content: #  Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
  #
  #  Licensed under the Apache License, Version 2.0 (the "License");
  #  you may not use this file except in compliance with the License.
  #  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  #  Unless required by applicable law or agreed to in writing, software
  #  distributed under the License is distributed on an "AS IS" BASIS,
  #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #  See the License for the specific language governing permissions and
  #  limitations under the License.
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import random
  
  import numpy as np
  
  from sklearn import datasets
  from sklearn.metrics import accuracy_score, mean_squared_error
  
  import tensorflow as tf
  from tensorflow.contrib.skflow.python import skflow
  
  
  class MultiOutputTest(tf.test.TestCase):
  
      def testMultiRegression(self):
          random.seed(42)
          rng = np.random.RandomState(1)
          X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
          y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
          regressor = skflow.TensorFlowLinearRegressor(learning_rate=0.01)
          regressor.fit(X, y)
          score = mean_squared_error(regressor.predict(X), y)
          self.assertLess(score, 10, "Failed with score = {0}".format(score))
  
  
  if __name__ == "__main__":
      tf.test.main()
  