feat: complete all practice modules (Git basics, Jupyter analysis, Networking, Spectral Clustering)

2026-05-27 21:43:59 +03:00
parent 08ac5885fa
commit afcdfb38cf
10 changed files with 1106 additions and 0 deletions
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+import time
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter
+from skimage.data import coins
+from skimage.transform import rescale
+from sklearn.cluster import spectral_clustering
+from sklearn.feature_extraction import image
+import logging
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+
+logging.info("Загрузка и препроцессинг изображения...")
+orig = coins()
+smoothed = gaussian_filter(orig, sigma=2)
+rescaled = rescale(smoothed, 0.2, mode="reflect", anti_aliasing=False)
+
+logging.info("Преобразование в граф...")
+graph = image.img_to_graph(rescaled)
+beta, eps = 10, 1e-6
+graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
+
+n_regions, n_plus = 26, 3
+logging.info(f"Запуск спектральной кластеризации ({n_regions} регионов)...")
+
+results = {}
+for method in ("kmeans", "discretize", "cluster_qr"):
+    logging.info(f"Метод: {method}")
+    t0 = time.time()
+    labels = spectral_clustering(graph, n_clusters=n_regions+n_plus,
+                                 eigen_tol=1e-7, assign_labels=method, random_state=42)
+    labels = labels.reshape(rescaled.shape)
+    results[method] = (labels, time.time()-t0)
+
+logging.info("Визуализация результатов...")
+for method, (labels, dur) in results.items():
+    plt.figure(figsize=(6,6))
+    plt.imshow(rescaled, cmap="gray")
+    plt.title(f"{method}, {dur:.2f} сек.")
+    plt.axis("off")
+    for l in range(n_regions):
+        plt.contour(labels == l, colors=[plt.cm.nipy_spectral((l+4)/float(n_regions+4))])
+    plt.show()