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VEX_FUNCTION(float, l2_distance, (size_t, idx)(uint32_t, num_dim)(float*, query)(float*, candidates), | |
float d = 0; | |
for (uint i = 0; i < num_dim; ++i) { | |
d += pow(query[i] - candidates[idx * num_dim + i], 2); | |
} | |
return d; | |
); | |
Knn::ValueVec Knn::search_h(const ValueVec& query, size_t query_offset) const { | |
prof_.tic_cl("distance"); | |
ValueVec distance(current_context(), num_row_); | |
switch (distance_) { | |
case L2: | |
distance = l2_distance(element_index(), num_col_, | |
raw_pointer(query) + query_offset, raw_pointer(h_)); | |
break; | |
case COSINE: | |
distance = cosine_distance(element_index(), num_col_, | |
raw_pointer(query) + query_offset, raw_pointer(h_)); | |
} | |
prof_.toc("distance"); | |
return distance; | |
} | |
pair<Knn::ValueVec, Knn::IndexVec> Knn::sort_internal(ValueVec& distance, uint32_t top_k) const { | |
prof_.tic_cl("sort_by_key"); | |
IndexVec index(current_context(), distance.size()); | |
index = element_index(); | |
sort_by_key(distance, index); | |
prof_.toc("sort_by_key"); | |
prof_.tic_cl("slice"); | |
slicer<1> slice(extents[top_k]); | |
auto result = make_pair(slice[range(0, top_k)](distance), slice[range(0, top_k)](index)); | |
prof_.toc("slice"); | |
return result; | |
} | |
pair<Knn::ValueVec, Knn::IndexVec> Knn::search_similar(uint32_t obj, uint32_t top_k) const { | |
auto distance = search_h(h_, obj * num_col_); | |
return sort_internal(distance, top_k); | |
} | |
[ distance: 4658.446 sec.] ( 81.39%) (428566x; avg: 1.078919e+04 usec.) |
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