Source code for openmdao.surrogate_models.nearest_neighbor

Surrogate model based on the N-Dimensional Interpolation library by Stephen Marone.

from collections import OrderedDict
from openmdao.surrogate_models.surrogate_model import SurrogateModel
from openmdao.surrogate_models.nn_interpolators.linear_interpolator import \
from openmdao.surrogate_models.nn_interpolators.weighted_interpolator import \
from openmdao.surrogate_models.nn_interpolators.rbf_interpolator import \

_interpolators = OrderedDict([('linear', LinearInterpolator),
                              ('weighted', WeightedInterpolator),
                              ('rbf', RBFInterpolator)])

[docs]class NearestNeighbor(SurrogateModel): """ Surrogate model that approximates values using a nearest neighbor approximation. Parameters ---------- **kwargs : dict Options dictionary. Attributes ---------- interpolant : object Interpolator object interpolant_init_args : dict Input keyword arguments for the interpolator. """
[docs] def __init__(self, **kwargs): """ Initialize all attributes. Parameters ---------- **kwargs : dict options dictionary. """ super().__init__() # Note: don't pass kwargs to parent because most of them are specific to choice of # interpolant. if 'interpolant_type' in kwargs: self.options['interpolant_type'] = kwargs.pop('interpolant_type') self.interpolant_init_args = kwargs self.interpolant = None
def _declare_options(self): """ Declare options before kwargs are processed in the init method. """ self.options.declare('interpolant_type', default='rbf', values=['linear', 'weighted', 'rbf'], desc="Type of interpolant, must be 'linear', 'weighted', or 'rbf'")
[docs] def train(self, x, y): """ Train the surrogate model with the given set of inputs and outputs. Parameters ---------- x : array-like Training input locations. y : array-like Model responses at given inputs. """ super().train(x, y) self.interpolant = _interpolators[self.options['interpolant_type']]( x, y, **self.interpolant_init_args)
[docs] def predict(self, x, **kwargs): """ Calculate a predicted value of the response based on the current trained model. Parameters ---------- x : array-like Point(s) at which the surrogate is evaluated. **kwargs : dict Additional keyword arguments passed to the interpolant. Returns ------- float Predicted value. """ super().predict(x) return self.interpolant(x, **kwargs)
[docs] def linearize(self, x, **kwargs): """ Calculate the jacobian of the interpolant at the requested point. Parameters ---------- x : array-like Point at which the surrogate Jacobian is evaluated. **kwargs : dict Additional keyword arguments passed to the interpolant. Returns ------- ndarray Jacobian of surrogate output wrt inputs. """ jac = self.interpolant.gradient(x, **kwargs) if jac.shape[0] == 1 and len(jac.shape) > 2: return jac[0, ...] return jac