class_docstr: Build a Curve Model to predict the performance
  
  Algorithm: https://github.com/Microsoft/nni/blob/master/src/sdk/pynni/nni/curvefitting_assessor/README.md
  
  Parameters
  ----------
  target_pos : int
      The point we need to predict
class_docstr_tok: ["Build","a","Curve","Model","to","predict","the","performance","Algorithm",":","https",":","\/\/github.com\/Microsoft\/nni\/blob\/master\/src\/sdk\/pynni\/nni\/curvefitting_assessor\/README.md","Parameters","--","--","--","--","--","target_pos",":","int","The","point","we","need","to","predict"]
docstr: returns the value of normal distribution, given the weight's sample and target position
  
  Parameters
  ----------
  pos : int
      the epoch number of the position you want to predict
  sample : list
      sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
  
  Returns
  -------
  float
      the value of normal distribution
docstr_tok: ["returns","the","value","of","normal","distribution",",","given","the","weight","'s","sample","and","target","position","Parameters","--","--","--","--","--","pos",":","int","the","epoch","number","of","the","position","you","want","to","predict","sample",":","list","sample","is","a","(","1","*","NUM_OF_FUNCTIONS",")","matrix",",","representing","{","w1",",","w2",",","...","wk","}","Returns","--","--","--","-","float","the","value","of","normal","distribution"]
code: def normal_distribution(self, pos, sample):
      """returns the value of normal distribution, given the weight's sample and target position
  
      Parameters
      ----------
      pos : int
          the epoch number of the position you want to predict
      sample : list
          sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
  
      Returns
      -------
      float
          the value of normal distribution
      """
      curr_sigma_sq = self.sigma_sq(sample)
      delta = self.trial_history[pos - 1] - self.f_comb(pos, sample)
      return np.exp(
          np.square(delta) / (-2.0 * curr_sigma_sq)
      ) / np.sqrt(2 * np.pi * np.sqrt(curr_sigma_sq))
  
file_path: nni/nni/algorithms/hpo/curvefitting_assessor/model_factory.py
imports: import logging
  import numpy
  from scipy import optimize
  from .curvefunctions import *
label_desc: Use image node_id 8 for calling the CurveModel obj's underlying member method code with example usage: obj.normal_distribution(pos, sample) and returns: unknown