Python numpy.greater_equal方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0):
  """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2.

  For each box in boxlist1, we want its IOA to be more than minoverlap with
  at least one of the boxes in boxlist2. If it does not, we remove it.

  Args:
    boxlist1: BoxList holding N boxes.
    boxlist2: BoxList holding M boxes.
    minoverlap: Minimum required overlap between boxes, to count them as
                overlapping.

  Returns:
    A pruned boxlist with size [N', 4].
  """
  intersection_over_area = ioa(boxlist2, boxlist1)  # [M, N] tensor
  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
  keep_inds = np.nonzero(keep_bool)[0]
  new_boxlist1 = gather(boxlist1, keep_inds)
  return new_boxlist1 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0):
  """Prunes the boxes in list1 that overlap less than thresh with list2.

  For each mask in box_mask_list1, we want its IOA to be more than minoverlap
  with at least one of the masks in box_mask_list2. If it does not, we remove
  it. If the masks are not full size image, we do the pruning based on boxes.

  Args:
    box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks.
    box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks.
    minoverlap: Minimum required overlap between boxes, to count them as
                overlapping.

  Returns:
    A pruned box_mask_list with size [N', 4].
  """
  intersection_over_area = ioa(box_mask_list2, box_mask_list1)  # [M, N] tensor
  intersection_over_area = np.amax(intersection_over_area, axis=0)  # [N] tensor
  keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap))
  keep_inds = np.nonzero(keep_bool)[0]
  new_box_mask_list1 = gather(box_mask_list1, keep_inds)
  return new_box_mask_list1 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def test_datetime_compare_nat(self):
        dt_nat = np.datetime64('NaT', 'D')
        dt_other = np.datetime64('2000-01-01')
        td_nat = np.timedelta64('NaT', 'h')
        td_other = np.timedelta64(1, 'h')

        for op in [np.equal, np.less, np.less_equal,
                   np.greater, np.greater_equal]:
            assert_(not op(dt_nat, dt_nat))
            assert_(not op(dt_nat, dt_other))
            assert_(not op(dt_other, dt_nat))

            assert_(not op(td_nat, td_nat))
            assert_(not op(td_nat, td_other))
            assert_(not op(td_other, td_nat))

        assert_(np.not_equal(dt_nat, dt_nat))
        assert_(np.not_equal(dt_nat, dt_other))
        assert_(np.not_equal(dt_other, dt_nat))

        assert_(np.not_equal(td_nat, td_nat))
        assert_(np.not_equal(td_nat, td_other))
        assert_(np.not_equal(td_other, td_nat)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def equal(x1, x2):
    """
    Return (x1 == x2) element-wise.

    Unlike `numpy.equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    not_equal, greater_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '==', True) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def not_equal(x1, x2):
    """
    Return (x1 != x2) element-wise.

    Unlike `numpy.not_equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, greater_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '!=', True) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def greater_equal(x1, x2):
    """
    Return (x1 >= x2) element-wise.

    Unlike `numpy.greater_equal`, this comparison is performed by
    first stripping whitespace characters from the end of the string.
    This behavior is provided for backward-compatibility with
    numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, less_equal, greater, less
    """
    return compare_chararrays(x1, x2, '>=', True) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def greater(x1, x2):
    """
    Return (x1 > x2) element-wise.

    Unlike `numpy.greater`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, less_equal, less
    """
    return compare_chararrays(x1, x2, '>', True) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def less(x1, x2):
    """
    Return (x1 < x2) element-wise.

    Unlike `numpy.greater`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, less_equal, greater
    """
    return compare_chararrays(x1, x2, '<', True) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def less_equal(x1, x2):
    """
    Return (x1 <= x2) element-wise.

    Unlike `numpy.less_equal`, this comparison is performed by first
    stripping whitespace characters from the end of the string.  This
    behavior is provided for backward-compatibility with numarray.

    Parameters
    ----------
    x1, x2 : array_like of str or unicode
        Input arrays of the same shape.

    Returns
    -------
    out : ndarray or bool
        Output array of bools, or a single bool if x1 and x2 are scalars.

    See Also
    --------
    equal, not_equal, greater_equal, greater, less
    """
    return compare_chararrays(x1, x2, '<=', True) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import greater_equal [as 别名]
def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod
            ]

        # These functions still return NotImplemented. Will be fixed in
        # future.
        # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]

        a = np.array('1')
        b = 1
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)