text: import sys
  import re
  import pytest
  
  import numpy as np
  from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
  from sklearn.utils._testing import (
      assert_almost_equal,
      assert_array_equal,
      assert_allclose,
  )
  
  from sklearn.datasets import load_digits
  from io import StringIO
  from sklearn.neural_network import BernoulliRBM
  from sklearn.utils.validation import assert_all_finite
  
  Xdigits, _ = load_digits(return_X_y=True)
  Xdigits -= Xdigits.min()
  Xdigits /= Xdigits.max()
  
  
  def test_fit():
      X = Xdigits.copy()
  
      rbm = BernoulliRBM(
          n_components=64, learning_rate=0.1, batch_size=10, n_iter=7, random_state=9
      )
      rbm.fit(X)
  
      assert_almost_equal(rbm.score_samples(X).mean(), -21.0, decimal=0)
  
      # in-place tricks shouldn't have modified X
      assert_array_equal(X, Xdigits)
  
  
  def test_partial_fit():
      X = Xdigits.copy()
      rbm = BernoulliRBM(
          n_components=64, learning_rate=0.1, batch_size=20, random_state=9
      )
      n_samples = X.shape[0]
      n_batches = int(np.ceil(float(n_samples) / rbm.batch_size))
      batch_slices = np.array_split(X, n_batches)
  
      for i in range(7):
          for batch in batch_slices:
              rbm.partial_fit(batch)
  
      assert_almost_equal(rbm.score_samples(X).mean(), -21.0, decimal=0)
      assert_array_equal(X, Xdigits)
  
  
  def test_transform():
      X = Xdigits[:100]
      rbm1 = BernoulliRBM(n_components=16, batch_size=5, n_iter=5, random_state=42)
      rbm1.fit(X)
  
      Xt1 = rbm1.transform(X)
      Xt2 = rbm1._mean_hiddens(X)
  
      assert_array_equal(Xt1, Xt2)
  
  
  def test_small_sparse():
      # BernoulliRBM should work on small sparse matrices.
      X = csr_matrix(Xdigits[:4])
      BernoulliRBM().fit(X)  # no exception
  
  
  def test_small_sparse_partial_fit():
      for sparse in [csc_matrix, csr_matrix]:
          X_sparse = sparse(Xdigits[:100])
          X = Xdigits[:100].copy()
  
          rbm1 = BernoulliRBM(
              n_components=64, learning_rate=0.1, batch_size=10, random_state=9
          )
          rbm2 = BernoulliRBM(
              n_components=64, learning_rate=0.1, batch_size=10, random_state=9
          )
  
          rbm1.partial_fit(X_sparse)
          rbm2.partial_fit(X)
  
          assert_almost_equal(
              rbm1.score_samples(X).mean(), rbm2.score_samples(X).mean(), decimal=0
          )
  
  
  def test_sample_hiddens():
      rng = np.random.RandomState(0)
      X = Xdigits[:100]
      rbm1 = BernoulliRBM(n_components=2, batch_size=5, n_iter=5, random_state=42)
      rbm1.fit(X)
  
      h = rbm1._mean_hiddens(X[0])
      hs = np.mean([rbm1._sample_hiddens(X[0], rng) for i in range(100)], 0)
  
      assert_almost_equal(h, hs, decimal=1)
  
  
  def test_fit_gibbs():
      # Gibbs on the RBM hidden layer should be able to recreate [[0], [1]]
      # from the same input
      rng = np.random.RandomState(42)
      X = np.array([[0.0], [1.0]])
      rbm1 = BernoulliRBM(n_components=2, batch_size=2, n_iter=42, random_state=rng)
      # you need that much iters
      rbm1.fit(X)
      assert_almost_equal(
          rbm1.components_, np.array([[0.02649814], [0.02009084]]), decimal=4
      )
      assert_almost_equal(rbm1.gibbs(X), X)
      return rbm1
  
  
  def test_fit_gibbs_sparse():
      # Gibbs on the RBM hidden layer should be able to recreate [[0], [1]] from
      # the same input even when the input is sparse, and test against non-sparse
      rbm1 = test_fit_gibbs()
      rng = np.random.RandomState(42)
      from scipy.sparse import csc_matrix
  
      X = csc_matrix([[0.0], [1.0]])
      rbm2 = BernoulliRBM(n_components=2, batch_size=2, n_iter=42, random_state=rng)
      rbm2.fit(X)
      assert_almost_equal(
          rbm2.components_, np.array([[0.02649814], [0.02009084]]), decimal=4
      )
      assert_almost_equal(rbm2.gibbs(X), X.toarray())
      assert_almost_equal(rbm1.components_, rbm2.components_)
  
  
  def test_gibbs_smoke():
      # Check if we don't get NaNs sampling the full digits dataset.
      # Also check that sampling again will yield different results.
      X = Xdigits
      rbm1 = BernoulliRBM(n_components=42, batch_size=40, n_iter=20, random_state=42)
      rbm1.fit(X)
      X_sampled = rbm1.gibbs(X)
      assert_all_finite(X_sampled)
      X_sampled2 = rbm1.gibbs(X)
      assert np.all((X_sampled != X_sampled2).max(axis=1))
  
  
  def test_score_samples():
      # Test score_samples (pseudo-likelihood) method.
      # Assert that pseudo-likelihood is computed without clipping.
      # See Fabian's blog, http://bit.ly/1iYefRk
      rng = np.random.RandomState(42)
      X = np.vstack([np.zeros(1000), np.ones(1000)])
      rbm1 = BernoulliRBM(n_components=10, batch_size=2, n_iter=10, random_state=rng)
      rbm1.fit(X)
      assert (rbm1.score_samples(X) < -300).all()
  
      # Sparse vs. dense should not affect the output. Also test sparse input
      # validation.
      rbm1.random_state = 42
      d_score = rbm1.score_samples(X)
      rbm1.random_state = 42
      s_score = rbm1.score_samples(lil_matrix(X))
      assert_almost_equal(d_score, s_score)
  
      # Test numerical stability (#2785): would previously generate infinities
      # and crash with an exception.
      with np.errstate(under="ignore"):
          rbm1.score_samples([np.arange(1000) * 100])
  
  
  def test_rbm_verbose():
      rbm = BernoulliRBM(n_iter=2, verbose=10)
      old_stdout = sys.stdout
      sys.stdout = StringIO()
      try:
          rbm.fit(Xdigits)
      finally:
          sys.stdout = old_stdout
  
  
  def test_sparse_and_verbose():
      # Make sure RBM works with sparse input when verbose=True
      old_stdout = sys.stdout
      sys.stdout = StringIO()
      from scipy.sparse import csc_matrix
  
      X = csc_matrix([[0.0], [1.0]])
      rbm = BernoulliRBM(
          n_components=2, batch_size=2, n_iter=1, random_state=42, verbose=True
      )
      try:
          rbm.fit(X)
          s = sys.stdout.getvalue()
          # make sure output is sound
          assert re.match(
              r"\[BernoulliRBM\] Iteration 1,"
              r" pseudo-likelihood = -?(\d)+(\.\d+)?,"
              r" time = (\d|\.)+s",
              s,
          )
      finally:
          sys.stdout = old_stdout
  
  
  @pytest.mark.parametrize(
      "dtype_in, dtype_out",
      [(np.float32, np.float32), (np.float64, np.float64), (int, np.float64)],
  )
  def test_transformer_dtypes_casting(dtype_in, dtype_out):
      X = Xdigits[:100].astype(dtype_in)
      rbm = BernoulliRBM(n_components=16, batch_size=5, n_iter=5, random_state=42)
      Xt = rbm.fit_transform(X)
  
      # dtype_in and dtype_out should be consistent
      assert Xt.dtype == dtype_out, "transform dtype: {} - original dtype: {}".format(
          Xt.dtype, X.dtype
      )
  
  
  def test_convergence_dtype_consistency():
      # float 64 transformer
      X_64 = Xdigits[:100].astype(np.float64)
      rbm_64 = BernoulliRBM(n_components=16, batch_size=5, n_iter=5, random_state=42)
      Xt_64 = rbm_64.fit_transform(X_64)
  
      # float 32 transformer
      X_32 = Xdigits[:100].astype(np.float32)
      rbm_32 = BernoulliRBM(n_components=16, batch_size=5, n_iter=5, random_state=42)
      Xt_32 = rbm_32.fit_transform(X_32)
  
      # results and attributes should be close enough in 32 bit and 64 bit
      assert_allclose(Xt_64, Xt_32, rtol=1e-06, atol=0)
      assert_allclose(
          rbm_64.intercept_hidden_, rbm_32.intercept_hidden_, rtol=1e-06, atol=0
      )
      assert_allclose(
          rbm_64.intercept_visible_, rbm_32.intercept_visible_, rtol=1e-05, atol=0
      )
      assert_allclose(rbm_64.components_, rbm_32.components_, rtol=1e-03, atol=0)
      assert_allclose(rbm_64.h_samples_, rbm_32.h_samples_)
  