mxnet.ndarray.equal¶

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

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

For each element in input arrays, return 1(true) if corresponding elements are same, otherwise return 0(false).

Equivalent to lhs == rhs and mx.nd.broadcast_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 mxnet.ndarray.array) – First array to be compared.
rhs (scalar or mxnet.ndarray.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.equal(x,y).asnumpy()
array([[ 0.,  0.,  0.],
       [ 1.,  1.,  1.]], dtype=float32)
>>> (z == y).asnumpy()
array([[ 1.,  0.],
       [ 0.,  1.]], dtype=float32)