Updates standardization code for Uniform and Normal

docs/source/sampling/theta_criterion.rst

@@ -1,19 +1,18 @@
 Theta Criterion
 ---------------
-
-
-ThetaCriterionPCE Class
-^^^^^^^^^^^^^^^^^^^^^^^^
-
 The technique enables one-by-one extension of an experimental design while trying to obtain an optimal sample at each stage of the adaptive sequential surrogate model
-construction process. The sequential sampling strategy based on :math:`\Theta` criterion selects from a pool of candidate points by trying to cover the design domain 
-proportionally to their local variance contribution. The proposed criterion for the sample selection balances both exploitation of the surrogate model using variance 
-density derived analytically from Polynomial Chaos Expansion and exploration of the design domain. The active learning technique based on  :math:`\Theta` criterion can be 
+construction process. The sequential sampling strategy based on :math:`\Theta` criterion selects from a pool of candidate points by trying to cover the design domain
+proportionally to their local variance contribution. The proposed criterion for the sample selection balances both exploitation of the surrogate model using variance
+density derived analytically from Polynomial Chaos Expansion and exploration of the design domain. The active learning technique based on  :math:`\Theta` criterion can be
 combined with arbitrary sampling technique employed for construction of a pool of candidate points. More details can be found in:
 
-L. Novák, M. Vořechovský, V. Sadílek, M. D. Shields, *Variance-based adaptive sequential sampling for polynomial chaos expansion*, 
+L. Novák, M. Vořechovský, V. Sadílek, M. D. Shields, *Variance-based adaptive sequential sampling for polynomial chaos expansion*,
 637 Computer Methods in Applied Mechanics and Engineering 386 (2021) 114105. doi:10.1016/j.cma.2021.114105
 
+
+ThetaCriterionPCE Class
+^^^^^^^^^^^^^^^^^^^^^^^^
+
 The :class:`.ThetaCriterionPCE` class is imported using the following command:
 
 >>> from UQpy.sampling.ThetaCriterionPCE import ThetaCriterionPCE

src/UQpy/surrogates/polynomial_chaos/polynomials/baseclass/Polynomials.py

@@ -47,10 +47,8 @@ def standardize_sample(x, joint_distribution):
             if type(marginals[i]) == Normal:
                 s[:, i] = Polynomials.standardize_normal(x[:, i], mean=marginals[i].parameters['loc'],
                                                          std=marginals[i].parameters['scale'])
-
-            if type(marginals[i]) == Uniform:
+            elif type(marginals[i]) == Uniform:
                 s[:, i] = Polynomials.standardize_uniform(x[:, i], marginals[i])
-
             else:
                 raise TypeError("standarize_sample is defined only for Uniform and Gaussian marginal distributions")
         return s
@@ -77,7 +75,7 @@ def standardize_pdf(x, joint_distribution):
         for i in range(inputs_number):
             if type(marginals[i]) == Normal:
                 pdf_val *= (stats.norm.pdf(s[:, i]))
-            if type(marginals[i]) == Uniform:
+            elif type(marginals[i]) == Uniform:
                 pdf_val *= (stats.uniform.pdf(s[:, i], loc=-1, scale=2))
             else:
                 raise TypeError("standardize_pdf is defined only for Uniform and Gaussian marginal distributions")

tests/unit_tests/surrogates/test_pce.py

@@ -405,35 +405,36 @@ def test_22():
     pos = ThetaSampling_complete.run(adapted_x, candidates_x, nsamples=2)
     best_candidates = candidates_x[pos, :]
     assert best_candidates[0, 0] == 1.1 and best_candidates[1, 0] == 9
-
+
+
 def test_23():
     """
     Test Standardization of sample and associated PDF 
     """
-    dist1 = Uniform(loc=0, scale=10)  
+    dist1 = Uniform(loc=0, scale=10)
     dist2 = Normal(loc=6, scale=2)
     marg = [dist1, dist2]
     joint = JointIndependent(marginals=marg)
 
     polynomial_basis = TotalDegreeBasis(joint, max_degree)
     least_squares = LeastSquareRegression()
     pce = PolynomialChaosExpansion(polynomial_basis=polynomial_basis, regression_method=least_squares)
-    X=np.zeros((3, 2))
+    X = np.zeros((3, 2))
     X[:, 1] = np.array([0, 6, 10])
     X[:, 0] = np.array([0, 5, 10])
-    
+
     pce.fit(X, X)
-    
-    standardized_samples=polynomial_chaos.Polynomials.standardize_sample(X,pce.polynomial_basis.distributions)   
-    standardized_pdf=polynomial_chaos.Polynomials.standardize_pdf(X,pce.polynomial_basis.distributions)
-    
+
+    standardized_samples = polynomial_chaos.Polynomials.standardize_sample(X, pce.polynomial_basis.distributions)
+    standardized_pdf = polynomial_chaos.Polynomials.standardize_pdf(X, pce.polynomial_basis.distributions)
+
     ref_sample1 = np.array([-1, 0, 1])
     ref_pdf1 = np.array([0.5, 0.5, 0.5])
     ref_sample2 = np.array([-3, 0, 2])
     ref_pdf2 = stats.norm.pdf(ref_sample2)
-    
+
     ref_sample = np.zeros((3, 2))
     ref_sample[:, 0] = ref_sample1
     ref_sample[:, 1] = ref_sample2
     ref_pdf = ref_pdf1 * ref_pdf2
-    assert (standardized_samples==ref_sample).all() and (standardized_pdf==ref_pdf).all()
+    assert (standardized_samples == ref_sample).all() and (standardized_pdf == ref_pdf).all()