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utils
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@@ -878,7 +878,7 @@ def extract_latent_semantics_from_feature_model(
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# singular value decomposition
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# singular value decomposition
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# sparse version of SVD to get only k singular values
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# sparse version of SVD to get only k singular values
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case 1:
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case 1:
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U, S, V_T = svd(feature_vectors, k=k)
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U, S, V_T = svds(feature_vectors, k=k)
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all_latent_semantics = {
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all_latent_semantics = {
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"image-semantic": U.tolist(),
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"image-semantic": U.tolist(),
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