removed pull request trigger from codeql analysis
This commit is contained in:
Quinn Dougherty
parent
656ad92f40
commit
76a0f254ea
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.github/workflows/codeql-analysis.yml
vendored
7
.github/workflows/codeql-analysis.yml
vendored
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@ -18,13 +18,6 @@ on:
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- production
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- staging
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- develop
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pull_request:
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# The branches below must be a subset of the branches above
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branches:
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- master
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- production
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- staging
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- develop
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schedule:
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- cron: "42 19 * * 0"
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@ -1,6 +0,0 @@
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{
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"extends": "@parcel/config-default",
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"transformers": {
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"*.res": ["@parcel/transformer-raw"]
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}
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}
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@ -1,69 +1,101 @@
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---
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title: Functions reference
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sidebar_position: 7
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---
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import { SquiggleEditor } from "../../src/components/SquiggleEditor";
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# Squiggle Functions Reference
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_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)_
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## Distributions
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# Inventory distributions
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### Normal distribution
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We provide starter distributions, computed symbolically.
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## Normal distribution
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The `normal(mean, sd)` function creates a normal distribution with the given mean
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and standard deviation.
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<SquiggleEditor initialSquiggleString="normal(5, 1)" />
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### Uniform distribution
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### Validity
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- `sd > 0`
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## Uniform distribution
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The `uniform(low, high)` function creates a uniform distribution between the
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two given numbers.
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<SquiggleEditor initialSquiggleString="uniform(3, 7)" />
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### Lognormal distribution
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### Validity
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- `low < high`
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## Lognormal distribution
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The `lognormal(mu, sigma)` returns the log of a normal distribution with parameters
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mu and sigma. The log of lognormal(mu, sigma) is a normal distribution with parameters
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mean mu and standard deviation sigma.
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`mu` and `sigma`. The log of `lognormal(mu, sigma)` is a normal distribution with mean `mu` and standard deviation `sigma`.
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<SquiggleEditor initialSquiggleString="lognormal(0, 0.7)" />
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An alternative format is also available. The "to" notation creates a lognormal
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An alternative format is also available. The `to` notation creates a lognormal
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distribution with a 90% confidence interval between the two numbers. We add
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this convinience as lognormal distributions are commonly used in practice.
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<SquiggleEditor initialSquiggleString="2 to 10" />
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### Future feature:
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Furthermore, it's also possible to create a lognormal from it's actual mean
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and standard deviation, using `lognormalFromMeanAndStdDev`.
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<SquiggleEditor initialSquiggleString="lognormalFromMeanAndStdDev(20, 10)" />
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### Beta distribution
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### Validity
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- `sigma > 0`
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- In `x to y` notation, `x < y`
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The `beta(a, b)` function creates a beta distribution with parameters a and b:
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## Beta distribution
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The `beta(a, b)` function creates a beta distribution with parameters `a` and `b`:
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<SquiggleEditor initialSquiggleString="beta(20, 20)" />
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### Exponential distribution
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### Validity
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- `a > 0`
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- `b > 0`
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- Empirically, we have noticed that numerical instability arises when `a < 1` or `b < 1`
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The `exponential(mean)` function creates an exponential distribution with the given
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mean.
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## Exponential distribution
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The `exponential(rate)` function creates an exponential distribution with the given
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rate.
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<SquiggleEditor initialSquiggleString="exponential(1)" />
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### The Triangular distribution
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### Validity
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- `rate > 0`
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## Triangular distribution
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The `triangular(a,b,c)` function creates a triangular distribution with lower
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bound a, mode b and upper bound c.
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bound `a`, mode `b` and upper bound `c`.
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### Validity
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- `a < b < c`
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<SquiggleEditor initialSquiggleString="triangular(1, 2, 4)" />
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### Multimodal distriutions
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## Scalar (constant dist)
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The multimodal function combines 2 or more other distributions to create a weighted
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Squiggle, when the context is right, automatically casts a float to a constant distribution.
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# Operating on distributions
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Here are the ways we combine distributions.
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## Mixture of distributions
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The `mx` function combines 2 or more other distributions to create a weighted
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combination of the two. The first positional arguments represent the distributions
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to be combined, and the last argument is how much to weigh every distribution in the
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combination.
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@ -78,37 +110,114 @@ As well as mixed distributions:
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<SquiggleEditor initialSquiggleString="mx(3, 8, 1 to 10, [0.2, 0.3, 0.5])" />
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## Other Functions
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An alias of `mx` is `mixture`
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### PDF of a distribution
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### Validity
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Using javascript's variable arguments notation, consider `mx(...dists, weights)`:
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- `dists.length == weights.length`
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The `pdf(distribution, x)` function returns the density of a distribution at the
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## Addition (horizontal right shift)
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<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 + dist2">
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## Subtraction (horizontal left shift)
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<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 - dist2">
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## Multiplication (??)
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<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 * dist2">
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## Division (???)
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<SquiggleEditor initialSquiggleString="dist1 = 1 to 10; dist2 = triangular(1,2,3); dist1 / dist2">
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## Taking the base `e` exponential
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<SquiggleEditor initialSquiggleString="dist = triangular(1,2,3); exp(dist)">
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## Taking the base `e` and base `10` logarithm
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<SquiggleEditor initialSquiggleString="dist = triangular(1,2,3); log(dist)">
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<SquiggleEditor initialSquiggleString="dist = beta(1,2); log10(dist)">
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### Validity
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- See [the current discourse](https://github.com/quantified-uncertainty/squiggle/issues/304)
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# Standard functions on distributions
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## Probability density function
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The `pdf(dist, x)` function returns the density of a distribution at the
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given point x.
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<SquiggleEditor initialSquiggleString="pdf(normal(0,1),0)" />
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### Inverse of a distribution
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### Validity
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- `x` must be a scalar
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- `dist` must be a distribution
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The `inv(distribution, prob)` gives the value x or which the probability for all values
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lower than x is equal to prob. It is the inverse of `cdf`.
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## Cumulative density function
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<SquiggleEditor initialSquiggleString="inv(normal(0,1),0.5)" />
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### CDF of a distribution
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The `cdf(distribution,x)` gives the cumulative probability of the distribution
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The `cdf(dist, x)` gives the cumulative probability of the distribution
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or all values lower than x. It is the inverse of `inv`.
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<SquiggleEditor initialSquiggleString="cdf(normal(0,1),0)" />
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### Mean of a distribution
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### Validity
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- `x` must be a scalar
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- `dist` must be a distribution
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## Inverse CDF
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The `inv(dist, prob)` gives the value x or which the probability for all values
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lower than x is equal to prob. It is the inverse of `cdf`.
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<SquiggleEditor initialSquiggleString="inv(normal(0,1),0.5)" />
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### Validity
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- `prob` must be a scalar (please only put it in `(0,1)`)
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- `dist` must be a distribution
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## Mean
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The `mean(distribution)` function gives the mean (expected value) of a distribution.
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<SquiggleEditor initialSquiggleString="mean(normal(5, 10))" />
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### Sampling a distribution
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## Sampling a distribution
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The `sample(distribution)` samples a given distribution.
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<SquiggleEditor initialSquiggleString="sample(normal(0, 10))" />
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# Normalization
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Some distribution operations (like horizontal shift) return an unnormalized distriibution.
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We provide a `normalize` function
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<SquiggleEditor initialSquiggleString="normalize((1e-1 to 1e0) + triangular(1e-1, 1e0, 1e1))" />
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### Valdity
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- Input to `normalize` must be a dist
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We provide a predicate `isNormalized`, for when we have simple control flow
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<SquiggleEditor initialSquiggleString="isNormalized((1e-1 to 1e0) * triangular(1e-1, 1e0, 1e1))" />
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### Validity
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- Input to `isNormalized` must be a dist
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# Convert any distribution to a sample set distribution
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`toSampleSet` has two signatures
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It is unary when you use an internal hardcoded number of samples
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<SquiggleEditor initialSquiggleString="toSampleSet(1e-1 to 1e0)" />
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And binary when you provide a number of samples (truncated)
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<SquiggleEditor initialSquiggleString="toSampleSet(1e-1 to 1e0, 1e2)" />
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@ -1,5 +1,5 @@
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---
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title: Statistical properties of algebraic combinations of distributions for property testing.
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title: Invariants
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urlcolor: blue
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author:
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- Nuño Sempere
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@ -7,8 +7,12 @@ author:
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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.
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---
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Invariants to check with property tests.
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_This document right now is normative and aspirational, not a description of the testing that's currently done_.
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# Algebraic combinations
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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.
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## Means and standard deviations
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@ -118,6 +122,20 @@ TODO
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TODO
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# `pdf`, `cdf`, and `inv`
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With $\forall dist, pdf := x \mapsto \texttt{pdf}(dist, x) \land cdf := x \mapsto \texttt{cdf}(dist, x) \land inv := p \mapsto \texttt{inv}(dist, p)$,
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## `cdf` and `inv` are inverses
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$$
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\forall x \in (0,1), cdf(inv(x)) = x \land \forall x \in \texttt{dom}(cdf), x = inv(cdf(x))
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$$
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## The codomain of `cdf` equals the open interval `(0,1)` equals the codomain of `pdf`
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$$
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\texttt{cod}(cdf) = (0,1) = \texttt{cod}(pdf)
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$$
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# To do:
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- Provide sources or derivations, useful as this document becomes more complicated
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