removed pull request trigger from codeql analysis

This commit is contained in:
Quinn Dougherty 2022-04-20 11:55:56 -04:00
parent 656ad92f40
commit 76a0f254ea
4 changed files with 158 additions and 44 deletions

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@ -18,13 +18,6 @@ on:
- production
- staging
- develop
pull_request:
# The branches below must be a subset of the branches above
branches:
- master
- production
- staging
- develop
schedule:
- cron: "42 19 * * 0"

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@ -1,6 +0,0 @@
{
"extends": "@parcel/config-default",
"transformers": {
"*.res": ["@parcel/transformer-raw"]
}
}

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@ -1,69 +1,101 @@
---
title: Functions reference
sidebar_position: 7
---
import { SquiggleEditor } from "../../src/components/SquiggleEditor";
# Squiggle Functions Reference
_The source of truth for this document is [this file of code](https://github.com/quantified-uncertainty/squiggle/blob/develop/packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res)_
## Distributions
# Inventory distributions
### Normal distribution
We provide starter distributions, computed symbolically.
## Normal distribution
The `normal(mean, sd)` function creates a normal distribution with the given mean
and standard deviation.
<SquiggleEditor initialSquiggleString="normal(5, 1)" />
### Uniform distribution
### Validity
- `sd > 0`
## Uniform distribution
The `uniform(low, high)` function creates a uniform distribution between the
two given numbers.
<SquiggleEditor initialSquiggleString="uniform(3, 7)" />
### Lognormal distribution
### Validity
- `low < high`
## Lognormal distribution
The `lognormal(mu, sigma)` returns the log of a normal distribution with parameters
mu and sigma. The log of lognormal(mu, sigma) is a normal distribution with parameters
mean mu and standard deviation sigma.
`mu` and `sigma`. The log of `lognormal(mu, sigma)` is a normal distribution with mean `mu` and standard deviation `sigma`.
<SquiggleEditor initialSquiggleString="lognormal(0, 0.7)" />
An alternative format is also available. The "to" notation creates a lognormal
An alternative format is also available. The `to` notation creates a lognormal
distribution with a 90% confidence interval between the two numbers. We add
this convinience as lognormal distributions are commonly used in practice.
<SquiggleEditor initialSquiggleString="2 to 10" />
### Future feature:
Furthermore, it's also possible to create a lognormal from it's actual mean
and standard deviation, using `lognormalFromMeanAndStdDev`.
<SquiggleEditor initialSquiggleString="lognormalFromMeanAndStdDev(20, 10)" />
### Beta distribution
### Validity
- `sigma > 0`
- In `x to y` notation, `x < y`
The `beta(a, b)` function creates a beta distribution with parameters a and b:
## Beta distribution
The `beta(a, b)` function creates a beta distribution with parameters `a` and `b`:
<SquiggleEditor initialSquiggleString="beta(20, 20)" />
### Exponential distribution
### Validity
- `a > 0`
- `b > 0`
- Empirically, we have noticed that numerical instability arises when `a < 1` or `b < 1`
The `exponential(mean)` function creates an exponential distribution with the given
mean.
## Exponential distribution
The `exponential(rate)` function creates an exponential distribution with the given
rate.
<SquiggleEditor initialSquiggleString="exponential(1)" />
### The Triangular distribution
### Validity
- `rate > 0`
## Triangular distribution
The `triangular(a,b,c)` function creates a triangular distribution with lower
bound a, mode b and upper bound c.
bound `a`, mode `b` and upper bound `c`.
### Validity
- `a < b < c`
<SquiggleEditor initialSquiggleString="triangular(1, 2, 4)" />
### Multimodal distriutions
## Scalar (constant dist)
The multimodal function combines 2 or more other distributions to create a weighted
Squiggle, when the context is right, automatically casts a float to a constant distribution.
# Operating on distributions
Here are the ways we combine distributions.
## Mixture of distributions
The `mx` function combines 2 or more other distributions to create a weighted
combination of the two. The first positional arguments represent the distributions
to be combined, and the last argument is how much to weigh every distribution in the
combination.
@ -78,37 +110,114 @@ As well as mixed distributions:
<SquiggleEditor initialSquiggleString="mx(3, 8, 1 to 10, [0.2, 0.3, 0.5])" />
## Other Functions
An alias of `mx` is `mixture`
### PDF of a distribution
### Validity
Using javascript's variable arguments notation, consider `mx(...dists, weights)`:
- `dists.length == weights.length`
The `pdf(distribution, x)` function returns the density of a distribution at the
## Addition (horizontal right shift)
<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 + dist2">
## Subtraction (horizontal left shift)
<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 - dist2">
## Multiplication (??)
<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 * dist2">
## Division (???)
<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 / dist2">
## Taking the base `e` exponential
<SquiggleEditor initialSquiggleString="dist = triangular(1,2,3); exp(dist)">
## Taking the base `e` and base `10` logarithm
<SquiggleEditor initialSquiggleString="dist = triangular(1,2,3); log(dist)">
<SquiggleEditor initialSquiggleString="dist = beta(1,2); log10(dist)">
### Validity
- See [the current discourse](https://github.com/quantified-uncertainty/squiggle/issues/304)
# Standard functions on distributions
## Probability density function
The `pdf(dist, x)` function returns the density of a distribution at the
given point x.
<SquiggleEditor initialSquiggleString="pdf(normal(0,1),0)" />
### Inverse of a distribution
### Validity
- `x` must be a scalar
- `dist` must be a distribution
The `inv(distribution, prob)` gives the value x or which the probability for all values
lower than x is equal to prob. It is the inverse of `cdf`.
## Cumulative density function
<SquiggleEditor initialSquiggleString="inv(normal(0,1),0.5)" />
### CDF of a distribution
The `cdf(distribution,x)` gives the cumulative probability of the distribution
The `cdf(dist, x)` gives the cumulative probability of the distribution
or all values lower than x. It is the inverse of `inv`.
<SquiggleEditor initialSquiggleString="cdf(normal(0,1),0)" />
### Mean of a distribution
### Validity
- `x` must be a scalar
- `dist` must be a distribution
## Inverse CDF
The `inv(dist, prob)` gives the value x or which the probability for all values
lower than x is equal to prob. It is the inverse of `cdf`.
<SquiggleEditor initialSquiggleString="inv(normal(0,1),0.5)" />
### Validity
- `prob` must be a scalar (please only put it in `(0,1)`)
- `dist` must be a distribution
## Mean
The `mean(distribution)` function gives the mean (expected value) of a distribution.
<SquiggleEditor initialSquiggleString="mean(normal(5, 10))" />
### Sampling a distribution
## Sampling a distribution
The `sample(distribution)` samples a given distribution.
<SquiggleEditor initialSquiggleString="sample(normal(0, 10))" />
# Normalization
Some distribution operations (like horizontal shift) return an unnormalized distriibution.
We provide a `normalize` function
<SquiggleEditor initialSquiggleString="normalize((1e-1 to 1e0) + triangular(1e-1, 1e0, 1e1))" />
### Valdity
- Input to `normalize` must be a dist
We provide a predicate `isNormalized`, for when we have simple control flow
<SquiggleEditor initialSquiggleString="isNormalized((1e-1 to 1e0) * triangular(1e-1, 1e0, 1e1))" />
### Validity
- Input to `isNormalized` must be a dist
# Convert any distribution to a sample set distribution
`toSampleSet` has two signatures
It is unary when you use an internal hardcoded number of samples
<SquiggleEditor initialSquiggleString="toSampleSet(1e-1 to 1e0)" />
And binary when you provide a number of samples (truncated)
<SquiggleEditor initialSquiggleString="toSampleSet(1e-1 to 1e0, 1e2)" />

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@ -1,5 +1,5 @@
---
title: Statistical properties of algebraic combinations of distributions for property testing.
title: Invariants
urlcolor: blue
author:
- Nuño Sempere
@ -7,8 +7,12 @@ author:
abstract: This document outlines some properties about algebraic combinations of distributions. It is meant to facilitate property tests for [Squiggle](https://squiggle-language.com/), an estimation language for forecasters. So far, we are focusing on the means, the standard deviation and the shape of the pdfs.
---
Invariants to check with property tests.
_This document right now is normative and aspirational, not a description of the testing that's currently done_.
# Algebraic combinations
The academic keyword to search for in relation to this document is "[algebra of random variables](https://wikiless.org/wiki/Algebra_of_random_variables?lang=en)". Squiggle doesn't yet support getting the standard deviation, denoted by $\sigma$, but such support could yet be added.
## Means and standard deviations
@ -118,6 +122,20 @@ TODO
TODO
# `pdf`, `cdf`, and `inv`
With $\forall dist, pdf := x \mapsto \texttt{pdf}(dist, x) \land cdf := x \mapsto \texttt{cdf}(dist, x) \land inv := p \mapsto \texttt{inv}(dist, p)$,
## `cdf` and `inv` are inverses
$$
\forall x \in (0,1), cdf(inv(x)) = x \land \forall x \in \texttt{dom}(cdf), x = inv(cdf(x))
$$
## The codomain of `cdf` equals the open interval `(0,1)` equals the codomain of `pdf`
$$
\texttt{cod}(cdf) = (0,1) = \texttt{cod}(pdf)
$$
# To do:
- Provide sources or derivations, useful as this document becomes more complicated