Triangular distribution

[xuhan-incore]

Can the triangular distribution be added to the distribution collections so that reliability analysis can be carried out when a random variable is of triangular distribution? Thank you.

[connor-krill]

Thanks for suggesting this feature! It's a good idea and we hope to include it in a future release. In the meantime, the distribution can be implemented as a subclass of the existing DistributionContinuous1D class. I hope this helps!

from scipy import stats
from UQpy.distributions.baseclass import DistributionContinuous1D

class Triangular(DistributionContinuous1D):

    def __init__(self, c: float, loc: float = 0.0, scale: float = 1.0):
        """

        :param c: Shape parameter between :math:`0 \leq c \leq 1`
        :param loc: The non-zero part distribution starts at ``loc``. Default: 0.0
        :param scale: The width of the non-zero part of the distribution. Default: 1.0
        """
        super().__init__(c=c, loc=loc, scale=scale, ordered_parameters=("c", "loc", "scale"))
        self._construct_from_scipy(scipy_name=stats.triang)

    def __repr__(self):
        s = "c={c}"
        if self.parameters["loc"] != 0.0:
            s += ", loc={loc}"
        if self.parameters["scale"] != 1.0:
            s += ", scale={scale}"
        s = s.format(**self.parameters)
        return f"Triangular({s})"

[xuhan-incore]

Hi Connor, Thank you so much for providing the code. I can now consider the triangular distribution in FORM analysis. Yours sincerely, Xu Han

…

________________________________ From: Connor Krill ***@***.***> Sent: Friday, March 28, 2025 11:49 AM To: SURGroup/UQpy ***@***.***> Cc: xuhan-incore ***@***.***>; Author ***@***.***> Subject: Re: [SURGroup/UQpy] Triangular distribution (Issue #262) Thanks for suggesting this feature! It's a good idea and we hope to include it in a future release. In the meantime, the distribution can be implemented as a subclass of the existing DistributionContinuous1D class. I hope this helps! from scipy import stats from UQpy.distributions.baseclass import DistributionContinuous1D class Triangular(DistributionContinuous1D): def __init__(self, c: float, loc: float = 0.0, scale: float = 1.0): """ :param c: Shape parameter between :math:`0 \leq c \leq 1` :param loc: The non-zero part distribution starts at ``loc``. Default: 0.0 :param scale: The width of the non-zero part of the distribution. Default: 1.0 """ super().__init__(c=c, loc=loc, scale=scale, ordered_parameters=("c", "loc", "scale")) self._construct_from_scipy(scipy_name=stats.triang) def __repr__(self): s = "c={c}" if self.parameters["loc"] != 0.0: s += ", loc={loc}" if self.parameters["scale"] != 1.0: s += ", scale={scale}" s = s.format(**self.parameters) return f"Triangular({s})" — Reply to this email directly, view it on GitHub<#262 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/A66BPNSD5VIVAF3V5V2OGND2WVVOZAVCNFSM6AAAAABZSZ65VOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDONRRG42DENZYHA>. You are receiving this because you authored the thread.Message ID: ***@***.***> [connor-krill]connor-krill left a comment (SURGroup/UQpy#262)<#262 (comment)> Thanks for suggesting this feature! It's a good idea and we hope to include it in a future release. In the meantime, the distribution can be implemented as a subclass of the existing DistributionContinuous1D class. I hope this helps! from scipy import stats from UQpy.distributions.baseclass import DistributionContinuous1D class Triangular(DistributionContinuous1D): def __init__(self, c: float, loc: float = 0.0, scale: float = 1.0): """ :param c: Shape parameter between :math:`0 \leq c \leq 1` :param loc: The non-zero part distribution starts at ``loc``. Default: 0.0 :param scale: The width of the non-zero part of the distribution. Default: 1.0 """ super().__init__(c=c, loc=loc, scale=scale, ordered_parameters=("c", "loc", "scale")) self._construct_from_scipy(scipy_name=stats.triang) def __repr__(self): s = "c={c}" if self.parameters["loc"] != 0.0: s += ", loc={loc}" if self.parameters["scale"] != 1.0: s += ", scale={scale}" s = s.format(**self.parameters) return f"Triangular({s})" — Reply to this email directly, view it on GitHub<#262 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/A66BPNSD5VIVAF3V5V2OGND2WVVOZAVCNFSM6AAAAABZSZ65VOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDONRRG42DENZYHA>. You are receiving this because you authored the thread.Message ID: ***@***.***>