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)
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