astype
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)
astype has 21 facts recorded in Dontopedia across 10 references, with 3 live disagreements.
21 facts·11 predicates·10 sources·3 in dispute
Mostly:rdf:type(6), converts to(3), converts(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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.
methodMethod(1)
- Type Conversion
ex:type-conversion
providesFunctionProvides Function(1)
- Numpy Library
ex:numpy_library
undergoesUndergoes(1)
- Embeddings
ex:embeddings
Other facts (19)
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.
19 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Method | [1] |
| Rdf:type | Method | [2] |
| Rdf:type | Numpy Method | [3] |
| Rdf:type | Method | [4] |
| Rdf:type | Method | [5] |
| Rdf:type | Method | [6] |
| Converts to | Float32 | [3] |
| Converts to | np.float32 | [5] |
| Converts to | float32 | [9] |
| Converts | Vectors to Float32 | [8] |
| Converts | Data Type to Float32 | [8] |
| Called on | Data Model[field] | [1] |
| Method of | Vectors | [2] |
| Parameter | Float32 | [2] |
| Applied to | Vectors | [6] |
| Ensures Memory Efficiency | true | [7] |
| Ensures | Float32 Data Type | [8] |
| Is Method of | Numpy Array | [8] |
| Method | numpy.ndarray.astype | [10] |
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/08afe6f4-c9af-4228-b4d5-4c65b909fa6a
ex:Method
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calledOnbeam/08afe6f4-c9af-4228-b4d5-4c65b909fa6a
ex:data_model[field]
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typebeam/01d47e70-2678-4424-bb6e-17ebfb57cf51
ex:Method
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methodOfbeam/01d47e70-2678-4424-bb6e-17ebfb57cf51
ex:vectors
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parameterbeam/01d47e70-2678-4424-bb6e-17ebfb57cf51
ex:float32
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typebeam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7
ex:NumpyMethod
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convertsTobeam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7
ex:float32
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typebeam/406dd8a8-9b3a-4822-bc8b-168d05c875b4
ex:Method
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labelbeam/406dd8a8-9b3a-4822-bc8b-168d05c875b4
astype
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typebeam/926f1488-328b-43c2-9fba-d5492a192351
ex:Method
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convertsTobeam/926f1488-328b-43c2-9fba-d5492a192351
np.float32
—
typebeam/49101dfd-4fc4-460c-9cd9-8e0457730c83
ex:Method
—
labelbeam/49101dfd-4fc4-460c-9cd9-8e0457730c83
astype
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appliedTobeam/49101dfd-4fc4-460c-9cd9-8e0457730c83
ex:vectors
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ensures-memory-efficiencybeam/9aef4a43-c110-4730-bed6-18e6312b77ad
true
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convertsbeam/f9d7604e-d22e-4ead-884d-c0c9204f8d52
ex:vectors to float32
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ensuresbeam/f9d7604e-d22e-4ead-884d-c0c9204f8d52
ex:float32 data type
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isMethodOfbeam/f9d7604e-d22e-4ead-884d-c0c9204f8d52
ex:numpy array
—
convertsbeam/f9d7604e-d22e-4ead-884d-c0c9204f8d52
ex:data type to float32
—
convertsTobeam/fbf615f8-f981-4f39-81d3-8564b83a0629
float32
—
methodbeam/c265cf07-6352-44cd-ba03-ed8f4af4e9ca
numpy.ndarray.astype
References (10)
10 references
ctx:claims/beam/08afe6f4-c9af-4228-b4d5-4c65b909fa6a- full textbeam-chunktext/plain1 KB
doc:beam/08afe6f4-c9af-4228-b4d5-4c65b909fa6aShow excerpt
data_model[field] = data_model[field].astype(bool) return data_model # Example usage fields = ['field1', 'field2', 'field3', 'field4', 'field5', 'field6', 'field7', 'field8', 'field9'] relationships = […
ctx:claims/beam/01d47e70-2678-4424-bb6e-17ebfb57cf51ctx:claims/beam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7- full textbeam-chunktext/plain1 KB
doc:beam/950d79f8-bdd2-4d0c-a7a6-39f813b82ca7Show excerpt
index = faiss.IndexFlatL2(embedding_dim) # Add the document embeddings to the index index.add(document_embeddings) # Generate a random query embedding query_embedding = np.random.rand(1, embedding_dim).astype('float32') # Search the inde…
ctx:claims/beam/406dd8a8-9b3a-4822-bc8b-168d05c875b4ctx:claims/beam/926f1488-328b-43c2-9fba-d5492a192351- full textbeam-chunktext/plain1 KB
doc:beam/926f1488-328b-43c2-9fba-d5492a192351Show excerpt
FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=128) ] schema = CollectionSchema(fields, "Document Embeddings") # Create the collection collection = Collection("document_embeddings", schema) ``` #### 3. Insert Vectors …
ctx: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/9aef4a43-c110-4730-bed6-18e6312b77adctx:claims/beam/f9d7604e-d22e-4ead-884d-c0c9204f8d52- full textbeam-chunktext/plain1 KB
doc:beam/f9d7604e-d22e-4ead-884d-c0c9204f8d52Show excerpt
3. **Multi-threading**: - `faiss.omp_set_num_threads(8)` enables multi-threading to take advantage of multiple CPU cores. Adjust the number of threads based on your CPU capabilities. 4. **Training the Index**: - The index needs to be…
ctx:claims/beam/fbf615f8-f981-4f39-81d3-8564b83a0629- full textbeam-chunktext/plain1 KB
doc:beam/fbf615f8-f981-4f39-81d3-8564b83a0629Show excerpt
client = redis.Redis(host='localhost', port=6379, db=0) # Create a FAISS index d = 128 # dimension index = faiss.IndexFlatL2(d) # Add vectors to the index vectors = np.random.rand(10000, d).astype('float32') index.add(vectors) # Define …
ctx:claims/beam/c265cf07-6352-44cd-ba03-ed8f4af4e9ca
See also
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