def test_sample_prior(self): isa = ISA(5, 10) samples = isa.sample_prior(20) # simple sanity checks self.assertEqual(samples.shape[0], 10) self.assertEqual(samples.shape[1], 20)
def test_sample_prior(self): isa = ISA(5, 10) samples = isa.sample_prior(20) # simple sanity checks self.assertEqual(samples.shape[0], 10) self.assertEqual(samples.shape[1], 20)
def test_sample_posterior(self): isa = ISA(2, 3, num_scales=10) isa.A = asarray([[1., 0., 1.], [0., 1., 1.]]) isa.initialize() params = isa.default_parameters() params['gibbs']['verbosity'] = 0 params['gibbs']['num_iter'] = 100 states_post = isa.sample_posterior(isa.sample(1000), params) states_prio = isa.sample_prior(states_post.shape[1]) states_post = states_post.flatten() states_post = states_post[permutation(states_post.size)] states_prio = states_prio.flatten() states_prio = states_prio[permutation(states_prio.size)] # on average, posterior samples should be distributed like prior samples p = ks_2samp(states_post, states_prio)[1] self.assertGreater(p, 0.0001) samples = isa.sample(100) states = isa.sample_posterior(samples, params) # reconstruction should be perfect self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()), 1e-10)
def test_subspaces(self): isa = ISA(2, 4, 2) # simple sanity checks self.assertEqual(isa.subspaces()[0].dim, 2) self.assertEqual(isa.subspaces()[1].dim, 2) self.assertEqual(sys.getrefcount(isa.subspaces()), 1) self.assertEqual(sys.getrefcount(isa.subspaces()[0]), 1)
def test_subspaces(self): isa = ISA(2, 4, 2) # simple sanity checks self.assertEqual(isa.subspaces()[0].dim, 2) self.assertEqual(isa.subspaces()[1].dim, 2) self.assertEqual(sys.getrefcount(isa.subspaces()), 1) self.assertEqual(sys.getrefcount(isa.subspaces()[0]), 1)
def test_sample(self): isa = ISA(3, 4) samples = isa.sample(100) samples_prior = isa.sample_prior(100) # simple sanity checks self.assertEqual(samples.shape[0], isa.num_visibles) self.assertEqual(samples.shape[1], 100) self.assertEqual(samples_prior.shape[0], isa.num_hiddens) self.assertEqual(samples_prior.shape[1], 100)
def test_sample(self): isa = ISA(3, 4) samples = isa.sample(100) samples_prior = isa.sample_prior(100) # simple sanity checks self.assertEqual(samples.shape[0], isa.num_visibles) self.assertEqual(samples.shape[1], 100) self.assertEqual(samples_prior.shape[0], isa.num_hiddens) self.assertEqual(samples_prior.shape[1], 100)
def test_nullspace_basis(self): isa = ISA(2, 5) B = isa.nullspace_basis() # simple sanity checks self.assertTrue(B.shape[0], 3) self.assertTrue(B.shape[1], 5) # B should be orthogonal to A and orthonormal self.assertLess(max(abs(dot(isa.A, B.T).flatten())), 1e-10) self.assertLess(max(abs((dot(B, B.T) - eye(3)).flatten())), 1e-10) self.assertEqual(sys.getrefcount(B) - 1, 1)
def test_nullspace_basis(self): isa = ISA(2, 5) B = isa.nullspace_basis() # simple sanity checks self.assertTrue(B.shape[0], 3) self.assertTrue(B.shape[1], 5) # B should be orthogonal to A and orthonormal self.assertLess(max(abs(dot(isa.A, B.T).flatten())), 1e-10) self.assertLess(max(abs((dot(B, B.T) - eye(3)).flatten())), 1e-10) self.assertEqual(sys.getrefcount(B) - 1, 1)
def test_default_parameters(self): isa = ISA(2, 4) params = isa.default_parameters() # simple sanity checks self.assertTrue(isinstance(params, dict)) self.assertEqual(sys.getrefcount(params) - 1, 1) self.assertEqual(sys.getrefcount(params['sgd']) - 1, 1) self.assertEqual(sys.getrefcount(params['lbfgs']) - 1, 1) self.assertEqual(sys.getrefcount(params['mp']) - 1, 1) self.assertEqual(sys.getrefcount(params['gsm']) - 1, 1) self.assertEqual(sys.getrefcount(params['gibbs']) - 1, 1) self.assertEqual(sys.getrefcount(params['ais']) - 1, 1) self.assertEqual(sys.getrefcount(params['merge']) - 1, 1)
def test_default_parameters(self): isa = ISA(2, 4) params = isa.default_parameters() # simple sanity checks self.assertTrue(isinstance(params, dict)) self.assertEqual(sys.getrefcount(params) - 1, 1) self.assertEqual(sys.getrefcount(params['sgd']) - 1, 1) self.assertEqual(sys.getrefcount(params['lbfgs']) - 1, 1) self.assertEqual(sys.getrefcount(params['mp']) - 1, 1) self.assertEqual(sys.getrefcount(params['gsm']) - 1, 1) self.assertEqual(sys.getrefcount(params['gibbs']) - 1, 1) self.assertEqual(sys.getrefcount(params['ais']) - 1, 1) self.assertEqual(sys.getrefcount(params['merge']) - 1, 1)
def test_sample_scales(self): isa = ISA(2, 5, num_scales=4) # get a copy of subspaces subspaces = isa.subspaces() # replace scales for gsm in subspaces: gsm.scales = asarray([1., 2., 3., 4.]) isa.set_subspaces(subspaces) samples = isa.sample_prior(100000) scales = isa.sample_scales(samples) # simple sanity checks self.assertEqual(scales.shape[0], isa.num_hiddens) self.assertEqual(scales.shape[1], samples.shape[1]) priors = mean( abs(scales.flatten() - asarray([[1., 2., 3., 4.]]).T) < 0.5, 1) # prior probabilities of scales should be equal and sum up to one self.assertLess(max(abs(priors - 1. / subspaces[0].num_scales)), 0.01) self.assertLess(abs(sum(priors) - 1.), 1e-10)
def test_prior_energy_gradient(self): isa = ISA(4) samples = isa.sample_prior(100) grad = isa.prior_energy_gradient(samples) # simple sanity checks self.assertEqual(grad.shape[0], samples.shape[0]) self.assertEqual(grad.shape[1], samples.shape[1]) f = lambda x: isa.prior_energy(x.reshape(-1, 1)).flatten() df = lambda x: isa.prior_energy_gradient(x.reshape(-1, 1)).flatten() for i in range(samples.shape[1]): relative_error = check_grad(f, df, samples[:, i]) / sqrt(sum(square(df(samples[:, i])))) # comparison with numerical gradient self.assertLess(relative_error, 0.001)
def test_pickle(self): isa0 = ISA(4, 16, ssize=3) isa0.set_hidden_states(randn(16, 100)) tmp_file = mkstemp()[1] # store model with open(tmp_file, 'w') as handle: dump({'isa': isa0}, handle) # load model with open(tmp_file) as handle: isa1 = load(handle)['isa'] # make sure parameters haven't changed self.assertEqual(isa0.num_visibles, isa1.num_visibles) self.assertEqual(isa0.num_hiddens, isa1.num_hiddens) self.assertLess(max(abs(isa0.A - isa1.A)), 1e-20) self.assertLess(max(abs(isa0.hidden_states() - isa1.hidden_states())), 1e-20) self.assertLess(max(abs(isa0.subspaces()[1].scales - isa1.subspaces()[1].scales)), 1e-20)
def test_train_lbfgs(self): isa = ISA(2) isa.initialize() isa.A = eye(2) samples = isa.sample(10000) # initialize close to original parameters isa.A = asarray([[cos(0.4), sin(0.4)], [-sin(0.4), cos(0.4)]]) params = isa.default_parameters() params['training_method'] = 'LBFGS' params['train_prior'] = False params['max_iter'] = 1 params['lbfgs']['max_iter'] = 50 isa.train(samples, params) # L-BFGS should be able to recover the parameters self.assertLess(sqrt(sum(square(isa.A.flatten() - eye(2).flatten()))), 0.1)
def test_callback(self): isa = ISA(2) # callback function def callback(i, isa_): callback.count += 1 self.assertTrue(isa == isa_) callback.count = 0 # set callback function parameters = { 'verbosity': 0, 'max_iter': 7, 'callback': callback, 'sgd': {'max_iter': 0} } isa.train(randn(2, 1000), parameters=parameters) # test how often callback function was called self.assertEqual(callback.count, parameters['max_iter'] + 1) def callback(i, isa_): if i == 5: return False callback.count += 1 callback.count = 0 parameters['callback'] = callback isa.train(randn(2, 1000), parameters=parameters) # test how often callback function was called self.assertEqual(callback.count, 5) # make sure referece counts stay stable self.assertEqual(sys.getrefcount(isa) - 1, 1) self.assertEqual(sys.getrefcount(callback) - 1, 2)
def test_sample_posterior_ais(self): isa = ISA(2, 3, num_scales=10) isa.A = asarray([[1., 0., 1.], [0., 1., 1.]]) isa.initialize() params = isa.default_parameters() params['ais']['verbosity'] = 0 params['ais']['num_iter'] = 100 samples = isa.sample(100) states, _ = isa.sample_posterior_ais(samples, params) # reconstruction should be perfect self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()), 1e-10)
def test_loglikelihood(self): isa = ISA(7, ssize=3) samples = isa.sample(100) energy = isa.prior_energy(dot(inv(isa.A), samples)) loglik = isa.loglikelihood(samples) # difference between loglik and -energy should be const self.assertTrue(var(loglik + energy) < 1e-10) isa = ISA(2, 3) samples = isa.sample(20) params = isa.default_parameters() params['ais']['num_samples'] = 5 params['ais']['num_iter'] = 10 loglik = isa.loglikelihood(samples, params, return_all=True) # simple sanity checks self.assertTrue(loglik.shape[0], params['ais']['num_samples']) self.assertTrue(loglik.shape[1], samples.shape[1])
def test_train_lbfgs(self): isa = ISA(2) isa.initialize() isa.A = eye(2) samples = isa.sample(10000) # initialize close to original parameters isa.A = asarray([[cos(0.4), sin(0.4)], [-sin(0.4), cos(0.4)]]) params = isa.default_parameters() params['training_method'] = 'LBFGS' params['train_prior'] = False params['max_iter'] = 1 params['lbfgs']['max_iter'] = 50 isa.train(samples, params) # L-BFGS should be able to recover the parameters self.assertLess(sqrt(sum(square(isa.A.flatten() - eye(2).flatten()))), 0.1)
def test_initialize(self): def sqrtmi(mat): """ Compute matrix inverse square root. @type mat: array_like @param mat: matrix for which to compute inverse square root """ # find eigenvectors eigvals, eigvecs = eig(mat) # eliminate eigenvectors whose eigenvalues are zero eigvecs = eigvecs[:, eigvals > 0.] eigvals = eigvals[eigvals > 0.] # inverse square root return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) # white data data = randn(5, 1000) data = dot(sqrtmi(cov(data)), data) isa = ISA(5, 10) isa.initialize(data) # rows of A should be roughly orthogonal self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(5)).flatten()), 1e-3) p = kstest( isa.sample_prior(100).flatten(), lambda x: laplace.cdf(x, scale=1. / sqrt(2.)))[1] # prior marginals should be roughly Laplace self.assertGreater(p, 0.0001) # test initialization with larger subspaces isa = ISA(5, 10, ssize=2) isa.initialize(data)
def test_train_mp(self): isa = ISA(5, 10) params = isa.default_parameters() params['training_method'] = 'MP' params['mp']['num_coeff'] = 4 samples = isa.sample(100) states = isa.matching_pursuit(samples, params) # simple sanity checks self.assertEqual(states.shape[1], 100) self.assertEqual(states.shape[0], 10) self.assertFalse(any(sum(states > 0., 0) > 4)) # make sure training with MP doesn't throw any errors isa.train(isa.sample(1011), params)
def test_sample_posterior_ais(self): isa = ISA(2, 3, num_scales=10) isa.A = asarray([[1., 0., 1.], [0., 1., 1.]]) isa.initialize() params = isa.default_parameters() params['ais']['verbosity'] = 0 params['ais']['num_iter'] = 100 samples = isa.sample(100) states, _ = isa.sample_posterior_ais(samples, params) # reconstruction should be perfect self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()), 1e-10)
def test_train_mp(self): isa = ISA(5, 10) params = isa.default_parameters() params['training_method'] = 'MP' params['mp']['num_coeff'] = 4 samples = isa.sample(100) states = isa.matching_pursuit(samples, params) # simple sanity checks self.assertEqual(states.shape[1], 100) self.assertEqual(states.shape[0], 10) self.assertFalse(any(sum(states > 0., 0) > 4)) # make sure training with MP doesn't throw any errors isa.train(isa.sample(1011), params)
def test_loglikelihood(self): isa = ISA(7, ssize=3) samples = isa.sample(100) energy = isa.prior_energy(dot(inv(isa.A), samples)) loglik = isa.loglikelihood(samples) # difference between loglik and -energy should be const self.assertTrue(var(loglik + energy) < 1e-10) isa = ISA(2, 3) samples = isa.sample(20) params = isa.default_parameters() params['ais']['num_samples'] = 5 params['ais']['num_iter'] = 10 loglik = isa.loglikelihood(samples, params, return_all=True) # simple sanity checks self.assertTrue(loglik.shape[0], params['ais']['num_samples']) self.assertTrue(loglik.shape[1], samples.shape[1])
def test_initialize(self): def sqrtmi(mat): """ Compute matrix inverse square root. @type mat: array_like @param mat: matrix for which to compute inverse square root """ # find eigenvectors eigvals, eigvecs = eig(mat) # eliminate eigenvectors whose eigenvalues are zero eigvecs = eigvecs[:, eigvals > 0.] eigvals = eigvals[eigvals > 0.] # inverse square root return dot(eigvecs, dot(diag(1. / sqrt(eigvals)), eigvecs.T)) # white data data = randn(5, 1000) data = dot(sqrtmi(cov(data)), data) isa = ISA(5, 10) isa.initialize(data) # rows of A should be roughly orthogonal self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(5)).flatten()), 1e-3) p = kstest( isa.sample_prior(100).flatten(), lambda x: laplace.cdf(x, scale=1. / sqrt(2.)))[1] # prior marginals should be roughly Laplace self.assertGreater(p, 0.0001) # test initialization with larger subspaces isa = ISA(5, 10, ssize=2) isa.initialize(data)
def test_prior_energy_gradient(self): isa = ISA(4) samples = isa.sample_prior(100) grad = isa.prior_energy_gradient(samples) # simple sanity checks self.assertEqual(grad.shape[0], samples.shape[0]) self.assertEqual(grad.shape[1], samples.shape[1]) f = lambda x: isa.prior_energy(x.reshape(-1, 1)).flatten() df = lambda x: isa.prior_energy_gradient(x.reshape(-1, 1)).flatten() for i in range(samples.shape[1]): relative_error = check_grad(f, df, samples[:, i]) / sqrt( sum(square(df(samples[:, i])))) # comparison with numerical gradient self.assertLess(relative_error, 0.001)
def test_callback(self): isa = ISA(2) # callback function def callback(i, isa_): callback.count += 1 self.assertTrue(isa == isa_) callback.count = 0 # set callback function parameters = { 'verbosity': 0, 'max_iter': 7, 'callback': callback, 'sgd': { 'max_iter': 0 } } isa.train(randn(2, 1000), parameters=parameters) # test how often callback function was called self.assertEqual(callback.count, parameters['max_iter'] + 1) def callback(i, isa_): if i == 5: return False callback.count += 1 callback.count = 0 parameters['callback'] = callback isa.train(randn(2, 1000), parameters=parameters) # test how often callback function was called self.assertEqual(callback.count, 5) # make sure referece counts stay stable self.assertEqual(sys.getrefcount(isa) - 1, 1) self.assertEqual(sys.getrefcount(callback) - 1, 2)
def test_pickle(self): isa0 = ISA(4, 16, ssize=3) isa0.set_hidden_states(randn(16, 100)) tmp_file = mkstemp()[1] # store model with open(tmp_file, 'w') as handle: dump({'isa': isa0}, handle) # load model with open(tmp_file) as handle: isa1 = load(handle)['isa'] # make sure parameters haven't changed self.assertEqual(isa0.num_visibles, isa1.num_visibles) self.assertEqual(isa0.num_hiddens, isa1.num_hiddens) self.assertLess(max(abs(isa0.A - isa1.A)), 1e-20) self.assertLess(max(abs(isa0.hidden_states() - isa1.hidden_states())), 1e-20) self.assertLess( max(abs(isa0.subspaces()[1].scales - isa1.subspaces()[1].scales)), 1e-20)
def test_sample_scales(self): isa = ISA(2, 5, num_scales=4) # get a copy of subspaces subspaces = isa.subspaces() # replace scales for gsm in subspaces: gsm.scales = asarray([1., 2., 3., 4.]) isa.set_subspaces(subspaces) samples = isa.sample_prior(100000) scales = isa.sample_scales(samples) # simple sanity checks self.assertEqual(scales.shape[0], isa.num_hiddens) self.assertEqual(scales.shape[1], samples.shape[1]) priors = mean(abs(scales.flatten() - asarray([[1., 2., 3., 4.]]).T) < 0.5, 1) # prior probabilities of scales should be equal and sum up to one self.assertLess(max(abs(priors - 1. / subspaces[0].num_scales)), 0.01) self.assertLess(abs(sum(priors) - 1.), 1e-10)
def test_orthogonalize(self): isa = ISA(4, 8) isa.orthogonalize() self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(4)).flatten()), 1e-3)
def test_merge(self): isa1 = ISA(5, ssize=2) isa2 = ISA(5) isa1.initialize() isa1.orthogonalize() isa2.initialize() isa2.A = isa1.A params = isa2.default_parameters() params['train_basis'] = False params['merge_subspaces'] = True params['merge']['verbosity'] = 0 isa2.train(isa1.sample(10000), params) ssizes1 = [gsm.dim for gsm in isa1.subspaces()] ssizes2 = [gsm.dim for gsm in isa2.subspaces()] # algorithm should be able to recover subspace sizes self.assertTrue(all(sort(ssizes1) == sort(ssizes2)))
def test_evaluate(self): isa1 = ISA(2) isa1.A = eye(2) subspaces = isa1.subspaces() for gsm in subspaces: gsm.scales = ones(gsm.num_scales) isa1.set_subspaces(subspaces) # equivalent overcomplete model isa2 = ISA(2, 4) A = copy(isa2.A) A[:, :2] = isa1.A / sqrt(2.) A[:, 2:] = isa1.A / sqrt(2.) isa2.A = A subspaces = isa2.subspaces() for gsm in subspaces: gsm.scales = ones(gsm.num_scales) isa2.set_subspaces(subspaces) data = isa1.sample(100) # the results should not depend on the parameters ll1 = isa1.evaluate(data) ll2 = isa2.evaluate(data) self.assertLess(abs(ll1 - ll2), 1e-5) isa1 = ISA(2) isa1.initialize() # equivalent overcomplete model isa2 = ISA(2, 4) isa2.set_subspaces(isa1.subspaces() * 2) A = isa2.basis() A[:, :2] = isa1.basis() A[:, 2:] = 0. isa2.set_basis(A) data = isa1.sample(100) params = isa2.default_parameters() params['ais']['num_iter'] = 100 params['ais']['num_samples'] = 100 ll1 = isa1.evaluate(data) ll2 = isa2.evaluate(data, params) self.assertLess(abs(ll1 - ll2), 0.1)
def test_evaluate(self): isa1 = ISA(2) isa1.A = eye(2) subspaces = isa1.subspaces() for gsm in subspaces: gsm.scales = ones(gsm.num_scales) isa1.set_subspaces(subspaces) # equivalent overcomplete model isa2 = ISA(2, 4) A = copy(isa2.A) A[:, :2] = isa1.A / sqrt(2.) A[:, 2:] = isa1.A / sqrt(2.) isa2.A = A subspaces = isa2.subspaces() for gsm in subspaces: gsm.scales = ones(gsm.num_scales) isa2.set_subspaces(subspaces) data = isa1.sample(100) # the results should not depend on the parameters ll1 = isa1.evaluate(data) ll2 = isa2.evaluate(data) self.assertLess(abs(ll1 - ll2), 1e-5) isa1 = ISA(2) isa1.initialize() # equivalent overcomplete model isa2 = ISA(2, 4) isa2.set_subspaces(isa1.subspaces() * 2) A = isa2.basis() A[:, :2] = isa1.basis() A[:, 2:] = 0. isa2.set_basis(A) data = isa1.sample(100) params = isa2.default_parameters() params['ais']['num_iter'] = 100 params['ais']['num_samples'] = 100 ll1 = isa1.evaluate(data) ll2 = isa2.evaluate(data, params) self.assertLess(abs(ll1 - ll2), 0.1)
def test_sample_posterior(self): isa = ISA(2, 3, num_scales=10) isa.A = asarray([[1., 0., 1.], [0., 1., 1.]]) isa.initialize() params = isa.default_parameters() params['gibbs']['verbosity'] = 0 params['gibbs']['num_iter'] = 100 states_post = isa.sample_posterior(isa.sample(1000), params) states_prio = isa.sample_prior(states_post.shape[1]) states_post = states_post.flatten() states_post = states_post[permutation(states_post.size)] states_prio = states_prio.flatten() states_prio = states_prio[permutation(states_prio.size)] # on average, posterior samples should be distributed like prior samples p = ks_2samp(states_post, states_prio)[1] self.assertGreater(p, 0.0001) samples = isa.sample(100) states = isa.sample_posterior(samples, params) # reconstruction should be perfect self.assertLess(sum(square(dot(isa.A, states) - samples).flatten()), 1e-10)
def __init__(self): self.cpu = CPU() self.memory = Memory() self.isa = ISA(self)
def test_merge(self): isa1 = ISA(5, ssize=2) isa2 = ISA(5) isa1.initialize() isa1.orthogonalize() isa2.initialize() isa2.A = isa1.A params = isa2.default_parameters() params['train_basis'] = False params['merge_subspaces'] = True params['merge']['verbosity'] = 0 isa2.train(isa1.sample(10000), params) ssizes1 = [gsm.dim for gsm in isa1.subspaces()] ssizes2 = [gsm.dim for gsm in isa2.subspaces()] # algorithm should be able to recover subspace sizes self.assertTrue(all(sort(ssizes1) == sort(ssizes2)))
def test_orthogonalize(self): isa = ISA(4, 8) isa.orthogonalize() self.assertLess(sum(square(dot(isa.A, isa.A.T) - eye(4)).flatten()), 1e-3)
def test_train(self): # make sure train() doesn't throw any errors isa = ISA(2) params = isa.default_parameters() params['verbosity'] = 0 params['max_iter'] = 2 params['training_method'] = 'SGD' params['sgd']['max_iter'] = 1 params['sgd']['batch_size'] = 57 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params) isa = ISA(4, ssize=2) isa.initialize(randn(4, 1000)) isa.train(randn(4, 1000), params) isa = ISA(2, 3) params['gibbs']['ini_iter'] = 2 params['gibbs']['num_iter'] = 2 params['verbosity'] = 0 params['gibbs']['verbosity'] = 0 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params)
* IN THE SOFTWARE. */ #ifndef _${guard}_ #define _${guard}_ #include "${isa}" #endif /* _${guard}_ */ """ xml = sys.argv[1] glue_h = sys.argv[2] dst_c = sys.argv[3] dst_h = sys.argv[4] isa = ISA(xml) with open(glue_h, 'w') as f: guard = os.path.basename(glue_h).upper().replace("-", "_").replace(".", "_") f.write(Template(glue).render(guard=guard, isa=os.path.basename(dst_h))) with open(dst_c, 'w') as f: f.write(Template(template).render(isa=isa)) with open(dst_h, 'w') as f: guard = os.path.basename(dst_h).upper().replace("-", "_").replace(".", "_") f.write(Template(header).render(isa=isa, guard=guard))
def test_train(self): # make sure train() doesn't throw any errors isa = ISA(2) params = isa.default_parameters() params['verbosity'] = 0 params['max_iter'] = 2 params['training_method'] = 'SGD' params['sgd']['max_iter'] = 1 params['sgd']['batch_size'] = 57 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params) isa = ISA(4, ssize=2) isa.initialize(randn(4, 1000)) isa.train(randn(4, 1000), params) isa = ISA(2, 3) params['gibbs']['ini_iter'] = 2 params['gibbs']['num_iter'] = 2 params['verbosity'] = 0 params['gibbs']['verbosity'] = 0 isa.initialize(randn(2, 1000)) isa.train(randn(2, 1000), params)