# annoy.

The nearest neighbor problem is very common in data science. It's useful in recommender situations but also with neural embeddings in general. It's an expensive thing to calculate so it is common to calculate approximate distances as a proxy. In python a very likeable tool for this is annoy.

**Episode Notes**

You'll first need to install annoy via;

```
pip install annoy
```

Once installed you can run the code from the video below.

This code generates the random data.

```
import numpy as np
import matplotlib.pylab as plt
from annoy import AnnoyIndex
columns = 2
vecs = np.concatenate([
np.random.normal(-1, 1, (5000, columns)),
np.random.normal(0, 0.5, (5000, columns)),
])
plt.scatter(vecs[:, 0], vecs[:, 1], s=1);
```

This code generates the annoy index.

```
annoy = AnnoyIndex(columns, 'euclidean')
for i in range(vecs.shape[0]):
annoy.add_item(i, vecs[i, :])
annoy.build(n_trees=1)
```

This code fetches the indices of the neighbors;

```
indices = annoy.get_nns_by_vector(np.array([0., 0.]), 20)
```

Feedback? See an issue? Feel free to mention it here.

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