ctef 0.0.4

Cayley transform ellipsoid fitting

Cayley transform ellispoid fitting (CTEF)

Python implementation of Cayley tranform ellipsoid fitting (CTEF).

Associated paper

IEEE Transactions on Signal Processing: https://ieeexplore.ieee.org/abstract/document/10324847

arXiv: https://arxiv.org/abs/2304.10630

Installation

Install from PyPI:

pip install ctef

Clone from GitHub:

git clone [e-mail]:omelikechi/ctef.git

Usage

Fit an ellipsoid to data arranged in an n-by-p numpy array X (n = number of samples, p = dimension):

from ctef.ctef import ctef

X = np.load('PATH_TO_DATA/X.npy')
fit = ctef(X)

With all arguments (and their default values) explicitly expressed:

fit = ctef(X, k=None, w=0.5, w_axis=10, ellipsoid_dimensions=None, trr_params=None)

The output of ctef, in this case fit, is a dictionary:

print(fit.keys())

dict_keys(['center', 'Lambda', 'Lambda_inv', 'result'])

The dictionay items are:

• $c = $center is a p-dimensional numpy array. It is the ellipsoid center.

• $\Lambda = $Lambda is a p-by-k numpy array (matrix).

• $\widetilde\Lambda = $Lambda_inv is a k-by-p numpy array (matrix). $\widetilde\Lambda = \Lambda^{-1}$ when k = p.

• result is the output of the STIR optimization algorithm implemented in scipy

Ellipsoid of best fit

The ellipsoid of best fit is $\mathcal{E} = \{\Lambda\eta+c : \eta\in\mathbb{R}^k, \lVert\eta\rVert=1\} = \{x\in\mathbb{R}^p : \lVert \widetilde\Lambda(x-c)\rVert=1\}$.

Example

Examples are available in the examples folder. Here we highlight ellipsoid_gaussian.py.

Open ellipsoid_gaussian.ipynb in Google Colab:

2d version of ellipsoid_gaussian.py:

from ctef.ctef import ctef
from examples.helpers import generate_truth, simulate_data
import matplotlib.pyplot as plt
import numpy as np

p, tau, axis_ratio, noise_level, n_samples = 2, 2, 3, .01, 50

# generate data from Ellipsoid-Gaussian model
truth = generate_truth(p, tau, axis_ratio)
X = simulate_data(n_samples, noise_level, truth)

# fit ellipsoid to data X
fit = ctef(X)
Lambda, center = fit['Lambda'], fit['center']

# plot result
n_mesh = 100
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(1, 1, 1)
ax.scatter(X[:,0], X[:,1], color='black', s=5, alpha=1)
theta = np.linspace(0, 2*np.pi, n_mesh)
x = np.cos(theta)
y = np.sin(theta)
for j in range(n_mesh):
    eta = np.array([x[j],y[j]])
    [x[j],y[j]] = center + Lambda @ eta
ax.plot(x, y, alpha=1, lw=2, color='deepskyblue')

plt.show()

Lambda and center yield the best fit ellipsoid $\{\Lambda\eta+c : \lVert\eta\rVert=1\}$ pictured below:

Clustering

ctef_clustering.py in the ctef folder implements our ellipsoid clustering algorithm. This algorithm is tested against other clustering algorithms on two toy examples in the compare.py file. Here is its output.