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Viewing larger datasets #528

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Nov 4, 2024
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2 changes: 1 addition & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ Linux • MacOS • Windows • iOS • Android • WebAssembly •
- ✅ [Trusted](#integrations) by giants like Google and DBs like [ClickHouse][clickhouse-docs] & [DuckDB][duckdb-docs].
- ✅ [SIMD][simd]-optimized and [user-defined metrics](#user-defined-functions) with JIT compilation.
- ✅ Hardware-agnostic `f16` & `i8` - [half-precision & quarter-precision support](#memory-efficiency-downcasting-and-quantization).
- ✅ [View large indexes from disk](#serving-index-from-disk) without loading into RAM.
- ✅ [View large indexes from disk](#serialization--serving-index-from-disk) without loading into RAM.
- ✅ Heterogeneous lookups, renaming/relabeling, and on-the-fly deletions.
- ✅ Binary Tanimoto and Sorensen coefficients for [Genomics and Chemistry applications](#usearch--rdkit--molecular-search).
- ✅ Space-efficient point-clouds with `uint40_t`, accommodating 4B+ size.
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4 changes: 2 additions & 2 deletions include/usearch/index_plugins.hpp
Original file line number Diff line number Diff line change
Expand Up @@ -1276,7 +1276,7 @@ struct casts_punned_t {
* > warning: loop not vectorized: the optimizer was unable to perform the requested transformation;
* > the transformation might be disabled or specified as part of an unsupported transformation ordering
*/
#if defined(__clang__)
#if defined(USEARCH_DEFINED_CLANG)
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wpass-failed"
#endif
Expand Down Expand Up @@ -1990,7 +1990,7 @@ class metric_punned_t {
};

/* Allow complaining about vectorization after this point. */
#if defined(__clang__)
#if defined(USEARCH_DEFINED_CLANG)
#pragma clang diagnostic pop
#endif

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2 changes: 1 addition & 1 deletion python/usearch/io.py
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def load_matrix(
return None

with open(filename, "rb") as f:
rows, cols = np.fromfile(f, count=2, dtype=np.int32)
rows, cols = np.fromfile(f, count=2, dtype=np.int32).astype(np.uint64)
rows = (rows - start_row) if count_rows is None else count_rows
row_offset = start_row * scalar_size * cols

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