Added documentation
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@@ -25,8 +25,6 @@
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#include "u/nvasil/tree/binary_kd_tree_mmapmm.h"
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struct Parameters {
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int32 dimension_;
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int64 num_of_points_;
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std::string train_file_;
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std::string test_file_;
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std::string out_file_;
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@@ -36,7 +34,7 @@ struct Parameters {
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BinaryDataset<float32> test_data_;
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uint64 capacity_;
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};
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std::string Usage();
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template<typename TREE>
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void DuallTreeAllNearestNeighborsSpecializedForKnn(Parameters &args);
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@@ -44,11 +42,15 @@ int main(int argc, char *argv[]) {
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Parameters args;
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// initialize command line parameter
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fx_init(argc, argv);
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args.train_file_=fx_param_str_req(NULL, "train_file");
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args.test_file_=fx_param_str_req(NULL, "test_file");
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if (fx_param_exists(NULL, "help")) {
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printf("%s\n", Usage().c_str());
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return -1;
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}
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args.train_file_=fx_param_str(NULL, "train_file", "");
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args.test_file_=fx_param_str(NULL, "test_file", "");
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args.knns_=fx_param_int(NULL, "knns",5);
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args.out_file_=fx_param_str_req(NULL, "out_file");
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args.memory_file_=fx_param_str_req(NULL, "memory_file");
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args.out_file_=fx_param_str(NULL, "out_file", "allnn");
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args.memory_file_=fx_param_str(NULL, "memory_file", "temp_mem");
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args.capacity_=fx_param_int(NULL, "capacity", 16777216);
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if (sizeof(index_t)==sizeof(int32)) {
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NONFATAL("index_t is int32, good for small scale problems");
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@@ -141,4 +143,16 @@ void DuallTreeAllNearestNeighborsSpecializedForKnn(Parameters &args) {
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fx_timer_stop(fx_root, "collecting_results");
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}
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}
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std::string Usage() {
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std::string ret =
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string("Computing all k-nearest neighbors with dual tree method...\n") +
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string("timit_nn --option=value\n")+
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string("--train_file : the dataset that contains the training data (reference)\n")+
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string("--test_file : the dataset that contains the test data (query)\n")+
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string("--out_file : stores the results in a text file, usually big\n")+
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string("--memory_file: the file wher the tree will be stored\n")+
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string("--knns : number of neighbors default is 5\n")+
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string("--capacity : the capacity of the memory file, keep it big enough\n");
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return ret;
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}
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