import numpy as np
[docs]def eval_point_decoding(conn, sim, eval_points=None):
"""Get the targets and actual decoded values for a set of eval points.
This function evaluates the static decoding (i.e. using the neuron type's
``rates`` function) of a connection for a given set of evaluation points.
Parameters
----------
conn : Connection
The Connection to evaluate the decoding of.
sim : Simulator
A Nengo simulator storing the built connection.
eval_points : array_like (N, E) (optional)
An N x E array of evaluation points to evaluate the decoding for, where
N is the number of points and E is the dimensionality of the input
ensemble (i.e. ``conn.size_in``). If None (default), use the connection's
training evaluation points.
Returns
-------
eval_points : ndarray (N, E)
A shallow copy of the evaluation points used. E is the dimensionality
of the connection input ensemble (i.e. ``conn.size_in``).
targets : ndarray (N, D)
The target function value at each evaluation point.
decoded : ndarray (N, D)
The decoded function value at each evaluation point.
"""
# pylint: disable=import-outside-toplevel
# note: these are imported here to avoid circular imports
from nengo import rc
from nengo.builder.ensemble import get_activities
from nengo.builder.connection import get_targets
dtype = rc.float_dtype
if eval_points is None:
eval_points = sim.data[conn].eval_points
else:
eval_points = np.asarray(eval_points, dtype=dtype)
ens = conn.pre_obj
weights = sim.data[conn].weights
activities = get_activities(sim.data[ens], ens, eval_points)
decoded = np.dot(activities, weights.T)
targets = get_targets(conn, eval_points, dtype=dtype)
return eval_points, targets, decoded
