'v2.0.5'

UQPyL/__init__.py

@@ -1,6 +1,6 @@
 from . import problems, surrogates, optimization, sensibility, DoE, utility
 
-__version__ = "2.0.4"
+__version__ = "2.0.5"
 __author__ = "wmtSky"
 
 __all__=[

UQPyL/optimization/asmo.py

@@ -113,7 +113,7 @@ def run(self, oneStep=False):
                 history_best_decs[FE]=BestX
                 history_best_objs[FE]=BestY
 
-        Result={'best_dec':BestX, 'best_obj':BestY, 'history_best_decs': history_best_decs, 'history_best_objs':history_best_objs ,'FE':FE}
+        Result={'best_dec':BestX, 'best_obj':BestY, 'history_best_decs': history_best_decs, 'history_best_objs':history_best_objs ,'FEs':FE}
 
         return Result
 

UQPyL/sensibility/morris.py

@@ -94,7 +94,7 @@ def analyze(self, X: Optional[np.ndarray]=None, Y: Optional[np.ndarray]=None, ve
 
         N=int(X.shape[0]/self.num_levels)
 
-        for i in range(N):
+        for i in range(num_trajectory):
             X_sub=X[i*(n_input+1):(i+1)*(n_input+1), :]
             Y_sub=Y[i*(n_input+1):(i+1)*(n_input+1), :]
 

examples/draw_p.py

@@ -0,0 +1,33 @@
+import os
+os.chdir('./examples')
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+values=np.loadtxt('./sensibility.txt', dtype=np.float32)
+with open('./si_names.txt', 'r') as f:
+    name=f.readlines()
+    name=[n.strip() for n in name]
+colors=['#03AED2', '#FFC55A', '#75A47F', '#D2649A']
+labels=['Sobol\'', 'FAST', 'Morris', 'MARS']
+x=np.arange(len(name))
+bar_width=0.2
+for i in range(4):
+    plt.bar(x + i * bar_width, values[:, i], bar_width, label=labels[i], color=colors[i])
+for i in range(len(name) - 1):
+    plt.axvline(x=i+1-bar_width, color='black', linestyle='--', linewidth=0.75)
+
+
+plt.xlim(-0.5, 25.8)
+plt.xticks(x+0.3, [f'P{i+1}' for i in range(len(name))], fontsize=14)
+plt.yticks(fontsize=14)
+plt.tick_params(axis='both', which='both', length=0)
+plt.xlabel('Parameters', fontsize=14)
+plt.ylabel('Sensibility', fontsize=14)
+plt.legend(loc='upper right', fancybox=True, shadow=True, ncol=4, fontsize=14)
+ax = plt.gca()
+ax.spines['top'].set_linewidth(2)
+ax.spines['bottom'].set_linewidth(2)
+ax.spines['left'].set_linewidth(2)
+ax.spines['right'].set_linewidth(2)
+plt.show()

examples/example_optimization.py

@@ -8,35 +8,32 @@
     - 6. MOEAD/D - Multi-objective
     - 7. MO_ASMO - Multi-objective - Surrogate
 '''
-#tmp
+#--------------tmp-------------------#
 import sys
 sys.path.append(".")
-from scipy.io import loadmat
-print(sys.path)
 import os
 os.chdir('./examples')
-#
+#-----------tmp--------------------#
 import numpy as np
 
 ################1. Genetic Algorithm (GA) - Single objective################
-# print('################1. Genetic Algorithm (GA) - Single objective################')
-# from UQPyL.optimization import GA
-# from UQPyL.problems import Sphere
+print('################1. Genetic Algorithm (GA) - Single objective################')
+from UQPyL.optimization import GA
+from UQPyL.problems import Sphere
+
+problem=Sphere(n_input=30, ub=100, lb=-100)
+ga=GA(problem, n_samples=50)
+res=ga.run()
+print('Best objective:', res['best_obj'])
+print('Best decisions:', res['best_dec'])
+print('FE:', res['FEs'])
 
-# problem=Sphere(n_input=30, ub=100, lb=-100)
-# ga=GA(problem, n_samples=50)
-# res=ga.run()
-# print('Best objective:', res['best_obj'])
-# print('Best decisions:', res['best_dec'])
-# print('FE:', res['FEs'])
-
 # import matplotlib.pyplot as plt
 # FEs_objs=res['history_best_objs']
 # FEs=list(FEs_objs.keys())
 # objs=list(FEs_objs.values())
 # plt.plot(FEs, np.log10(objs))
 
-
 ################2. SCE-UA - Single objective################
 # print('################2. SCE-UA - Single objective################')
 # from UQPyL.optimization import SCE_UA
@@ -83,7 +80,7 @@
 problem=Sphere(n_input=30, ub=100, lb=-100)
 rbf=RBF(kernel=Cubic())
 asmo=ASMO(problem, rbf, n_init=50)
-res=asmo.run(maxFE=1000)
+res=asmo.run()
 print('Best objective:', res['best_obj'])
 print('Best decisions:', res['best_dec'])
 print('FE:', res['FEs'])