Coverage for src/sensai/util/math.py: 0%

1import math 

2from typing import List 

3 

4from .string import object_repr, ToStringMixin 

5 

6 

7class NormalDistribution(ToStringMixin): 

8 def __init__(self, mean=0, std=1, unit_max=False): 

9 """ 

10 :param mean: the mean 

11 :param std: the standard deviation 

12 :param unit_max: if True, scales the distribution's pdf such that its maximum value becomes 1 

13 """ 

14 import scipy.stats 

15 self.unitMax = unit_max 

16 self.mean = mean 

17 self.std = std 

18 self.norm = scipy.stats.norm(loc=mean, scale=std) 

19 

20 def _tostring_includes(self) -> List[str]: 

21 return ["mean", "std", "unitMax"] 

22 

23 def pdf(self, x): 

24 v = self.norm.pdf(x) 

25 if self.unitMax: 

26 v /= self.norm.pdf(self.mean) 

27 return v 

28 

29 def __str__(self): 

30 return object_repr(self, ["mean", "std", "unitMax"]) 

31 

32 

33def sigmoid(x: float, min_value=0, max_value=1, k=1, x0=0): 

34 return min_value + (max_value - min_value) / (1 + math.exp(-k * (x - x0))) 

35 

36 

37def reverse_sigmoid(x: float, max_value=1, min_value=0, k=1, x0=0): 

38 return max_value - sigmoid(x, min_value=0, max_value=max_value - min_value, k=k, x0=x0) 

39 

40 

41def reduce_float_precision_decimals(f: float, decimals: int) -> float: 

42 return float(format(f, '.%df' % decimals))