
adata = dc.fvec([1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
bdata = dc.fvec([8.0,7.0,6.0,5.0,4.0,3.0,2.0,1.0])
a.load(adata)
b.load(bdata)