code/notebooks/stft.ipynb

@@ -636,11 +636,11 @@
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.decomposition import PCA\n",
-    "from xgboost import XGBClassifier\n",
-    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.neighbors import KNeighborsClassifier\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+    "# from xgboost import XGBClassifier\n",
+    "# from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
+    "# from sklearn.tree import DecisionTreeClassifier\n",
+    "# from sklearn.neighbors import KNeighborsClassifier\n",
+    "# from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
     "from sklearn.neural_network import MLPClassifier\n",
     "\n"
    ]
@@ -669,11 +669,11 @@
     "    #     StandardScaler(),\n",
     "    #     SVC(kernel='rbf', probability=True)\n",
     "    # ),\n",
-    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    #                                     StandardScaler(),\n",
-    "    #                                     PCA(n_components=10),\n",
-    "    #                                     SVC(kernel='rbf')\n",
-    "    #                                     ),\n",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "                                        StandardScaler(),\n",
+    "                                        PCA(n_components=10),\n",
+    "                                        SVC(kernel='rbf', probability=True)\n",
+    "                                        ),\n",
     "\n",
     "    # \"XGBoost\": XGBClassifier()\n",
     "    \"MLPClassifier\": make_pipeline(\n",
@@ -818,8 +818,22 @@
    "source": [
     "# 4. Sensor A Validate on Dataset B\n",
     "from joblib import load\n",
-    "svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
-    "y_pred_svm_1 = svm_model.predict(X1b)"
+    "svm_model = load('D:/thesis/models/Sensor A/SVM with StandardScaler and PCA.joblib')\n",
+    "y_pred_svm_1 = svm_model.predict_proba(X1b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Set NumPy to display full decimal values\n",
+    "np.set_printoptions(suppress=True, precision=6)  # Suppress scientific notation, set precision to 6 decimals\n",
+    "\n",
+    "y_pred_svm_1[1]"
    ]
   },
   {