flopscope.numpy.array_equiv
fnp.array_equiv(a1, a2)[flopscope source][numpy source]
Returns True if input arrays are shape consistent and all elements equal.
Adapted from NumPy docs np.array_equiv
Cost
per-operation
Flopscope Context
Element-wise equivalence; cost = numel(a).
Shape consistent means they are either the same shape, or one input array can be broadcasted to create the same shape as the other one.
Parameters
- a1, a2:array_like
Input arrays.
Returns
- out:bool
True if equivalent, False otherwise.
Examples
>>> import flopscope.numpy as fnp
>>> flops.array_equiv([1, 2], [1, 2])
True
>>> flops.array_equiv([1, 2], [1, 3])
FalseShowing the shape equivalence:
>>> flops.array_equiv([1, 2], [[1, 2], [1, 2]])
True
>>> flops.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
False>>> flops.array_equiv([1, 2], [[1, 2], [1, 3]])
False