Added descriptions to last API types
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@ -292,7 +292,7 @@ quantile(normal(5, 2), 0.5);
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**TODO: Will soon be called "PointSet.make"**
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Converts a distribution to the pointSet format
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Converts a distribution to the pointSet format.
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```
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toPointSet: (distribution) => pointSetDistribution
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@ -308,7 +308,7 @@ toPointSet(normal(5, 2));
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**TODO: Will soon be called "SampleSet.make"**
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Converts a distribution to the sampleSet format, with n samples
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Converts a distribution to the sampleSet format, with n samples.
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```
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toSampleSet: (distribution, number) => sampleSetDistribution
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@ -2,6 +2,13 @@
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sidebar_position: 4
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title: Point Set Distribution
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---
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:::danger
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These functions aren't yet implemented with these specific names. This should be changed soon
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:::
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Point set distributions are one of the three distribution formats. They are stored as a list of x-y coordinates representing both discrete and continuous distributions.
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One complication is that it's possible to represent invalid probability distributions in the point set format. For example, you can represent shapes with negative values, or shapes that are not normalized.
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### make
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@ -2,13 +2,22 @@
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sidebar_position: 5
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title: Sample Set Distribution
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---
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:::danger
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These functions aren't yet implemented with these specific names. This should be added soon.
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:::
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Sample set distributions are one of the three distribution formats. Internally, they are stored as a list of numbers. It's useful to distinguish point set distributions from arbitrary lists of numbers to make it clear which functions are applicable.
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Monte Carlo calculations typically result in sample set distributions.
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All regular distribution function work on sample set distributions. In addition, there are several functions that only work on sample set distributions.
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### make
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```
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SampleSet.make: (distribution) => sampleSet
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SampleSet.make: (() => number) => sampleSet
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SampleSet.make: (list<number>) => sampleSet
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SampleSet.make: (() => number) => sampleSet // not yet implemented
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```
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### map
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@ -35,7 +44,7 @@ SampleSet.map3: (sampleSet, sampleSet, sampleSet, ((number, number, number) => n
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SampleSet.toList: (sampleSet) => list<number>
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```
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Gets the internal samples of a sampleSet distribution. This is separate from the sampleN() function, which would shuffle the samples. toList() maintains order and length. Gets the internal samples of a sampleSet distribution. This is separate from the sampleN() function, which would shuffle the samples. toList() maintains order and length.
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Gets the internal samples of a sampleSet distribution. This is separate from the sampleN() function, which would shuffle the samples. toList() maintains order and length.
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**Examples**
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@ -3,13 +3,21 @@ sidebar_position: 7
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title: List
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---
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Squiggle lists are a lot like Python lists or Ruby arrays. They accept all types.
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```javascript
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myList = [3, normal(5,2), "random"]
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```
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### make
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**Note: currently just called ``makeList``, without the preix**
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```
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List.make: (number, 'a) => list<'a>
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```
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Returns an array of size `n` filled with value `e`.
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Returns an array of size `n` filled with the value.
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```js
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List.make(4, 1); // creates the list [1, 1, 1, 1]
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@ -31,6 +39,8 @@ length: (list<'a>) => number
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### up to
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**Note: currently just called ``upTo``, without the preix**
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```
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List.upTo: (low:number, high:number) => list<number>
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```
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@ -3,6 +3,10 @@ sidebar_position: 9
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title: Number
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---
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Squiggle ``numbers`` are Javascript floats.
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Many of the functions below work on lists or pairs of numbers.
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import TOCInline from "@theme/TOCInline";
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<TOCInline toc={toc} />
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