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celinelee
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python_fn_calls

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self.assertEqual(x.dtype, self.dtype)
self.assertEqual(x.format, self.format)
cupy.random.RandomState(1)
getattr(sparse, self.random_method)
self.assertTrue((x.toarray()
y.toarray()
all()
test_random_with_data_rvs(self)
pytest.skip('cupyx.scipy.sparse.rand does not support data_rvs')
mock.MagicMock(side_effect=cupy.zeros)
getattr(sparse, self.random_method)
self.assertEqual(x.shape, (3, 4)
self.assertEqual(x.dtype, self.dtype)
self.assertEqual(x.format, self.format)
self.assertEqual(data_rvs.call_count, 1)
self.assertIsInstance(data_rvs.call_args[0][0], int)
testing.with_requires('scipy')
TestRandomInvalidArgument(unittest.TestCase)
testing.numpy_cupy_raises(sp_name='sp', accept_error=ValueError)
test_too_small_density(self, xp, sp)
sp.random(3, 4, density=-0.1)
testing.numpy_cupy_raises(sp_name='sp', accept_error=ValueError)
test_too_large_density(self, xp, sp)
sp.random(3, 4, density=1.1)
testing.numpy_cupy_raises(sp_name='sp', accept_error=NotImplementedError)
test_invalid_dtype(self, xp, sp)
sp.random(3, 4, dtype='i')
testing.with_requires('scipy')
TestDiags(unittest.TestCase)
testing.numpy_cupy_allclose(sp_name='sp')
test_diags_scalar_offset(self, xp, sp)
xp.arange(16)
self.assertIsInstance(x, sp.spmatrix)
self.assertEqual(x.format, self.format)
testing.numpy_cupy_allclose(sp_name='sp')
test_diags_single_element_lists(self, xp, sp)
xp.arange(16)
self.assertIsInstance(x, sp.spmatrix)
self.assertEqual(x.format, self.format)
testing.numpy_cupy_allclose(sp_name='sp')
test_diags_multiple(self, xp, sp)
xp.arange(15)
xp.arange(16)
xp.arange(15)
xp.arange(13)
self.assertIsInstance(x, sp.spmatrix)
self.assertEqual(x.format, self.format)
testing.numpy_cupy_allclose(sp_name='sp')
test_diags_offsets_as_array(self, xp, sp)
xp.arange(15)
xp.arange(16)
xp.arange(15)
xp.arange(13)
xp.array([-1, 0, 1, 3])
self.assertIsInstance(x, sp.spmatrix)
self.assertEqual(x.format, self.format)
testing.numpy_cupy_allclose(sp_name='sp')
test_diags_non_square(self, xp, sp)
xp.arange(5)
xp.arange(3)
self.assertIsInstance(x, sp.spmatrix)
self.assertEqual(x.format, self.format)
normal_std(x)
x.std()
np.sqrt((len(x)
len(x)
Data_utility(object)
__init__(self, dSet, train, valid, cuda, horizon, window, normalize = 2)
np.zeros(self.rawdat.shape)
np.ones(self.m)
self._normalized(normalize)
self._split(int(train * self.n)
int((train+valid)
torch.from_numpy(self.scale)
float()
self.scale.expand(self.test[1].size(0)
self.scale.cuda()
Variable(self.scale)
normal_std(tmp)
torch.mean(torch.abs(tmp - torch.mean(tmp)
_normalized(self, normalize)
if (normalize == 0)
if (normalize == 1)
np.max(self.rawdat)
row(sensor)
if (normalize == 2)
range(self.m)
np.max(np.abs(self.rawdat[:,i])
np.max(np.abs(self.rawdat[:,i])
_split(self, train, valid, test)
range(self.P+self.h-1, train)
range(train, valid)
range(valid, self.n)
self._batchify(train_set, self.h)
self._batchify(valid_set, self.h)
self._batchify(test_set, self.h)
_batchify(self, idx_set, horizon)
len(idx_set)
torch.zeros((n,self.P,self.m)
torch.zeros((n,self.m)