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from tools.load import LoadMatrix
from sg import sg
lm=LoadMatrix()
traindna=lm.load_dna('../data/fm_train_dna.dat')
cubedna=lm.load_cubes('../data/fm_train_cube.dat')
parameter_list=[[traindna,cubedna,3,0,'n'],[traindna,cubedna,4,0,'n']]
def distribution_histogram(fm_train=traindna,fm_cube=cubedna,order=3,
gap=0,reverse='n'):
# sg('new_distribution', 'HISTOGRAM')
sg('add_preproc', 'SORTWORDSTRING')
sg('set_features', 'TRAIN', fm_train, 'DNA')
sg('convert', 'TRAIN', 'STRING', 'CHAR', 'STRING', 'WORD', order, order-1, gap, reverse)
sg('attach_preproc', 'TRAIN')
# sg('train_distribution')
# histo=sg('get_histogram')
# num_examples=11
# num_param=sg('get_histogram_num_model_parameters')
# for i in xrange(num_examples):
# for j in xrange(num_param):
# sg('get_log_derivative %d %d' % (j, i))
# sg('get_log_likelihood')
# return sg('get_log_likelihood_sample')
if __name__=='__main__':
print('Histogram')
distribution_histogram(*parameter_list[0])
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