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{"type":"item.completed","item":{"id":"item_2","type":"agent_message","text":"```python\nimport numpy as np\nfrom scipy.linalg import svd\nfrom scipy.stats import skew\nfrom scipy.optimize import quadratic_assignment\nfrom sklearn.decomposition import FastICA\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(limits=2)\n\n# Weight-only reconstruction of the two static coordinate comparisons from run 9.\nd=384\nP=np.eye(d)-np.ones((d,d))/d\nrs=[]\nfor h in [1,5]:\n    _,_,r=svd(np.asarray(W[\"W_v.0\"][64*h:64*(h+1)],float)@P,full_matrices=False)\n    rs.append(r[:28])\nee,U=np.linalg.eigh(sum(r.T@r for r in rs))\nU=U[:,np.argsort(ee)[::-1][:28]]\nF=np.asarray(W[\"W_fc.0\"],float)*W[\"ln2_w.0\"][None,:]\nica=FastICA(n_components=28,whiten=\"unit-variance\",random_state=17,max_iter=4000,tol=.001)\nZ=ica.fit_transform(F@U)\nZ*=np.where(skew(Z,axis=0)>=0,1.,-1.)[None,:]\nO=np.asarray(W[\"W_proj.0\"],float)\nC=np.zeros((d,28))\nfor k in range(28):\n    ids=np.argsort(np.abs(Z[:,k]))[-3:]\n    cc=Z[ids,k]\n    C[:,k]=O[:,ids]@cc/np.linalg.norm(cc)\nC/=np.linalg.norm(C,axis=0,keepdims=True)\nout_u,_,_=svd(np.asarray(W[\"W_proj.3\"],float),full_matrices=False)\nout_u=out_u[:,:28]\nJ=out_u.T@C\nmaps={}\nfor h in [1,5]:\n    s=slice(64*h,64*(h+1))\n    b=np.asarray(W[\"W_o.3\"][:,s],float)@(\n        np.asarray(W[\"W_v.3\"][s],float)*W[\"ln1_w.3\"][None,:])@P\n    a=out_u.T@b@C\n    uu,ss,vv=svd(a,full_matrices=False)\n    inverse=(vv.T*(ss/(ss*ss+(ss[0]*.15)**2))[None,:])@uu.T\n    maps[h]=(inverse@J).T\n\n# These short, invented controls are ONLY counted as text.\n# They are NOT fed to the network, any layer, or any weight matrix.\n# Matching their unlabeled bigram graphs is exploratory, not a calibrated test.\nsamples={}\nsamples[\"english\"]=\"\"\"\nthe history of the city is closely related to the development of the region\nmany people lived in small villages before the construction of the railway\nthe population increased during the twentieth century and the town became an important industrial centre\nthe university was founded in the nineteenth century and has several departments\nits research includes mathematics physics chemistry biology and computer science\nthe company produces equipment for commercial and domestic use\nthe first edition of the book was published in london and was later translated into several languages\nshe wrote about her experiences and the lives of the people she had met\nthe river flows through the northern part of the country before reaching the sea\nthe climate is generally mild although temperatures can vary considerably\nthe national government is responsible for education health and public services\nthe team won the championship after defeating their opponents in the final match\nthe organization has members in many countries and holds an annual conference\na number of different methods have been proposed to explain these results\nthis process requires a significant amount of energy and may take several years\nhe studied at the local school and later worked as a teacher and journalist\nthe species is found in forests and grasslands where it feeds on insects and small animals\nthe music was composed for a large orchestra and performed for the first time in paris\nthere are several buildings of historical interest including the church and the old castle\nthe system consists of a central processor connected to a network of other devices\n\"\"\"\nsamples[\"python\"]=\"\"\"\nimport os\nimport sys\nfrom collections import defaultdict\nfrom pathlib import path\n\nclass connection\n    def init self host port timeout none\n        self host host\n        self port port\n        self timeout timeout\n        self closed false\n        self data dict\n    def connect self\n        if self closed\n            raise valueerror invalid connection\n        return self client connect self host self port\n    def close self\n        self closed true\n        self client close\n    def get self key default none\n        return self data get key default\n    def update self values\n        for key value in values items\n            self data key value\n\ndef process items limit none\n    result list\n    for index item in enumerate items\n        if item is none\n            continue\n        value str item strip lower\n        if not value\n            continue\n        result append value\n        if limit is not none and len result limit\n            break\n    return result\n\ndef load path encoding utf\n    with open path mode r encoding encoding as stream\n        return stream read\n\ndef validate value expected type\n    if not isinstance value expected type\n        raise typeerror expected value\n    return value\n\nclass handler\n    def init self config\n        self config config\n        self cache dict\n    def call self request args kwargs\n        try\n            response self process request\n        except exception as error\n            self logger error str error\n            raise\n        else\n            return response\n    def process self request\n        key request get name\n        if key not in self cache\n            self cache key self create key\n        return self cache key\n    def create self name\n        options self config get name dict\n        return objectfactory name options\n\ndef main args none\n    parser argumentparser\n    parser add argument input\n    parser add argument output\n    options parser parse args args\n    data load options input\n    result process data splitlines\n    with open options output w as stream\n        for line in result\n            stream write line\n    return none\n\"\"\"\nsamples[\"romaji\"]=\"\"\"\nwatashi wa sono toki mada kodomo de atta\nsore wa aru aki no yoru no koto de aru\nkare wa shizuka ni heya no naka ni haitte kita\nwatashi wa nani mo iwanai de sono kao o mite ita\nsoto ni wa ame ga futte ite kaze no oto ga kikoeta\nkono machi ni kita no wa mou zuibun mae no koto da\nhaha wa itsumo yasashii koe de watashi o yonda\nsono hito wa mukashi kara koko ni sunde iru rashikatta\ndare mo inai michi o hitori de aruite iru to\ntooku no yama no ue ni tsuki ga dete ita\nsore kara futari wa nagai aida hanashi o shita\ndoushite sonna koto o suru no ka watashi ni wa wakaranakatta\nkare no kotoba ni wa fushigi na chikara ga atta\nashita no asa ni wa mata tabi ni denakereba naranai\nwatashi wa hon o tojite mado no soto o nagameta\nchiisana tori ga niwa no ki no eda ni tomatte ita\nsensei wa sukoshi waratte sore wa omoshiroi to itta\nkanojo wa nanimo shiranai you na kao o shite iru\nyagate haru ga kite hana ga sakihajimeta\nsoshite subete no koto ga yume no you ni omoeta\n\"\"\"\nvocab=\"\\n abcdefghijklmnopqrstuvwxyz\"\ndef counts(text):\n    lines=[]\n    for line in text.lower().splitlines():\n        lead=len(line)-len(line.lstrip(\" \"))\n        body=\"\".join(c if \"a\"<=c<=\"z\" else \" \" for c in line[lead:])\n        lines.append(\" \"*lead+\" \".join(body.split()))\n    text=\"\\n\".join(lines)\n    a=np.zeros((28,28))\n    for x,y in zip(text,text[1:]):\n        a[vocab.index(x),vocab.index(y)]+=1\n    return a\n\ndef standardized(a):\n    a=a.copy()\n    a=a-a.mean(0,keepdims=True)-a.mean(1,keepdims=True)+a.mean()\n    np.fill_diagonal(a,0)\n    return a/np.linalg.norm(a)\n\nA=standardized(maps[1])\ntest=standardized(maps[5])\nrng=np.random.default_rng(927)\nprint(\"EXPLORATORY graph matching against invented controls; scores are NOT probabilities.\")\nfor transform in [\"sqrt_counts\",\"log_conditional\"]:\n    print(\"\\nTransform:\",transform)\n    for name,txt in samples.items():\n        cnt=counts(txt)\n        if transform==\"sqrt_counts\":\n            ref=np.sqrt(cnt)\n        else:\n            ref=np.log((cnt+.25)/(cnt.sum(1,keepdims=True)+7))\n        ref=standardized(ref)\n        fits=[]\n        for trial in range(80):\n            result=quadratic_assignment(A,ref,method=\"faq\",\n                options={\"maximize\":True,\"P0\":\"randomized\",\"rng\":rng,\n                         \"maxiter\":60,\"tol\":.001})\n            perm=result.col_ind\n            rr=ref[np.ix_(perm,perm)]\n            fits.append((float(np.sum(A*rr)),float(np.sum(test*rr)),perm))\n        fits.sort(key=lambda x:x[0],reverse=True)\n        best=fits[0]\n        print(name,\"fit\",round(best[0],3),\"other-head agreement\",round(best[1],3),\n              \"top10 mean other-head\",round(np.mean([x[1] for x in fits[:10]]),3))\n        print(\"  tentative axis labels:\",\n              \" \".join(f\"{i}:{repr(vocab[j])}\" for i,j in enumerate(best[2])))\n```"}}
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