bignmf 1.0.2

Non-negative matrix factorization

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.