This example shows you how to run a global optimisation with SNES.
For a more elaborate example of an optimisation, see: basic optimisation example.
from __future__ import print_function
import pints
import pints.toy as toy
import numpy as np
import matplotlib.pyplot as pl
# Load a forward model
model = toy.LogisticModel()
# Create some toy data
real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 1000)
values = model.simulate(real_parameters, times)
# Add noise
values += np.random.normal(0, 10, values.shape)
# Create an object with links to the model and time series
problem = pints.SingleOutputProblem(model, times, values)
# Select a score function
score = pints.SumOfSquaresError(problem)
# Select some boundaries
boundaries = pints.RectangularBoundaries([0, 400], [0.03, 600])
# Perform an optimization with boundaries and hints
x0 = 0.01, 450
sigma0 = [0.01, 100]
found_parameters, found_value = pints.optimise(
score,
x0,
sigma0,
boundaries,
method=pints.SNES,
)
# Show score of true solution
print('Score at true solution: ')
print(score(real_parameters))
# Compare parameters with original
print('Found solution: True parameters:' )
for k, x in enumerate(found_parameters):
print(pints.strfloat(x) + ' ' + pints.strfloat(real_parameters[k]))
# Show quality of fit
pl.figure()
pl.xlabel('Time')
pl.ylabel('Value')
pl.plot(times, values, label='Nosiy data')
pl.plot(times, problem.evaluate(found_parameters), label='Fit')
pl.legend()
pl.show()
Minimising error measure using Seperable Natural Evolution Strategy (SNES) Running in sequential mode. Iter. Eval. Best Time m:s 0 6 1032189 0:03.3 1 12 1032189 0:03.3 2 18 1032189 0:03.3 3 24 415890.9 0:03.3 20 126 99951.86 0:03.3 40 246 97790.04 0:03.3 60 366 97712.96 0:03.4 80 486 97712.92 0:03.4 100 606 97712.92 0:03.4 120 726 97712.92 0:03.4 140 846 97712.92 0:03.4 160 966 97712.92 0:03.4 180 1086 97712.92 0:03.5 200 1206 97712.92 0:03.5 220 1326 97712.92 0:03.5 240 1446 97712.92 0:03.5 260 1566 97712.92 0:03.5 280 1686 97712.92 0:03.5 300 1806 97712.92 0:03.6 320 1926 97712.92 0:03.6 340 2046 97712.92 0:03.6 360 2166 97712.92 0:03.6 380 2286 97712.92 0:03.6 381 2286 97712.92 0:03.6 Halting: No significant change for 200 iterations. Score at true solution: 97762.3046752 Found solution: True parameters: 1.49887429771769151e-02 1.49999999999999994e-02 5.00225855587396723e+02 5.00000000000000000e+02