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src/p/y/pylearn2-HEAD/pylearn2/training_algorithms/tests/test_default.py   pylearn2(Download)
import numpy as np
 
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.models.rbm import RBM
from pylearn2.models.s3c import S3C, E_Step, Grad_M_Step
                min_alpha=1., max_alpha=1., init_mu=0.,
                m_step=Grad_M_Step(learning_rate=0.),
                e_step=E_Step(h_new_coeff_schedule=[1.]))
 
    algorithm.setup(model=model, dataset=train)

src/p/y/pylearn2-HEAD/pylearn2/tests/test_monitor.py   pylearn2(Download)
from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
from pylearn2.models.model import Model
from pylearn2.models.s3c import S3C, E_Step, Grad_M_Step
from pylearn2.monitor import _err_ambig_data
from pylearn2.monitor import _err_no_data
            min_alpha = 1., max_alpha = 1., init_mu = 0.,
            m_step = Grad_M_Step( learning_rate = 0.),
            e_step = E_Step( h_new_coeff_schedule = [ 1. ]))
    algorithm.setup(model = model, dataset = dataset)
    algorithm.train(dataset = dataset)

src/p/y/pylearn2-HEAD/pylearn2/models/tests/test_s3c_inference.py   pylearn2(Download)
from pylearn2.models.s3c import S3C
from pylearn2.models.s3c import E_Step_Scan
from pylearn2.models.s3c import Grad_M_Step
from pylearn2.models.s3c import E_Step
from theano import function
    def test_match_unrolled(self):
        """ tests that inference with scan matches result using unrolled loops """
 
        unrolled_e_step = E_Step(h_new_coeff_schedule = self.h_new_coeff_schedule)
        unrolled_e_step.register_model(self.model)