Re-applied Spectral clustering on time series data using Aeon

Author: SalmanDeveloperz

Diff for: examples/clustering/sklearn_clustering_with_aeon_distances.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -284,18 +284,15 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from sklearn.cluster import SpectralClustering\n",
-    "from sklearn.metrics import pairwise_distances\n",
-    "\n",
-    "X = np.vstack((np.random.normal(loc=[2, 2], scale=0.5, size=(50, 2)), \n",
-    "               np.random.normal(loc=[5, 5], scale=0.5, size=(50, 2))))\n",
-    "distance_matrix = pairwise_distances(X, metric='euclidean')\n",
+    "X, _ = load_basic_motions(split=\"train\", return_X_y=True)\n",
+    "flat_X = np.array([flatten(ts) for ts in X])\n",
+    "distance_matrix = pairwise_distances(flat_X, metric=\"euclidean\")\n",
     "inverse_distance_matrix = 1 - (distance_matrix / distance_matrix.max())\n",
     "spectral = SpectralClustering(n_clusters=2, affinity=\"precomputed\", random_state=42)\n",
-    "spectral_labels = spectral.fit_predict(inverse_distance_matrix)\n",
+    "labels = spectral.fit_predict(inverse_distance_matrix)\n",
     "plt.figure(figsize=(10, 6))\n",
-    "for label in np.unique(spectral_labels):\n",
-    "    plt.scatter(X[spectral_labels == label, 0], X[spectral_labels == label, 1], label=f\"Cluster {label}\", alpha=0.7)\n",
+    "for label in np.unique(labels):\n",
+    "    plt.plot(flat_X[labels == label].T, alpha=0.5, label=f\"Cluster {label}\")\n",
     "plt.title(\"Spectral Clustering with Normalized Similarity Matrix\")\n",
     "plt.legend()\n",
     "plt.show()"