Bound prior sampling (#69)

pybop/optimisation.py

@@ -44,8 +44,7 @@ def __init__(
         if x0 is None:
             self.x0 = np.zeros(self.n_parameters)
             for i, param in enumerate(self.parameters):
-                self.x0[i] = param.prior.rvs(1)[0]
-                # Update to capture dimensions per parameter
+                self.x0[i] = param.rvs(1)
 
         # Add the initial values to the parameter definitions
         for i, param in enumerate(self.parameters):

pybop/parameters/base_parameter.py

@@ -8,12 +8,21 @@ def __init__(self, name, value=None, prior=None, bounds=None):
         self.prior = prior
         self.value = value
         self.bounds = bounds
+        self.lower_bound = self.bounds[0]
+        self.upper_bound = self.bounds[1]
 
-        # To Do:
-        # priors implementation
-        # parameter check
-        # bounds checks and set defaults
-        # implement methods to assign and retrieve parameters
+        if self.lower_bound > self.upper_bound:
+            raise ValueError("Lower bound must be less than upper bound")
+
+    def rvs(self, n_samples):
+        sample = self.prior.rvs(n_samples)
+
+        if sample < self.lower_bound:
+            return self.lower_bound
+        elif sample > self.upper_bound:
+            return self.upper_bound
+        else:
+            return sample
 
     def update(self, value):
         self.value = value

tests/unit/test_optimisation.py

@@ -0,0 +1,42 @@
+import pybop
+import numpy as np
+import pytest
+
+
+class TestOptimisation:
+    """
+    A class to test the optimisation class.
+    """
+
+    @pytest.mark.unit
+    def test_prior_sampling(self):
+        # Tests prior sampling
+        model = pybop.lithium_ion.SPM()
+
+        Dataset = [
+            pybop.Dataset("Time [s]", np.linspace(0, 3600, 100)),
+            pybop.Dataset("Current function [A]", np.zeros(100)),
+            pybop.Dataset("Terminal voltage [V]", np.ones(100)),
+        ]
+
+        param = [
+            pybop.Parameter(
+                "Negative electrode active material volume fraction",
+                prior=pybop.Gaussian(0.75, 0.2),
+                bounds=[0.73, 0.77],
+            )
+        ]
+
+        signal = "Terminal voltage [V]"
+        cost = pybop.RMSE()
+
+        for i in range(50):
+            opt = pybop.Optimisation(
+                cost,
+                model,
+                optimiser=pybop.NLoptOptimize(n_param=len(param)),
+                parameters=param,
+                dataset=Dataset,
+                signal=signal,
+            )
+            assert opt.x0 <= 0.77 and opt.x0 >= 0.73