old_contents: try:
      from sklearn.model_selection import train_test_split
  except ImportError:
      from sklearn.cross_validation import train_test_split
  from sklearn.datasets import make_regression
  
  from mla.linear_models import LinearRegression
  from mla.metrics.metrics import mean_squared_error
  from mla.neuralnet import NeuralNet
  from mla.neuralnet.layers import Activation, Dense
  from mla.neuralnet.optimizers import Adam, RMSprop
  from mla.neuralnet.parameters import Parameters
  from mla.knn import KNNRegressor
  
  # Generate a random regression problem
  X, y = make_regression(n_samples=1000, n_features=10,
                         n_informative=10, n_targets=1, noise=0.05,
                         random_state=1111, bias=0.5)
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                      random_state=1111)
  
  
  def test_linear():
      model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      assert mean_squared_error(y_test, predictions) < 0.25
  
  
  def test_mlp():
      model = NeuralNet(
          layers=[
              Dense(16, Parameters(init='normal')),
              Activation('linear'),
              Dense(8, Parameters(init='normal')),
              Activation('linear'),
              Dense(1),
          ],
          loss='mse',
          optimizer=RMSprop(),
          metric='mse',
          batch_size=256,
          max_epochs=175,
      )
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      assert mean_squared_error(y_test, predictions.flatten()) < 1.0
  
  
  def test_knn():
      model = KNNRegressor(k=5)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      assert mean_squared_error(y_test, predictions) < 10000
  
new_contents: try:
      from sklearn.model_selection import train_test_split
  except ImportError:
      from sklearn.cross_validation import train_test_split
  from sklearn.datasets import make_regression
  
  from mla.knn import KNNRegressor
  from mla.linear_models import LinearRegression
  from mla.metrics.metrics import mean_squared_error
  from mla.neuralnet import NeuralNet
  from mla.neuralnet.layers import Activation, Dense
  from mla.neuralnet.optimizers import Adam
  from mla.neuralnet.parameters import Parameters
  
  
  # Generate a random regression problem
  X, y = make_regression(n_samples=1000, n_features=10,
                         n_informative=10, n_targets=1, noise=0.05,
                         random_state=1111, bias=0.5)
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
                                                      random_state=1111)
  
  
  def test_linear():
      model = LinearRegression(lr=0.01, max_iters=2000, penalty='l2', C=0.03)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      assert mean_squared_error(y_test, predictions) < 0.25
  
  
  def test_mlp():
      model = NeuralNet(
          layers=[
              Dense(16, Parameters(init='normal')),
              Activation('linear'),
              Dense(8, Parameters(init='normal')),
              Activation('linear'),
              Dense(1),
          ],
          loss='mse',
          optimizer=Adam(),
          metric='mse',
          batch_size=64,
          max_epochs=150,
      )
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      assert mean_squared_error(y_test, predictions.flatten()) < 1.0
  
  
  def test_knn():
      model = KNNRegressor(k=5)
      model.fit(X_train, y_train)
      predictions = model.predict(X_test)
      assert mean_squared_error(y_test, predictions) < 10000
  
subject: Fix mlp test