[mlpack-svn] r10110 - mlpack/trunk/src/mlpack/methods/emst
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Tue Nov 1 14:48:40 EDT 2011
Author: march
Date: 2011-11-01 14:48:39 -0400 (Tue, 01 Nov 2011)
New Revision: 10110
Added:
mlpack/trunk/src/mlpack/methods/emst/emst_test.cc
mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv
Removed:
mlpack/trunk/src/mlpack/methods/emst/README
mlpack/trunk/src/mlpack/methods/emst/test_data.csv
Modified:
mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt
mlpack/trunk/src/mlpack/methods/emst/dtb.hpp
mlpack/trunk/src/mlpack/methods/emst/emst_main.cpp
Log:
wrote emst_tests, fixed a few problems created by blindly replacing index_t with size_t
Modified: mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt 2011-11-01 17:36:36 UTC (rev 10109)
+++ mlpack/trunk/src/mlpack/methods/emst/CMakeLists.txt 2011-11-01 18:48:39 UTC (rev 10110)
@@ -33,3 +33,11 @@
mlpack
boost_unit_test_framework
)
+
+add_executable(emst_test
+ emst_test.cc
+)
+target_link_libraries(emst_test
+ mlpack
+ boost_unit_test_framework
+)
Deleted: mlpack/trunk/src/mlpack/methods/emst/README
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/README 2011-11-01 17:36:36 UTC (rev 10109)
+++ mlpack/trunk/src/mlpack/methods/emst/README 2011-11-01 18:48:39 UTC (rev 10110)
@@ -1,19 +0,0 @@
-This folder contains an implementation of the DualTreeBoruvka algorithm for finding a Euclidean Minimum Spanning Tree. NOTE: this algorithm is awaiting publication and is not for distribution.
-
-Compile with fl-build emst_main
-
-Run with ./emst_main --data="filename" for basic use. This will create a file "output.txt" with the minimum spanning tree represented as an edge list.
-
-Other options:
-
-bool --using_thor -> defaults to 0, thor is not yet supported, so setting it to 1 will cause an error message to be printed
-
-string --data -> the name of the file with the data points
-
-int --dtb/leaf_size -> defaults to 1, the number of points in the leaves of the tree. For the DualTreeBoruvka algorithm, 1 gives the fastest performance. I recommend only changing this parameter to conserve memory.
-
-bool --do_naive -> defaults to 0. If it is 1, then the algorithm will compute the MST with both DualTreeBoruvka and a naive implementation of Boruvka's algorithm. It will compare the two results and exit with an error if they are different.
-
-string --naive/output_filename -> The name of the file where the naive edge list should be printed. Defaults to naive_output.txt
-
-string --dtb/output_filename -> The name of the file where the DTB edge list should be printed. Defaults to output.txt
\ No newline at end of file
Modified: mlpack/trunk/src/mlpack/methods/emst/dtb.hpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/dtb.hpp 2011-11-01 17:36:36 UTC (rev 10109)
+++ mlpack/trunk/src/mlpack/methods/emst/dtb.hpp 2011-11-01 18:48:39 UTC (rev 10110)
@@ -4,7 +4,7 @@
* @author Bill March (march at gatech.edu)
*
* Contains an implementation of the DualTreeBoruvka algorithm for finding a
- * Euclidean Minimum Spanning Tree.
+ * Euclidean Minimum Spanning Tree using the kd-tree data structure.
*
* Citation: March, W. B.; Ram, P.; and Gray, A. G. Fast Euclidean Minimum Spanning
* Tree: Algorithm, Analysis, Applications. In KDD, 2010.
@@ -21,17 +21,9 @@
#include <mlpack/core/tree/binary_space_tree.hpp>
#include <mlpack/core/kernels/lmetric.hpp>
-PARAM(size_t, "leaf_size", "Size of the leaves.", "naive", 1, false);
-
namespace mlpack {
namespace emst {
-/*
-const fx_submodule_doc dtb_submodules[] = {
-FX_SUBMODULE_DOC_DONE
-};
- */
-
/**
* A Stat class for use with fastlib's trees. This one only stores two values.
*
@@ -43,10 +35,11 @@
* points in this node. If points in this node are in different components,
* this value will be negative.
*/
+
class DTBStat {
private:
double max_neighbor_distance_;
- size_t component_membership_;
+ int component_membership_;
public:
void set_max_neighbor_distance(double distance) {
@@ -57,11 +50,11 @@
return max_neighbor_distance_;
}
- void set_component_membership(size_t membership) {
+ void set_component_membership(int membership) {
component_membership_ = membership;
}
- size_t component_membership() {
+ int component_membership() {
return component_membership_;
}
@@ -108,8 +101,6 @@
*/
class DualTreeBoruvka {
-// FORBID_ACCIDENTAL_COPIES(DualTreeBoruvka);
-
public:
// For now, everything is in Euclidean space
static const size_t metric = 2;
@@ -144,7 +135,7 @@
size_t number_r_recursions_;
size_t number_both_recursions_;
- int do_naive_;
+ bool do_naive_;
DTBTree* tree_;
@@ -286,6 +277,9 @@
//pruned by component membership
mlpack::Log::Assert(reference_node->stat().component_membership() >= 0);
+
+ mlpack::Log::Info << query_node->stat().component_membership() << "q mem\n";
+ mlpack::Log::Info << reference_node->stat().component_membership() << "r mem\n";
number_component_prunes_++;
}
@@ -411,7 +405,7 @@
struct SortEdgesHelper_ {
bool operator() (const EdgePair& pairA, const EdgePair& pairB) {
- return (pairA.distance() > pairB.distance());
+ return (pairA.distance() < pairB.distance());
}
} SortFun;
@@ -432,29 +426,33 @@
SortEdges_();
mlpack::Log::Assert(number_of_edges_ == number_of_points_ - 1);
- results.set_size(3, number_of_edges_);
+ results.set_size(number_of_edges_, 3);
+ // need to unpermute the point labels
if (!do_naive_) {
for (size_t i = 0; i < (number_of_points_ - 1); i++) {
- edges_[i].set_lesser_index(old_from_new_permutation_[edges_[i]
- .lesser_index()]);
+ // Make sure the edge list stores the smaller index first to
+ // make checking correctness easier
+ size_t ind1, ind2;
+ ind1 = old_from_new_permutation_[edges_[i].lesser_index()];
+ ind2 = old_from_new_permutation_[edges_[i].greater_index()];
+
+ edges_[i].set_lesser_index(std::min(ind1, ind2));
+ edges_[i].set_greater_index(std::max(ind1, ind2));
- edges_[i].set_greater_index(old_from_new_permutation_[edges_[i]
- .greater_index()]);
+ results(i, 0) = edges_[i].lesser_index();
+ results(i, 1) = edges_[i].greater_index();
+ results(i, 2) = sqrt(edges_[i].distance());
- results(0, i) = edges_[i].lesser_index();
- results(1, i) = edges_[i].greater_index();
- results(2, i) = sqrt(edges_[i].distance());
-
}
}
else {
for (size_t i = 0; i < number_of_edges_; i++) {
- results(0, i) = edges_[i].lesser_index();
- results(1, i) = edges_[i].greater_index();
- results(2, i) = sqrt(edges_[i].distance());
+ results(i, 0) = edges_[i].lesser_index();
+ results(i, 1) = edges_[i].greater_index();
+ results(i, 2) = sqrt(edges_[i].distance());
}
}
@@ -532,9 +530,10 @@
fx_result_int(module_, "number_r_recursions", number_r_recursions_);
fx_result_int(module_, "number_both_recursions", number_both_recursions_);*/
// TODO, not sure how I missed this last time.
- mlpack::Log::Info << "total_squared_length" << total_dist_ << std::endl;
- mlpack::Log::Info << "number_of_points" << number_of_points_ << std::endl;
- mlpack::Log::Info << "dimension" << data_points_.n_rows << std::endl;
+ mlpack::Log::Info << "Total squared length: " << total_dist_ << std::endl;
+ mlpack::Log::Info << "Number of points: " << number_of_points_ << std::endl;
+ mlpack::Log::Info << "Dimension: " << data_points_.n_rows << std::endl;
+ /*
mlpack::Log::Info << "number_of_loops" << std::endl;
mlpack::Log::Info << "number_distance_prunes" << std::endl;
mlpack::Log::Info << "number_component_prunes" << std::endl;
@@ -542,6 +541,10 @@
mlpack::Log::Info << "number_q_recursions" << std::endl;
mlpack::Log::Info << "number_r_recursions" << std::endl;
mlpack::Log::Info << "number_both_recursions" << std::endl;
+ */
+
+ mlpack::CLI::GetParam<double>("dtb/total_squared_length") = total_dist_;
+
} // OutputResults_
/////////// Public Functions ///////////////////
@@ -554,11 +557,9 @@
/**
- * Takes in a reference to the data set and a module. Copies the data,
+ * Takes in a reference to the data set. Copies the data,
* builds the tree, and initializes all of the member variables.
*
- * This module will be checked for the optional parameters "leaf_size" and
- * "do_naive".
*/
void Init(const arma::mat& data) {
@@ -572,17 +573,20 @@
// This gives best pruning empirically
// Use leaf_size=1 unless space is a big concern
CLI::GetParam<int>("tree/leaf_size") =
- CLI::GetParam<size_t>("naive/leaf_size");
+ CLI::GetParam<int>("emst/leaf_size");
- Timers::StartTimer("naive/tree_building");
+ Timers::StartTimer("emst/tree_building");
tree_ = new DTBTree(data_points_, old_from_new_permutation_);
- Timers::StopTimer("naive/tree_building");
+ Timers::StopTimer("emst/tree_building");
+
}
else {
+
tree_ = NULL;
old_from_new_permutation_.resize(0);
+
}
number_of_points_ = data_points_.n_cols;
@@ -614,24 +618,31 @@
Timers::StartTimer("emst/MST_computation");
while (number_of_edges_ < (number_of_points_ - 1)) {
+
ComputeNeighbors_();
AddAllEdges_();
Cleanup_();
- Log::Info << "number_of_loops = " << number_of_loops_ << std::endl;
+
+ Log::Info << "Finished loop number: " << number_of_loops_ << std::endl;
+ Log::Info << number_of_edges_ << " edges found so far.\n\n";
+ /*
+ Log::Info << number_leaf_computations_ << " base cases.\n";
+ Log::Info << number_distance_prunes_ << " distance prunes.\n";
+ Log::Info << number_component_prunes_ << " component prunes.\n";
+ Log::Info << number_r_recursions_ << " reference recursions.\n";
+ Log::Info << number_q_recursions_ << " query recursions.\n";
+ Log::Info << number_both_recursions_ << " dual recursions.\n\n";
+ */
+
}
Timers::StopTimer("emst/MST_computation");
-// if (results != NULL) {
+ EmitResults_(results);
- EmitResults_(results);
-
-// }
-
-
OutputResults_();
} // ComputeMST
Modified: mlpack/trunk/src/mlpack/methods/emst/emst_main.cpp
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/emst_main.cpp 2011-11-01 17:36:36 UTC (rev 10109)
+++ mlpack/trunk/src/mlpack/methods/emst/emst_main.cpp 2011-11-01 18:48:39 UTC (rev 10110)
@@ -3,159 +3,88 @@
*
* Calls the DualTreeBoruvka algorithm from dtb.h
* Can optionally call Naive Boruvka's method
- * See README for command line options.
+ *
+ * For algorithm details, see:
+ * March, W.B., Ram, P., and Gray, A.G.
+ * Fast Euclidean Minimum Spanning Tree: Algorithm, Analysis, Applications.
+ * In KDD, 2010.
*
* @author Bill March (march at gatech.edu)
*/
+#include "dtb.hpp"
#include <mlpack/core.h>
-#include "dtb.hpp"
-PARAM_FLAG("using_thor", "For when an implementation of thor is around",
- "emst");
PARAM_STRING_REQ("input_file", "Data input file.", "emst");
-PARAM_STRING("output_file", "Data output file.", "emst", "emst_output.csv");
-
-PARAM_FLAG("do_naive", "Check against naive.", "naive");
+PARAM_STRING("output_file", "Data output file. Stored as an edge list.", "emst", "emst_output.csv");
+PARAM_FLAG("do_naive", "Compute the MST using .", "naive");
PARAM_STRING("output_file", "Naive data output file.", "naive",
"naive_output.csv");
+PARAM_INT("leaf_size", "Leaf size in the kd-tree. Singleton leaves give the empirically best performance at the cost of greater memory requirements.", "emst", 1);
+PARAM_DOUBLE("total_squared_length", "Squared length of the computed tree.", "dtb", 0.0);
-PARAM(double, "total_squared_length", "Calculation result.", "dtb", 0.0, false);
-
using namespace mlpack;
using namespace mlpack::emst;
int main(int argc, char* argv[]) {
+
CLI::ParseCommandLine(argc, argv);
- // For when I implement a thor version
- bool using_thor = CLI::GetParam<bool>("emst/using_thor");
+ ///////////////// READ IN DATA //////////////////////////////////
+ std::string data_file_name = CLI::GetParam<std::string>("emst/input_file");
- if (using_thor) {
- Log::Warn << "thor is not yet supported" << std::endl;
+ Log::Info << "Reading in data.\n";
+
+ arma::mat data_points;
+ data_points.load(data_file_name.c_str());
+
+ // Do naive
+ if (CLI::GetParam<bool>("naive/do_naive")) {
+
+ Log::Info << "Running naive algorithm.\n";
+
+ DualTreeBoruvka naive;
+ //CLI::GetParam<bool>("naive/do_naive") = true;
+
+ naive.Init(data_points);
+
+ arma::mat naive_results;
+ naive.ComputeMST(naive_results);
+
+ std::string naive_output_filename =
+ CLI::GetParam<std::string>("naive/output_file");
+
+ naive_results.save(naive_output_filename.c_str(), arma::csv_ascii, false,
+ true);
+
}
else {
+
+ Log::Info << "Data read, building tree.\n";
- ///////////////// READ IN DATA //////////////////////////////////
-
- std::string data_file_name = CLI::GetParam<std::string>("emst/input_file");
-
- arma::mat data_points;
- data_points.load(data_file_name.c_str());
-
/////////////// Initialize DTB //////////////////////
DualTreeBoruvka dtb;
-
+ dtb.Init(data_points);
+
+ Log::Info << "Tree built, running algorithm.\n\n";
+
////////////// Run DTB /////////////////////
arma::mat results;
dtb.ComputeMST(results);
- //////////////// Check against naive //////////////////////////
- if (CLI::GetParam<bool>("naive/do_naive")) {
- DualTreeBoruvka naive;
- CLI::GetParam<bool>("naive/do_naive") = true;
-
- naive.Init(data_points);
-
- arma::mat naive_results;
- naive.ComputeMST(naive_results);
-
- /* Compare the naive output to the DTB output */
-
- Timers::StartTimer("naive/comparison");
-
-
- // Check if the edge lists are the same
- // Loop over the naive edge list
- int is_correct = 1;
- /*
- for (size_t naive_index = 0; naive_index < results.size();
- naive_index++) {
-
- int this_loop_correct = 0;
- size_t naive_lesser_index = results[naive_index].lesser_index();
- size_t naive_greater_index = results[naive_index].greater_index();
- double naive_distance = results[naive_index].distance();
-
- // Loop over the DTB edge list and compare against naive
- // Break when an edge is found that matches the current naive edge
- for (size_t dual_index = 0; dual_index < naive_results.size();
- dual_index++) {
-
- size_t dual_lesser_index = results[dual_index].lesser_index();
- size_t dual_greater_index = results[dual_index].greater_index();
- double dual_distance = results[dual_index].distance();
-
- if (naive_lesser_index == dual_lesser_index) {
- if (naive_greater_index == dual_greater_index) {
- DEBUG_ASSERT(naive_distance == dual_distance);
- this_loop_correct = 1;
- break;
- }
- }
-
- }
-
- if (this_loop_correct == 0) {
- is_correct = 0;
- break;
- }
-
- }
- */
- if (is_correct == 0) {
-
- Log::Warn << "Naive check failed!" << std::endl <<
- "Edge lists are different." << std::endl << std::endl;
-
- // Check if the outputs have the same length
- if (CLI::GetParam<double>("naive/total_squared_length") !=
- CLI::GetParam<double>("naive/total_squared_length")) {
-
- Log::Fatal << "Total lengths are different! "
- << " One algorithm has failed." << std::endl;
-
- return 1;
- }
- else {
- // NOTE: if the edge lists are different, but the total lengths are
- // the same, the algorithm may still be correct. The MST is not
- // uniquely defined for some point sets. For example, an equilateral
- // triangle has three minimum spanning trees. It is possible for
- // naive and DTB to find different spanning trees in this case.
- Log::Info << "Total lengths are the same.";
- Log::Info << "It is possible the point set";
- Log::Info << "has more than one minimum spanning tree." << std::endl;
- }
-
- }
- else {
- Log::Info << "Naive and DualTreeBoruvka produced the same MST." <<
- std::endl << std::endl;
- }
-
- Timers::StopTimer("naive/comparison");
-
- std::string naive_output_filename =
- CLI::GetParam<std::string>("naive/output_file");
-
- naive_results.save(naive_output_filename.c_str(), arma::csv_ascii, false,
- true);
- }
-
//////////////// Output the Results ////////////////
std::string output_filename =
CLI::GetParam<std::string>("emst/output_file");
results.save(output_filename.c_str(), arma::csv_ascii, false, true);
+
+ }
- }// end else (if using_thor)
-
return 0;
}
Added: mlpack/trunk/src/mlpack/methods/emst/emst_test.cc
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/emst_test.cc (rev 0)
+++ mlpack/trunk/src/mlpack/methods/emst/emst_test.cc 2011-11-01 18:48:39 UTC (rev 10110)
@@ -0,0 +1,138 @@
+/**
+ * @file emst_test.cc
+ *
+ * Test file for EMST methods
+ */
+#include <mlpack/core.h>
+#include "dtb.hpp"
+
+#define BOOST_TEST_MODULE EMST Test
+#include <boost/test/unit_test.hpp>
+
+using namespace mlpack;
+using namespace mlpack::emst;
+
+/***
+ * Simple emst test with small, synthetic dataset. This is an
+ * exhaustive test, which checks that each method for performing the calculation
+ * (dual-tree, single-tree, naive) produces the correct results. The dataset is
+ * in one dimension for simplicity -- the correct functionality of distance
+ * functions is not tested here.
+ */
+BOOST_AUTO_TEST_CASE(exhaustive_synthetic_test) {
+ // Set up our data.
+ arma::mat data(1, 11);
+ data[0] = 0.05; // Row addressing is unnecessary (they are all 0).
+ data[1] = 0.37;
+ data[2] = 0.15;
+ data[3] = 1.25;
+ data[4] = 5.05;
+ data[5] = -0.22;
+ data[6] = -2.00;
+ data[7] = -1.30;
+ data[8] = 0.45;
+ data[9] = 0.91;
+ data[10] = 1.00;
+
+
+ // Now perform the actual calculation.
+ arma::mat results;
+
+
+ DualTreeBoruvka dtb;
+ dtb.Init(data);
+ dtb.ComputeMST(results);
+
+ // Now the exhaustive check for correctness.
+
+ BOOST_REQUIRE(results(0, 0) == 1);
+ BOOST_REQUIRE(results(0, 1) == 8);
+ BOOST_REQUIRE_CLOSE(results(0, 2), 0.08, 1e-5);
+
+ BOOST_REQUIRE(results(1, 0) == 9);
+ BOOST_REQUIRE(results(1, 1) == 10);
+ BOOST_REQUIRE_CLOSE(results(1, 2), 0.09, 1e-5);
+
+ BOOST_REQUIRE(results(2, 0) == 0);
+ BOOST_REQUIRE(results(2, 1) == 2);
+ BOOST_REQUIRE_CLOSE(results(2, 2), 0.1, 1e-5);
+
+ BOOST_REQUIRE(results(3, 0) == 1);
+ BOOST_REQUIRE(results(3, 1) == 2);
+ BOOST_REQUIRE_CLOSE(results(3, 2), 0.22, 1e-5);
+
+ BOOST_REQUIRE(results(4, 0) == 3);
+ BOOST_REQUIRE(results(4, 1) == 10);
+ BOOST_REQUIRE_CLOSE(results(4, 2), 0.25, 1e-5);
+
+ BOOST_REQUIRE(results(5, 0) == 0);
+ BOOST_REQUIRE(results(5, 1) == 5);
+ BOOST_REQUIRE_CLOSE(results(5, 2), 0.27, 1e-5);
+
+ BOOST_REQUIRE(results(6, 0) == 8);
+ BOOST_REQUIRE(results(6, 1) == 9);
+ BOOST_REQUIRE_CLOSE(results(6, 2), 0.46, 1e-5);
+
+ BOOST_REQUIRE(results(7, 0) == 6);
+ BOOST_REQUIRE(results(7, 1) == 7);
+ BOOST_REQUIRE_CLOSE(results(7, 2), 0.7, 1e-5);
+
+ BOOST_REQUIRE(results(8, 0) == 5);
+ BOOST_REQUIRE(results(8, 1) == 7);
+ BOOST_REQUIRE_CLOSE(results(8, 2), 1.08, 1e-5);
+
+ BOOST_REQUIRE(results(9, 0) == 3);
+ BOOST_REQUIRE(results(9, 1) == 4);
+ BOOST_REQUIRE_CLOSE(results(9, 2), 3.8, 1e-5);
+
+
+}
+
+/**
+ * Test the dual tree method against the naive computation.
+ *
+ * Errors are produced if the results are not identical.
+ */
+BOOST_AUTO_TEST_CASE(dual_tree_vs_naive) {
+ arma::mat input_data;
+
+ // Hard-coded filename: bad!
+ // Code duplication: also bad!
+ if (!input_data.load("test_data_3_1000.csv", arma::auto_detect, false,
+ true))
+ BOOST_FAIL("Cannot load test dataset test_data_3_1000.csv!");
+
+ // Set up matrices to work with (may not be necessary with no ALIAS_MATRIX?).
+ arma::mat dual_data = arma::trans(input_data);
+ arma::mat naive_data = arma::trans(input_data);
+
+ // Reset parameters from last test.
+ DualTreeBoruvka dtb;
+ dtb.Init(dual_data);
+
+ arma::mat dual_results;
+ dtb.ComputeMST(dual_results);
+
+ // Set naive mode.
+ CLI::GetParam<bool>("naive/do_naive") = true;
+
+ DualTreeBoruvka dtb_naive;
+ dtb_naive.Init(naive_data);
+
+ arma::mat naive_results;
+ dtb_naive.ComputeMST(naive_results);
+
+ BOOST_REQUIRE(dual_results.n_cols == naive_results.n_cols);
+ BOOST_REQUIRE(dual_results.n_rows == naive_results.n_rows);
+
+ for (size_t i = 0; i < dual_results.n_rows; i++) {
+
+ BOOST_REQUIRE(dual_results(i,0) == naive_results(i,0));
+ BOOST_REQUIRE(dual_results(i,1) == naive_results(i,1));
+ BOOST_REQUIRE_CLOSE(dual_results(i,2), naive_results(i,2), 1e-5);
+
+ }
+
+
+}
+
Deleted: mlpack/trunk/src/mlpack/methods/emst/test_data.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/test_data.csv 2011-11-01 17:36:36 UTC (rev 10109)
+++ mlpack/trunk/src/mlpack/methods/emst/test_data.csv 2011-11-01 18:48:39 UTC (rev 10110)
@@ -1,20 +0,0 @@
-0.3964647737602753 0.8404853694114252
-0.3533360972452435 0.4465834347965441
-0.3186927723118806 0.8864284332230312
-0.01558284940832877 0.5840902203172718
-0.1593686265318048 0.3837158748071943
-0.6910043733821958 0.05885891359273643
-0.899854306161604 0.1635459506303647
-0.1590715025818064 0.5330647140218545
-0.6041441897112385 0.5826990212072189
-0.2699711179070157 0.3904781954634089
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Added: mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv
===================================================================
--- mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv (rev 0)
+++ mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv 2011-11-01 18:48:39 UTC (rev 10110)
@@ -0,0 +1,1000 @@
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Property changes on: mlpack/trunk/src/mlpack/methods/emst/test_data_3_1000.csv
___________________________________________________________________
Added: svn:executable
+ *
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