M
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-20.)
M is Number of subquantizers.
Mostly:rdf:type(14), description(4), has value(4)
Maturity scale
raw canonical shape-checked rule-derived certifiedRdf:typein disputerdf:type
- Parameter[5]all time · Aaea2d5a 2786 4bf1 840d 700a9d6307af
- Subquantizer Count Parameter[6]all time · 2923b0ab 4ec2 4f48 9528 Ef9982bfeed5
- Subquantizer Count[6]all time · 2923b0ab 4ec2 4f48 9528 Ef9982bfeed5
- Parameter[6]all time · 2923b0ab 4ec2 4f48 9528 Ef9982bfeed5
- Parameter[7]all time · 01d47e70 2678 4424 Bb6e 17ebfb57cf51
- Subdomain[10]sourceall time · 2
- Variable[11]all time · B296f27d A550 49c1 Ae24 6118c21f96b1
- Parameter[12]all time · 49101dfd 4fc4 460c 9cd9 8e0457730c83
- Variable[13]sourceall time · C987e07c Dc22 48c0 Aadb 1075131743e6
- Index Parameter[14]all time · 4efeeb64 8572 49af 812f E5accd46c4ad
Inbound mentions (27)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
hasParameterHas Parameter(7)
- Create Ivfpq Index
ex:create_ivfpq_index - Create Ivfpq Index
ex:create_ivfpq_index - Index Ivfpq
ex:IndexIVFPQ - Index Ivfpq
ex:IndexIVFPQ - Index Ivfpq
ex:IndexIVFPQ - Ivfpq Index
ex:IVFPQ-index - Ivfpq Index
ex:IVFPQIndex
assignedToAssigned to(2)
- 8
ex:8 - Quantization Level
ex:quantization_level
affectedByAffected by(1)
- Query Speed
ex:query-speed
appliedToApplied to(1)
- Balance
ex:balance
balancedByBalanced by(1)
- Query Speed
ex:query-speed
constructedWithConstructed With(1)
- Index Ivfpq
ex:IndexIVFPQ
createdWithParametersCreated With Parameters(1)
- Index
ex:index
describedForDescribed for(1)
- Tradeoff
ex:tradeoff
describesDescribes(1)
- Subquantizers Comment
ex:subquantizers-comment
equalsEquals(1)
- Eff Anc
ex:eff-anc
hasHypothesisOnHas Hypothesis on(1)
- Universal Sync Mechanism
ex:universal-sync-mechanism
hasMHas M(1)
- Ivf Pq Index
ex:ivf-pq-index
hasModuleHas Module(1)
- Arbitrary Clifford Algebra
ex:arbitrary-clifford-algebra
hasMParameterHas M Parameter(1)
- Faiss Index Ivf Pq
ex:faiss-index-ivf-pq
hasNumAnchorsHas Num Anchors(1)
- Anchors
ex:anchors
involvesParameterInvolves Parameter(1)
- Parameter Tuning
ex:parameter-tuning
isSumOverIs Sum Over(1)
- Sum M G K M T W Im X M T
ex:sum-m-g-k-m-t-w-im-x-m-t
parametersParameters(1)
- Index Constructor
index-constructor
sentBySent by(1)
- Letter 9948 1889
ex:letter-9948-1889
usesParameterUses Parameter(1)
- Index Creation
ex:index-creation
Other facts (39)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Description | Number of subquantizers | [7] |
| Description | Number of subquantizers | [8] |
| Description | number-of-sub-quantizers | [12] |
| Description | Parameter to balance memory usage and query speed | [16] |
| Has Value | 8 | [8] |
| Has Value | 8 | [9] |
| Has Value | 8 | [13] |
| Has Value | 8 | [15] |
| Affects | Subquantizer Count | [14] |
| Affects | Memory Usage | [16] |
| Affects | Query Speed | [16] |
| Affects | subquantizer-count | [17] |
| Donated to | Maryborough Benevolent Institution | [1] |
| Donated to | Maryborough Benevolent Institution | [2] |
| Is Parameter of | Index Ivfpq | [6] |
| Is Parameter of | IndexIVFPQ | [14] |
| Controls | Subquantizer Count | [8] |
| Controls | subquantizer_count | [14] |
| Represents | number of subquantizers | [13] |
| Represents | number of subquantizers | [14] |
| Equals | 8 | [17] |
| Equals | nbytes | [17] |
| Donation Amount | £2 | [1] |
| Offers | French Music Needlework Lessons | [3] |
| Has Address | Box 9 Office This Paper | [3] |
| Stands for | Minute | [4] |
| Role | number of subquantizers | [5] |
| Typical Value | 8 | [5] |
| Parameter Value | 8 | [7] |
| Value | 8 | [12] |
| Comment | number of subquantizers | [15] |
| Used in | Faiss.index Ivfpq | [15] |
| Purpose | Balance Memory and Speed | [16] |
| Controlled by | Create Ivfpq Index | [16] |
| Default | 8 | [17] |
| Describes | Number of subquantizers | [17] |
| Assigned Value | 8 | [18] |
| Haspublicationdate | Christmas 1894 | [20] |
| Haspagerange | 97–100 | [20] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (20)
ctx:genes/trove-cooktown/beche-de-merctx:genes/brackenridge-cairns-1880-1900/trove-new/146785816_Saturday-7-May-1887_LOCAL-NEWSctx:genes/rosie-reynolds-massacre-connection/trove-hartley-sykes-oconnor-cape-bedford-289344095ctx:research/blucher-uhr/sqlite--qsa-32042220--qsa847166-1885-telegram-from-reginald-uhr-to-under-colonial-secretary-27-august,-colonial-ctx:claims/beam/aaea2d5a-2786-4bf1-840d-700a9d6307afctx:claims/beam/2923b0ab-4ec2-4f48-9528-ef9982bfeed5ctx:claims/beam/01d47e70-2678-4424-bb6e-17ebfb57cf51ctx:claims/beam/9c3d6c77-2b58-4a3b-9618-59e705c00dfd- full textbeam-chunktext/plain1 KB
doc:beam/9c3d6c77-2b58-4a3b-9618-59e705c00dfdShow excerpt
# Normalize the vectors for cosine similarity faiss.normalize_L2(vectors) # Create an IVFPQ index nlist = 100 # Number of clusters m = 8 # Number of subquantizers index = faiss.IndexIVFPQ(faiss.IndexFlatL2(128), 128, nlist, m, 8) # 8 is…
ctx:claims/beam/ea1c880d-666a-428b-9f18-ae4bdd751abe- full textbeam-chunktext/plain1 KB
doc:beam/ea1c880d-666a-428b-9f18-ae4bdd751abeShow excerpt
index = faiss.IndexHNSWFlat(128, M) index.hnsw.efConstruction = efConstruction index.hnsw.efSearch = efSearch index.add(vectors) # Measure initial performance start_time = time.time() distances, indices = search_similar_vectors(query_vecto…
ctx:discord/blah/atlas-ai/2- full textctx:discord/blah/atlas-ai/2text/plain3 KB
doc:discord/blah/atlas-ai/2Show excerpt
[2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…
- full textatlas-ai-2text/plain3 KB
doc:agent/atlas-ai-2/3a79ad11-fcb3-4da8-b38e-c15390bfab94Show excerpt
[2025-04-04 05:23] lisamegawatts: I had a polisci professor that worked on this, he used to say theory is fine but no match for data https://correlatesofwar.org/ [2025-04-04 05:23] lisamegawatts: Trying to catalog and predict all factors th…
ctx:claims/beam/b296f27d-a550-49c1-ae24-6118c21f96b1ctx:claims/beam/49101dfd-4fc4-460c-9cd9-8e0457730c83- full textbeam-chunktext/plain1 KB
doc:beam/49101dfd-4fc4-460c-9cd9-8e0457730c83Show excerpt
- Adjust the search parameters like `efSearch` for `IndexHNSW` to balance between speed and accuracy. ### Example Implementation Here's an optimized version of your code using `IndexIVFPQ` and enabling multi-threading: ```python impor…
ctx:claims/beam/c987e07c-dc22-48c0-aadb-1075131743e6- full textbeam-chunktext/plain1 KB
doc:beam/c987e07c-dc22-48c0-aadb-1075131743e6Show excerpt
1. **Create an Index**: Choose an appropriate index type that balances speed and accuracy. 2. **Add Embeddings**: Add your embeddings to the index. 3. **Search for Nearest Neighbors**: Perform the search and optimize the parameters for bett…
ctx:claims/beam/4efeeb64-8572-49af-812f-e5accd46c4ad- full textbeam-chunktext/plain1 KB
doc:beam/4efeeb64-8572-49af-812f-e5accd46c4adShow excerpt
query_vector = np.random.rand(1, 128).astype("float32") # Search for nearest neighbors k = 10 # number of nearest neighbors to retrieve D, I = index.search(query_vector, k) # Print the results print("Distances:", D) print("Indices:", I) …
ctx:claims/beam/c5e65b2e-6289-4399-808e-64fe4e0eddce- full textbeam-chunktext/plain1 KB
doc:beam/c5e65b2e-6289-4399-808e-64fe4e0eddceShow excerpt
m = 8 # number of subquantizers index = faiss.IndexIVFPQ(faiss.MetricType.L2, d, nlist, m, 8) # Train the index index.train(embeddings) # Add the embeddings to the index index.add(embeddings) # Generate a query embedding in a different …
ctx:claims/beam/16e72a23-0e74-4398-83f0-1a6963cbc18d- full textbeam-chunktext/plain1 KB
doc:beam/16e72a23-0e74-4398-83f0-1a6963cbc18dShow excerpt
- `nprobe`: Number of clusters to probe during the search. 2. **Training the Index**: - The `train` method is used to train the index on the dataset. 3. **Adding Vectors**: - The `add` method adds the vectors to the index. 4. **…
ctx:claims/beam/3aa97b5d-2401-4a53-a5d0-4cd1d9b8e042ctx:claims/beam/9170f193-72c4-43d3-9c09-87f869d91b8b- full textbeam-chunktext/plain1 KB
doc:beam/9170f193-72c4-43d3-9c09-87f869d91b8bShow excerpt
index.nprobe = nprobe return index # Example usage: vectors = np.random.rand(10000, 128).astype(np.float32) index = create_ivfpq_index(vectors, nlist=200, m=8, nprobe=15) print(index.ntotal) # Test the index query_vectors = np.ran…
ctx:claims/beam/a5fc8118-22f9-47dc-ab75-3a5765c02306- custom
ctx:src/moore-making-mala-ch3- text/plain89 KB
doc:research/rosie-research/south-sea-islander/moore-making-mala-ch3Show excerpt
Previous Making Mala 3 Malaitan Christians Overseas, 1880s–1910s It is easy to understand why labourers in Queensland should have become Christians. They were cut off from all home influences, separated from their relatives, and i…
See also
- Maryborough Benevolent Institution
- French Music Needlework Lessons
- Box 9 Office This Paper
- Parameter
- Subquantizer Count Parameter
- Subquantizer Count
- Index Ivfpq
- Subquantizer Count
- Subdomain
- Variable
- Variable
- Index Parameter
- Subquantizer Count
- Faiss.index Ivfpq
- Balance Memory and Speed
- Memory Usage
- Query Speed
- Create Ivfpq Index
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