Documentation¶
beta_nmf.py¶
Contents
The beta_nmf module includes the beta_nmf class, fit function and theano functions to compute updates and cost.
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class
beta_nmf.BetaNMF(data_shape, n_components=50, beta=2, n_iter=100, fixed_factors=None, verbose=0, cold_start=True)[source]¶ BetaNMF class
Performs nonnegative matrix factorization with Theano.
Parameters: data_shape : tuple composed of integers
the shape of the data to approximate
n_components : positive integer (default 50)
the number of latent components for the NMF model
beta : arbitrary float (default 2)
- the beta-divergence to consider, particular cases of interest are
- beta=2 : Euclidean distance
- beta=1 : Kullback Leibler
- beta=0 : Itakura-Saito
n_iter : Positive integer (default 100)
number of iterations
fixed_factors : array (default Null)
- list of factors that are not updated
e.g. fixed_factors = [0] -> H is not updated
fixed_factors = [1] -> W is not updated
verbose : Integer
the frequence at which the score should be computed and displayed (number of iterations between each computation)
Attributes
factors (list of arrays) The estimated factors (factors[0] = H) Methods
check_shape()Check that all the matrix have consistent shapes fit(data[, warm_start])Learns NMF model get_div_function()Compile the theano-based divergence function get_updates_functions()Compile the theano based update functions score()Compute factorisation score set_factors(X[, fixed_factors])reset factors transform(X[, warm_start])Project data X on the basis W -
fit(data, warm_start=False)[source]¶ Learns NMF model
Parameters: X : ndarray with nonnegative entries
The input array
warm_start : Boolean (default False)
start from new values
base.py¶
Contents
The base module includes the basic functions such as beta-divergence, nonnegative random matrices generator or load_data.
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base.beta_div(X, W, H, beta)[source]¶ Compute beta divergence D(X|WH)
Parameters: X : Theano tensor
data
W : Theano tensor
Bases
H : Theano tensor
activation matrix
beta : Theano scalar
Returns: div : Theano scalar
beta divergence D(X|WH)
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base.load_data(f_name, scale=True, rnd=True)[source]¶ Get data from H5FS file.
Parameters: f_name : String
file name
scale : Boolean (default True)
scale data to unit variance (scikit-learn function)
rnd : Boolean (default True)
randomize the data along time axis
Returns: data : Dictionnary
dictionary containing the data
x_train: numpy array
train data matrix
cost.py¶
Contents
The cost module regroups the cost functions used for the beta NMF
updates.py¶
Contents
The update module regroupse the update functions used for the beta NMF