0.33.0

Kriging updated

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -4714,32 +4714,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Seed set to 123\n",
-      "Seed set to 123\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Experiment saved to TEST_SAVE_exp.pkl\n",
-      "Anisotropic model: n_theta set to 15\n",
-      "Anisotropic model: n_theta set to 15\n",
-      "spotpython tuning: 311.9606862434023 [#---------] 13.84% \n",
-      "Anisotropic model: n_theta set to 15\n",
-      "spotpython tuning: 311.9606862434023 [##########] 100.00% Done...\n",
-      "\n",
-      "Experiment saved to TEST_SAVE_res.pkl\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import numpy as np\n",
@@ -4851,24 +4828,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded experiment from TEST_SAVE_exp.pkl\n",
-      "Anisotropic model: n_theta set to 15\n",
-      "Anisotropic model: n_theta set to 15\n",
-      "spotpython tuning: 311.9608671871864 [#---------] 14.28% \n",
-      "Anisotropic model: n_theta set to 15\n",
-      "spotpython tuning: 311.9608671871864 [##########] 100.00% Done...\n",
-      "\n",
-      "Experiment saved to TEST_SAVE_res.pkl\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from spotpython.utils.file import load_and_run_spot_python_experiment\n",
     "# spot_load_res = load_and_run_spot_python_experiment(filename=\"TEST_SAVE_res.pkl\")\n",
@@ -14177,12 +14139,46 @@
     "S_3.get_spot_attributes_as_df()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test new prediction "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Standard Kriging. Dimensions: 2\n",
+      "Anisotropic model: n_theta set to 2\n",
+      "bounds for differential_evolution: [(-3.0, 2.0), (-3.0, 2.0), (-9.0, 0.0)]\n",
+      "Prediction at [0.25 0.25]: f=0.14995512874385375, s=8.354296177760167e-05, SigmaSqr=1.3948369741908675, SSqr=6.979426462573815e-09\n",
+      "Prediction at [0.75 0.75]: f=0.2500448712561424, s=8.354296270441831e-05, SigmaSqr=1.3948369741908675, SSqr=6.9794266174318275e-09\n",
+      "Predictions: [[1.49955129e-01 8.35429618e-05]\n",
+      " [2.50044871e-01 8.35429627e-05]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from spotpython.surrogate.kriging import Kriging\n",
+    "# Training data\n",
+    "X_train = np.array([[0.0, 0.0], [0.5, 0.5], [1.0, 1.0]])\n",
+    "y_train = np.array([0.1, 0.2, 0.3])\n",
+    "# Fit the Kriging model\n",
+    "model = Kriging().fit(X_train, y_train)\n",
+    "# Test data\n",
+    "X_test = np.array([[0.25, 0.25], [0.75, 0.75]])\n",
+    "# Predict responses\n",
+    "y_pred = model.predict(X_test)\n",
+    "print(\"Predictions:\", y_pred)"
+   ]
   },
   {
    "cell_type": "code",

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.32.3"
+version = "0.33.0"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]