def getAdjustedExponential(params):
dimPars = map(extractDimension, params['dimensions'])
factor = params['adjustmentFactor']
for d in dimPars:
d.decayRate = d.decayRate * 1. / factor
parameters = e.Parameters(dimPars)
return ae.getSelector(parameters, params['numSelectedPoints'])
def test_ExponentialFileInput(self):
cf = Exponential.ExponentialCostFunction()
cf.FileInit('Values/TestExponential.txt')
guess = libUnfitPython.std_vector_double()
guess[:] = [0, 0]
cost = 0
for r in cf(guess):
cost = cost + r * r
self.assertAlmostEqual(cost, 1.58190784, cost_tol)
self.lm.FindMin(cf, guess)
self.assertAlmostEqual(guess[0], 0.99999682538, guess_tol)
self.assertAlmostEqual(guess[1], 0.500017605642, guess_tol)
def test_ExponentialFileInput(self):
cf = Exponential.ExponentialCostFunction()
cf.FileInit('Values/TestExponential.txt')
guess = libUnfitPython.std_vector_double()
guess[:] = [0, 0]
cost = 0
for r in cf(guess):
cost = cost + r * r
self.assertAlmostEqual(cost, 1.58190784, cost_tol)
self.ga.FindMin(cf, guess)
self.assertAlmostEqual(guess[0], 1.02684400144, guess_tol)
self.assertAlmostEqual(guess[1], 0.517748487134, guess_tol)
def test_ExponentialDirectInput(self):
cf = Exponential.ExponentialCostFunction([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
[1.0, 0.6065, 0.3679, 0.2231, 0.1353, 0.0821, 0.0498, 0.0302, 0.0183, 0.0111, 0.0067])
guess = libUnfitPython.std_vector_double()
guess[:] = [0, 0]
cost = 0
for r in cf(guess):
cost = cost + r * r
self.assertAlmostEqual(cost, 1.58190784, cost_tol)
self.ga.FindMin(cf, guess)
self.assertAlmostEqual(guess[0], 1.02684400144, guess_tol)
self.assertAlmostEqual(guess[1], 0.517748487134, guess_tol)
def extractDimension(dimPars):
sweepWidth = dimPars['sweepWidth']
decayRate = dimPars['decayRate']
exponent = dimPars['exponent']
return e.DimensionParameters(sweepWidth, decayRate, exponent)
def getExponential(params):
dimPars = map(extractDimension, params['dimensions'])
parameters = e.Parameters(dimPars)
return e.getSelector(parameters, params['numSelectedPoints'])
8/28: plt.xlabel("$k$")
8/29: plt.title("Probability mass funciton of a Poisson random \$\lambda$ values")
8/30: plt.show()
9/1: from pymc import DiscreteUniform, Exponential, deterministic, Poisson, Uniform
9/2: import numpy as np
9/3:
disasters_array = \
np.array([ 4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])
9/4: switchPoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')
9/5: early_mean = Exponential('early_mean', beta=1.)
9/6: late_mean = Exponential('late_mean', beta=1.)
9/7:
@deterministic(plot=false)
def rate(s=switchPoint, e=early_mean, l=late_mean):
''' Concatyente Poisson means '''
out = np.empty(len(disasters_array))
out[:s] = e
out[s:] = l
return out
9/8:
@deterministic(plot=False)
def rate(s=switchPoint, e=early_mean, l=late_mean):
''' Concatyente Poisson means '''
out = np.empty(len(disasters_array))
out[:s] = e
def getSelector(params, numPointsToSelect):
return e.getSelector(params, numPointsToSelect)
# DISTRIBUCION EXPONENCIAL
elif optionSelected == 3:
os.system("cls")
nombreDistribucion = "Distribución Exponencial"
print("\t::", nombreDistribucion, "::")
# Parametros necesarios para la generació1n
media = float(input("->Ingrese la media :"))
numeroDatos = int(input("->Ingrese el número de variables aleatorias a generar :"))
#nivelDeSignificacia = float(input("->Ingrese el nivel de significancia para la prueba chi cuadrado :"))
nivelDeSignificacia = 0.05
tipoDistribucion = st.expon(1, media)
objExpon = exponDistribution.Exponential(numeroDatos, tipoDistribucion, nivelDeSignificacia, nombreDistribucion,
media)
legendHistogram = r'$\mu$' + "="+ str("{0:.2f}".format(objExpon.meanGenerated))
legendDensity = r'$\mu$' + "=" + str(objExpon.mean)
axisX = np.linspace(1, objExpon.mean + 6 * objExpon.des, 100)
objExpon.chiSquareTest()
objExpon.graph(legendHistogram, legendDensity, axisX)
# DISTRIBUCION ERLANG
elif optionSelected == 4:
os.system("cls")
nombreDistribucion = "Distribución Erlang"
##################################
### Fibonacci Search
start = time.time()
# # Algorithem starts here
x = Fibonacci.Search(dic , search)
print("Fibonacci Search Result: " + str(x))
# # Algoritem stops here
end = time.time()
print("Fibonacci Search Time: " + str(end-start) )
print()
##################################
### Exponential Search
start = time.time()
# # Algorithem starts here
x = Exponential.Search(dic , search)
print("Exponential Search Result: " + str(x))
# # Algoritem stops here
end = time.time()
print("Exponential Search Time: " + str(end-start))
print()
##################################
# NOTE: Only works with int, and not sting.
# ### Interpolation Search
# start = time.time()
# # # Algorithem starts here
# x = Interpolation.Search(dic , search)
# print("Interpolation Search Result: " + str(x))
# # # Algoritem stops here
import Exponential as ex
import ANNTrain as ann
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('eggs_new.csv')
usage = data['CPU usage [%]']
exPred, exError = ex.expo(usage)
mse_history = ann.ret_mse()
annPred = []
for i in range(len(mse_history)):
annPred.append(usage[i] + mse_history[i])
exContri = []
annContri = []
finPred = []
enError = []
enAcc = []
axis = []
actual = []
for i in exError:
exContri.append(1/i)
for i in mse_history:
def getExponential(params):
dimPars = map(extractDimension, params['dimensions'])
parameters = e.Parameters(dimPars)
return e.getSelector(parameters, params['numSelectedPoints'])
def setUp(self):
self.numToSelect = 840
dim = exp.DimensionParameters()
params = exp.Parameters([dim])
self.selector = exp.getSelector(params, self.numToSelect)
Test3D.setUp(self)
def getSelector(params, numPointsToSelect):
return e.getSelector(params, numPointsToSelect)
