if __name__ == '__main__':
# global parameters for distance measures (Manhatten/Euclidean, sym. gem)
squared, symmetric = True, True
# read split number
try:
splitN, splitL = int(sys.argv[1]), int(sys.argv[2])
except:
raise Exception \
("python2 MetalError.py splitnumber(int) splitlearn(int)")
# read data from database
(parentLabels, parent), (childLabels, child) = mdb.read()
print "done reading metal data"
# downsample time series to 1024 points to reduce computational complexity
parent = map(lambda series: mdb.scale(series, length=2**10), parent)
child = map(lambda series: mdb.scale(series, length=2**10), child)
print "done with the scaling"
# znormalize data for all distance measures but gem
zparent = map(mdb.znormalize, parent)
zchild = map(mdb.znormalize, child)
# open file for the logging of results
f = open("./results/dn_M-sn_%s-lp_%s-sq_%s-sy_%s" %
(splitN, splitL, squared, symmetric), "w")
"""concatenate a bunch of time series"""
return np.hstack(tuple(listOfSeries))
def extract(subject, length):
"""extract a subsequence from subject"""
# get left position of extraction window
left = np.random.uniform(0, len(subject)-length-1)
return subject[left:left+length]
if __name__ == '__main__':
# read data from database
(parentLabels, parent), (childLabels, child) = mdb.read()
print "done reading metal data"
# read length
try:
L, N = int(sys.argv[1]), int(sys.argv[2])
except:
raise Exception \
("python2 QueryAndSubjectGeneratorMetall.py L(int) N(int)")
# for reproducibility
np.random.seed(L)
# concatenate database
subject, queries = cat(parent), cat(child)
import numpy as np
import pylab as pl
import scipy.cluster.hierarchy as h
import utils.distances as ds
import utils.MetalDatabase as mdb
# read data from database
(parentLabels, parent), (childLabels, child) = mdb.read()
print "done reading metal data"
# downsample time series to 1024 points to reduce computational complexity
parent = map(lambda series: mdb.scale(series, length=2**10), parent)
child = map(lambda series: mdb.scale(series, length=2**10), child)
print "done with the scaling"
# znormalize data for all distance measures but gem
zparent = map(mdb.znormalize, parent)
zchild = map(mdb.znormalize, child)
# taken from dn_M-sn_0-lp_100-sq_True-sy_True LISTCONSDTWONE
mask = [
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,