class documentation

class BoundTransform(BoundaryHandlerBase):

Constructor: BoundTransform(bounds)

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Handle boundaries by a smooth, piecewise linear and quadratic transformation into the feasible domain.

>>> import numpy as np
>>> import cma
>>> from cma.constraints_handler import BoundTransform
>>> from cma import fitness_transformations as ft
>>> veq = cma.utilities.math.Mh.vequals_approximately
>>> b = BoundTransform([0, None])
>>> assert b.bounds == [[0], [None]]
>>> assert veq(b.repair([-0.1, 0, 1, 1.2]), np.array([0.0125, 0.0125, 1, 1.2])), b.repair([-0.1, 0, 1, 1.2])
>>> assert b.is_in_bounds([0, 0.5, 1])
>>> assert veq(b.transform([-1, 0, 1, 2]), [0.9, 0.0125,  1,  2  ]), b.transform([-1, 0, 1, 2])
>>> bounded_sphere = ft.ComposedFunction([
...         cma.ff.sphere,
...         BoundTransform([[], 5 * [-1] + [np.inf]]).transform
...     ])
>>> o1 = cma.fmin(bounded_sphere, 6 * [-2], 0.5)  # doctest: +ELLIPSIS
(4_w,9)-aCMA-ES (mu_w=2.8,w_1=49%) in dimension 6 (seed=...
>>> o2 = cma.fmin(cma.ff.sphere, 6 * [-2], 0.5, options={
...    'BoundaryHandler': cma.s.ch.BoundTransform,
...    'bounds': [[], 5 * [-1] + [np.inf]] })  # doctest: +ELLIPSIS
(4_w,9)-aCMA-ES (mu_w=2.8,w_1=49%) in dimension 6 (seed=...
>>> assert o1[1] < 5 + 1e-8 and o2[1] < 5 + 1e-8
>>> b = BoundTransform([-np.random.rand(120), np.random.rand(120)])
>>> for i in range(0, 100, 9):
...     x = (-i-1) * np.random.rand(120) + i * np.random.randn(120)
...     x_to_b = b.repair(x)
...     x2 = b.inverse(x_to_b)
...     x2_to_b = b.repair(x2)
...     x3 = b.inverse(x2_to_b)
...     x3_to_b = b.repair(x3)
...     assert veq(x_to_b, x2_to_b)
...     assert veq(x2, x3)
...     assert veq(x2_to_b, x3_to_b)
>>> for _ in range(5):
...     lb = np.random.randn(4)
...     ub = lb + 1e-7 + np.random.rand(4)
...     b = BoundTransform([lb, ub])
...     for x in [np.random.randn(4) / np.sqrt(np.random.rand(4)) for _ in range(22)]:
...         assert all(lb <= b.transform(x)), (lb, ub, b.__dict__)
...         assert all(b.transform(x) <= ub), (lb, ub, b.__dict__)

Details: this class uses class BoxConstraintsLinQuadTransformation

Method __init__ Argument bounds can be None or bounds[0] and bounds[1] are lower and upper domain boundaries, each is either None or a scalar or a list or array of appropriate size.
Method inverse inverse transform of x from the bounded domain.
Method repair transforms x into the bounded domain.
Method transform Undocumented
Instance Variable bounds_tf Undocumented

Inherited from BoundaryHandlerBase:

Method __call__ return penalty or list of penalties, by default zero(s).
Method amend_bounds_for_integer_variables set bounds away from at=0.5 such that
Method get_bound return lower and upper bound of variable with index index
Method get_bounds get_bounds('lower', 8) returns the lower bounds in 8-D
Method has_bounds return True if any variable is bounded
Method idx_out_of_bounds return index list of out-of-bound values in x.
Method is_in_bounds not yet tested
Method to_dim_times_two return boundaries in format [[lb0, ub0], [lb1, ub1], ...], as used by BoxConstraints... class.
Method update end-iteration callback of boundary handler (abstract/empty)
Instance Variable bounds Undocumented
Method _get_bounds ib == 0/1 means lower/upper bound, return a vector of length dimension
def __init__(self, bounds=None):

Argument bounds can be None or bounds[0] and bounds[1] are lower and upper domain boundaries, each is either None or a scalar or a list or array of appropriate size.

def inverse(self, x, copy_if_changed=True):

inverse transform of x from the bounded domain.

def repair(self, x, copy_if_changed=True):

transforms x into the bounded domain.

def transform(self, x):

Undocumented

bounds_tf =

Undocumented