0.33.10

cleanup kriging

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -14477,41 +14477,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "# Initialize and fit the Kriging model\n",
+    "model = Kriging()\n",
+    "model.fit(X_train, y_train)\n",
+    "for param, value in model.get_params(deep=True).items():\n",
+    "    print(f\"{param} -> {value}\")\n",
+    "theta_values = model.get_params()[\"theta\"]\n",
+    "print(\"Fitted theta values:\", theta_values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "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",
+    "# Build the correlation matrix Psi\n",
+    "Psi = model.build_Psi()\n",
+    "print(\"Correlation matrix Psi:\\n\", Psi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Anisotropic model: n_theta set to 2\n",
-      "Lambda -> [-8.98453674]\n",
-      "counter -> None\n",
-      "eps -> 1.4901161193847656e-08\n",
-      "isotropic -> False\n",
-      "log_level -> 50\n",
-      "max_Lambda -> 0.0\n",
-      "max_p -> 2.0\n",
-      "max_theta -> 2.0\n",
-      "method -> regression\n",
-      "metric_factorial -> canberra\n",
-      "min_Lambda -> -9.0\n",
-      "min_p -> 1.0\n",
-      "min_theta -> -3.0\n",
-      "model_fun_evals -> 100\n",
-      "model_optimizer -> <function differential_evolution at 0x1173237e0>\n",
-      "n_p -> 1\n",
-      "n_theta -> 2\n",
-      "name -> Kriging\n",
-      "optim_p -> False\n",
-      "p_val -> 2.0\n",
-      "penalty -> 10000.0\n",
-      "seed -> 124\n",
-      "spot_writer -> None\n",
-      "theta -> [-2.73827427 -2.74539395]\n",
-      "theta_init_zero -> False\n",
-      "var_type -> ['num', 'num']\n",
-      "Fitted theta values: [-2.73827427 -2.74539395]\n"
+      "Negative Log-Likelihood: -7.829213113186723\n",
+      "Correlation matrix Psi:\n",
+      " [[1.000001   0.60653066 0.13533528]\n",
+      " [0.60653066 1.000001   0.60653066]\n",
+      " [0.13533528 0.60653066 1.000001  ]]\n",
+      "Cholesky factor U:\n",
+      " [[1.0000005  0.         0.        ]\n",
+      " [0.60653036 0.79506096 0.        ]\n",
+      " [0.13533522 0.6596296  0.73930655]]\n"
      ]
     }
    ],
@@ -14521,14 +14541,50 @@
     "# 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",
-    "# Initialize and fit the Kriging model\n",
-    "model = Kriging()\n",
-    "model.fit(X_train, y_train)\n",
-    "for param, value in model.get_params(deep=True).items():\n",
-    "    print(f\"{param} -> {value}\")\n",
-    "theta_values = model.get_params()[\"theta\"]\n",
-    "print(\"Fitted theta values:\", theta_values)"
+    "# Fit the Kriging model\n",
+    "model = Kriging().fit(X_train, y_train)\n",
+    "# Example log(theta) parameters\n",
+    "log_theta = np.array([0.0, 0.0, -6.0]) # -6 => 10**(-6) = 1e-6\n",
+    "negLnLike, Psi, U = model.likelihood(log_theta)\n",
+    "print(\"Negative Log-Likelihood:\", negLnLike)\n",
+    "print(\"Correlation matrix Psi:\\n\", Psi)\n",
+    "print(\"Cholesky factor U:\\n\", U)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Anisotropic model: n_theta set to 2\n",
+      "Psi vector for new point:\n",
+      " [0.99975896 0.99975896 0.99783273]\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",
+    "x_new = np.array([0.25, 0.25])\n",
+    "psi_vector = model.build_psi_vec(x_new)\n",
+    "print(\"Psi vector for new point:\\n\", psi_vector)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {