e955c94ed3
Listing the "Blender Foundation" as copyright holder implied the Blender Foundation holds copyright to files which may include work from many developers. While keeping copyright on headers makes sense for isolated libraries, Blender's own code may be refactored or moved between files in a way that makes the per file copyright holders less meaningful. Copyright references to the "Blender Foundation" have been replaced with "Blender Authors", with the exception of `./extern/` since these this contains libraries which are more isolated, any changed to license headers there can be handled on a case-by-case basis. Some directories in `./intern/` have also been excluded: - `./intern/cycles/` it's own `AUTHORS` file is planned. - `./intern/opensubdiv/`. An "AUTHORS" file has been added, using the chromium projects authors file as a template. Design task: #110784 Ref !110783.
92 lines
2.9 KiB
C++
92 lines
2.9 KiB
C++
/* SPDX-FileCopyrightText: 2023 Blender Authors
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*
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* SPDX-License-Identifier: GPL-2.0-or-later */
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#pragma once
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#include "BLI_linear_allocator.hh"
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#include "BLI_span.hh"
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#include "BLI_string_ref.hh"
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#include "BLI_vector.hh"
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namespace blender::string_search {
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struct SearchItem {
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Span<blender::StringRef> normalized_words;
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int length;
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void *user_data;
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int weight;
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};
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/**
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* Non templated base class so that its methods can be implemented outside of this header.
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*/
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class StringSearchBase {
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protected:
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LinearAllocator<> allocator_;
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Vector<SearchItem> items_;
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protected:
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void add_impl(StringRef str, void *user_data, int weight);
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Vector<void *> query_impl(StringRef query) const;
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};
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/**
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* #StringSearch filters and sorts search items based on a string query. Every search item has data
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* of type T attached that is used to identify it.
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*
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* When querying, the a match score is computed between the query string and each item. Items that
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* don't match are filtered out, the rest is sorted by the score. Elements with the same score are
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* further sorted based on the optionally provided weight and other heuristics.
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*
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* The usage is simple. First #add all the search items and then use the #query method.
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*/
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template<typename T> class StringSearch : private StringSearchBase {
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public:
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/**
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* Add a new possible result to the search.
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*
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* \param weight: Can be used to customize the order when multiple items have the same match
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* score.
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*/
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void add(const StringRefNull str, T *user_data, const int weight = 0)
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{
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this->add_impl(str, (void *)user_data, weight);
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}
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/**
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* Filter and sort all previously added search items.
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* Returns an array containing the filtered user data.
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*/
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Vector<T *> query(const StringRef query) const
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{
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Vector<void *> result = this->query_impl(query);
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Vector<T *> result_typed = result.as_span().cast<T *>();
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return result_typed;
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}
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};
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/**
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* Computes the cost of transforming string a into b. The cost/distance is the minimal number of
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* operations that need to be executed. Valid operations are deletion, insertion, substitution and
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* transposition.
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*
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* This function is utf8 aware in the sense that it works at the level of individual code points
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* (1-4 bytes long) instead of on individual bytes.
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*/
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int damerau_levenshtein_distance(StringRef a, StringRef b);
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/**
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* Returns -1 when this is no reasonably good match.
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* Otherwise returns the number of errors in the match.
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*/
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int get_fuzzy_match_errors(StringRef query, StringRef full);
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/**
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* Splits a string into words and normalizes them (currently that just means converting to lower
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* case). The returned strings are allocated in the given allocator.
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*/
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void extract_normalized_words(StringRef str,
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LinearAllocator<> &allocator,
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Vector<StringRef, 64> &r_words);
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} // namespace blender::string_search
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