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Provides functions for fast matrix inversion, creation of empirical CDF from sample data including handling of asymmetric errors, and fitting to a funtion using chi-squared. The fitting procedure return the full covariance matrix describing the fitted parameters.
Retired package: Release invalid - Does not build and run properly
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lib/distributions/poisson.ex
defmodule Chi2fit.Distribution.Poisson do
# Copyright 2019 Pieter Rijken
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
@moduledoc """
The Poisson distribution.
For the implementation, see https://en.wikipedia.org/wiki/Poisson_distribution, 'Generating Poisson-distributed random variables'
"""
alias Exboost.Math, as: M
defstruct [:pars, period: 1.0, name: "poisson"]
@type t() :: %__MODULE__{
pars: [number()] | nil,
period: number(),
name: String.t
}
end
defimpl Chi2fit.Distribution, for: Chi2fit.Distribution.Poisson do
alias Chi2fit.Distribution, as: D
import D.Poisson
alias D.Poisson
alias Exboost.Math, as: M
@step 500
@expstep :math.exp(@step)
defp iterate(p,r) when p<1 and r>0 and r>@step, do: iterate(p*@expstep,r-@step)
defp iterate(p,r) when p<1 and r>0, do: iterate(p*:math.exp(r),0)
defp iterate(p,r), do: {p,r}
defp _poisson(rate, k \\ 0, p \\ 1.0)
defp _poisson(rate,k,p) do
k = k+1
p = p*:rand.uniform()
{p,rate} = iterate p,rate
if p>1, do: _poisson(rate,k,p), else: k-1
end
defp poisson(rate), do: fn -> _poisson(rate) end
defp poissonCDF(rate) when rate >= 0.0 do
fn t -> 1.0 - M.gamma_p(Float.floor(t+1.0),rate) end
end
defp poissonCDF(rate) when rate < 0.0, do: fn _t -> 0.0 end
def skewness(%Poisson{pars: nil, period: factor}), do: fn [lambda] -> 1/:math.sqrt(lambda*factor) end
def kurtosis(%Poisson{pars: nil, period: factor}), do: fn [lambda] -> 1/lambda/factor end
def size(%Poisson{}), do: 1
def cdf(%Poisson{pars: nil, period: factor}), do: fn x, [lambda] -> poissonCDF(lambda*factor).(x) end
def cdf(%Poisson{pars: [lambda], period: factor}), do: fn x -> poissonCDF(lambda*factor).(x) end
def pdf(%Poisson{pars: nil, period: factor}), do: fn x, [lambda] -> :math.exp( x*:math.log(lambda*factor) - lambda*factor - M.lgamma(x+1) ) end
def pdf(%Poisson{pars: [lambda], period: factor}), do: fn x -> :math.exp( x*:math.log(lambda*factor) - lambda*factor - M.lgamma(x+1) ) end
def random(%Poisson{pars: [lambda], period: factor}), do: poisson(lambda*factor).()
def random(%Poisson{pars: nil, period: factor}), do: fn [lambda] -> poisson(lambda*factor).() end
def name(model), do: model.name
end
defimpl Inspect, for: Chi2fit.Distribution.Poisson do
import Inspect.Algebra
def inspect(dict, opts) do
case dict.pars do
nil ->
"#Poisson<>"
[rate] ->
concat ["#Poisson<", to_doc("rate=#{rate}", opts), ">"]
list ->
concat ["#Poisson<", to_doc(list, opts), ">"]
end
end
end