Merge pull request #6 from PAIR-code/transform

Implement transform method

README.md

@@ -9,7 +9,6 @@ Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction te
 There are a few important differences between the python implementation and the JS port.
 
 - The optimization step is seeded with a random embedding rather than a spectral embedding. This gives comparable results for smaller datasets. The spectral embedding computation relies on efficient eigenvalue / eigenvector computations that are not easily done in JS.
-- There is no implementation of any supervised dimension reduction or adding new points to an existing embedding.
 - The only distance function used is euclidean distance.
 - There is no specialized functionality for angular distances or sparse data representations.
 
@@ -62,15 +61,25 @@ umap.setSupervisedProjection(labels);
 const embedding = umap.fit(data);
 ```
 
+#### Transforming additional points after fitting
+
+```typescript
+import { UMAP } from 'umap-js';
+
+const umap = new UMAP();
+umap.fit(data);
+const transformed = umap.transform(additionalData);
+```
+
 #### Parameters
 
-The UMAP constructor can accept a number of parameters via a `UMAPParameters` object:
+The UMAP constructor can accept a number of hyperparameters via a `UMAPParameters` object, with the most common described below. See [umap.ts](./src/umap.ts) for more details.
 
 | Parameter     | Description                                                                                                                         | default                  |
 | ------------- | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------ |
 | `nComponents` | The number of components (dimensions) to project the data to                                                                        | 2                        |
 | `nEpochs`     | The number of epochs to optimize embeddings via SGD                                                                                 | (computed automatically) |
-| `nNeighbors`   | The number of nearest neighbors to construct the fuzzy manifold                                                                      | 15                       |
+| `nNeighbors`  | The number of nearest neighbors to construct the fuzzy manifold                                                                     | 15                       |
 | `minDist`     | The effective minimum distance between embedded points, used with `spread` to control the clumped/dispersed nature of the embedding | 0.1                      |
 | `spread`      | The effective scale of embedded points, used with `minDist` to control the clumped/dispersed nature of the embedding                | 1.0                      |
 | `random`      | A pseudo-random-number generator for controlling stochastic processes                                                               | `Math.random`            |