---
sidebar_position: 4
title: Point Set Distribution
---

### make

Converts the distribution in question into a point set distribution. If the distribution is symbolic, then it does this by taking the quantiles. If the distribution is a sample set, then it uses a version of kernel density estimation to approximate the point set format. One complication of this latter process is that if there is a high proportion of overlapping samples (samples that are exactly the same as each other), it will convert these samples into discrete point masses. Eventually we'd like to add further methods to help adjust this process.

```
PointSet.make: (distribution) => pointSetDist
```

### makeContinuous

**TODO: Now called "toContinuousPointSet"**

Converts a set of x-y coordinates directly into a continuous distribution.

```
PointSet.makeContinuous: (list<{x: number, y: number}>) => pointSetDist
```

```javascript
PointSet.makeContinuous([
  { x: 0, y: 0.1 },
  { x: 1, y: 0.2 },
  { x: 2, y: 0.15 },
  { x: 3, y: 0.1 },
]);
```

### makeDiscrete

**TODO: Now called "toDiscretePointSet"**

Converts a set of x-y coordinates directly into a discrete distribution.

```
PointSet.makeDiscrete: (list<{x: number, y: number}>) => pointSetDist
```

```javascript
PointSet.makeDiscrete([
  { x: 0, y: 0.1 },
  { x: 1, y: 0.2 },
  { x: 2, y: 0.15 },
  { x: 3, y: 0.1 },
]);
```