Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

2026-09-24 13:22:23 +07:00
commit 642cc11a9f
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+# mypy: allow-untyped-defs
+
+import torch
+from torch import Tensor
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import broadcast_all
+from torch.types import _Number, Number
+
+
+__all__ = ["Poisson"]
+
+
+class Poisson(ExponentialFamily):
+    r"""
+    Creates a Poisson distribution parameterized by :attr:`rate`, the rate parameter.
+
+    Samples are nonnegative integers, with a pmf given by
+
+    .. math::
+      \mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}
+
+    Example::
+
+        >>> # xdoctest: +SKIP("poisson_cpu not implemented for 'Long'")
+        >>> m = Poisson(torch.tensor([4]))
+        >>> m.sample()
+        tensor([ 3.])
+
+    Args:
+        rate (Number, Tensor): the rate parameter
+    """
+
+    # pyrefly: ignore [bad-override]
+    arg_constraints = {"rate": constraints.nonnegative}
+    support = constraints.nonnegative_integer
+
+    @property
+    def mean(self) -> Tensor:
+        return self.rate
+
+    @property
+    def mode(self) -> Tensor:
+        return self.rate.floor()
+
+    @property
+    def variance(self) -> Tensor:
+        return self.rate
+
+    def __init__(
+        self,
+        rate: Tensor | Number,
+        validate_args: bool | None = None,
+    ) -> None:
+        (self.rate,) = broadcast_all(rate)
+        if isinstance(rate, _Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.rate.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Poisson, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.rate = self.rate.expand(batch_shape)
+        super(Poisson, new).__init__(batch_shape, validate_args=False)
+        new._validate_args = self._validate_args
+        return new
+
+    def sample(self, sample_shape=torch.Size()):
+        shape = self._extended_shape(sample_shape)
+        with torch.no_grad():
+            return torch.poisson(self.rate.expand(shape))
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        rate, value = broadcast_all(self.rate, value)
+        return value.xlogy(rate) - rate - (value + 1).lgamma()
+
+    @property
+    def _natural_params(self) -> tuple[Tensor]:
+        return (torch.log(self.rate),)
+
+    # pyrefly: ignore [bad-override]
+    def _log_normalizer(self, x):
+        return torch.exp(x)