## Quickshift++

This is not an officially supported Google product

Density-based clustering algorithm based on mode-seeking.

## Usage

**Initializiation**:

`QuickshiftPP(k, beta)`

k: number of neighbors in k-NN

beta: fluctuation parameter which ranges between 0 and 1.

**Finding Clusters**:

`fit(X)`

X is the data matrix, where each row is a datapoint in euclidean space.

fit performs the clustering. The final result can be found in QuickshiftPP.memberships.

**Example** (mixture of two gaussians):

```
from QuickshiftPP import *
import numpy as np
X = [np.random.normal(0, 1, 2) for i in range(100)] + [np.random.normal(5, 1, 2) for i in range(100)]
y = [0] * 100 + [1] * 100
# Declare a Quickshift++ model with tuning hyperparameters.
model = QuickshiftPP(k=20, beta=.5)
# Compute the clustering.
model.fit(X)
y_hat = model.memberships
from sklearn.metrics.cluster import adjusted_rand_score, adjusted_mutual_info_score
print("Adj. Rand Index Score: %f." % adjusted_rand_score(y_hat, y))
print("Adj. Mutual Info Score: %f." % adjusted_mutual_info_score(y_hat, y))
```

## Install

This package uses distutils, which is the default way of installing python modules.

To install for all users on Unix/Linux:

sudo python setup.py build; python setup.py install

## Dependencies

python 3.9, scikit-learn