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| Original file line number | Diff line number | Diff line change |
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| /* | ||
| * The MIT License (MIT) | ||
| * Copyright (c) 2018, Benjamin Maier | ||
| * | ||
| * Permission is hereby granted, free of charge, to any person | ||
| * obtaining a copy of this software and associated documentation | ||
| * files (the "Software"), to deal in the Software without | ||
| * restriction, including without limitation the rights to use, | ||
| * copy, modify, merge, publish, distribute, sublicense, and/or | ||
| * sell copies of the Software, and to permit persons to whom the | ||
| * Software is furnished to do so, subject to the following conditions: | ||
| * | ||
| * The above copyright notice and this permission notice shall | ||
| * be included in all copies or substantial portions of the Software. | ||
| * | ||
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, | ||
| * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES | ||
| * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON- | ||
| * INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS | ||
| * BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN | ||
| * AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF | ||
| * OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS | ||
| * IN THE SOFTWARE. | ||
| */ | ||
|
|
||
| #ifndef __MODEL_MARKOV_H__ | ||
| #define __MODEL_MARKOV_H__ | ||
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| #include "Utilities.h" | ||
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| #include <iostream> | ||
| #include <algorithm> | ||
| #include <stdexcept> | ||
| #include <vector> | ||
| #include <set> | ||
| #include <utility> | ||
| #include <random> | ||
| #include <cmath> | ||
| #include <numeric> | ||
| #include <random> | ||
| #include <ctime> | ||
| #include <tuple> | ||
| #include <assert.h> | ||
|
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||
| using namespace std; | ||
|
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||
| template <typename T1, typename T2> | ||
| void | ||
| markov_on_model( | ||
| T1 & this_model_object, | ||
| T2 & this_markov_object, | ||
| double max_dt, | ||
| bool reset_simulation_objects = true, | ||
| bool verbose = false | ||
| ) | ||
| { | ||
|
|
||
| if (this_model_object.N != this_markov_object.N) | ||
| throw domain_error("Both model and markov object need to have the same node number."); | ||
|
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| if (verbose) | ||
| cout << "started markov integration on model." << endl; | ||
|
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| // deal with random numbers | ||
| mt19937_64 &generator = this_markov_object.generator; | ||
| uniform_real_distribution<double> randuni(0.0,1.0); | ||
| this_model_object.set_generator(this_markov_object.generator); | ||
|
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|
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| // reset the simulation objects | ||
| if (reset_simulation_objects) | ||
| { | ||
| if (verbose) | ||
| cout << "Resetting models..." << endl; | ||
|
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| this_markov_object.reset(); | ||
| this_model_object.reset(); | ||
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| if (verbose) | ||
| cout << "Done." << endl; | ||
| } | ||
|
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| // initialize time variables | ||
| double t0 = this_model_object.t0; | ||
| double t = t0; | ||
|
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| double t_simulation = this_markov_object.t_simulation; | ||
| this_model_object.edg_chg.tmax = t_simulation; | ||
|
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| // initialize a graph and pass it to the markov object | ||
| this_markov_object.update_network(this_model_object.G,t); | ||
|
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| if (verbose) | ||
| cout << "Start integration" << endl; | ||
|
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| while ( (t-t0 < t_simulation) and (not this_markov_object.simulation_ended()) ) | ||
| { | ||
|
|
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| // inititalize rate containers | ||
| vector < double > rates_model; | ||
| double Lambda_model; | ||
|
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| // get the updated rates and lambda | ||
| this_model_object.get_rates_and_Lambda(rates_model,Lambda_model); | ||
|
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| if (verbose) | ||
| { | ||
| cout << "Lambda_model = " << Lambda_model << endl; | ||
| for(auto const &rate: rates_model) | ||
| cout << " rate = " << rate << endl; | ||
| } | ||
|
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| double rProduct = randuni(generator) * Lambda_model; | ||
|
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| vector<double>::iterator this_rate; | ||
| size_t n_rates; | ||
|
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| this_rate = rates_model.begin(); | ||
| n_rates = rates_model.size(); | ||
|
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| double sum_event = 0.0; | ||
| size_t event = 0; | ||
|
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| while ( (event < n_rates) and not ( (sum_event < rProduct) and (rProduct <= sum_event + (*this_rate)) ) ) | ||
| { | ||
| sum_event += (*this_rate); | ||
| ++this_rate; | ||
| ++event; | ||
| } | ||
|
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| if (verbose) | ||
| { | ||
| cout << "rProduct = " << rProduct << endl; | ||
| cout << "event = " << event << endl; | ||
| cout << "this_rate = " << *this_rate << endl; | ||
| } | ||
|
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| double tau = log(1.0/randuni(generator)) / Lambda_model; | ||
|
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| while ((tau > max_dt) and (t-t0+max_dt < t_simulation) and (not this_markov_object.simulation_ended()) ) | ||
| { | ||
| this_markov_object.step(t,max_dt); | ||
| tau -= max_dt; | ||
| t += max_dt; | ||
| } | ||
|
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| if ((t-t0+tau < t_simulation) and (not this_markov_object.simulation_ended())) | ||
| { | ||
| this_markov_object.step(t, tau); | ||
|
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| vector < pair < size_t, size_t > > edges_in; | ||
| vector < pair < size_t, size_t > > edges_out; | ||
|
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| this_model_object.make_event(event,t,edges_in,edges_out); | ||
| this_markov_object.update_network(this_model_object.G,t+tau); | ||
| } | ||
| t += tau; | ||
|
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| if (verbose) | ||
| cout << "new time: " << t << " / " << t_simulation << endl; | ||
|
|
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| } | ||
| } | ||
|
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||
| #endif |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| import sys | ||
|
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| import tacoma as tc | ||
| import matplotlib.pyplot as pl | ||
| import numpy as np | ||
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| import _tacoma as _tc | ||
|
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| N = 100 | ||
|
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| t_run_total = 10000/N | ||
| recovery_rate = 1.0 | ||
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| seed = 12 | ||
|
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| R0s = [0.5,1.0,1.2,1.5,2,4,6] | ||
|
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| for k in range(1,8,3): | ||
| pl.figure() | ||
| pl.title('$k={0:d}$'.format(k)) | ||
| for omega in np.logspace(-2,0,4,base=N): | ||
| pl.figure() | ||
| curve_1 = [] | ||
| curve_2 = [] | ||
| for R0 in R0s: | ||
|
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| print(k, omega, R0) | ||
| AM = _tc.EdgeActivityModel(N, | ||
| k/(N-1.), | ||
| omega, | ||
| verbose = False, | ||
| ) | ||
| infection_rate = R0 / k * recovery_rate | ||
|
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|
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| SIS = _tc.SIS(N,t_run_total,infection_rate,recovery_rate, | ||
| number_of_initially_infected=N, | ||
| verbose=False, | ||
| seed=seed, | ||
| sampling_dt=1, | ||
| ) | ||
|
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| _tc.gillespie_SIS_on_EdgeActivityModel(AM,SIS,verbose=False) | ||
|
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| t = np.array(SIS.time) | ||
| I = np.array(SIS.I) | ||
|
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| this_pl, = pl.plot(t,I,'s',ms=2,alpha=0.5,mfc='None') | ||
| mean_I_1 = tc.time_average(t, I, tmax=t_run_total) | ||
|
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| AM = _tc.EdgeActivityModel(N, | ||
| k/(N-1.), | ||
| omega, | ||
| verbose =False, | ||
| ) | ||
|
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| print("generated new AM") | ||
|
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| SIS = _tc.MARKOV_SIS(N, | ||
| t_run_total, | ||
| infection_rate, | ||
| recovery_rate, | ||
| minimum_I=0.01, | ||
| number_of_initially_infected=N, | ||
| sampling_dt=1) | ||
|
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| print("starting integration") | ||
|
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| _tc.markov_SIS_on_EdgeActivityModel(AM,SIS,max_dt=1.0,verbose=False) | ||
|
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| t = np.array(SIS.time) | ||
| I = np.array(SIS.I) | ||
|
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| mean_I_2 = tc.time_average(t, I, tmax=t_run_total) | ||
|
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| curve_1.append(mean_I_1) | ||
| curve_2.append(mean_I_2) | ||
| this_pl, = pl.plot(t,I,'-',lw=1,alpha=0.5,c=this_pl.get_color()) | ||
|
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| #this_pl, = pl.plot(R0s,curve_1,'s',ms=2,alpha=0.5,mfc='None') | ||
| #this_pl, = pl.plot(R0s,curve_2,'-',lw=1,alpha=0.5,c=this_pl.get_color()) | ||
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| pl.show() | ||
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