bayesvalidrox.surrogate_models.glexindex.cross_truncate

bayesvalidrox.surrogate_models.glexindex.cross_truncate(indices, bound, norm)

Truncate of indices using L_p norm. .. math:

L_p(x) = sum_i |x_i/b_i|^p ^{1/p} leq 1

where \(b_i\) are bounds that each \(x_i\) should follow. Args:

indices (Sequence[int]):

Indices to be truncated.

bound (int, Sequence[int]):

The bound function for witch the indices can not be larger than.

norm (float, Sequence[float]):

The p in the L_p-norm. Support includes both L_0 and L_inf.

Returns:

Boolean indices to indices with True for each index where the truncation criteria holds.

Examples:

>>> indices = numpy.array(numpy.mgrid[:10, :10]).reshape(2, -1).T
>>> indices[cross_truncate(indices, 2, norm=0)].T
array([[0, 0, 0, 1, 2],
       [0, 1, 2, 0, 0]])
>>> indices[cross_truncate(indices, 2, norm=1)].T
array([[0, 0, 0, 1, 1, 2],
       [0, 1, 2, 0, 1, 0]])
>>> indices[cross_truncate(indices, [0, 1], norm=1)].T
array([[0, 0],
       [0, 1]])