def main():
tau = pm.rdiscrete_uniform(0, 80)
print tau
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
print lambda_1, lambda_2
data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]
def plot_artificial_sms_dataset():
tau = pm.rdiscrete_uniform(0, 80)
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
data = np.r_[pm.rpoisson(lambda_1, tau),
pm.rpoisson(lambda_2, 80 - tau)]
plt.bar(np.arange(80), data, color="#348ABD")
plt.bar(tau - 1,
data[tau - 1],
color="r",
label="user behaviour changed")
plt.xlim(0, 80)
plt.title("More example of artificial datasets")
for i in range(1, 5):
plt.subplot(4, 1, i)
plot_artificial_sms_dataset()
plt.show()
def step(self):
"""
Slice step method
"""
y = self.loglike - rexponential(1)
# Stepping out procedure
L = self.stochastic.value - self.w*runiform(0,1)
R = L + self.w
J = floor(self.m*runiform(0,1))
K = (self.m-1)-J
while(J>0 and y<self.fll(L)):
L = L - self.w
J = J - 1
while(K>0 and y<self.fll(R)):
R = R + self.w
K = K - 1
#self.stochastic.last_value = self.stochastic.value
self.stochastic.value = runiform(L,R)
try:
y_new = self.loglike
except ZeroProbability:
y_new = -infty
while(y_new<y):
if (self.stochastic.value < self.stochastic.last_value):
L = float(self.stochastic.value)
else:
R = float(self.stochastic.value)
self.stochastic.revert()
self.stochastic.value = runiform(L,R)
try:
y_new = self.loglike
except ZeroProbability:
y_new = -infty
def plot_artificial_sms_dataset():
tau = pm.rdiscrete_uniform(0, 80)
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]
plt.bar(np.arange(80), data, color="#348ABD")
plt.bar(tau - 1, data[tau - 1], color="r", label="user behaviour changed")
plt.xlim(0, 80)
def plot_artificial_sms_dataset():
maxdays = 80
tau = pm.rdiscrete_uniform( 0, maxdays )
alpha = 1 / 20.
lambda_1, lambda_2 = pm.rexponential( alpha, 2 )
data = np.r_[
pm.rpoisson( lambda_1, tau ),
pm.rpoisson( lambda_2, maxdays-tau )]
plt.bar( np.arange(maxdays), data )
plt.bar( tau - 1, data[tau-1], color = 'r', label='change point' )
plt.xlim( 0, 80 )
def test_selection():
N = 40
r = pymc.rexponential(2.0, size=N)
def prior_x(x):
"""Triangle distribution.
A = h*L/2 = 1
"""
L=5
h = 2./L
if np.all(0.< x) and np.all(x < L):
return np.sum(log(h - h/L *x))
else:
return -np.inf
W = select_distribution(r, [pymc.Exponweib, pymc.Exponential, pymc.Weibull, pymc.Chi2], prior_x)
return W
def plot_artificail_sms_dataset():
#----------------------------------
# initialize both deterministic and stochastic variables
tau = pm.rdiscrete_uniform(0, 80)
print("tau = {0}".format(tau))
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
print("lambda_1 = {0}\nlambda_2 = {1}".format(lambda_1, lambda_2))
lambda_ = np.r_[lambda_1 * np.ones(tau), lambda_2 * np.ones(80 - tau)]
print("lambda = \n{0}".format(lambda_))
data = pm.rpoisson(lambda_)
print("data = \n{0}".format(data))
#-----------------------------------
# plot the artificial
plt.bar(np.arange(80), data, color="#348ABD")
plt.bar(tau - 1, data[tau - 1], color="r", label="user behavior changed")
plt.xlabel("Time(days)")
plt.ylabel("Text messages received")
plt.xlim(0, 80)
def test_selection():
N = 40
r = pymc.rexponential(2.0, size=N)
def prior_x(x):
"""Triangle distribution.
A = h*L/2 = 1
"""
L = 5
h = 2. / L
if np.all(0. < x) and np.all(x < L):
return np.sum(log(h - h / L * x))
else:
return -np.inf
W = select_distribution(
r, [pymc.Exponweib, pymc.Exponential, pymc.Weibull, pymc.Chi2],
prior_x)
return W
def plot_artifical_sms_dataset():
# specify when the user's behaviour (amount of sms received) switches by sampling from DiscreteUniform
tau = rdiscrete_uniform(0, 80)
print('τ = {}'.format(tau,))
alpha = 1. / 20.
lambda_1, lambda_2 = rexponential(alpha, 2)
print(lambda_1, lambda_2)
# for days before tau, repr. the user's received sms count by sampling from a
# Poisson(lambda_1), and for days after tau by sampling from Poisson(lambda_2)
data = np.r_[rpoisson(lambda_1, tau), rpoisson(lambda_2, 80 - tau)]
print(data)
# plot artificial data set
pyplot.bar(np.arange(80), data, color="#348ABD")
pyplot.bar(tau - 1, data[tau - 1], color="r", label="user behaviour changed")
pyplot.xlabel("time (days)")
pyplot.ylabel("count of sms received")
pyplot.xlim(0, 80)
pyplot.legend()
def main():
tau = pm.rdiscrete_uniform(0, 80)
print tau
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
print lambda_1, lambda_2
data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]
def plot_artificial_sms_dataset():
tau = pm.rdiscrete_uniform(0, 80)
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]
plt.bar(np.arange(80), data, color="#348ABD")
plt.bar(tau - 1, data[tau - 1], color="r", label="user behaviour changed")
plt.xlim(0, 80)
plt.title("More example of artificial datasets")
for i in range(1, 5):
plt.subplot(4, 1, i)
plot_artificial_sms_dataset()
plt.show()
import numpy as np
import pymc as pm
from matplotlib import pyplot as plt
tau = pm.rdiscrete_uniform(0, 80)
print(tau)
alpha = 1. / 20.
lambda_1, lambda_2 = pm.rexponential(alpha, 2)
print(lambda_1, lambda_2)
data = np.r_[pm.rpoisson(lambda_1, tau), pm.rpoisson(lambda_2, 80 - tau)]
plt.bar(np.arange(80), data, color="#348ABD")
plt.bar(tau - 1, data[tau - 1], color="r", label="user behaviour changed")
plt.xlabel("Time (days)")
plt.ylabel("count of text-msgs received")
plt.title("Artificial dataset")
plt.xlim(0, 80)
plt.legend();
plt.show()
data = np.array( [10, 20, 15, 20, 49] )
obs = pm.Poisson( "obs", lambda_, value=data, observed=True )
obs.value
#
# Model class - analyze variables as a single unit
model = pm.Model( [obs, lambda_, lambda_1, lambda_2, taus] )
#
# Creating new datasets
#
maxdays = 80
tau = pm.rdiscrete_uniform( 0, maxdays )
alpha = 1 / 20.
lambda_1, lambda_2 = pm.rexponential( alpha, 2 )
data = np.r_[
pm.rpoisson( lambda_1, tau ),
pm.rpoisson( lambda_2, maxdays-tau )]
plt.bar( np.arange(maxdays), data )
plt.bar( tau - 1, data[tau-1], color = 'r', label='change point' )
plt.xlabel( "Time (days)" )
plt.ylabel( "count" )
plt.title( "Artificial Data" )
plt.xlim( 0, 80 )
plt.legend()
plt.show()
def plot_artificial_sms_dataset():
import numpy as np
import pymc as pm
from matplotlib import pyplot as plt
alpha = 1. / 20.
lambda_ = pm.rexponential(alpha)
print(lambda_)
data = np.r_[pm.rpoisson(lambda_, 80)]
np.savetxt("txtdata_sim.csv", data)
plt.bar(np.arange(80), data, color="#348ABD")
plt.xlabel("Time (days)")
plt.ylabel("count of text-msgs received")
plt.title("Artificial dataset")
plt.xlim(0, 80)
plt.show()