'1214'

Author: smasky

README.md

@@ -4,14 +4,19 @@
 
 [![PyPI version](https://badge.fury.io/py/UQPyL.svg?icon=si%3Apython&icon_color=%2331aadd)](https://badge.fury.io/py/UQPyL) ![PyPI - Downloads](https://img.shields.io/pypi/dm/UQPyL) ![PyPI - License](https://img.shields.io/pypi/l/UQPyL) ![GitHub last commit](https://img.shields.io/github/last-commit/smasky/UQPyL) ![Static Badge](https://img.shields.io/badge/Author-wmtSky-orange) ![Static Badge](https://img.shields.io/badge/Contact-wmtsmasky%40gmail.com-blue)
 
-**UQPyL** is a Python package for **Uncertainty Quantification** and **Optimization** of computational models and their associated problems (e.g., model calibration, resource scheduling, product design). It includes a wide range of methods and algorithms for Design of Experiments, Sensitivity Analysis, Optimization (Single- and Multi-objective). Additionally, **Surrogate Models** are built-in for solving computationally expensive problems. 
+**UQPyL** is a Python package for **Uncertainty Quantification** and **Optimization** of computational models and their associated problems (e.g., model calibration, resource scheduling, product design). It includes a wide range of methods and algorithms for Design of Experiments, Sensitivity Analysis, Bayesian Inference, Optimization (Single- and Multi-objective). Additionally, **Surrogate Models** are built-in for solving computationally expensive problems. 
 
 👉[中文简介](https://github.com/smasky/UQPyL/blob/dev/README_CN.md)
 
 👉[中文文档](https://swat-uq.readthedocs.io/en/latest/zh/index.html)
 
 👉[Documentation](https://uqpyl.readthedocs.io)
 
+## Changelog
+
+- 2025.12.14:  Added the **inference** module. And results are now saved in **NetCDF** format, replacing the previous HDF output.
+
+
 ## Contents
 
 - [Main Features](#-main-features)

docs/quick_start.md

@@ -95,15 +95,15 @@ def objFunc_(X):
 # Array Mode
 def conFunc(X):
 
-    cons = X[:, 0]**2 + X[:, 1]**2 + X[:, 2]**2 - 4 
+    cons = 4 - X[:, 0]**2 + X[:, 1]**2 + X[:, 2]**2
 
     return cons[:, None] #keep 2D numpy array
 
 # Single-run Mode
 @singleFunc
 def conFunc(X):
 
-    con = X[0]**2 + X[1]**2 + X[2]**2 - 4 
+    con = 4 - X[0]**2 + X[1]**2 + X[2]**2
 
     return con # scaler value is feasible
 

examples/temp.py

@@ -1,5 +1,3 @@
-import sys
-sys.path.insert(0, '.')
 import numpy as np
 import xarray as xr
 
@@ -33,43 +31,47 @@ def objFunc(X):
 
 gauss = Problem(nInput = 2, nOutput = 1, objFunc = objFunc, ub = 10, lb = -10)
 
-
 # ------------------------------- #
 #      Gaussian Mixture 4D        #
 # ------------------------------- #
 
-def objFunc(X):
+# def objFunc(X):
 
-    X = np.atleast_2d(X)
-    _, D = X.shape
+#     X = np.atleast_2d(X)
+#     _, D = X.shape
 
-    mu1 = np.array([-5.0, 0.0, 0.0, 0.0])
-    mu2 = np.array([ 5.0, 0.0, 0.0, 0.0])
+#     mu1 = np.array([-5.0, 0.0, 0.0, 0.0])
+#     mu2 = np.array([ 5.0, 0.0, 0.0, 0.0])
 
-    Sigma1 = np.eye(D)
-    Sigma2 = np.eye(D)
+#     Sigma1 = np.eye(D)
+#     Sigma2 = np.eye(D)
 
-    inv1 = np.linalg.inv(Sigma1)
-    inv2 = np.linalg.inv(Sigma2)
+#     inv1 = np.linalg.inv(Sigma1)
+#     inv2 = np.linalg.inv(Sigma2)
 
-    e1 = 0.5 * np.sum((X - mu1) @ inv1 * (X - mu1), axis=1)
-    e2 = 0.5 * np.sum((X - mu2) @ inv2 * (X - mu2), axis=1)
+#     e1 = 0.5 * np.sum((X - mu1) @ inv1 * (X - mu1), axis=1)
+#     e2 = 0.5 * np.sum((X - mu2) @ inv2 * (X - mu2), axis=1)
 
-    f = -np.log(0.5 * np.exp(-e1) + 0.5 * np.exp(-e2))
+#     f = -np.log(0.5 * np.exp(-e1) + 0.5 * np.exp(-e2))
 
-    return f[:, np.newaxis]
+#     return f[:, np.newaxis]
 
-gauss4 = Problem(nInput = 4, nOutput = 1, objFunc = objFunc, ub = 10, lb = -10, name = "Gaussian4")
+# gauss4 = Problem(nInput = 4, nOutput = 1, objFunc = objFunc, ub = 10, lb = -10, name = "Gaussian4")
 
 # ------------------------------------------- #
 #             Metropolis-Hastings             # 
 # ------------------------------------------- #
 
-from UQPyL.inference import MH
+# from UQPyL.inference import MH
+
+# mh = MH(nChains = 10, warmUp = 10, maxIters = 1000, propDist = 'gauss', 
+#             verboseFlag = True, verboseFreq = 1)
 
-mh = MH(nChains = 10, warmUp = 10, maxIters = 1000, propDist = 'gauss', verboseFlag = True, verboseFreq = 1)
+# res = mh.run(problem = gauss)
 
-res = mh.run(problem = gauss4)
+# print(res['posterior'].info)
+# print(res['stats'].info)
+# print(res['optimization'].info)
 
 # ------------------------------------------- #
 #                  MH-Gibbs                   # 

examples/test_inference.py

@@ -1,22 +1,19 @@
-import sys
-sys.path.insert(0, '.')
-
 #---------------------------------------------#
 # Single Objective Optimization
 from UQPyL.problem.sop import Sphere
 
-problem = Sphere(30)
+problem = Sphere(4)
 
 # ------------------------------------------- #
 #                    GA                       # 
 # ------------------------------------------- #
 
 # from UQPyL.optimization.soea import GA
 
-# for i in range(9):
-#     ga = GA()
-#     res = ga.run(problem)
+# ga = GA()
+# res = ga.run(problem)
 
+# A=1
 # ------------------------------------------- #
 #                    PSO                      # 
 # ------------------------------------------- #
@@ -37,16 +34,16 @@
 #                   CSA                       # 
 # ------------------------------------------- #
 
-# from UQPyL.optimization.soea import CSA
-# csa = CSA()
-# res = csa.run(problem)
+from UQPyL.optimization.soea import CSA
+csa = CSA()
+res = csa.run(problem)
 
 # ------------------------------------------- #
 #                  SCE-UA                     # 
 # ------------------------------------------- #
 
 # from UQPyL.optimization.soea import SCE_UA
-# sce_ua = SCE_UA()
+# sce_ua = SCE_UA(verboseFreq = 10)
 # res = sce_ua.run(problem)
 
 # ------------------------------------------- #

examples/test_optimization.py

@@ -1,6 +1,3 @@
-import sys
-sys.path.insert(0, '.')
-
 from UQPyL.util import plot_op_curve, plot_op_curve_stat, plot_op_pareto, plot_sa, plot_surrogate
 
 # source = {
@@ -10,6 +7,12 @@
 #     "ABC": "./Result/Data/ABC_Sphere_D30_M1_1.nc",
 # }
 
+# source = {
+#     "SCE-UA" : "./Result/Data/SCE-UA_Sphere_D4_M1_1.nc",
+#     "PSO" : "./Result/Data/PSO_Sphere_D4_M1_1.nc",
+#     "GA" : "./Result/Data/GA_Sphere_D4_M1_1.nc",
+# }
+
 # plot_op_curve(source = source, yLog = True, ySmooth = True, markevery = 40, xlim = [0, 1000], xMajorLocator = 100)
 
 # folder = "./Result/Data"
@@ -31,13 +34,13 @@
 
 # three-objective problems
 
-# from UQPyL.problem import DTLZ7
+# from UQPyL.problem import DTLZ2
 
-# dtlz = DTLZ7(10)
+# dtlz = DTLZ2(10)
 
 # optima = dtlz.getPF()
 
-# filepath = "./Result/Data/NSGAII_DTLZ7_D10_M3_1.nc"
+# filepath = "./Result/Data/NSGAII_DTLZ2_D10_M3_1.nc"
 
 # plot_op_pareto(filepath = filepath, optima = optima, xlim = [0, 1], xMajorLocator = 0.1, ylim = [0, 1], yMajorLocator = 0.1)
 
@@ -48,6 +51,13 @@
 #     "MARS" : "./Result/Data/MARS_Problem_D8_M1_1.nc",
 # }
 
+# source = {
+#     "Sobol" : "./Result/Data/Sobol_Problem_D3_M1_1.nc",
+#     "RBD-FAST" : "./Result/Data/RBDFAST_Problem_D3_M1_1.nc",
+#     "FAST" : "./Result/Data/FAST_Problem_D3_M1_1.nc",
+#     "MARS" : "./Result/Data/MARS_Problem_D3_M1_1.nc",
+# }
+
 # plot_sa(source = source, fontsize = 20)
 
 # from UQPyL.problem import Sphere
@@ -83,10 +93,8 @@
 
 # from UQPyL.util import plot_infer_stat, plot_infer_stat_combined
 
-# filepath = "./Result/Data/MH_Gaussian4_D4_M1_1.nc"
+# filepath = "./Result/Data/MH_Problem_D2_M1_1.nc"
 
 # plot_infer_stat(filepath = filepath, fontsize = 20, burnIn = 100)
 
 # plot_infer_stat_combined(filepath = filepath, fontsize = 20, burnIn = 100, showCI = True, CI = 0.95)
-
-

examples/test_plot.py

@@ -1,6 +1,3 @@
-import sys
-sys.path.insert(0, '.')
-
 import numpy as np
 from UQPyL.problem import Problem, ProblemABC
 from UQPyL.doe import LHS