nuluh/thesis
1
0
Fork 0
Code Issues 47 Pull Requests Actions Packages Projects Releases 1 Wiki Activity

refactor(stft): comment out unused imports and update SVM model loading for consistency

Browse Source
This commit is contained in:
nuluh
2025-07-28 05:22:24 +07:00
parent 3cbef17b0c
commit 3e2b153d11
1 changed files with 26 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
code/notebooks/stft.ipynb
View File

@@ -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]"
]
},
{