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{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"```python\nimport numpy as np\nfrom scipy.linalg import svd\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nfrom threadpoolctl import threadpool_limits\n\nwith threadpool_limits(limits=4):\n    g=W[\"ln1_w.0\"].astype(float)\n    v=W[\"W_v.0\"][192:256].astype(float)*g\n    v-=v.mean(1,keepdims=True)\n    b=W[\"W_o.0\"][:,192:256].astype(float)@v\n    u,s,vt=svd(b,full_matrices=False)\n    basis=vt[:20].T\n    f=W[\"W_fc.0\"].astype(float)*W[\"ln2_w.0\"].astype(float)\n    f-=f.mean(1,keepdims=True)\n    cur=f@basis\n    prev=f@b@basis\n    nc=np.linalg.norm(cur,axis=1)\n    npv=np.linalg.norm(prev,axis=1)\n    print(\"Numbers of neurons above current/prior coefficient-norm thresholds\")\n    for t in (.2,.4,.6,.8,1.0):\n        print(t,int((nc>t).sum()),int((npv>t).sum()),int(((nc>t)&(npv>t)).sum()))\n    print(\"\\nClustering high-signal current coefficient rows, cosine geometry\")\n    mask=nc>.5\n    x=cur[mask]\n    x=x/np.linalg.norm(x,axis=1,keepdims=True)\n    for k in (8,12,16,20,24,28,32):\n        km=KMeans(n_clusters=k,n_init=20,random_state=0).fit(x)\n        print(k,\"silhouette\",round(silhouette_score(x,km.labels_),4),\n              \"sizes\",np.sort(np.bincount(km.labels_))[::-1])\n\n    print(\"\\nJoint current/prior coefficient clustering\")\n    mc=nc>.5\n    mp=npv>.4\n    xc=cur[mc]/nc[mc,None]\n    xp=prev[mp]/npv[mp,None]\n    xx=np.vstack([xc,xp])\n    for k in (16,20,24,28):\n        km=KMeans(n_clusters=k,n_init=20,random_state=0).fit(xx)\n        centers=km.cluster_centers_\n        centers=centers/np.linalg.norm(centers,axis=1,keepdims=True)\n        lc=np.argmax((cur/nc[:,None])@centers.T,axis=1)\n        lp=np.argmax((prev/npv[:,None])@centers.T,axis=1)\n        keep=mc&mp\n        counts=np.zeros((k,k))\n        np.add.at(counts,(lp[keep],lc[keep]),1)\n        adj=counts+counts.T\n        deg=adj.sum(1)\n        normal=adj/np.sqrt(np.maximum(deg[:,None]*deg[None,:],1))\n        ev,vec=np.linalg.eigh(normal)\n        signs=vec[:,0]>0\n        cross=adj[signs][:,~signs].sum()*2/(adj.sum()+1e-30)\n        print(\"k\",k,\"silhouette\",round(silhouette_score(xx,km.labels_),4),\n              \"min eigs\",np.round(ev[:4],3),\"bipartite-cut fraction\",round(cross,3),\n              \"partition sizes\",int(signs.sum()),int((~signs).sum()))\n        if k==24:\n            print(\"current cluster sizes\",np.bincount(lc[mc],minlength=k))\n            print(\"prior cluster sizes\",np.bincount(lp[mp],minlength=k))\n            print(\"joint counts, rows=prior coefficient cluster, columns=current coefficient cluster\")\n            print(counts.astype(int))\n```\n"}}
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