mxnet.ndarray.logical_and¶

mxnet.ndarray.logical_and(lhs, rhs)[source]¶

Returns the result of element-wise logical and comparison operation with broadcasting.

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

Equivalent to lhs and rhs and mx.nd.broadcast_logical_and(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 mxnet.ndarray.array) – First input of the function.
rhs (scalar or mxnet.ndarray.array) – Second input of the function. 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)
>>> mx.nd.logical_and(x, 1).asnumpy()
array([[ 1.,  1.,  1.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> mx.nd.logical_and(x, y).asnumpy()
array([[ 0.,  0.,  0.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> mx.nd.logical_and(z, y).asnumpy()
array([[ 0.,  0.],
       [ 0.,  1.]], dtype=float32)