'''
Created on 05/06/2014
@author: nikolay
'''
from __builtin__ import map
import sys
import time
import collections
import logging
from os.path import os
from sys import maxint
from autoscale.FANNWrapper import FANNWrapper
from math import *
fann = FANNWrapper(topology=(1, 250, 2));
for k in range(1, 1000):
for i in range(k, 10000, k):
expOutput = [log(i * 10), log(i * 10)]
rmse = fann.train(n=i, expOutput=expOutput, trainTimes = 1, revert=False, maxRMSE=0.05)
print "Training with i = %d, RMSE: %.4f, output=[%.4f, %.4f]" % (i, rmse, expOutput[0], expOutput[1])
for i in range(1, 10000, 100):
result = fann.run(i)
rmse = fann.rmse(i, result)
print "Result: %d, RMSE: %.4f, output=[%.4f, %.4f]" % (i, rmse, result[0], result[1])
rmsePart = max(1.0, rmsePre / avgRMSE)
lrk = getLrk(k=k,
lr=lr,
rmsePart=rmsePart,
annPart=annPart,
epochCode=epochCode)
mk = getMk(k=k, lr=lr, lrk=lrk, epochCode=epochCode)
ek = nextEpoch(lrk=lrk, lr=lr, epochCode=epochCode)
# sleepPeriod = defSleepPeriod if ek < 2 else defSleepPeriod / 2.0
# Speculated run ...
fann.config(momentum=mk, learning_rate=lrk)
rmsePost = fann.train(measurement.numberOfUsers(), (cpu, mem),
trainTimes=1,
revert=True)
lrkBuff = lrk
if rmsePost > rmsePre:
lrk = lr
fann.config(momentum=mk, learning_rate=lrk)
fann.train(measurement.numberOfUsers(), (cpu, mem),
trainTimes=ek,
revert=False,
maxRMSE=None)
values = (formatCurrTime(), server.readableName,
server.vmType.readableName, iteration, k,
measurement.numberOfUsers(), cpu, mem, ank, rmsePre,
rmses.append(rmsePre)
run = fann.run(measurement.numberOfUsers())
annPart = 2**len(annomalies) * reduce(lambda x, y : x * y, map(lambda x : sigmoid(x), annomalies))
rmsePart = max(1.0, rmsePre / avgRMSE)
lrk = getLrk(k=k, lr=lr, rmsePart=rmsePart, annPart=annPart, epochCode = epochCode)
mk = getMk(k=k, lr=lr, lrk = lrk, epochCode = epochCode)
ek = nextEpoch(lrk=lrk, lr=lr, epochCode=epochCode)
# sleepPeriod = defSleepPeriod if ek < 2 else defSleepPeriod / 2.0
# Speculated run ...
fann.config(momentum = mk, learning_rate = lrk)
rmsePost=fann.train(measurement.numberOfUsers(), (cpu, mem), trainTimes=1, revert = True)
lrkBuff = lrk
if rmsePost > rmsePre:
lrk=lr
fann.config(momentum = mk, learning_rate = lrk)
fann.train(measurement.numberOfUsers(), (cpu, mem), trainTimes=ek, revert = False, maxRMSE=None)
values = (formatCurrTime(), server.readableName, server.vmType.readableName, iteration, k, measurement.numberOfUsers(),
cpu, mem, ank, rmsePre, run[0], run[1], ek, mk, lrk, lrkBuff, annPart, rmsePart, annPart*rmsePart, avgRMSE )
statLine(trainingResFile, values)
k = k + 1
else :
if measurement is not None:
txt = "SKIP: %s: [%.3d] %3d, %.5f, %.5f" % (firstAppServer.readableName, iteration, measurement.numberOfUsers(), measurement.cpuUtil(), measurement.ramUtil())