Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the gallery for the big picture.

mri.operators.linear.utils.learn_dictionary(flat_patches_subjects, nb_atoms=100, alpha=1, n_iter=1, fit_algorithm='lars', transform_algorithm='lasso_lars', batch_size=100, n_jobs=1, verbose=1)[source]¶

Learn the dictionary from the real/imaginary part or complex paches from a training set

Parameters

flat_patches : generator of 1d array of flat patches (floats)

a list per subject

nb_atoms : int,

number of components of the dictionary (default=100)

alpha : float,

regulation term (default=1)

n_iter : int

number of iterations (default=1)

fit_algorithm : ‘lars’

for more details see MiniBatchDictionaryLearning from the sklearn library

transform_algorithm : ‘lasso_lars’,

for more details see MiniBatchDictionaryLearning from the sklearn library

batch_size : int (default 100),

number of patches taken per iteration to fit the model

n_jobs : int defaul 6,

number of cpu to run the learning

verbose : int default1,

The level of verbosity

Returns

dico : MiniBatchDictionaryLearning object