Undoing expansion definition. Modifying deepcopy

BAMresearch · Sep 16, 2024 · b3ea4f5 · b3ea4f5
1 parent a960824
commit b3ea4f5
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Showing 4 changed files with 13 additions and 13 deletions.
diff --git a/docs/examples/bias.py b/docs/examples/bias.py
@@ -95,11 +95,7 @@ class LinearModel(ForwardModelBase):
     def __init__(self, name):
         super().__init__(name)
         self.pce_order = 1
-        self.bias_dist = chaospy.Normal(0.0, 1.0)
-
-        # generate the polynomial chaos expansion
-        self.expansion = chaospy.generate_expansion(self.pce_order, self.bias_dist)
-
+
     def interface(self):
         self.parameters = ["a", "b"]
         self.input_sensors = Sensor("x")
@@ -116,6 +112,10 @@ def response(self, inp: dict) -> dict:
 
         # define the distribution for the bias term
         b_dist = chaospy.Normal(0.0, b)
+
+        # generate the polynomial chaos expansion
+        expansion = chaospy.generate_expansion(self.pce_order, bias_dist)
+
         # generate quadrature nodes and weights
         sparse_quads = chaospy.generate_quadrature(
             self.pce_order, b_dist, rule="Gaussian"
@@ -127,7 +127,7 @@ def response(self, inp: dict) -> dict:
 
         # fit the polynomial chaos expansion
         fitted_sparse = chaospy.fit_quadrature(
-            self.expansion, sparse_quads[0], sparse_quads[1], sparse_evals
+            expansion, sparse_quads[0], sparse_quads[1], sparse_evals
         )
         return {"y": fitted_sparse, "dist": b_dist}
 

diff --git a/probeye/inference/solver.py b/probeye/inference/solver.py
@@ -31,7 +31,7 @@ def __init__(
     ):
 
         # attributes from arguments
-        self.problem = cp.deepcopy(problem)
+        self.problem = cp.copy(problem)
         self.show_progress = show_progress
         self.seed = seed
 

diff --git a/tests/unit_tests/inference/bias/test_likelihood_models.py b/tests/unit_tests/inference/bias/test_likelihood_models.py
@@ -30,8 +30,6 @@ def __init__(self, name):
                 super().__init__(name)
 
                 self.pce_order = 1
-                self.b_dist = chaospy.Normal(0.0, 1.0)
-                self.expansion = chaospy.generate_expansion(self.pce_order, self.b_dist)
 
             def interface(self):
                 self.parameters = ["a", "b"]
@@ -49,6 +47,8 @@ def response(self, inp: dict) -> dict:
 
                 # define the distribution for the bias term
                 b_dist = chaospy.Normal(0.0, b)
+                expansion = chaospy.generate_expansion(self.pce_order, b_dist)
+
                 # generate quadrature nodes and weights
                 sparse_quads = chaospy.generate_quadrature(
                     self.pce_order, b_dist, rule="Gaussian"
@@ -59,7 +59,7 @@ def response(self, inp: dict) -> dict:
                 )
                 # fit the polynomial chaos expansion
                 fitted_sparse = chaospy.fit_quadrature(
-                    self.expansion, sparse_quads[0], sparse_quads[1], sparse_evals
+                    expansion, sparse_quads[0], sparse_quads[1], sparse_evals
                 )
                 return {"y": fitted_sparse, "dist": b_dist}
 

diff --git a/tests/unit_tests/inference/bias/test_solver.py b/tests/unit_tests/inference/bias/test_solver.py
@@ -26,8 +26,6 @@ def __init__(self, name):
                 super().__init__(name)
 
                 self.pce_order = 1
-                self.b_dist = chaospy.Normal(0.0, 1.0)
-                self.expansion = chaospy.generate_expansion(self.pce_order, self.b_dist)
 
             def interface(self):
                 self.parameters = ["a", "b"]
@@ -45,6 +43,8 @@ def response(self, inp: dict) -> dict:
 
                 # define the distribution for the bias term
                 b_dist = chaospy.Normal(0.0, b)
+                expansion = chaospy.generate_expansion(self.pce_order, b_dist)
+
                 # generate quadrature nodes and weights
                 sparse_quads = chaospy.generate_quadrature(
                     self.pce_order, b_dist, rule="Gaussian"
@@ -55,7 +55,7 @@ def response(self, inp: dict) -> dict:
                 )
                 # fit the polynomial chaos expansion
                 fitted_sparse = chaospy.fit_quadrature(
-                    self.expansion, sparse_quads[0], sparse_quads[1], sparse_evals
+                    expansion, sparse_quads[0], sparse_quads[1], sparse_evals
                 )
                 return {"y": fitted_sparse, "dist": b_dist}