'''
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])
minUsers, maxUsers = (min(measurement.numberOfUsers(), minUsers),
max(measurement.numberOfUsers(), maxUsers))
if k % 50 == 0:
statHeader(trainingResFile, overwrite=False)
rmsePre = fann.rmse(measurement.numberOfUsers(), (cpu, mem))
avgRMSE = rmsePre if len(rmses) == 0 else sum(rmses) / len(rmses)
if rmsePre < 0.01:
log.info("Predicted well!!! RMSE: %.5f" % (rmsePre))
time.sleep(sleepPeriod)
continue
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
if measurement is not None and measurement.isValid() :
minUsers, maxUsers = (min(measurement.numberOfUsers(), minUsers), max(measurement.numberOfUsers(), maxUsers))
if k % 50 == 0:
statHeader(trainingResFile, overwrite=False);
rmsePre = fann.rmse(measurement.numberOfUsers(), (cpu, mem))
avgRMSE = rmsePre if len(rmses) == 0 else sum(rmses) / len(rmses)
if rmsePre < 0.01:
log.info("Predicted well!!! RMSE: %.5f" % (rmsePre))
time.sleep(sleepPeriod)
continue
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
