Table Of Contents
Table Of Contents

lesser_equal

mxnet.ndarray.lesser_equal(lhs, rhs)[source]

Returns the result of element-wise lesser than or equal to (<=) comparison operation with broadcasting.

For each element in input arrays, return 1(true) if lhs elements are lesser than equal to rhs, otherwise return 0(false).

Equivalent to lhs <= rhs and mx.nd.broadcast_lesser_equal(lhs, rhs).

Note

If the corresponding dimensions of two arrays have the same size or one of them has size 1, then the arrays are broadcastable to a common shape.

Parameters:
  • lhs (scalar or array) – First array to be compared.
  • rhs (scalar or array) – Second array to be compared. If lhs.shape != rhs.shape, they must be broadcastable to a common shape.
Returns:

Output array of boolean values.

Return type:

NDArray

Examples

>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
       [ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0.,  1.]], dtype=float32)
>>> (x <= 1).asnumpy()
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> (x <= y).asnumpy()
array([[ 0.,  0.,  0.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> mx.nd.lesser_equal(x, y).asnumpy()
array([[ 0.,  0.,  0.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> (z <= y).asnumpy()
array([[ 1.,  0.],
       [ 1.,  1.]], dtype=float32)