74 lines
1.9 KiB
Python
74 lines
1.9 KiB
Python
from typing import Any, Dict
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import ast
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import torch
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from torch.distributions import Chi2
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import scipy.stats as scst
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class Chi2Ex(Chi2):
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"""
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A class to extend Chi2 to add missing functionality.
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pragma: skip_doc
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"""
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def icdf(self, q: torch.Tensor):
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"""
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Computes the inverse of cumulative distribution function (CDF) of the Chi-squared distribution.
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Args:
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q (torch.Tensor): Input tensor containing values where the ICDF is evaluated.
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Returns:
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torch.Tensor: Tensor containing the inverse of cumulative probabilities.
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"""
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a = q.detach().cpu().numpy()
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df = self.df.detach().cpu().item()
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outputs = scst.chi2.ppf(a, df)
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return torch.tensor(outputs, dtype=torch.float32).to(q.device)
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class PtdChi2:
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"""
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Ptd Chi2:
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Instantiates a Chi-squared distribution object.
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Args:
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df (str): Degree of freedom. You can specify a scalar or a tuple of float.
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category: PyTorch wrapper - Distribution
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"""
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@classmethod
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def INPUT_TYPES(cls) -> Dict[str, Any]:
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"""
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Defines the input types for the function.
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Returns:
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Dict[str, Any]: A dictionary specifying the required input types.
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"""
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return {
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"required": {
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"df": ("STRING", {"default": ""})
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}
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}
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RETURN_TYPES: tuple = ("PTDISTRIBUTION",)
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FUNCTION: str = "f"
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CATEGORY: str = "Distribution"
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def f(self,
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df: str) -> tuple:
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"""
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Instantiates a Chi-squared distribution object.
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Args:
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df (str): Degree of freedom. You can specify a scalar or a tuple of float.
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Returns:
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tuple: (torch.distributions.binomial.Chi2)
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"""
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d = ast.literal_eval(df)
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dist = Chi2Ex(
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df=torch.tensor(d, dtype=torch.float32))
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return (dist,)
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