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Clustering via hierarchical agglomerative learning

Project description

Hierarchical Agglomerative Learning (HAL)

Package for performing clustering for high-dimensional data. This packages uses heavily scikit-learn and fft accelerated t-SNE.

Installing (once)

Activate an Anaconda Python 3 environment

conda config --add channels conda-forge
conda install cython numpy fftw
pip install hal-x

Updating

Again from your Anaconda Python 3 environment:

pip install hal-x --upgrade

Minimum use

from hal import HAL
from sklearn.datasets import make_blobs

# generate some data
X,y = make_blobs(10000,12,10) # 10 blobs in 12 dimensions, 10000 data points

model = HAL(clf_type='rf') # many optional parameters here

# builds model and outputs intermediate plots/results
model.fit(X)

# predict new labels
ypred = model.predict(X)

Project details


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