github地址：https://github.com/pymc-devs/pymc3

案例說(shuō)明：https://docs.pymc.io/notebooks/getting_started

API參考：https://docs.pymc.io/api/inference.html#module-pymc3.sampling

代碼：

# -*- coding: utf-8 -*- ''' Created on 2019年3月20日@author: cvter ''' from matplotlib import pyplot as plt import numpy as np import pymc3 as pmdef CoalMiningDisasters():disaster_data = np.ma.masked_values([4, 5, 4, 0, 1, 4, 3, 4, 0, 6, 3, 3, 4, 0, 2, 6,3, 3, 5, 4, 5, 3, 1, 4, 4, 1, 5, 5, 3, 4, 2, 5,2, 2, 3, 4, 2, 1, 3, -999, 2, 1, 1, 1, 1, 3, 0, 0,1, 0, 1, 1, 0, 0, 3, 1, 0, 3, 2, 2, 0, 1, 1, 1,0, 1, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 1, 0, 2,3, 3, 1, -999, 2, 1, 1, 1, 1, 2, 4, 2, 0, 0, 1, 4,0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], value=-999)years = np.arange(1851, 1962)'''plt.plot(years, disaster_data, 'o', markersize=8);plt.ylabel("Disaster count")plt.xlabel("Year")plt.show()'''with pm.Model() as disaster_model:switchpoint = pm.DiscreteUniform('switchpoint', lower=years.min(), upper=years.max(), testval=1900)# Priors for pre- and post-switch rates number of disastersearly_rate = pm.Exponential('early_rate', .1)late_rate = pm.Exponential('late_rate', .1)# Allocate appropriate Poisson rates to years before and after currentrate = pm.math.switch(switchpoint >= years, early_rate, late_rate)disasters = pm.Poisson('disasters', rate, observed=disaster_data)with disaster_model:trace = pm.sample(10000)print (pm.traceplot(trace))plt.figure(figsize=(10, 8))plt.plot(years, disaster_data, '.')plt.ylabel("Number of accidents", fontsize=16)plt.xlabel("Year", fontsize=16)plt.vlines(trace['switchpoint'].mean(), disaster_data.min(), disaster_data.max(), color='C1')average_disasters = np.zeros_like(disaster_data, dtype='float')for i, year in enumerate(years):idx = year < trace['switchpoint']average_disasters[i] = (trace['early_rate'][idx].sum() + trace['late_rate'][~idx].sum()) / (len(trace) * trace.nchains)sp_hpd = pm.hpd(trace['switchpoint'])plt.fill_betweenx(y=[disaster_data.min(), disaster_data.max()],x1=sp_hpd[0], x2=sp_hpd[1], alpha=0.5, color='C1');plt.plot(years, average_disasters, 'k--', lw=2);plt.show()def main():CoalMiningDisasters()if __name__ == "__main__":main()

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