تنفيذ

base_g = ( graphistry .bind(source=”src”, destination=”dst”, node=”id”) .edges(edges_df) .nodes(nodes_df) .bind( edge=”edge_id”, edge_title=”edge_title”, edge_label=”edge_label”, edge_weight=”event_count”, edge_size=”edge_size”, point_title=”point_title”, point_label=”label”, point_color=”node_color”, point_size=”node_size”, point_x=”x”,…

poi_gdf[“cx”] = poi_gdf.geometry.x poi_gdf[“cy”] = poi_gdf.geometry.y coords = poi_gdf[[“cx”, “cy”]].to_numpy() nn = NearestNeighbors(radius=150.0).fit(coords) poi_gdf[“local_density”] = [len(idx) – 1 for idx…

في هذا البرنامج التعليمي، قمنا ببناء خط أنابيب شامل لتجزئة الصور الطبية ثلاثية الأبعاد باستخدام موناي لتقسيم الطحال في مجموعة…

k = RUN_KNOBS train_out = run_cli([“python”,”scripts/train.py”,”–config”,CFG,”–split_dir”,SPLIT, “–optimizer_model”,OPTIMIZER_MODEL,”–target_model”,TARGET_MODEL,”–out_root”,RUN, *COMMON, “train.train_size=0″, f”train.num_epochs={k[‘num_epochs’]}”, f”train.batch_size={k[‘batch_size’]}”, f”gradient.minibatch_size={k[‘minibatch’]}”, f”gradient.merge_batch_size={k[‘merge_batch’]}”, f”gradient.analyst_workers={k[‘workers’]}”, f”optimizer.learning_rate={k[‘lr’]}”, f”optimizer.lr_scheduler={k[‘lr_sched’]}”, “optimizer.use_slow_update=true”, “optimizer.use_meta_skill=true”, f”env.workers={k[‘workers’]}”, f”env.limit={k[‘limit’]}”],…

scenarios = [ { “name”: “Safe database read”, “tool”: research_db, “kwargs”: { “table”: “customers”, “operation”: “select”, “type”: “select”, “sensitivity”: “medium”…

banner(“1) logger.configure(): handlers + custom level + extra + patcher”) mem = MemorySink() logger.configure( handlers=[ {“sink”: sys.stderr, “format”: console_formatter, “level”:…