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1 # -*- coding: utf-8 -*-
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2 """
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3 Created on Thu May 28 13:02:25 2015
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4
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5 @author: mmxgn
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6 """
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7
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8 from pymc import DiscreteUniform, Exponential, deterministic, Poisson, Uniform
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9
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10 import numpy as np
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11
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12 disasters_array = \
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13 np.array([ 4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,
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14 3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,
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15 2, 2, 3, 4, 2, 1, 3, 2, 2, 1, 1, 1, 1, 3, 0, 0,
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16 1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,
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17 0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,
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18 3, 3, 1, 1, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,
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19 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1])
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20
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21 switchpoint = DiscreteUniform('switchpoint', lower=0, upper=110, doc='Switchpoint[year]')
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22
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23 early_mean = Exponential('early_mean', beta=1.)
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24 late_mean = Exponential('late_mean', beta=1.)
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25
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26
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27 @deterministic(plot=False)
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28 def rate(s=switchpoint, e=early_mean, l=late_mean):
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29 ''' Concatenate Poisson means '''
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30 out = np.empty(len(disasters_array))
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31 out[:s] = e
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32 out[s:] = l
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33 return out
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34
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35
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36 disasters = Poisson('disasters', mu=rate, value=disasters_array, observed=True) |
