Python _umath_tests.inner1d方法代码示例

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_broadcast(self):
        msg = "broadcast"
        a = np.arange(4).reshape((2, 1, 2))
        b = np.arange(4).reshape((1, 2, 2))
        assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
        msg = "extend & broadcast loop dimensions"
        b = np.arange(4).reshape((2, 2))
        assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
        # Broadcast in core dimensions should fail
        a = np.arange(8).reshape((4, 2))
        b = np.arange(4).reshape((4, 1))
        assert_raises(ValueError, umt.inner1d, a, b)
        # Extend core dimensions should fail
        a = np.arange(8).reshape((4, 2))
        b = np.array(7)
        assert_raises(ValueError, umt.inner1d, a, b)
        # Broadcast should fail
        a = np.arange(2).reshape((2, 1, 1))
        b = np.arange(3).reshape((3, 1, 1))
        assert_raises(ValueError, umt.inner1d, a, b) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_endian(self):
        msg = "big endian"
        a = np.arange(6, dtype='>i4').reshape((2, 3))
        assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
                           err_msg=msg)
        msg = "little endian"
        a = np.arange(6, dtype='<i4').reshape((2, 3))
        assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
                           err_msg=msg)

        # Output should always be native-endian
        Ba = np.arange(1, dtype='>f8')
        La = np.arange(1, dtype='<f8')
        assert_equal((Ba+Ba).dtype, np.dtype('f8'))
        assert_equal((Ba+La).dtype, np.dtype('f8'))
        assert_equal((La+Ba).dtype, np.dtype('f8'))
        assert_equal((La+La).dtype, np.dtype('f8'))

        assert_equal(np.absolute(La).dtype, np.dtype('f8'))
        assert_equal(np.absolute(Ba).dtype, np.dtype('f8'))
        assert_equal(np.negative(La).dtype, np.dtype('f8'))
        assert_equal(np.negative(Ba).dtype, np.dtype('f8')) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_get_signature(self):
        assert_equal(umt.inner1d.signature, "(i),(i)->()") 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_inner1d(self):
        a = np.arange(6).reshape((2, 3))
        assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1))
        a = np.arange(6)
        assert_array_equal(umt.inner1d(a, a), np.sum(a*a)) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_type_cast(self):
        msg = "type cast"
        a = np.arange(6, dtype='short').reshape((2, 3))
        assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
                           err_msg=msg)
        msg = "type cast on one argument"
        a = np.arange(6).reshape((2, 3))
        b = a + 0.1
        assert_array_almost_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1),
                                  err_msg=msg) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_output_argument(self):
        msg = "output argument"
        a = np.arange(12).reshape((2, 3, 2))
        b = np.arange(4).reshape((2, 1, 2)) + 1
        c = np.zeros((2, 3), dtype='int')
        umt.inner1d(a, b, c)
        assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
        c[:] = -1
        umt.inner1d(a, b, out=c)
        assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)

        msg = "output argument with type cast"
        c = np.zeros((2, 3), dtype='int16')
        umt.inner1d(a, b, c)
        assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
        c[:] = -1
        umt.inner1d(a, b, out=c)
        assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)

        msg = "output argument with incontiguous layout"
        c = np.zeros((2, 3, 4), dtype='int16')
        umt.inner1d(a, b, c[..., 0])
        assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
        c[:] = -1
        umt.inner1d(a, b, out=c[..., 0])
        assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import inner1d [as 别名]
def test_gufunc_override(self):
        # gufunc are just ufunc instances, but follow a different path,
        # so check __array_ufunc__ overrides them properly.
        class A(object):
            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                return self, ufunc, method, inputs, kwargs

        inner1d = ncu_tests.inner1d
        a = A()
        res = inner1d(a, a)
        assert_equal(res[0], a)
        assert_equal(res[1], inner1d)
        assert_equal(res[2], '__call__')
        assert_equal(res[3], (a, a))
        assert_equal(res[4], {})

        res = inner1d(1, 1, out=a)
        assert_equal(res[0], a)
        assert_equal(res[1], inner1d)
        assert_equal(res[2], '__call__')
        assert_equal(res[3], (1, 1))
        assert_equal(res[4], {'out': (a,)})

        # wrong number of arguments in the tuple is an error too.
        assert_raises(TypeError, inner1d, a, out='two')
        assert_raises(TypeError, inner1d, a, a, 'one', out='two')
        assert_raises(TypeError, inner1d, a, a, 'one', 'two')
        assert_raises(ValueError, inner1d, a, a, out=('one', 'two'))
        assert_raises(ValueError, inner1d, a, a, out=())