add a tutorial that shows how to continue GL from a previous iteration

Author: chyalexcheng

tutorials/simple_regression/linear_regression/linear_load_and_run.py

@@ -0,0 +1,96 @@
+"""
+This tutorial shows how to run one iteration of Bayesian calibration for a linear regression model.
+"""
+import os
+from math import floor, log
+from grainlearning import BayesianCalibration
+from grainlearning.dynamic_systems import IODynamicSystem, DynamicSystem
+
+PATH = os.path.abspath(os.path.dirname(__file__))
+executable = f'python {PATH}/linear_model.py'
+
+
+def run_sim(calib):
+    """
+    Run the external executable and passes the parameter sample to generate the output file.
+    """
+    system = calib.system
+    # keep the naming convention consistent between iterations
+    mag = floor(log(system.num_samples, 10)) + 1
+    # check the software name and version
+    print("*** Running external software... ***\n")
+    # loop over and pass parameter samples to the executable
+    for i, params in enumerate(system.param_data):
+        description = 'Iter' + str(system.curr_iter) + '_Sample' + str(i).zfill(mag)
+        linear(executable, params, system.sim_name, description)
+
+
+def linear(executable, params, sim_name, description):
+    print(" ".join([executable, "%.8e %.8e" % tuple(params), sim_name, description]))
+    os.system(' '.join([executable, "%.8e %.8e" % tuple(params), sim_name, description]))
+
+
+calibration = BayesianCalibration.from_dict(
+    {
+        "num_iter": 3,
+        "callback": run_sim,
+        "system": {
+            "system_type": IODynamicSystem,
+            "param_min": [0.001, 0.001],
+            "param_max": [1, 10],
+            "param_names": ['a', 'b'],
+            "num_samples": 20,
+            "obs_data_file": PATH + '/linear_obs.dat',
+            "obs_names": ['f'],
+            "ctrl_name": 'u',
+            "sim_name": 'linear',
+            "sim_data_dir": PATH + '/sim_data/',
+            "sim_data_file_ext": '.txt',
+            "sigma_tol": 0.01,
+        },
+        "calibration": {
+            "inference": {"ess_target": 0.3},
+            "sampling": {
+                "max_num_components": 1,
+                "covariance_type": "full",
+            },
+        },
+        "save_fig": -1,
+    }
+)
+
+# Run one iteration of Bayesian calibration
+calibration.run()
+
+# Continue Bayesian calibration from a previous iteration until the total number of iterations or tolerance is reached
+curr_iter = 0
+calibration_continued = BayesianCalibration.from_dict(
+    {
+        "curr_iter": curr_iter,
+        "num_iter": 3,
+        "callback": run_sim,
+        "system": {
+            "system_type": IODynamicSystem,
+            "param_min": [0.001, 0.001],
+            "param_max": [1, 10],
+            "obs_data_file": PATH + '/linear_obs.dat',
+            "obs_names": ['f'],
+            "ctrl_name": 'u',
+            "sim_name": 'linear',
+            "sim_data_dir": PATH + '/sim_data/',
+            "sim_data_file_ext": ".txt",
+            "param_names": ['a', 'b'],
+        },
+        "calibration": {
+            "inference": {"ess_target": 0.3},
+            "sampling": {
+                "max_num_components": 1,
+                "covariance_type": "full",
+            },
+        },
+        "save_fig": 0,
+    }
+)
+
+calibration_continued.load_and_run_one_iteration()
+calibration_continued.run()