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Parallel k-NN ImHex Pattern
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| import std.array; | |
| fn extract_comp(u64 num_points, u64 num_dims, std::Array<std::Array<float, num_dims>, | |
| num_points> points, u8 dim) { | |
| std::Array<float, num_points> ret; | |
| for (u64 point = 0, point < num_points, point += 1) { | |
| ret.data[point] = points.data[point].data[dim]; | |
| } | |
| return ret; | |
| }; | |
| struct TrainPoints<auto NumPoints, auto NumDims> { | |
| std::Array<std::Array<float, NumDims>, NumPoints> p [[inline]]; | |
| std::Array<float, NumPoints> x_arr = extract_comp(NumPoints, NumDims, p, 0); | |
| std::Array<float, NumPoints> y_arr = extract_comp(NumPoints, NumDims, p, 1); | |
| } [[hex::visualize("scatter_plot", x_arr.data, y_arr.data)]]; | |
| struct TrainingFile { | |
| u64 id; | |
| u64 num_points; | |
| u64 num_dims; | |
| TrainPoints<num_points, num_dims> points; | |
| }; | |
| struct QueryFile { | |
| u64 id; | |
| u64 num_queries; | |
| u64 num_dims; | |
| u64 num_neighbors; | |
| TrainPoints<num_queries, num_dims> queries; | |
| }; | |
| struct Results<auto NumDims, auto NumQueries, auto NumNeighbors> { | |
| std::Array<TrainPoints<NumNeighbors, NumDims>, NumQueries> r [[inline]]; | |
| }; | |
| struct ResultFile { | |
| u64 training_file_id; | |
| u64 query_file_id; | |
| u64 result_file_id; | |
| u64 num_queries; | |
| u64 num_dims; | |
| u64 num_neighbors; | |
| Results<num_dims, num_queries, num_neighbors> results; | |
| }; | |
| struct File { | |
| char file_type[8]; | |
| match (file_type) { | |
| ("TRAINING"): TrainingFile training; | |
| ("QUERY\0\0\0"): QueryFile query; | |
| ("RESULT\0\0"): ResultFile r; | |
| } | |
| }; | |
| File f @ 0x0 [[inline]]; |
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