def ratio(cd, models, suppress=False):
"""For a compressed data set and a set of models, calculate the ratio
of data elements that make up the data set.
"""
mc = [0] * len(models)
if suppress:
suppress.suppress(2)
for d in cd:
bm = optimalModel(d, models)[0]
if not (bm == -1):
mc[bm] += 1
if suppress:
suppress.restore(2)
r = [(t * 1.0) / len(cd) for t in mc]
return r, mc
def ratio(cd, models, suppress = False):
"""For a compressed data set and a set of models, calculate the ratio
of data elements that make up the data set.
"""
mc = [0] * len(models)
if suppress:
suppress.suppress(2)
for d in cd:
bm = optimalModel(d, models)[0]
if not (bm == -1):
mc[bm] += 1
if suppress:
suppress.restore(2)
r = [(t * 1.0)/len(cd) for t in mc]
return r, mc
"""cluster_score.py
Author: James Howard
Short program to give the score of a given clustering
"""
import pybb.math.markov_anneal as markov_anneal
import pybb.data.dataio as dataio
import pybb.math.hmmextra as hmmextra
import os
import pybb.suppress as suppress
suppress.suppress(2)
from ghmm import *
suppress.restore(2)
readLocation = "../../runs/clean/models/"
if __name__ == "__main__":
files = os.listdir(readLocation)
suppress.suppress(2)
for f in files:
print f
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open files
fn = dataio.loadData(readLocation + str(f))
fn.matrixToModel(fn.modelList)
"""cluster_score.py
Author: James Howard
Short program to give the score of a given clustering
"""
import pybb.math.markov_anneal as markov_anneal
import pybb.data.dataio as dataio
import pybb.math.hmmextra as hmmextra
import os
import pybb.suppress as suppress
suppress.suppress(2)
from ghmm import *
suppress.restore(2)
readLocation = "../../runs/clean/models/"
if __name__ == "__main__":
files = os.listdir(readLocation)
suppress.suppress(2)
for f in files:
print f
#It is a data file.
if f.split('.')[-1] == 'dat':
#Open files
fn = dataio.loadData(readLocation + str(f))
fn.matrixToModel(fn.modelList)
sigma = IntegerRange(0, fn.obs)