Python umath_tests.inner1d方法代碼示例

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
def HausdorffDist(A,B):
    # Hausdorf Distance: Compute the Hausdorff distance between two point
    # clouds.
    # Let A and B be subsets of metric space (Z,dZ),
    # The Hausdorff distance between A and B, denoted by dH(A,B),
    # is defined by:
    # dH(A,B) = max(h(A,B),h(B,A)),
    # where h(A,B) = max(min(d(a,b))
    # and d(a,b) is a L2 norm
    # dist_H = hausdorff(A,B)
    # A: First point sets (MxN, with M observations in N dimension)
    # B: Second point sets (MxN, with M observations in N dimension)
    # ** A and B may have different number of rows, but must have the same
    # number of columns.
    #
    # Edward DongBo Cui; Stanford University; 06/17/2014

    # Find pairwise distance
    D_mat = np.sqrt(inner1d(A,A)[np.newaxis].T + inner1d(B,B)-2*(np.dot(A,B.T)))
    # Find DH
    dH = np.max(np.array([np.max(np.min(D_mat,axis=0)),np.max(np.min(D_mat,axis=1))]))
    return(dH) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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')) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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)
        msg = "broadcast in core dimensions"
        a = np.arange(8).reshape((4, 2))
        b = np.arange(4).reshape((4, 1))
        assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
        msg = "extend & broadcast core and loop dimensions"
        a = np.arange(8).reshape((4, 2))
        b = np.array(7)
        assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
        msg = "broadcast should fail"
        a = np.arange(2).reshape((2, 1, 1))
        b = np.arange(3).reshape((3, 1, 1))
        try:
            ret = umt.inner1d(a, b)
            assert_equal(ret, None, err_msg=msg)
        except ValueError: None 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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')) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
def ModHausdorffDist(A,B):
    #This function computes the Modified Hausdorff Distance (MHD) which is
    #proven to function better than the directed HD as per Dubuisson et al.
    #in the following work:
    #
    #M. P. Dubuisson and A. K. Jain. A Modified Hausdorff distance for object
    #matching. In ICPR94, pages A:566-568, Jerusalem, Israel, 1994.
    #http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=576361
    #
    #The function computed the forward and reverse distances and outputs the
    #maximum/minimum of both.
    #Optionally, the function can return forward and reverse distance.
    #
    #Format for calling function:
    #
    #[MHD,FHD,RHD] = ModHausdorffDist(A,B);
    #
    #where
    #MHD = Modified Hausdorff Distance.
    #FHD = Forward Hausdorff Distance: minimum distance from all points of B
    #      to a point in A, averaged for all A
    #RHD = Reverse Hausdorff Distance: minimum distance from all points of A
    #      to a point in B, averaged for all B
    #A -> Point set 1, [row as observations, and col as dimensions]
    #B -> Point set 2, [row as observations, and col as dimensions]
    #
    #No. of samples of each point set may be different but the dimension of
    #the points must be the same.
    #
    #Edward DongBo Cui Stanford University; 06/17/2014

    # Find pairwise distance
    D_mat = np.sqrt(inner1d(A,A)[np.newaxis].T + inner1d(B,B)-2*(np.dot(A,B.T)))
    # Calculating the forward HD: mean(min(each col))
    FHD = np.mean(np.min(D_mat,axis=1))
    # Calculating the reverse HD: mean(min(each row))
    RHD = np.mean(np.min(D_mat,axis=0))
    # Calculating mhd
    MHD = np.max(np.array([FHD, RHD]))
    return(MHD, FHD, RHD) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
def test_get_signature(self):
        assert_equal(umt.inner1d.signature, "(i),(i)->()") 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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)) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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=()) 

# 需要導入模塊: from numpy.core import umath_tests [as 別名]
# 或者: from numpy.core.umath_tests import inner1d [as 別名]
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, a), np.sum(a*a, axis=-1),
            err_msg=msg)