Dontopedia

fuse_scores

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-08.)

fuse_scores is fuse scores from sparse and dense searches.

86 facts·54 predicates·4 sources·11 in dispute

Mostly:has parameter(9), called with(6), rdf:type(4)

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Inbound mentions (15)

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assignedFromAssigned From(1)

callsFunctionCalls Function(1)

computedByComputed by(1)

containsContains(1)

definesFunctionDefines Function(1)

demonstratesDemonstrates(1)

describesDescribes(1)

isCalledByIs Called by(1)

isUsedByIs Used by(1)

isUsedInIs Used in(1)

providesInputToProvides Input to(1)

Other facts (83)

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.

83 facts
PredicateValueRef
Has ParameterSparse Scores[2]
Has ParameterDense Scores[2]
Has ParameterSparse Weight[2]
Has ParameterDense Weight[2]
Has Parameterscores1[3]
Has Parameterscores2[3]
Has ParameterScores1[4]
Has ParameterScores2[4]
Has ParameterWeights[4]
Called WithSparse Scores[2]
Called WithDense Scores[2]
Called With0.6[2]
Called With0.4[2]
Called Withscores1[3]
Called Withscores2[3]
Rdf:typeImplementation Task[1]
Rdf:typeFunction[2]
Rdf:typeFunction[3]
Rdf:typeFunction[4]
Execution OrderConvert to Tensor[2]
Execution OrderNormalize[2]
Execution OrderWeighted Sum[2]
Execution OrderConvert Back to Numpy[2]
ReturnsNumpy Array[2]
ReturnsFused Scores[3]
ReturnsFused Scores[4]
Performs OperationConvert to Tensor[2]
Performs OperationNormalize[2]
Performs OperationWeighted Sum[2]
Has CommentConvert scores to PyTorch tensors[2]
Has CommentNormalize scores[2]
Has CommentFuse scores[2]
Combinesscores1[3]
Combinesscores2[3]
Inverse Has Parameterscores1[3]
Inverse Has Parameterscores2[3]
PerformsNormalization[4]
PerformsWeighted Sum Fusion[4]
NormalizesScores1[4]
NormalizesScores2[4]
Uses LibraryPy Torch[2]
Descriptionfuse scores from sparse and dense searches[2]
Source Codedef fuse_scores(sparse_scores, dense_scores, sparse_weight=0.5, dense_weight=0.5):[2]
Specifies Dtypetorch.float32[2]
Converts FromNumpy Array[2]
Converts toPy Torch Tensor[2]
Purposescore fusion for retrieval systems[2]
Return Typenumpy.ndarray[2]
Parameter Order["sparse_scores","dense_scores","sparse_weight","dense_weight"][2]
UsesNum Py Dot[3]
Sets Weights[0.6, 0.4][3]
Test Input1[0.8, 0.2, 0.4][3]
Test Input2[0.3, 0.7, 0.1][3]
Has Return TypeFused Scores Array[3]
ComputesDot Product[3]
OutputsFused Scores[3]
Test OutputFused Scores Value[3]
Test Input1 Length3[3]
Test Input2 Length3[3]
Is Pure Functiontrue[3]
Algorithm Typelinear-combination[3]
Parameter TypeNum Py Array[3]
Computational Operationdot-product[3]
Has Two Parameterstrue[3]
Returns Single Valuetrue[3]
Is Defined inFusion Code Block[3]
Follows Python Naming Conventiontrue[3]
Has No Error Handlingtrue[3]
Has No Input Validationtrue[3]
Uses Numpy Dottrue[3]
Test Input1 Element Count3[3]
Test Input2 Element Count3[3]
Has Fixed Weightstrue[3]
Computes Weighted Sumtrue[3]
Return Statementreturn fused_scores[3]
Parameter Names["scores1","scores2"][3]
Uses FormulaMin Max Normalization[4]
Uses OperationDot Product[4]
Consumes Output ofTune Weights[4]
TransformsArray Construction[4]
EnablesEnsemble Prediction[4]
CreatesFused Score Array[4]
Has Return StatementReturn Fused Scores[4]

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.

typebeam/b2901d01-4633-4513-84d1-1ea253e96bbf
ex:ImplementationTask
typebeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:Function
labelbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
fuse_scores
hasParameterbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
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hasParameterbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:dense-scores
hasParameterbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:sparse-weight
hasParameterbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:dense-weight
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usesLibrarybeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:PyTorch
performsOperationbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:convert-to-tensor
performsOperationbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:normalize
performsOperationbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:weighted-sum
descriptionbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
fuse scores from sparse and dense searches
calledWithbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:sparse-scores
calledWithbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:dense-scores
calledWithbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
0.6
calledWithbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
0.4
sourceCodebeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
def fuse_scores(sparse_scores, dense_scores, sparse_weight=0.5, dense_weight=0.5):
specifiesDtypebeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
torch.float32
convertsFrombeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
ex:numpy-array
convertsTobeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
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executionOrderbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
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executionOrderbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
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executionOrderbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
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executionOrderbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
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purposebeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
score fusion for retrieval systems
hasCommentbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
Convert scores to PyTorch tensors
hasCommentbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
Normalize scores
hasCommentbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
Fuse scores
returnTypebeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
numpy.ndarray
parameterOrderbeam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
["sparse_scores","dense_scores","sparse_weight","dense_weight"]
typebeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:Function
hasParameterbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
scores1
hasParameterbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
scores2
usesbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:NumPy-dot
setsWeightsbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
[0.6, 0.4]
returnsbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:fused-scores
testInput1beam/83d82fac-5668-4797-9ad9-b4b6b371089e
[0.8, 0.2, 0.4]
testInput2beam/83d82fac-5668-4797-9ad9-b4b6b371089e
[0.3, 0.7, 0.1]
labelbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
fuse_scores
hasReturnTypebeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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computesbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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outputsbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:fused-scores
calledWithbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
scores1
calledWithbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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scores1
combinesbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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testOutputbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:fused-scores-value
testInput1Lengthbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
3
testInput2Lengthbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
3
isPureFunctionbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
algorithmTypebeam/83d82fac-5668-4797-9ad9-b4b6b371089e
linear-combination
parameterTypebeam/83d82fac-5668-4797-9ad9-b4b6b371089e
ex:NumPyArray
computationalOperationbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
dot-product
hasTwoParametersbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
returnsSingleValuebeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
inverseHasParameterbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
scores1
inverseHasParameterbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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isDefinedInbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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followsPythonNamingConventionbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
hasNoErrorHandlingbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
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hasNoInputValidationbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
usesNumpyDotbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
testInput1ElementCountbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
3
testInput2ElementCountbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
3
hasFixedWeightsbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
computesWeightedSumbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
true
returnStatementbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
return fused_scores
parameterNamesbeam/83d82fac-5668-4797-9ad9-b4b6b371089e
["scores1","scores2"]
typebeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
ex:Function
labelbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
fuse_scores
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usesFormulabeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
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usesOperationbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
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consumesOutputOfbeam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
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References (4)

4 references
  1. ctx:claims/beam/b2901d01-4633-4513-84d1-1ea253e96bbf
  2. ctx:claims/beam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
    • full textbeam-chunk
      text/plain1 KBdoc:beam/2ba6cd1e-507f-44fe-bc7e-a6ea9503c472
      Show excerpt
      Use PyTorch to fuse the scores from sparse and dense searches: ```python def fuse_scores(sparse_scores, dense_scores, sparse_weight=0.5, dense_weight=0.5): # Convert scores to PyTorch tensors sparse_scores_tensor = torch.tensor(spa
  3. ctx:claims/beam/83d82fac-5668-4797-9ad9-b4b6b371089e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/83d82fac-5668-4797-9ad9-b4b6b371089e
      Show 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
  4. ctx:claims/beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
    • full textbeam-chunk
      text/plain1 KBdoc:beam/c2cfce3c-ef3d-4bc1-8ac6-e059a3dd9fbb
      Show 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

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