Welcome to BigNmf’s documentation!¶

Indices and tables¶

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)