title: Trouble fitting simple data with MLPRegressor
body: <p>I am trying out Python and scikit-learn. I cannot get MLPRegressor to come even close to the data. Where is this going wrong?</p>
  
  <pre><code>from sklearn.neural_network import MLPRegressor
  import numpy as np
  import matplotlib.pyplot as plt
  
  x = np.arange(0.0, 1, 0.01).reshape(-1, 1)
  y = np.sin(2 * np.pi * x).ravel()
  
  reg = MLPRegressor(hidden_layer_sizes=(10,),  activation='relu', solver='adam',    alpha=0.001,batch_size='auto',
                 learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=1000, shuffle=True,
                 random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,
                 nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999,
                 epsilon=1e-08)
  
  reg = reg.fit(x, y)
  
  test_x = np.arange(0.0, 1, 0.05).reshape(-1, 1)
  test_y = reg.predict(test_x)
  
  fig = plt.figure()
  ax1 = fig.add_subplot(111)
  ax1.scatter(x, y, s=10, c='b', marker="s", label='real')
  ax1.scatter(test_x,test_y, s=10, c='r', marker="o", label='NN Prediction')
  
  plt.show()
  </code></pre>
  
  <p>The result is not very good:
  <img src="https://i.stack.imgur.com/hzutQ.png" alt="failed fit">
  Thank you.</p>