rm garbage in test
@@ -5,63 +5,6 @@ import numpy
import cupy_testing as testing
-"""
-class TestA(unittest.TestCase):
-
- shape_a = (1, 1)
- shape_b = (3, 4)
- shape_c = (1, 1)
- subscripts = 'ij,jk,kl'
- optimize = True
-
- @testing.for_all_dtypes_combination(['dtype_x', 'dtype_y'], no_float16=True)
- # @testing.numpy_cupy_allclose(contiguous_check=False)
- def test_einsum_ternary(self, dtype_x, dtype_y):
- a = testing.shaped_arange(self.shape_a, numpy, dtype_x)
- b = testing.shaped_arange(self.shape_b, numpy, dtype_x)
- c = testing.shaped_arange(self.shape_c, numpy, dtype_y)
- return numpy.einsum(self.subscripts, a, b, c, optimize=self.optimize)
-
-
-a = np.array([[1.+1.j]])
-b = np.array([[ 1. +1.j, 2. +2.j, 3. +3.j, 4. +4.j],
- [ 5. +5.j, 6. +6.j, 7. +7.j, 8. +8.j],
- [ 9. +9.j, 10.+10.j, 11.+11.j, 12.+12.j]])
-c = np.array([[1]], dtype=np.int32)
-np.einsum('ij,jk,kl', a, b, c, optimize=True)
-# This raises error, due to numpy issue #10930.
-"""
-
-
-class TestB(unittest.TestCase):
-
- shape_a = (3, 4, 2)
- shape_b = (1, 1, 2)
- subscripts = 'ijk,jil->kl'
- # optimize = True
-
- @testing.for_all_dtypes_combination(['dtype_a', 'dtype_b'])
- # @testing.numpy_cupy_allclose(contiguous_check=False)
- def test_einsum_np(self, dtype_a, dtype_b):
- a = testing.shaped_arange(self.shape_a, numpy, dtype_a)
- b = testing.shaped_arange(self.shape_b, numpy, dtype_b)
- # c = testing.shaped_arange(self.shape_c, numpy, dtype_y)
- return numpy.einsum(self.subscripts, a, b)
-
-
-class TestC(unittest.TestCase):
- @testing.numpy_cupy_allclose(contiguous_check=False)
- def test1(self, xp):
- shape_a = (1, 1, 1, 1, 1, 1)
- dtype_a = numpy.complex128
- shape_b = (2, 3, 2, 2)
- dtype_b = numpy.int8
- subscripts = '...lmn,lmno->...o'
- a = testing.shaped_arange(shape_a, xp, dtype_a)
- b = testing.shaped_arange(shape_b, xp, dtype_b)
- return xp.einsum(subscripts, a, b, optimize=False)
-
-
def _dec_shape(shape, dec):
return tuple(1 if s == 1 else max(0, s - dec) for s in shape)
@@ -511,6 +454,3 @@ class TestEinSumLarge(unittest.TestCase):
def test_einsum_memory_limit(self, xp):
# I hope there's no problem with np.einsum for these cases...
return xp.einsum(*self.operands, optimize=('optimal', 20))
-
-
-testing.run_module(__name__, __file__)
