58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
from typing import Any, Dict
|
|
import torch
|
|
from torch.distributions import *
|
|
import ast
|
|
|
|
class PtdmIcdf:
|
|
"""
|
|
Computes the inverse of the cumulative distribution function for the input distribution.
|
|
|
|
**Note**
|
|
Icdf is not supported for all distributions in PyTorch.
|
|
|
|
Args:
|
|
distribution (torch.distributions.distribution.Distribution): Distribution.
|
|
q (float): Value
|
|
|
|
category: PyTorch wrapper - Distribution
|
|
"""
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls) -> Dict[str, Any]:
|
|
"""
|
|
Defines the input types for the function.
|
|
|
|
Returns:
|
|
Dict[str, Any]: A dictionary specifying the required input types.
|
|
"""
|
|
return {
|
|
"required": {
|
|
"distribution": ("PTDISTRIBUTION", {}),
|
|
"q": ("STRING", {"default": ""})
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES: tuple = ("TENSOR",)
|
|
FUNCTION: str = "f"
|
|
CATEGORY: str = "Distribution"
|
|
|
|
def f(self, distribution: torch.distributions.Distribution, q: str) -> tuple:
|
|
"""
|
|
Computes the inverse of the cumulative distribution function for the input distribution.
|
|
|
|
Args:
|
|
distribution (torch.distributions.Distribution): Distribution.
|
|
q (str): Value
|
|
|
|
Returns:
|
|
tuple containing the probability
|
|
"""
|
|
v2 = ast.literal_eval(q)
|
|
if isinstance(v2, int) is False and isinstance(v2, float) is False:
|
|
if isinstance(distribution, Dirichlet) is False:
|
|
raise ValueError("You need to specify a float or an int.")
|
|
|
|
tens = torch.tensor(v2, dtype=torch.float32)
|
|
|
|
return (distribution.icdf(tens),)
|