class_docstr: Gaussian process regressor.
  Attributes:
      alpha: A hyperparameter.
class_docstr_tok: ["Gaussian","process","regressor",".","Attributes",":","alpha",":","A","hyperparameter","."]
code: def __init__(self):
      self.alpha = 1e-10
      self._distance_matrix = None
      self._x = None
      self._y = None
      self._first_fitted = False
      self._l_matrix = None
      self._alpha_vector = None
  
imports: import math
  import random
  from copy import deepcopy
  from functools import total_ordering
  from queue import PriorityQueue
  import numpy
  from scipy.linalg import LinAlgError, cho_solve, cholesky, solve_triangular
  from scipy.optimize import linear_sum_assignment
  from sklearn.metrics.pairwise import rbf_kernel
  from nni.utils import OptimizeMode
  from .graph_transformer import transform
  from .utils import Constant
  from .layers import is_layer
label_desc: Use image node_id 1 to create a new IncrementalGaussianProcess object  with example: obj = IncrementalGaussianProcess()