Non-negative matrix factorization
Project description
BigNmf
BigNmf (Big Data NMF) is a python package for performing single NMF and joint NMF algorithms. NMF (Non-negative matrix factorization) is a unsupervised classification algorithm.
Installation
This package is available on the PyPi repository. Therefore you can install, by running the following.
pip3 install bignmf
Usage
The following is an example code snippet for running the nmf.
1. Single NMF
from bignmf.datasets.datasets import Datasets
from bignmf.models.snmf.standard import StandardNmf
Datasets.list_all()
data=Datasets.read("SimulatedX1")
k = 3
iter =100
trials = 50
model = StandardNmf(data,k)
model.run(trials, iter, verbose=0)
print(model.error)
model.cluster_data()
model.calc_consensus_matrices()
print(model.h_cluster)
2. Joint NMF
from bignmf.models.jnmf.integrative import IntegrativeJnmf
from bignmf.datasets.datasets import Datasets
Datasets.list_all()
data_dict = {}
data_dict["sim1"] = Datasets.read("SimulatedX1")
data_dict["sim2"] = Datasets.read("SimulatedX2")
k = 3
iter =100
trials = 50
lamb = 0.1
model = IntegrativeJnmf(data_dict, k, lamb)
model = StandardNmf(data,k)
model.run(trials, iter, verbose=0)
print(model.error)
model.cluster_data()
model.calc_consensus_matrices()
print(model.h_cluster)
Here is the extensive documentation for more details.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
bignmf-1.0.3.tar.gz
(6.8 kB
view hashes)
Built Distribution
bignmf-1.0.3-py3-none-any.whl
(19.5 kB
view hashes)