asw456

# Holt-Winters algorithms to forecasting
# Coded in Python 2 by: Andre Queiroz
# Description: This module contains three exponential smoothing algorithms. They are Holt's linear trend method and Holt-Winters seasonal methods (additive and multiplicative).
# References:
#  Hyndman, R. J.; Athanasopoulos, G. (2013) Forecasting: principles and practice. http://otexts.com/fpp/. Accessed on 07/03/2013.
#  Byrd, R. H.; Lu, P.; Nocedal, J. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing, 16, 5, pp. 1190-1208.

from sys import exit
from math import sqrt
from numpy import array

asw456

import numpy as np
import matplotlib.pyplot
mu, sigma = 3., 1. # mean and standard deviation
s = np.random.lognormal(mu, sigma, 10000)
log_s = np.log(s)

subplot(211)
count,bins,_ = hist(s, 100, normed=True, align='mid')
x = np.linspace(min(bins), max(bins), 10000)
pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2)) / (x * sigma * np.sqrt(2 * np.pi)))

asw456

from pylab import *
from numpy import *
from numpy.linalg import solve
from scipy.integrate import odeint
from scipy.stats import norm, uniform, beta
from scipy.special import jacobi



a = 0.0

asw456

from pylab import *
from numpy import *
from numpy.linalg import solve
from scipy.integrate import odeint
from scipy.stats import norm, uniform, beta
from scipy.special import jacobi

a = 0.0
b = 3.0
theta=1.0

asw456

from matplotlib import use
use('wx')
from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

asw456

from matplotlib import use
use('wx')
from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

asw456

from matplotlib import use
use('wx')
from pylab import *
from scipy.stats import beta, norm, uniform
from random import random
from numpy import *
import numpy as np
import os

# Input data

asw456

import matplotlib
matplotlib.use("WXAgg")
from pylab import *
from scipy.stats import beta, uniform, norm

class BetaBandit(object):
    def __init__(self, num_options=2, prior=(1.0,1.0)):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options

asw456

from pylab import *
import random
from scipy.stats import beta, uniform

prior = beta(1,1)

class Bandit(object):
    def __init__(self):
        self.history = [(1.0,1.0), (1.0,1.0)]

asw456

import org.apache.hadoop.io.*;

import java.util.*;
import java.io.*;

public class UUIDWritable implements WritableComparable<UUIDWritable> {
    private UUID value;

    public UUIDWritable(long mostSignificantBits, long leastSignificantBits) {
	value = new UUID(mostSignificantBits, leastSignificantBits);