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Added triangular distribution #2

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1 change: 1 addition & 0 deletions reno/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,7 @@
Normal,
Observation,
Uniform,
Triangular,
abs,
add,
assign,
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67 changes: 67 additions & 0 deletions reno/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,7 @@
# -- distributions --
"Normal",
"Uniform",
"Triangular",
"DiscreteUniform",
"Bernoulli",
"Categorical",
Expand Down Expand Up @@ -2176,6 +2177,72 @@ def pt_str(self, **refs: dict[str, str]) -> str:
return f'pm.Uniform("{name}", {self.sub_equation_parts[0].pt_str(**refs)}, {self.sub_equation_parts[1].pt_str(**refs)}{dist_dim_shape_args_str(self, refs)})'


class Triangular(reno.components.Distribution):
"""Triangular distribution with lower bound, upper bound, and mode.

String notation: ``Triangular(lower=0.0, upper=1.0, c=0.5, per_timestep=False)``
``c`` is the most likely value of the distribution. Must be between ``lower`` and ``upper``.
"""

def __init__(
self,
lower: float = 0.0,
upper: float = 1.0,
c: float = 0.5,
per_timestep: bool = False,
dim: int = 1,
):
super().__init__(lower, upper, c, per_timestep=per_timestep, dim=dim)

def op_latex(self, **kwargs: dict) -> str:
return (
f"\\text{{Triangular}}("
f"{self.sub_equation_parts[0].latex(**kwargs)}, "
f"{self.sub_equation_parts[1].latex(**kwargs)}, "
f"{self.sub_equation_parts[2].latex(**kwargs)})"
)

def get_type(self) -> type:
return float

def populate(self, steps: int = 0, dim: int = 1) -> None:
dims = dist_shape(self, steps, dim)
lower = self.sub_equation_parts[0].eval()
upper = self.sub_equation_parts[1].eval()
c = self.sub_equation_parts[2].eval()
self.value = np.random.triangular(lower, c, upper, size=dims)

def __repr__(self) -> str:
args = (
f"{self.clean_part_repr(0)}, "
f"{self.clean_part_repr(1)}, "
f"{self.clean_part_repr(2)}"
)
if not self.per_timestep:
return f"Triangular({args})"
return f"Triangular({args}, {self.per_timestep})"

def pt(self, **refs: dict[str, pt.TensorVariable]) -> pt.TensorVariable:
name, *_ = dist_params(self, refs)
return pm.Triangular(
name,
lower=self.sub_equation_parts[0].pt(**refs),
upper=self.sub_equation_parts[1].pt(**refs),
c=self.sub_equation_parts[2].pt(**refs),
**dist_dim_shape_args(self, refs),
)

def pt_str(self, **refs: dict[str, str]) -> str:
name, *_ = dist_params(self, refs)
lower = self.sub_equation_parts[0].pt_str(**refs)
upper = self.sub_equation_parts[1].pt_str(**refs)
c = self.sub_equation_parts[1].pt_str(**refs)
return (
f'pm.Triangular("{name}", lower={lower}, upper={upper}, c={c}'
f'{dist_dim_shape_args_str(self, refs)})'
)


class DiscreteUniform(reno.components.Distribution):
"""Uniform/flat distribution across a provided range, returning only integer values.

Expand Down
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