jax.numpy.alltrueΒΆ

jax.numpy.alltrue(a, axis=None, dtype=None, out=None, keepdims=False)ΒΆ

Test whether all array elements along a given axis evaluate to True.

LAX-backend implementation of all(). Original docstring below.

Parameters
  • a (array_like) – Input array or object that can be converted to an array.

  • axis (None or int or tuple of ints, optional) – Axis or axes along which a logical AND reduction is performed. The default (axis=None) is to perform a logical AND over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis.

  • out (ndarray, optional) – Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if dtype(out) is float, the result will consist of 0.0’s and 1.0’s). See ufuncs-output-type for more details.

  • keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

Returns

all – A new boolean or array is returned unless out is specified, in which case a reference to out is returned.

Return type

ndarray, bool

See also

ndarray.all()

equivalent method

any()

Test whether any element along a given axis evaluates to True.

Notes

Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero.

Examples

>>> np.all([[True,False],[True,True]])
False
>>> np.all([[True,False],[True,True]], axis=0)
array([ True, False])
>>> np.all([-1, 4, 5])
True
>>> np.all([1.0, np.nan])
True
>>> o=np.array(False)
>>> z=np.all([-1, 4, 5], out=o)
>>> id(z), id(o), z
(28293632, 28293632, array(True)) # may vary