200 lines
3.2 KiB
Markdown
200 lines
3.2 KiB
Markdown
---
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sidebar_position: 1
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title: Distribution Creation
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---
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## Normal Distribution
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**Definitions**
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```javascript
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normal(frValueDistOrNumber, frValueDistOrNumber)
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```
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```javascript
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normal(dict<{p5: frValueDistOrNumber, p95: frValueDistOrNumber}>)
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```
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```javascript
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normal(dict<{mean: frValueDistOrNumber, stdev: frValueDistOrNumber}>)
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```
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**Examples**
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```javascript
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normal(5,1)
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normal({p5: 4, p95: 10})
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normal({mean: 5, stdev: 2})
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```
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## Lognormal Distribution
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**Definitions**
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```javascript
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lognormal(frValueDistOrNumber, frValueDistOrNumber)
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```
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```javascript
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lognormal(dict<{p5: frValueDistOrNumber, p95: frValueDistOrNumber}>)
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```
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```javascript
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lognormal(dict<{mean: frValueDistOrNumber, stdev: frValueDistOrNumber}>)
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```
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**Examples**
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```javascript
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lognormal(0.5, 0.8)
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lognormal({p5: 4, p95: 10})
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lognormal({mean: 5, stdev: 2})
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```
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## Uniform Distribution
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**Definitions**
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```javascript
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uniform(frValueDistOrNumber, frValueDistOrNumber)
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```
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**Examples**
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```javascript
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uniform(10, 12)
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```
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## Beta Distribution
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**Definitions**
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```javascript
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beta(frValueDistOrNumber, frValueDistOrNumber)
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```
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**Examples**
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```javascript
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beta(20, 25)
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```
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## Cauchy Distribution
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**Definitions**
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```javascript
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cauchy(frValueDistOrNumber, frValueDistOrNumber)
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```
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**Examples**
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```javascript
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cauchy(5, 1)
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```
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## Gamma Distribution
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**Definitions**
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```javascript
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gamma(frValueDistOrNumber, frValueDistOrNumber)
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```
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**Examples**
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```javascript
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gamma(5, 1)
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```
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## Logistic Distribution
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**Definitions**
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```javascript
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logistic(frValueDistOrNumber, frValueDistOrNumber)
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```
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**Examples**
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```javascript
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gamma(5, 1)
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```
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## To (Distribution)
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**Definitions**
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```javascript
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to(frValueDistOrNumber, frValueDistOrNumber)
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```
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```javascript
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credibleIntervalToDistribution(frValueDistOrNumber, frValueDistOrNumber)
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```
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**Examples**
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```javascript
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5 to 10
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to(5,10)
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-5 to 5
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```
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## Exponential
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**Definitions**
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```javascript
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exponential(frValueDistOrNumber)
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```
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**Examples**
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```javascript
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exponential(2)
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```
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## Bernoulli
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**Definitions**
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```javascript
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bernoulli(frValueDistOrNumber)
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```
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**Examples**
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```javascript
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bernoulli(0.5)
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```
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## toContinuousPointSet
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Converts a set of points to a continuous distribution
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**Definitions**
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```javascript
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toContinuousPointSet(array<dict<{x: numeric, y: numeric}>>)
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```
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**Examples**
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```javascript
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toContinuousPointSet([
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{x: 0, y: 0.1},
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{x: 1, y: 0.2},
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{x: 2, y: 0.15},
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{x:3, y: 0.1}
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])
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```
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## toDiscretePointSet
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Converts a set of points to a discrete distribution
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**Definitions**
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```javascript
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toDiscretePointSet(array<dict<{x: numeric, y: numeric}>>)
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```
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**Examples**
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```javascript
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toDiscretePointSet([
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{x: 0, y: 0.1},
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{x: 1, y: 0.2},
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{x: 2, y: 0.15},
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{x:3, y: 0.1}
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])
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```
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## Declaration (Continuous Function)
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Adds metadata to a function of the input ranges. Works now for numeric and date inputs. This is useful when making predictions. It allows you to limit the domain that your prediction will be used and scored within.
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**Definitions**
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```javascript
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declareFn(dict<{fn: lambda, inputs: array<dict<{min: number, max: number}>>}>)
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```
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**Examples**
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```javascript
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declareFn({
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fn: {|a,b| a },
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inputs: [
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{min: 0, max: 100},
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{min: 30, max: 50}
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]
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})
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``` |