def generateAvgModel(model, length, genNum=50):
"""Creates an average array for a given model.
"""
#Number states
states = len(model.getEmission(0))
numSensors = int(math.log(states, 2))
a = numpy.zeros((length, numSensors), float)
for i in range(genNum):
tmp = model.sampleSingle(length)
b = bbdata.uncompressData(tmp, numSensors)
a += b
a /= genNum
return a
def drawHMMCluster(data, models, numSensors, \
writeLocation = "../output/out.png", \
spacing = 10, numberBase = 2, \
scaling = 5, \
cColor = (0, 255, 0), cCluster = (0, 0, 0)):
"""Draw all data associated with a given hidden markov model.
Space the data by the value spacing.
"""
lenInstances = 0
#Get length of each image.
for d in data:
lenInstances += len(d)
#Make the image
iLen = lenInstances * scaling + spacing * len(data)
if iLen == 0:
iLen = 1
im = Image.new("RGB", ((numSensors + 1) * scaling + 50, iLen))
d = ImageDraw.Draw(im)
currentY = 0
#Draw the instances
for i in range(len(data)):
#Invert the data to an array.
tmpData = bbdata.uncompressData(data[i], numSensors, numberBase)
#Caluclate values
sigma = IntegerRange(0, numberBase**numSensors)
values = hmmextra.hmmDistAll(data[i], models, sigma)
values.sort()
_drawArrayText(d,
tmpData,
0,
currentY,
values,
scale=scaling,
cCluster=cCluster)
currentY += tmpData.shape[0] * scaling + spacing
im.save(writeLocation, "PNG")
def generateAvgModel(model, length, genNum = 50):
"""Creates an average array for a given model.
"""
#Number states
states = len(model.getEmission(0))
numSensors = int(math.log(states, 2))
a = numpy.zeros((length, numSensors), float)
for i in range(genNum):
tmp = model.sampleSingle(length)
b = bbdata.uncompressData(tmp, numSensors)
a += b
a /= genNum
return a
def drawHMMCluster(data, models, numSensors, \
writeLocation = "../output/out.png", \
spacing = 10, numberBase = 2, \
scaling = 5, \
cColor = (0, 255, 0), cCluster = (0, 0, 0)):
"""Draw all data associated with a given hidden markov model.
Space the data by the value spacing.
"""
lenInstances = 0
#Get length of each image.
for d in data:
lenInstances += len(d)
#Make the image
iLen = lenInstances * scaling + spacing * len(data)
if iLen == 0:
iLen = 1
im = Image.new("RGB", ((numSensors + 1) * scaling + 50, iLen))
d = ImageDraw.Draw(im)
currentY = 0
#Draw the instances
for i in range(len(data)):
#Invert the data to an array.
tmpData = bbdata.uncompressData(data[i], numSensors, numberBase)
#Caluclate values
sigma = IntegerRange(0, numberBase**numSensors)
values = hmmextra.hmmDistAll(data[i], models, sigma)
values.sort()
_drawArrayText(d, tmpData, 0, currentY, values, scale = scaling, cCluster=cCluster)
currentY += tmpData.shape[0] * scaling + spacing
im.save(writeLocation, "PNG")
# val, counts = analysis.ratio(sData.values(), fn.models)
#except Exception, e:
# counts = [0] * len(fn.models)
# val = [0] * len(fn.models)
#tmpDoc += counts
cd, td = bbdata.getdata(oldSplit, newSplit, \
comp = compress, \
sens = bbdata.allSensors, \
vDays = validDays, \
readLocation = dataDirectory)
cd2 = bbdata.uncompressData(cd, 50)
b = numpy.array(cd2)
b = numpy.sum(b, axis = 0)
tmpDoc = list(b)
#if sum(tmpDoc) >= minBehaviour:
# tdMatrix.append(tmpDoc)
# print len(tdMatrix)
firstRun = False
tdMatrix.append(tmpDoc)
sfirst = [s[0] for s in splits]
# val, counts = analysis.ratio(sData.values(), fn.models)
#except Exception, e:
# counts = [0] * len(fn.models)
# val = [0] * len(fn.models)
#tmpDoc += counts
cd, td = bbdata.getdata(oldSplit, newSplit, \
comp = compress, \
sens = bbdata.allSensors, \
vDays = validDays, \
readLocation = dataDirectory)
cd2 = bbdata.uncompressData(cd, 50)
b = numpy.array(cd2)
b = numpy.sum(b, axis=0)
tmpDoc = list(b)
#if sum(tmpDoc) >= minBehaviour:
# tdMatrix.append(tmpDoc)
# print len(tdMatrix)
firstRun = False
tdMatrix.append(tmpDoc)
sfirst = [s[0] for s in splits]
