posterior / distributions

    scipy.misc.imsave(os.path.join(RESULTS, 'prior_gibbs.png'), image)

@parsable.command

def compress_seq_gibbs(passes=PASSES):

    '''

    Compress image via sequentiall-initialized gibbs sampling.

    '''

    assert passes >= 1

    assert os.path.exists(SAMPLES), 'first create dataset'

    print 'seq+gibbs start {} passes'.format(passes)

    model = ImageModel()

    mixture = ImageModel.Mixture()

    mixture.init(model)

    scores = numpy.zeros(1, dtype=numpy.float32)

    assignments = {}

    for i, xy in enumerate(json_stream_load(SAMPLES)):

        scores.resize(len(mixture))

        mixture.score_value(model, xy, scores)

        groupid = sample_discrete_log(scores)

        mixture.add_value(model, groupid, xy)

        assignments[i] = mixture.id_tracker.packed_to_global(groupid)

    print 'seq+gibbs init with {} components'.format(len(mixture))

    for _ in xrange(passes - 1):

        for i, xy in enumerate(json_stream_load(SAMPLES)):

            groupid = mixture.id_tracker.global_to_packed(assignments[i])

            mixture.remove_value(model, groupid, xy)

            scores.resize(len(mixture))

            mixture.score_value(model, xy, scores)

            groupid = sample_discrete_log(scores)

            mixture.add_value(model, groupid, xy)

            assignments[i] = mixture.id_tracker.packed_to_global(groupid)

    print 'seq+gibbs found {} components'.format(len(mixture))

    image = synthesize_image(model, mixture)

    scipy.misc.imsave(os.path.join(RESULTS, 'seq_gibbs.png'), image)

def json_loop_load(filename):

    scipy.misc.imsave(os.path.join(RESULTS, 'sequential.png'), image)

@parsable.command

def compress_gibbs(passes=PASSES):

    '''

    Compress image via gibbs sampling.

    '''

    assert passes >= 0

    assert os.path.exists(SAMPLES), 'first create dataset'

    print 'prior+gibbs start {} passes'.format(passes)

    model = ImageModel()

    mixture = ImageModel.Mixture()

    mixture.init(model)

    scores = numpy.zeros(1, dtype=numpy.float32)

    assignments = {}

    for i, xy in enumerate(json_stream_load(SAMPLES)):

        scores.resize(len(mixture))

        mixture.clustering.score_value(model.clustering, scores)

        groupid = sample_discrete_log(scores)

        mixture.add_value(model, groupid, xy)

        assignments[i] = mixture.id_tracker.packed_to_global(groupid)

    print 'prior+gibbs init with {} components'.format(len(mixture))

    for _ in xrange(passes):

        for i, xy in enumerate(json_stream_load(SAMPLES)):

            groupid = mixture.id_tracker.global_to_packed(assignments[i])

            mixture.remove_value(model, groupid, xy)

            scores.resize(len(mixture))

            mixture.score_value(model, xy, scores)

            groupid = sample_discrete_log(scores)

            mixture.add_value(model, groupid, xy)

            assignments[i] = mixture.id_tracker.packed_to_global(groupid)

    print 'prior+gibbs found {} components'.format(len(mixture))

    image = synthesize_image(model, mixture)

    scipy.misc.imsave(os.path.join(RESULTS, 'prior_gibbs.png'), image)

@parsable.command