def nmf(X, method='sklearn', **nmfparams):
"""
Calculates the non-negative matrix factorization of an input matrix
"""
#TODO: Documentation
if method == 'sklearn':
model = NMF(**nmfparams)
H = model.fit_transform(X)
W = model.components_
elif method == 'nimfa':
model_tmp = nimfa.mf(X, **nmfparams)
model = nimfa.mf_run(model_tmp)
H = model.coef()
W = model.basis()
return (H, W, model)
def nmf(X, method='sklearn', **nmfparams):
"""
Calculates the non-negative matrix factorization of an input matrix
"""
#TODO: Documentation
if method == 'sklearn':
model = NMF(**nmfparams)
H = model.fit_transform(X)
W = model.components_
elif method == 'nimfa':
model_tmp = nimfa.mf(X, **nmfparams)
model = nimfa.mf_run(model_tmp)
H = model.coef()
W = model.basis()
return (H, W, model)
