Python _umath_tests.matrix_multiply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 matrix_multiply [as 别名]
def test_signature4(self):
        # matrix_multiply signature from _umath_tests
        enabled, num_dims, ixs, flags, sizes = umt.test_signature(
            2, 1, "(n,k),(k,m)->(n,m)")
        assert_equal(enabled, 1)
        assert_equal(num_dims, (2, 2, 2))
        assert_equal(ixs, (0, 1, 1, 2, 0, 2))
        assert_equal(flags, (self.size_inferred,)*3)
        assert_equal(sizes, (-1, -1, -1)) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import matrix_multiply [as 别名]
def test_matrix_multiply_umath_empty(self):
        res = umt.matrix_multiply(np.ones((0, 10)), np.ones((10, 0)))
        assert_array_equal(res, np.zeros((0, 0)))
        res = umt.matrix_multiply(np.ones((10, 0)), np.ones((0, 10)))
        assert_array_equal(res, np.zeros((10, 10))) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import matrix_multiply [as 别名]
def test_axis_argument(self):
        # inner1d signature: '(i),(i)->()'
        inner1d = umt.inner1d
        a = np.arange(27.).reshape((3, 3, 3))
        b = np.arange(10., 19.).reshape((3, 1, 3))
        c = inner1d(a, b)
        assert_array_equal(c, (a * b).sum(-1))
        c = inner1d(a, b, axis=-1)
        assert_array_equal(c, (a * b).sum(-1))
        out = np.zeros_like(c)
        d = inner1d(a, b, axis=-1, out=out)
        assert_(d is out)
        assert_array_equal(d, c)
        c = inner1d(a, b, axis=0)
        assert_array_equal(c, (a * b).sum(0))
        # Sanity checks on innerwt and cumsum.
        a = np.arange(6).reshape((2, 3))
        b = np.arange(10, 16).reshape((2, 3))
        w = np.arange(20, 26).reshape((2, 3))
        assert_array_equal(umt.innerwt(a, b, w, axis=0),
                           np.sum(a * b * w, axis=0))
        assert_array_equal(umt.cumsum(a, axis=0), np.cumsum(a, axis=0))
        assert_array_equal(umt.cumsum(a, axis=-1), np.cumsum(a, axis=-1))
        out = np.empty_like(a)
        b = umt.cumsum(a, out=out, axis=0)
        assert_(out is b)
        assert_array_equal(b, np.cumsum(a, axis=0))
        b = umt.cumsum(a, out=out, axis=1)
        assert_(out is b)
        assert_array_equal(b, np.cumsum(a, axis=-1))
        # Check errors.
        # Cannot pass in both axis and axes.
        assert_raises(TypeError, inner1d, a, b, axis=0, axes=[0, 0])
        # Not an integer.
        assert_raises(TypeError, inner1d, a, b, axis=[0])
        # more than 1 core dimensions.
        mm = umt.matrix_multiply
        assert_raises(TypeError, mm, a, b, axis=1)
        # Output wrong size in axis.
        out = np.empty((1, 2, 3), dtype=a.dtype)
        assert_raises(ValueError, umt.cumsum, a, out=out, axis=0)
        # Regular ufuncs should not accept axis.
        assert_raises(TypeError, np.add, 1., 1., axis=0) 

# 需要导入模块: from numpy.core import _umath_tests [as 别名]
# 或者: from numpy.core._umath_tests import matrix_multiply [as 别名]
def compare_matrix_multiply_results(self, tp):
        d1 = np.array(np.random.rand(2, 3, 4), dtype=tp)
        d2 = np.array(np.random.rand(2, 3, 4), dtype=tp)
        msg = "matrix multiply on type %s" % d1.dtype.name

        def permute_n(n):
            if n == 1:
                return ([0],)
            ret = ()
            base = permute_n(n-1)
            for perm in base:
                for i in range(n):
                    new = perm + [n-1]
                    new[n-1] = new[i]
                    new[i] = n-1
                    ret += (new,)
            return ret

        def slice_n(n):
            if n == 0:
                return ((),)
            ret = ()
            base = slice_n(n-1)
            for sl in base:
                ret += (sl+(slice(None),),)
                ret += (sl+(slice(0, 1),),)
            return ret

        def broadcastable(s1, s2):
            return s1 == s2 or s1 == 1 or s2 == 1

        permute_3 = permute_n(3)
        slice_3 = slice_n(3) + ((slice(None, None, -1),)*3,)

        ref = True
        for p1 in permute_3:
            for p2 in permute_3:
                for s1 in slice_3:
                    for s2 in slice_3:
                        a1 = d1.transpose(p1)[s1]
                        a2 = d2.transpose(p2)[s2]
                        ref = ref and a1.base is not None
                        ref = ref and a2.base is not None
                        if (a1.shape[-1] == a2.shape[-2] and
                                broadcastable(a1.shape[0], a2.shape[0])):
                            assert_array_almost_equal(
                                umt.matrix_multiply(a1, a2),
                                np.sum(a2[..., np.newaxis].swapaxes(-3, -1) *
                                       a1[..., np.newaxis,:], axis=-1),
                                err_msg=msg + ' %s %s' % (str(a1.shape),
                                                          str(a2.shape)))

        assert_equal(ref, True, err_msg="reference check")