np.dot
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
np.dot has 15 facts recorded in Dontopedia across 6 references, with 4 live disagreements.
15 facts·4 predicates·6 sources·4 in dispute
Mostly:rdf:type(6), operates on(4), combines(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (11)
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.
usesOperationUses Operation(5)
- Cosine Similarity Calculation
ex:cosine-similarity-calculation - Fuse Scores
ex:fuse-scores - Fuse Scores Function
ex:fuse-scores-function - Similarity Calculation
ex:similarity-calculation - Symmetric Decoder
ex:symmetric-decoder
computed-viaComputed Via(1)
- Cosine Similarity
ex:cosine-similarity
computesComputes(1)
- Fuse Scores
ex:fuse-scores
decodingMethodDecoding Method(1)
- Symmetric Decoder
ex:symmetric-decoder
:definesOperation:defines Operation(1)
- Manifold Trait
ex:manifold-trait
mathematicalOperationMathematical Operation(1)
- Linear Combination Function
ex:linear-combination-function
usesVectorOperationUses Vector Operation(1)
- Contextual Similarity Function
ex:contextual-similarity-function
Other facts (14)
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.
14 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Mathematical Operation | [1] |
| Rdf:type | Mathematical Operation | [2] |
| Rdf:type | Linear Algebra Operation | [3] |
| Rdf:type | Mathematical Operation | [4] |
| Rdf:type | Mathematical Operation | [5] |
| Rdf:type | Vector Operation | [6] |
| Operates on | Vectors Array | [1] |
| Operates on | Target Vector | [1] |
| Operates on | Weights Variable | [3] |
| Operates on | Fused Scores Array | [3] |
| Combines | Weight Vector | [5] |
| Combines | Normalized Score Matrix | [5] |
| Operands | Term Embedding | [6] |
| Operands | Contextual Embedding | [6] |
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.
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typebeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
ex:MathematicalOperation
—
labelbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
np.dot
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operatesOnbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
ex:vectors-array
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operatesOnbeam/1c92d7b3-5e81-4735-8dba-06ce859d99dc
ex:target-vector
—
typebeam/5278119f-c632-4b91-b193-f1e7bddf1e64
ex:MathematicalOperation
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typebeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:LinearAlgebraOperation
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operatesOnbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:weights-variable
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operatesOnbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:fused-scores-array
—
typebeam/9723d5c7-7f1e-4fca-a6ab-7212129d3781
ex:MathematicalOperation
—
typebeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:Mathematical-Operation
—
combinesbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:weight-vector
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combinesbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:normalized-score-matrix
—
typebeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:VectorOperation
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operandsbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:term-embedding
—
operandsbeam/5e1fccc0-109f-4d58-b6c4-6482a168aad7
ex:contextual-embedding
References (6)
6 references
ctx:claims/beam/1c92d7b3-5e81-4735-8dba-06ce859d99dcctx:claims/beam/5278119f-c632-4b91-b193-f1e7bddf1e64- full textbeam-chunktext/plain1 KB
doc:beam/5278119f-c632-4b91-b193-f1e7bddf1e64Show excerpt
# Calculate the similarity between the query vector and each vector in the database similarities = [np.dot(query_vector, vector) for vector in self.vectors] # Return the indices of the top 10 most similar vectors …
ctx:claims/beam/83d82fac-5668-4797-9ad9-b4b6b371089e- full textbeam-chunktext/plain1 KB
doc:beam/83d82fac-5668-4797-9ad9-b4b6b371089eShow excerpt
[Turn 6684] User: I'm testing fusion on 3,000 queries and achieving 91% relevance improvement, but I need help optimizing the fusion algorithm. Can you review my code and suggest improvements? I'm using NumPy 1.25.0 for score calculations a…
ctx:claims/beam/9723d5c7-7f1e-4fca-a6ab-7212129d3781- full textbeam-chunktext/plain1 KB
doc:beam/9723d5c7-7f1e-4fca-a6ab-7212129d3781Show excerpt
3. **Advanced Fusion Techniques**: Consider more advanced fusion techniques such as weighted sum, min-max scaling, or even more sophisticated methods like logistic regression or neural networks. ### Current Implementation Review Your curr…
ctx:claims/beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb- full textbeam-chunktext/plain1 KB
doc:beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbbShow excerpt
#### 2. Normalization Normalize the scores to ensure they are on the same scale. #### 3. Advanced Fusion Techniques Consider using a weighted sum with normalization. ### Example Code ```python import numpy as np from sklearn.model_select…
ctx:claims/beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7- full textbeam-chunktext/plain1 KB
doc:beam/5e1fccc0-109f-4d58-b6c4-6482a168aad7Show excerpt
for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…
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