def test_choice(weights, rng):
n = 1000
choices = [[1, 1], [1, -1], [-1, 1], [-1, -1]]
N = len(choices)
dist = dists.Choice(choices, weights=weights)
# If d is passed, it has to match
with pytest.raises(ValueError):
dist.sample(n, d=4, rng=rng)
sample = dist.sample(n, rng=rng)
tsample, tchoices = list(map(tuple, sample)), list(map(tuple, choices))
# check that frequency of choices matches weights
inds = [tchoices.index(s) for s in tsample]
hist, bins = np.histogram(inds, bins=np.linspace(-0.5, N - 0.5, N + 1))
p_empirical = hist / float(hist.sum())
p = np.ones(N) / N if dist.p is None else dist.p
sterr = 1. / np.sqrt(n) # expected maximum standard error
assert np.allclose(p, p_empirical, atol=2 * sterr)
def ThresholdingEnsembles(threshold, intercept_width=0.15, radius=1.0):
"""Configuration preset for a thresholding ensemble.
This preset adjust ensemble parameters for thresholding. The ensemble's
neurons will only fire for values above threshold. One can either decode
the represented value (if it is above the threshold) or decode
a step function if binary classification is desired.
This preset:
- Sets intercepts to be between ``threshold`` and ``radius`` with an
exponential distribution (shape parameter of ``intercept_width``).
This clusters intercepts near the threshold for better approximation.
- Sets encoders to 1.
- Sets evaluation points to be uniformly distributed between
``threshold`` and ``radius``.
- Sets the radius.
Parameters
----------
threshold : float
Point at which ensembles should start firing.
intercept_width : float, optional
Controls how widely distributed the intercepts are. Smaller values
give more clustering at the threshold, larger values give a more
uniform distribution.
radius : float, optional
Ensemble radius.
Returns
-------
`nengo.Config`
Configuration with presets.
"""
config = Config(Ensemble)
config[Ensemble].radius = radius
config[Ensemble].intercepts = dists.Exponential(intercept_width, threshold,
radius)
config[Ensemble].encoders = dists.Choice([[1]])
config[Ensemble].eval_points = dists.Uniform(threshold / radius, 1)
return config