53 lines
1.6 KiB
Python
53 lines
1.6 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 Beta
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class PtdBeta:
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"""
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Instantiates a Beta distribution object.
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Args:
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alpha: The alpha parameter of the distsribution. You can specify a scalar or a tuple of float.
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beta: The beta parameter of the distribution. 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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"alpha": ("STRING", {"default": ""}),
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"beta": ("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, alpha: str, beta: str) -> tuple:
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"""
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Instantiates a Beta distribution object.
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Args:
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alpha: The alpha parameter of the distsribution. You can specify a scalar or a tuple of float.
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beta: The beta parameter of the distribution. You can specify a scalar or a tuple of float.
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Returns:
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tuple: (torch.distributions.binomial.Beta)
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"""
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c1 = ast.literal_eval(alpha)
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c2 = ast.literal_eval(beta)
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dist = Beta(
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concentration1=torch.tensor(c1, dtype=torch.float32), concentration0=torch.tensor(c2, dtype=torch.float32))
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return (dist,)
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