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
def cluster(data, k, mode='full'): d = data.shape[1] gm = GM(d, k, mode) gmm = GMM(gm) em = EM() em.train(data, gmm, maxiter=20) return gm
def cluster(data, k, mode='full'): d = data.shape[1] gm = GM(d, k, mode) gmm = GMM(gm, 'random') em = RegularizedEM(pcnt=pcnt, pval=pval) em.train(data, gmm, maxiter=20) return gm, gmm.bic(data)
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.compute_responsabilities(data) gmm.update_em(data, g) self.data = data self.gm = lgm
def cluster(data, k): d = data.shape[1] gm = GM(d, k) gmm = GMM(gm) em = EM() em.train(data, gmm, maxiter=20) return gm, gmm.bic(data)
def _test(self, dataset, log): dic = load_dataset(dataset) gm = GM.fromvalues(dic['w0'], dic['mu0'], dic['va0']) gmm = GMM(gm, 'test') EM().train(dic['data'], gmm, log = log) assert_array_almost_equal(gmm.gm.w, dic['w'], DEF_DEC) assert_array_almost_equal(gmm.gm.mu, dic['mu'], DEF_DEC) assert_array_almost_equal(gmm.gm.va, dic['va'], DEF_DEC)
def _create_model_and_run_em(self, d, k, mode, nframes): #+++++++++++++++++++++++++++++++++++++++++++++++++ # 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') em = EM() lk = em.train(data, gmm)
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)
k = 2 d = 2 mode = 'diag' nframes = 1e3 #+++++++++++++++++++++++++++++++++++++++++++ # Create an artificial GM model, samples it #+++++++++++++++++++++++++++++++++++++++++++ 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) #++++++++++++++++++++++++ # Learn the model with EM #++++++++++++++++++++++++ # Create a Model from a Gaussian mixture with kmean initialization lgm = GM(d, k, mode) gmm = GMM(lgm, 'kmean') # The actual EM, with likelihood computation. The threshold # is compared to the (linearly appromixated) derivative of the likelihood em = EM() like = em.train(data, gmm, maxiter = 30, thresh = 1e-8) # The computed parameters are in gmm.gm, which is the same than lgm # (remember, python does not copy most objects by default). You can for example # plot lgm against gm to compare