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Added lower bound option to UniformHypersphere dist #799

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Merged
merged 1 commit into from
May 11, 2017
Merged

Added lower bound option to UniformHypersphere dist #799

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2 changes: 2 additions & 0 deletions CHANGES.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,8 @@ Release History
- Added a new example showing how adjusting ensemble tuning curves can
improve function approximation.
(`#1129 <https://github.com/nengo/nengo/pull/1129>`_)
- Added a minimum magnitude option to ``UniformHypersphere``.
(`#799 <https://github.com/nengo/nengo/pull/799>`_)

**Changed**

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24 changes: 20 additions & 4 deletions nengo/dists.py
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@@ -1,4 +1,7 @@
from __future__ import absolute_import

import warnings

import numpy as np

from nengo.exceptions import ValidationError
Expand Down Expand Up @@ -222,17 +225,29 @@ class UniformHypersphere(Distribution):
Whether sample points should be distributed uniformly
over the surface of the hyperphere (True),
or within the hypersphere (False).
min_magnitude : Number, optional (Default: 0)
Lower bound on the returned vector magnitudes (such that they are in
the range ``[min_magnitude, 1]``). Must be in the range [0, 1).
Ignored if ``surface`` is ``True``.
"""

surface = BoolParam('surface')
min_magnitude = NumberParam('min_magnitude', low=0, high=1, high_open=True)

def __init__(self, surface=False):
def __init__(self, surface=False, min_magnitude=0):
super(UniformHypersphere, self).__init__()
if surface and min_magnitude > 0:
warnings.warn("min_magnitude ignored because surface is True")
self.surface = surface
self.min_magnitude = min_magnitude

def __repr__(self):
return "UniformHypersphere(%s)" % (
"surface=True" if self.surface else "")
args = []
if self.surface:
args.append("surface=%s" % self.surface)
if self.min_magnitude > 0:
args.append("min_magnitude=%r" % self.min_magnitude)
return "%s(%s)" % (type(self).__name__, ', '.join(args))

def sample(self, n, d, rng=np.random):
if d is None or d < 1: # check this, since other dists allow d = None
Expand All @@ -247,7 +262,8 @@ def sample(self, n, d, rng=np.random):
# Generate magnitudes for vectors from uniform distribution.
# The (1 / d) exponent ensures that samples are uniformly distributed
# in n-space and not all bunched up at the centre of the sphere.
samples *= rng.rand(n, 1) ** (1.0 / d)
samples *= rng.uniform(
low=self.min_magnitude**d, high=1, size=(n, 1)) ** (1. / d)

return samples

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33 changes: 20 additions & 13 deletions nengo/tests/test_dists.py
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Expand Up @@ -3,6 +3,7 @@

import nengo.dists as dists
import nengo.utils.numpy as npext
from nengo.utils.testing import warns
from nengo.exceptions import ValidationError


Expand Down Expand Up @@ -69,19 +70,15 @@ def test_exponential(scale, shift, high, rng):
assert abs(np.mean(samples - shift) - scale) < ci


@pytest.mark.parametrize("dimensions", [0, 1, 2, 5])
def test_hypersphere(dimensions, rng):
n = 150 * dimensions
if dimensions < 1:
with pytest.raises(ValueError):
dist = dists.UniformHypersphere().sample(1, dimensions)
else:
dist = dists.UniformHypersphere()
samples = dist.sample(n, dimensions, rng=rng)
assert samples.shape == (n, dimensions)
assert np.allclose(np.mean(samples, axis=0), 0, atol=0.1)
hist, _ = np.histogramdd(samples, bins=5)
assert np.allclose(hist - np.mean(hist), 0, atol=0.1 * n)
@pytest.mark.parametrize(
"min_magnitude,d", [(0, 1), (0, 2), (0, 5), (0.6, 1), (0.3, 2), (0.4, 5)])
def test_hypersphere_volume(min_magnitude, d, rng):
n = 150 * d
dist = dists.UniformHypersphere(min_magnitude=min_magnitude)
samples = dist.sample(n, d, rng=rng)
assert samples.shape == (n, d)
assert np.allclose(np.mean(samples, axis=0), 0, atol=0.1)
assert np.all(npext.norm(samples, axis=1) >= min_magnitude)


@pytest.mark.parametrize("dimensions", [1, 2, 5])
Expand All @@ -94,6 +91,16 @@ def test_hypersphere_surface(dimensions, rng):
assert np.allclose(np.mean(samples, axis=0), 0, atol=0.25 / dimensions)


def test_hypersphere_dimension_fail(rng):
with pytest.raises(ValueError):
dists.UniformHypersphere(0).sample(1, 0)


def test_hypersphere_warns(rng):
with warns(UserWarning):
dists.UniformHypersphere(surface=True, min_magnitude=0.1)


@pytest.mark.parametrize("weights", [None, [5, 1, 2, 9], [3, 2, 1, 0]])
def test_choice(weights, rng):
n = 1000
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