def squared_exponential(theta, d):
 theta = np.asarray(theta, dtype=np.float64)
 d = np.asarray(d, dtype=np.float64)
 if (d.ndim > 1):
  n_features = d.shape[1]
 else:
  n_features = 1
 if (theta.size == 1):
  return np.exp(((- theta[0]) * np.sum((d ** 2), axis=1)))
 elif (theta.size != n_features):
  raise ValueError(('Length of theta must be 1 or %s' % n_features))
 else:
  return np.exp((- np.sum((theta.reshape(1, n_features) * (d ** 2)), axis=1)))

def squared_exponential ( theta , d ) : theta = np . asarray ( theta , dtype = np . float64 ) d = np . asarray ( d , dtype = np . float64 ) if ( d . ndim > 1 ) : n_features = d . shape [ 1 ] else : n_features = 1 if ( theta . size == 1 ) : return np . exp ( ( ( - theta [ 0 ] ) * np . sum ( ( d ** 2 ) , axis = 1 ) ) ) elif ( theta . size != n_features ) : raise ValueError ( ( 'Length of theta must be 1 or %s' % n_features ) ) else : return np . exp ( ( - np . sum ( ( theta . reshape ( 1 , n_features ) * ( d ** 2 ) ) , axis = 1 ) ) )
squared exponential correlation model .