code: def main():
      try:
          import matplotlib.pyplot as plt
      except:
          pass
  
      # Let's start with some random values
      x = (
          np.linspace(0, 1, 10)[:, np.newaxis] * 10
      )  # np.random.rand(100)[:,np.newaxis]
      y = np.random.randn(10)
      mygp = GP(covar="ARDSE")
      mygp.real_init(x.shape[1], y)
  
      # Sample some functions given these hyperparameters and plot them
      for i in range(0, 5):
          x = np.linspace(0, 1, 100)[:, np.newaxis] * 10
          K = mygp.cov(x)
          y = np.random.randn(100)
  
          fsamp = mygp.mean + np.dot(spla.cholesky(K).transpose(), y)
          try:
              plt.plot(x, fsamp)
          except:
              pass
  
      print("Loglikelihood before optimizing: ", mygp.logprob(x, y))
      mygp.optimize_hypers(x, y)
      print("Loglikelihood after optimizing: ", mygp.logprob(x, y))
  
      try:
          plt.show()
      except:
          print("Install matplotlib to get figures")
  
file_path: auptimizer/src/aup/Proposer/spearmint/gp.py
imports: import numpy
  import scipy.linalg
  import scipy.optimize
  import scipy.io
label_desc: Use image node_id 10 for calling a global function with example usage: main() without return types