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2) DBSCAN_shortened_july.py
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2) DBSCAN_shortened_july.py
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import numpy as np
import pandas as pd
import os
import sklearn
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
print sklearn.__version__
if __name__ == '__main__':
for i in xrange(3,4):
pathList = []
path1
#pathList.append("") #path2
#pathList.append("") #path3
for path in pathList:
os.chdir(path)
fit = pd.read_table('FitResults.txt')
F = np.array(zip(fit['X'],fit['Y']))
try:
db = DBSCAN(eps=i, min_samples=10).fit(F)
labels = db.labels_
n_clusters_ = len(set(labels)) - (1 if-1 in labels else 0)
print('Estimated number of clusters: %d' % n_clusters_)
fit['Cluster'] = labels
fit.to_csv(path + '/' + 'Results'+str(i)+'.csv', sep = '\t')
except ValueError:
pass
#