# -*- coding: utf-8 -*-
"""
Created on Fri Jan 29 16:56:11 2016
@author: konopczynski
Perform the dictionary learning for a given settings,
on the provided patches
"""
import numpy as np
import pickle
from sklearn.decomposition import MiniBatchDictionaryLearning
from sklearn.decomposition import DictionaryLearning
import warnings
import sys
sys.path.append('../')
import config
warnings.filterwarnings("ignore")
def learn_dictionary_mini(patches, n_c=512, a=1, n_i=800, n_j=3, b_s=3, es=5, fit_algorithm='lars'):
"""
patches - patches to learn on (should be normalized before)
n_c - number of components (atoms) e.g. 512
a - alpha sparsity controlling parameter
n_i - total number of iterations to perform
b_s - batch size: number of samples in each mini-batch
fit_algorithm - {‘lars’, ‘cd’}
n_j - number of parallel jobs to run (number of threads)
e_s - size of each element in the dictionary
"""
dic = MiniBatchDictionaryLearning(n_components=n_c, alpha=a, n_iter=n_i,
n_jobs=n_j, batch_size=b_s, fit_algorithm=fit_algorithm)
print ("Start learning dictionary_mini: n_c: "+str(n_c)+", alpha: "+str(a)+", n_i: " +
str(n_i)+", n_j: "+str(n_j)+", es: "+str(es)+", b_s: "+str(b_s))
v1 = dic.fit(patches).components_
d1 = v1.reshape(n_c, es, es, es) # e.g. 512x5x5x5
return d1
def learn_dictionary(patches, n_c=512, a=1, n_i=100, n_j=3, es=5, fit_algorithm='lars'):
dic = DictionaryLearning(n_components=n_c, alpha=a, max_iter=n_i,
n_jobs=n_j, fit_algorithm=fit_algorithm)
print ("Start learning dictionary: n_c: "+str(n_c)+", alpha: "+str(a)+", n_i: " +
str(n_i)+", es: "+str(es)+", n_j: "+str(n_j))
v2 = dic.fit(patches).components_
d2 = v2.reshape(n_c, es, es, es) # e.g. 512x5x5x5
return d2
def serialize_dictionary(d, path2save):
full_saving_path = path2save
output = open(full_saving_path, 'wb')
pickle.dump(d, output)
output.close()
print("saved at: "+full_saving_path)
return None
def main():
param = config.read_parameters()
na = param.numOfAtoms
es = param.eS
bs = param.bS
ni = param.nI
ac = param.aC
file_with_patches = param.path2patches+param.fileWithPatches+'.npy'
patches = np.load(file_with_patches)
# Learn the dictionary
dictionary = learn_dictionary_mini(patches, n_c=na, a=ac, n_i=ni,
n_j=4, b_s=bs, es=es, fit_algorithm='lars')
# Serialize the dictionary
path2save_dictionary = param.path2dicts+param.dictionaryName+'.pkl'
serialize_dictionary(dictionary, path2save_dictionary)
return None
if __name__ == '__main__':
main()
