mars.tensor.nonzero¶

mars.tensor.
nonzero
(a)[source]¶ Return the indices of the elements that are nonzero.
Returns a tuple of tensors, one for each dimension of a, containing the indices of the nonzero elements in that dimension. The values in a are always tested and returned. The corresponding nonzero values can be obtained with:
a[nonzero(a)]
To group the indices by element, rather than dimension, use:
transpose(nonzero(a))
The result of this is always a 2D array, with a row for each nonzero element.
 Parameters
a (array_like) – Input tensor.
 Returns
tuple_of_arrays – Indices of elements that are nonzero.
 Return type
See also
flatnonzero
Return indices that are nonzero in the flattened version of the input tensor.
Tensor.nonzero
Equivalent tensor method.
count_nonzero
Counts the number of nonzero elements in the input tensor.
Examples
>>> import mars.tensor as mt
>>> x = mt.array([[1,0,0], [0,2,0], [1,1,0]]) >>> x.execute() array([[1, 0, 0], [0, 2, 0], [1, 1, 0]]) >>> mt.nonzero(x).execute() (array([0, 1, 2, 2]), array([0, 1, 0, 1]))
>>> x[mt.nonzero(x)].execute() # TODO(jisheng): accomplish this after fancy indexing is supported
>>> mt.transpose(mt.nonzero(x)).execute() # TODO(jisheng): accomplish this later
A common use for
nonzero
is to find the indices of an array, where a condition is True. Given an array a, the condition a > 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of the a where the condition is true.>>> a = mt.array([[1,2,3],[4,5,6],[7,8,9]]) >>> (a > 3).execute() array([[False, False, False], [ True, True, True], [ True, True, True]]) >>> mt.nonzero(a > 3).execute() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The
nonzero
method of the boolean array can also be called.>>> (a > 3).nonzero().execute() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))