def test_affine_transform(plot=False):
np.random.seed(1)
corrs = [0, 0.6, 0.95, 0.999]
for corr in corrs:
for scaleratio in [1]: #, 0.001]:
covmatrix = np.array([[1., corr], [corr, 1.]])
# should draw uniformly sampled points
points = genpoints_following_cov(covmatrix, size=400)
print('settings: corr:', corr, 'scaleratio:', scaleratio,
'covmatrix:', covmatrix.flatten(), points.shape)
points[:, 0] = points[:, 0] * 0.01 * scaleratio + 0.5
points[:, 1] = points[:, 1] * 0.01 + 0.5
layer = AffineLayer()
layer.optimize(points, points)
points3 = layer.untransform(
genpoints_following_cov(np.diag([1, 1]), size=400))
#print('cov:', layer.cov, 'covmatrix:', covmatrix, 'ratio:', layer.cov / covmatrix)
tpoints = layer.transform(points)
assert tpoints.shape == points.shape, (tpoints.shape, points.shape)
points2 = layer.untransform(tpoints)
assert tpoints.shape == points2.shape, (tpoints.shape,
points2.shape)
if plot and scaleratio == 1:
plt.figure(figsize=(9, 4))
plt.subplot(1, 2, 1)
plt.scatter(points[:, 0], points[:, 1])
plt.scatter(points2[:, 0], points2[:, 1], marker='x')
plt.scatter(points3[:, 0], points3[:, 1], marker='+')
plt.subplot(1, 2, 2)
plt.scatter(tpoints[:, 0], tpoints[:, 1])
lo, hi = plt.xlim()
lo2, hi2 = plt.ylim()
lo, hi = min(lo, lo2), max(hi, hi2)
plt.xlim(lo, hi)
plt.ylim(lo, hi)
plt.savefig("testtransform_affine_corr%s_scale%s.pdf" %
(corr, scaleratio),
bbox_inches='tight')
plt.close()
assert (points2 == points).all(), (points, tpoints, points2)
# transform a single point
points = points[0]
tpoints = layer.transform(points)
assert tpoints.shape == points.shape, (tpoints.shape, points.shape)
points2 = layer.untransform(tpoints)
assert tpoints.shape == points2.shape, (tpoints.shape,
points2.shape)
assert (points2 == points).all(), (points, tpoints, points2)
def test_region_mean_distances():
np.random.seed(1)
points = np.random.uniform(0.4, 0.6, size=(10000, 2))
#points[:,1] *= 0.5
mask = np.abs((points[:, 0] - 0.5)**2 +
(points[:, 1] - 0.5)**2 - 0.08**2) < 0.02**2
print('circle:', mask.sum())
points = points[mask]
mask = points[:, 0] < 0.5
print('half-circle:', mask.sum())
points = points[mask]
transformLayer = AffineLayer(wrapped_dims=[])
transformLayer.optimize(points, points)
region = MLFriends(points, transformLayer)
region.maxradiussq, region.enlarge = region.compute_enlargement(
nbootstraps=30)
print("enlargement factor:", region.enlarge, 1 / region.enlarge)
region.create_ellipsoid()
meandist = region.compute_mean_pair_distance()
t = transformLayer.transform(region.u)
d = 0
N = 0
for i in range(len(t)):
for j in range(i):
d += ((t[i, :] - t[j, :])**2).sum()**0.5
#print(i, j, t[i,:], t[j,:], ((t[i,:] - t[j,:])**2).sum())
N += 1
print((meandist, d, N, t))
assert np.isclose(meandist, d / N), (meandist, d, N)