code: import sys
  sys.path.append('/Users/phanquochuy/Projects/minimind/prototypes/george/build/lib.macosx-10.10-x86_64-2.7')
  import numpy as np
  import george
  from george.kernels import ExpSquaredKernel
  
  # Generate some fake noisy data.
  x = 10 * np.sort(np.random.rand(10))
  yerr = 0.2 * np.ones_like(x)
  y = np.sin(x) + yerr * np.random.randn(len(x))
  
  # Set up the Gaussian process.
  kernel = ExpSquaredKernel(1.0)
  gp = george.GP(kernel)
  
  # Pre-compute the factorization of the matrix.
  gp.compute(x, yerr)
  gp.optimize(x, y, verbose=True)
  
  # Compute the log likelihood.
  print(gp.lnlikelihood(y))
  
  t = np.linspace(0, 10, 500)
  mu, cov = gp.predict(y, t)
  std = np.sqrt(np.diag(cov))
apis: numpy.diag numpy.ones_like numpy.random.rand numpy.sin numpy.linspace