class_docstr: The Gaussian Naive Bayes classifier. 
class_docstr_tok: ["The","Gaussian","Naive","Bayes","classifier","."]
docstr: Gaussian likelihood of the data x given mean and var 
docstr_tok: ["Gaussian","likelihood","of","the","data","x","given","mean","and","var"]
code: def _calculate_likelihood(self, mean, var, x):
      """Gaussian likelihood of the data x given mean and var"""
      eps = 1e-4  # Added in denominator to prevent division by zero
      coeff = 1.0 / math.sqrt(2.0 * math.pi * var + eps)
      exponent = math.exp(-(math.pow(x - mean, 2) / (2 * var + eps)))
      return coeff * exponent
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/naive_bayes.py
imports: from __future__ import division, print_function
  import numpy
  import math
  from mlfromscratch.utils import train_test_split, normalize
  from mlfromscratch.utils import Plot, accuracy_score
label_desc: Use image node_id 2 for calling the NaiveBayes obj's underlying member method code with example usage: obj._calculate_likelihood(mean, var, x) and returns: unknown