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kmeans.py
52 lines (32 loc) · 1.22 KB
/
kmeans.py
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import argparse
import pickle
import numpy as np
from sklearn.cluster import KMeans
from mnist import load_mnist
def generate_kmeans(x, k, verbose=False):
kmeans = KMeans(n_clusters=k, verbose=int(verbose)).fit(x).cluster_centers_
return kmeans
def load_kmeans(path):
with open(path, 'rb+') as file:
kmeans = pickle.load(file)
return kmeans
def main():
parser = argparse.ArgumentParser(
prog='kmeans',
description='find kmeans in data'
)
parser.add_argument('--path', default='/home/data/ml/mnist',
help='path to the mnist data')
parser.add_argument('--k', default=10, type=int,
help='number of components')
parser.add_argument('--verbose', action='store_true', default=False)
parser.add_argument('--output', '-o', default='kmeans.dat')
args = parser.parse_args()
data = load_mnist(dataset='training', path=args.path, return_labels=False)
data = np.reshape(data, (60000, 784))
kmeans = generate_kmeans(data, int(args.k), args.verbose)
with open(args.output, 'wb+') as file:
pickle.dump(kmeans, file)
print('saved kmeans in {}'.format(args.output))
if __name__ == '__main__':
main()