feat: add SVM

Author: marcpinet

examples/tools-usages/decomposition-manifold/pca_tsne_kmeans_dbscan.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "from neuralnetlib.preprocessing import StandardScaler\n",
     "from neuralnetlib.preprocessing import PCA, TSNE\n",
-    "from neuralnetlib.cluster import KMeans, DBSCAN\n",
+    "from neuralnetlib.learners import KMeans, DBSCAN\n",
     "\n",
     "from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score\n",
     "from sklearn.datasets import make_blobs"
@@ -249,7 +249,7 @@
     "print(f\"Adjusted Mutual Information Score: {adjusted_mutual_info_score(y, y_pred):.3f}\")\n",
     "\n",
     "print(\"\\nCluster Centers (after de-standardization):\")\n",
-    "cluster_centers = scaler.inverse_transform(kmeans.cluster_centers_)\n",
+    "cluster_centers = scaler.inverse_transform(kmeans.learners_centers_)\n",
     "for i, center in enumerate(cluster_centers):\n",
     "    print(f\"Cluster {i}: {center}\")"
    ]

examples/tools-usages/unsupervised/unsupervised.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from neuralnetlib.ensemble import IsolationForest, RandomForest, AdaBoost, GradientBoostingMachine, XGBoost\n",
+    "from neuralnetlib.learners import IsolationForest, RandomForest, AdaBoost, GradientBoostingMachine, XGBoost\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",

neuralnetlib/__init__.py

@@ -8,5 +8,4 @@
 from . import preprocessing
 from . import regularizers
 from . import utils
-from . import ensemble
-from . import cluster
+from . import learners