def generate_dataset(d, k, mode, nframes):
    """Generate a dataset useful for EM anf GMM testing.
    
    returns:
        data : ndarray
            data from the true model.
        tgm : GM
            the true model (randomly generated)
        gm0 : GM
            the initial model
        gm : GM
            the trained model
    """
    # Generate a model
    w, mu, va = GM.gen_param(d, k, mode, spread = 2.0)
    tgm = GM.fromvalues(w, mu, va)

    # Generate data from the model
    data = tgm.sample(nframes)

    # Run EM on the model, by running the initialization separetely.
    gmm = GMM(GM(d, k, mode), 'test')
    gmm.init_random(data)
    gm0 = copy.copy(gmm.gm)

    gmm = GMM(copy.copy(gmm.gm), 'test')
    em = EM()
    em.train(data, gmm)

    return data, tgm, gm0, gmm.gm
Esempio n. 2
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    def _test_common(self, d, k, mode):
        dic = load_dataset('%s_%dd_%dk.mat' % (mode, d, k))

        gm = GM.fromvalues(dic['w0'], dic['mu0'], dic['va0'])
        gmm = GMM(gm, 'test')

        a, na = gmm.compute_responsabilities(dic['data'])
        la, nla = gmm.compute_log_responsabilities(dic['data'])

        ta = N.log(a)
        tna = N.log(na)
        if not N.all(N.isfinite(ta)):
            print "precision problem for %s, %dd, %dk, test need fixing" % (mode, d, k)
        else:
            assert_array_almost_equal(ta, la, DEF_DEC)

        if not N.all(N.isfinite(tna)):
            print "precision problem for %s, %dd, %dk, test need fixing" % (mode, d, k)
        else:
            assert_array_almost_equal(tna, nla, DEF_DEC)
def generate_dataset(d, k, mode, nframes):
    """Generate a dataset useful for EM anf GMM testing.
    
    returns:
        data : ndarray
            data from the true model.
        tgm : GM
            the true model (randomly generated)
        gm0 : GM
            the initial model
        gm : GM
            the trained model
    """
    # Generate a model
    w, mu, va = GM.gen_param(d, k, mode, spread=2.0)
    tgm = GM.fromvalues(w, mu, va)

    # Generate data from the model
    data = tgm.sample(nframes)

    # Run EM on the model, by running the initialization separetely.
    gmm = GMM(GM(d, k, mode), 'test')
    gmm.init_random(data)
    gm0 = copy.copy(gmm.gm)

    gmm = GMM(copy.copy(gmm.gm), 'test')
    em = EM()
    em.train(data, gmm)

    return data, tgm, gm0, gmm.gm
Esempio n. 4
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    def _create_model(self, d, k, mode, nframes, emiter):
        #+++++++++++++++++++++++++++++++++++++++++++++++++
        # Generate a model with k components, d dimensions
        #+++++++++++++++++++++++++++++++++++++++++++++++++
        w, mu, va   = GM.gen_param(d, k, mode, spread = 1.5)
        gm          = GM.fromvalues(w, mu, va)
        # Sample nframes frames  from the model
        data        = gm.sample(nframes)

        #++++++++++++++++++++++++++++++++++++++++++
        # Approximate the models with classical EM
        #++++++++++++++++++++++++++++++++++++++++++
        # Init the model
        lgm = GM(d, k, mode)
        gmm = GMM(lgm, 'kmean')
        gmm.init(data, niter = KM_ITER)

        self.gm0    = copy.copy(gmm.gm)
        # The actual EM, with likelihood computation
        for i in range(emiter):
            g, tgd  = gmm.sufficient_statistics(data)
            gmm.update_em(data, g)

        self.data   = data
        self.gm     = lgm