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 math
+
+import torch
+from torch import Tensor
+from torch.distributions import constraints
+from torch.distributions.exp_family import ExponentialFamily
+from torch.distributions.utils import _standard_normal, broadcast_all
+from torch.types import _Number, _size
+
+
+__all__ = ["Normal"]
+
+
+class Normal(ExponentialFamily):
+    r"""
+    Creates a normal (also called Gaussian) distribution parameterized by
+    :attr:`loc` and :attr:`scale`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = Normal(torch.tensor([0.0]), torch.tensor([1.0]))
+        >>> m.sample()  # normally distributed with loc=0 and scale=1
+        tensor([ 0.1046])
+
+    Args:
+        loc (float or Tensor): mean of the distribution (often referred to as mu)
+        scale (float or Tensor): standard deviation of the distribution
+            (often referred to as sigma)
+    """
+
+    # pyrefly: ignore [bad-override]
+    arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
+    support = constraints.real
+    has_rsample = True
+    _mean_carrier_measure = 0
+
+    @property
+    def mean(self) -> Tensor:
+        return self.loc
+
+    @property
+    def mode(self) -> Tensor:
+        return self.loc
+
+    @property
+    def stddev(self) -> Tensor:
+        return self.scale
+
+    @property
+    def variance(self) -> Tensor:
+        return self.stddev.pow(2)
+
+    def __init__(
+        self,
+        loc: Tensor | float,
+        scale: Tensor | float,
+        validate_args: bool | None = None,
+    ) -> None:
+        self.loc, self.scale = broadcast_all(loc, scale)
+        if isinstance(loc, _Number) and isinstance(scale, _Number):
+            batch_shape = torch.Size()
+        else:
+            batch_shape = self.loc.size()
+        super().__init__(batch_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(Normal, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new.loc = self.loc.expand(batch_shape)
+        new.scale = self.scale.expand(batch_shape)
+        super(Normal, 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.normal(self.loc.expand(shape), self.scale.expand(shape))
+
+    def rsample(self, sample_shape: _size = torch.Size()) -> Tensor:
+        shape = self._extended_shape(sample_shape)
+        eps = _standard_normal(shape, dtype=self.loc.dtype, device=self.loc.device)
+        return self.loc + eps * self.scale
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        # compute the variance
+        var = self.scale**2
+        log_scale = (
+            math.log(self.scale)
+            if isinstance(self.scale, _Number)
+            else self.scale.log()
+        )
+        return (
+            -((value - self.loc) ** 2) / (2 * var)
+            - log_scale
+            - math.log(math.sqrt(2 * math.pi))
+        )
+
+    def cdf(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        return 0.5 * (
+            1 + torch.erf((value - self.loc) * self.scale.reciprocal() / math.sqrt(2))
+        )
+
+    def icdf(self, value):
+        return self.loc + self.scale * torch.erfinv(2 * value - 1) * math.sqrt(2)
+
+    def entropy(self):
+        return 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(self.scale)
+
+    @property
+    def _natural_params(self) -> tuple[Tensor, Tensor]:
+        return (self.loc / self.scale.pow(2), -0.5 * self.scale.pow(2).reciprocal())
+
+    # pyrefly: ignore [bad-override]
+    def _log_normalizer(self, x, y):
+        return -0.25 * x.pow(2) / y + 0.5 * torch.log(-math.pi / y)