i BdZddlZddlZddlZddlZ ddlZdZn #e$rdZYnwxYwej e Z dej zZ d!dZd"d ed edd fd Zd#dZd$dZd%dZd d dd defdZd"ded edd fdZd"dedeed edd fdZdd defdZdedd fdZd ededefd ZdS)&uHolographic Reduced Representations (HRR) with phase encoding. HRRs are a vector symbolic architecture for encoding compositional structure into fixed-width distributed representations. This module uses *phase vectors*: each concept is a vector of angles in [0, 2π). The algebraic operations are: bind — circular convolution (phase addition) — associates two concepts unbind — circular correlation (phase subtraction) — retrieves a bound value bundle — superposition (circular mean) — merges multiple concepts Phase encoding is numerically stable, avoids the magnitude collapse of traditional complex-number HRRs, and maps cleanly to cosine similarity. Atoms are generated deterministically from SHA-256 so representations are identical across processes, machines, and language versions. References: Plate (1995) — Holographic Reduced Representations Gayler (2004) — Vector Symbolic Architectures answer Jackendoff's challenges NTF@returnc2tstddS)Nz,numpy is required for holographic operations) _HAS_NUMPY RuntimeErrorK/home/piyush/.hermes/hermes-agent/plugins/memory/holographic/holographic.py_require_numpyr &s& KIJJJKKr worddim np.ndarrayctd}tj||z }g}t|D]g}t j|d|}|tj d|htj |d|tj tdz z}|S)uDeterministic phase vector via SHA-256 counter blocks. Uses hashlib (not numpy RNG) for cross-platform reproducibility. Algorithm: - Generate enough SHA-256 blocks by hashing f"{word}:{i}" for i=0,1,2,... - Concatenate digests, interpret as uint16 values via struct.unpack - Scale to [0, 2π): phases = values * (2π / 65536) - Truncate to dim elements - Returns np.float64 array of shape (dim,) :z<16HNdtypeg@)r mathceilrangehashlibsha256encodedigestextendstructunpacknparrayfloat64_TWO_PI)r rvalues_per_block blocks_needed uint16_valuesirphasess r encode_atomr(+sIc$4455M!M = ! !<<4 ! 4 4 6 677>>@@V]66::;;;; XmDSD) < < <'@Q RF Mr abc8t||ztzS)zCircular convolution = element-wise phase addition. Binding associates two concepts into a single composite vector. The result is dissimilar to both inputs (quasi-orthogonal). r r"r)r*s r bindr.Fs  EW r memorykeyc8t||z tzS)uCircular correlation = element-wise phase subtraction. Unbinding retrieves the value associated with a key from a memory vector. unbind(bind(a, b), a) ≈ b (up to superposition noise) r,)r/r0s r unbindr2Ps  SLG ##r vectorscttjd|Dd}tj|tzS)zSuperposition via circular mean of complex exponentials. Bundling merges multiple vectors into one that is similar to each input. The result can hold O(sqrt(dim)) items before similarity degrades. c<g|]}tjd|zS)y?)rexp).0vs r zbundle..as$:::Q"&a..:::r r)axis)r rsumangler")r3 complex_sums r bundler>ZsH &::':::CCCK 8K 7 **r ctttjtj||z S)zPhase cosine similarity. Range [-1, 1]. Returns 1.0 for identical vectors, near 0.0 for random (unrelated) vectors, and -1.0 for perfectly anti-correlated vectors. )r floatrmeancosr-s r similarityrCes4  A'' ( ((r textctd|D}d|D}|stdSfd|D}t |S)a7Bag-of-words: bundle of atom vectors for each token. Tokenizes by lowercasing, splitting on whitespace, and stripping leading/trailing punctuation from each token. Returns bundle of all token atom vectors. If text is empty or produces no tokens, returns encode_atom("__hrr_empty__", dim). c8g|]}|dS)z.,!?;:"'()[]{})strip)r7tokens r r9zencode_text..zs5   %&&r cg|]}||Srr)r7ts r r9zencode_text..~s % % %A1 %a % % %r __hrr_empty__c0g|]}t|Sr)r()r7rHrs r r9zencode_text..s#@@@Ks++@@@r )r lowersplitr(r>)rDrtokens atom_vectorss ` r encode_textrQosZZ\\''))F& % % % %F 1?C000@@@@@@@L <  r contententitiesc >ttd|}td|}tt|||g}|D]E}|tt|||Ft |S)uStructured encoding: content bound to ROLE_CONTENT, each entity bound to ROLE_ENTITY, all bundled. Role vectors are reserved atoms: "__hrr_role_content__", "__hrr_role_entity__" Components: 1. bind(encode_text(content, dim), encode_atom("__hrr_role_content__", dim)) 2. For each entity: bind(encode_atom(entity.lower(), dim), encode_atom("__hrr_role_entity__", dim)) 3. bundle all components together This enables algebraic extraction: unbind(fact, bind(entity, ROLE_ENTITY)) ≈ content_vector __hrr_role_content____hrr_role_entity__)r r(r.rQappendrMr>)rRrSr role_content role_entity componentsentitys r encode_factr\s5s;;L3S99K [# & & 55$JOO${6<<>>3??MMNNNN : r r'cFt|S)uFSerialize phase vector to bytes. float64 tobytes — 8 KB at dim=1024.)r tobytes)r's r phases_to_bytesr_s >>  r datacttj|tjS)zDeserialize bytes back to phase vector. Inverse of phases_to_bytes. The .copy() call is required because frombuffer returns a read-only view backed by the bytes object; callers expect a mutable array. r)r r frombufferr!copy)r`s r bytes_to_phasesrds3  =RZ 0 0 0 5 5 7 77r n_itemsct|dkrtdStj||z }|dkrtd||||S)aSignal-to-noise ratio estimate for holographic storage. SNR = sqrt(dim / n_items) when n_items > 0, else inf. The SNR falls below 2.0 when n_items > dim / 4, meaning retrieval errors become likely. Logs a warning when this threshold is crossed. rinfrzHRR storage near capacity: SNR=%.2f (dim=%d, n_items=%d). Retrieval accuracy may degrade. Consider increasing dim or reducing stored items.)r r@rsqrtloggerwarning)rresnrs r snr_estimaterlsm!||U|| )C'M " "C Syy `        Jr )rN)r )r)rr*rrr)r/rr0rrr)r3rrr)__doc__rloggingrrnumpyrr ImportError getLogger__name__ripir"r strintr(r.r2r>r@rCrQlistr\bytesr_rdrlrr r rxs* JJJJJ  8 $ $ -KKKK c|6$$$$++++),)<)E))))!!c!!|!!!!0S |8LU 8%8L8888cCEs %%