class Processor(object):
def __init__(self, datafile='data.txt', statsNum=3):
self.analyzer = Analyzer()
self.classificator = Classificator()
self.datafile = datafile
self.statsNum = statsNum
def writeStats(self, files):
datafile = open(self.datafile, 'w')
for file in files:
ffts = self.analyzer.getFFTs(file)
stats = self.analyzer.getStats(ffts)
datafile.write(' '.join(str(x) for x in stats) + '\n')
datafile.close()
def normalize(self, data):
transposed = data.transpose()
meanVal = 1
for i in range(len(transposed)):
if i % self.statsNum == 0:
meanVal = np.mean(transposed[i])
transposed[i] /= meanVal
data = transposed.transpose()
return data
def cluster(self, files, clustersNum):
self.writeStats(files)
datafile = open(self.datafile)
data = datafile.split('\n')
data = np.array([[float(x) for x in row.split(' ')] for row in data[:-1]])
data = self.normalize(data)
net = self.classificator.newnet(clustersNum)
net.train(data, epochs=500)
result = net.sim(data)
self.classificator.savenet(net)
return self.classificator.getGroupedResult(result)
def classify(self, file):
ffts = self.analyzer.getFFTs(file)
stats = self.analyzer.getStats(ffts)
net = self.classificator.loadnet()
return net.sim(stats)
