def Reff(data, si_mean, si_sd, tau=7, conf=0.95, mu=5):
""" effective reproduction number
assuming exponential distribution for prior Reff
input:
data = daily number of incidence
si_mean = mean of serial interval
si_sd = standard deviation of serial interval
tau = length of time window (integer in days)
conf = confidence level of estimated Reff
mu = mean of prior ditribution of Reff
return:
R = daily Reff of shape (3,len(data))
R[0:3] = median, min, max
reference:
A. Cori et al
American Journal of Epidemiology 178 (2013) 1505
Web Appendix 1
"""
N = len(data)
w = si_distr(N, si_mean, si_sd)
L = np.convolve(data, w)[:N]
u = np.ones(tau)
a = 1 + np.convolve(data, u)[:N]
b = mu / (1 + mu * np.convolve(L, u)[:N])
return np.vstack([gamma.median(a, 0, b), gamma.interval(conf, a, 0, b)])
import numpy as np
from copy import deepcopy
from scipy.stats import gamma
from problems.vendor_problem.vendor import simulation
runlength = 5
n_customers = 1000
n_products = 2
cost = [5, 10]
sell_price = [8, 18]
set_constraints = {}
median = gamma.median(n_customers, scale=1)
intervals = [[0.0, median], [median, np.inf]]
for i in xrange(2):
for j in xrange(2):
set_constraints[i * 2 + j] = [intervals[i], intervals[j]]
# n=5
# x=[2, 3, 4, 1, 1]
# replications=5
# customers=10
# simulation(x, replications, customers, n, [5,6, 3, 1, 1], [8, 12, 7, 2, 3])
def toy_example(n_samples, x):
"""
def gammamedian(concentration, scale):
return gamma.median(a=concentration, scale=scale)
def median(self, n, p):
med = gamma.median(self, n, p)
return med
d3 = d3[0]/d0[0]
d0 = d0[0]/d0[0]
return dm2, dm1, d0, d1, d2, d3
#dm2, dm1, d0, d1, d2, d3 = getProbabilities(5)
# Example from:
# http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.gamma.html
# http://docs.scipy.org/doc/scipy-0.14.0/reference/tutorial/integrate.html
# gamma function
a = 5
rv = gamma(a, loc=0., scale = 1.) # scale = 1.0 / lambda.
median = gamma.median(a)
mean = gamma.mean(a)
x0 = scipy.optimize.fsolve(lambda x: rv.pdf(x), 0.1) # 0.3 is the starting point
# find the top
increase = True
df0 = 0
delta = 0.01
x = 0.01
while increase == True:
h = rv.pdf(x+delta) - rv.pdf(x)
if h < 0:
increase = False
else:
d0 = d0[0] / d0[0]
return dm2, dm1, d0, d1, d2, d3
# dm2, dm1, d0, d1, d2, d3 = getProbabilities(5)
# Example from:
# http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.gamma.html
# http://docs.scipy.org/doc/scipy-0.14.0/reference/tutorial/integrate.html
# gamma function
a = 5
rv = gamma(a, loc=0.0, scale=1.0) # scale = 1.0 / lambda.
median = gamma.median(a)
mean = gamma.mean(a)
x0 = scipy.optimize.fsolve(lambda x: rv.pdf(x), 0.1) # 0.3 is the starting point
# find the top
increase = True
df0 = 0
delta = 0.01
x = 0.01
while increase == True:
h = rv.pdf(x + delta) - rv.pdf(x)
if h < 0:
increase = False
else: