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import os | |
import sys | |
CWD = os.path.dirname(os.path.realpath(__file__)) | |
sys.path.insert(0, os.path.join(CWD, "lib")) | |
# The example below was taken and modified from the pyjags documentation | |
# https://pyjags.readthedocs.io/en/latest/getting_started.html | |
import pyjags | |
import numpy as np | |
np.random.seed(0) | |
np.set_printoptions(precision=1) | |
N = 500 | |
a = 70 | |
b = 4 | |
sigma = 50 | |
x = np.random.uniform(0, 100, size=N) | |
y = np.random.normal(a + x*b, sigma, size=N) | |
code = ''' | |
model { | |
for (i in 1:N) { | |
y[i] ~ dnorm(alpha + beta * x[i], tau) | |
} | |
alpha ~ dunif(-1e3, 1e3) | |
beta ~ dunif(-1e3, 1e3) | |
tau <- 1 / sigma^2 | |
sigma ~ dgamma(1e-4, 1e-4) | |
} | |
''' | |
model = pyjags.Model(code, data=dict(x=x, y=y, N=N), chains=4) | |
samples = model.sample(500, vars=['alpha', 'beta', 'sigma']) | |
def summary(samples, varname, p=95): | |
values = samples[varname] | |
ci = np.percentile(values, [100-p, p]) | |
return '{:<6} mean = {:>5.1f}, {}% credible interval [{:>4.1f} {:>4.1f}]'.format( | |
varname, np.mean(values), p, *ci) | |
def main(event, context): | |
print('Did stuff with pyjags') | |
return { | |
'Summary': summary(samples, 'alpha') | |
} | |
if __name__ == "__main__": | |
main('', '') |
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