The purpose of this guide is to illustrate the possible uses of geomstats.
INSTALL GEOMSTATS WITH PIP3
From a terminal (OS X & Linux), you can install geomstats and its requirements with
pip3 as follows:
pip3 install geomstats
This method installs the latest version of geomstats that is uploaded on PyPi.
INSTALL GEOMSTATS FROM GITHUB
From a terminal (OS X & Linux), you can install geomstats and its requirements via Git as follows:
git clone https://github.com/geomstats/geomstats.git pip3 install -r requirements.txt
This methods installs the latest GitHub version. Developers should install this version, together with the development requirements and the optional requirements to enable
pip3 install -r dev-requirements.txt -r opt-requirements.txt
CHOOSE THE BACKEND
Geomstats can run seemlessly with
pytorch. Note that
tensorflow requirements are optional, as geomstats can be used with
numpy only. By default, the
numpy backend is used. The visualizations are only available with this backend.
To get the
pytorch versions compatible with geomstats, install the optional requirements:
pip3 install -r opt-requirements.txt
You can choose your backend by setting the environment variable
pytorch, and importing the
backend module. From the command line:
and in the Python3 code:
import geomstats.backend as gs
To use geomstats for learning
algorithms on Riemannian manifolds, you need to follow three steps:
- instantiate the manifold of interest,
- instantiate the learning algorithm of interest,
- run the algorithm.
The data should be represented by the structure
gs.array, which represents numpy arrays, tensorflow or pytorch tensors, depending on the choice of backend.
As an example, the following code snippet illustrates the use of K-means on simulated data on the 5-dimensional hypersphere.
from geomstats.geometry.hypersphere import Hypersphere from geomstats.learning.online_kmeans import OnlineKMeans sphere = Hypersphere(dim=5) data = sphere.random_uniform(n_samples=10) clustering = OnlineKMeans(metric=sphere.metric, n_clusters=4) clustering = clustering.fit(data)
The following code snippet shows the use of tangent Principal Component Analysis on simulated data on the space of 3D rotations.
from geomstats.geometry.special_orthogonal import SpecialOrthogonal from geomstats.learning.pca import TangentPCA so3 = SpecialOrthogonal(n=3, point_type='vector') metric = so3.bi_invariant_metric data = so3.random_uniform(n_samples=10) tpca = TangentPCA(metric=metric, n_components=2) tpca = tpca.fit(data) tangent_projected_data = tpca.transform(data)
All geometric computations are performed behind the scenes. The user only needs a high-level understanding of Riemannian geometry. Each algorithm can be used with any of the manifolds and metric implemented in the package.
To see additional examples, visit the page examples.