diff --git a/docs/HTML/get_data_by_rand.html b/docs/HTML/get_data_by_rand.html
index 31c1918a..de138ef1 100644
--- a/docs/HTML/get_data_by_rand.html
+++ b/docs/HTML/get_data_by_rand.html
@@ -66,7 +66,7 @@
DataFrame<I, H>
get_data_by_rand(random_policy spec,
double n,
- std::size_t seed = 0) const;
+ seed_t seed = 0) const;
@@ -78,7 +78,7 @@
Ts: The list of types for all columns. A type should be specified only once.
random_policy: Please see random_policy in DataFrameTypes.h. It specifies how this function should proceed.
n: Depending on the random policy, it is either the number of rows to sample or a fraction of rows to sample. In case of fraction, for example 0.4 means 40% of rows.
- seed: Depending on the random policy, user could specify a seed. The same seed should always produce the same random selection.
+ seed: (unsigned int) Depending on the random policy, user could specify a seed. The same seed should always produce the same random selection.
|
@@ -89,7 +89,7 @@
PtrView
get_view_by_rand(random_policy spec,
double n,
- std::size_t seed = 0);
+ seed_t seed = 0);
@@ -101,7 +101,7 @@
Ts: The list of types for all columns. A type should be specified only once.
random_policy: Please see random_policy in DataFrameTypes.h. It specifies how this function should proceed.
n: Depending on the random policy, it is either the number of rows to sample or a fraction of rows to sample. In case of fraction, for example 0.4 means 40% of rows.
- seed: Depending on the random policy, user could specify a seed. The same seed should always produce the same random selection.
+ seed: (unsigned int) Depending on the random policy, user could specify a seed. The same seed should always produce the same random selection.
|
@@ -112,7 +112,7 @@
ConstPtrView
get_view_by_rand(random_policy spec,
double n,
- std::size_t seed = 0) const;
+ seed_t seed = 0) const;
@@ -122,7 +122,7 @@
Ts: The list of types for all columns. A type should be specified only once.
random_policy: Please see random_policy in DataFrameTypes.h. It specifies how this function should proceed.
n: Depending on the random policy, it is either the number of rows to sample or a fraction of rows to sample. In case of fraction, for example 0.4 means 40% of rows.
- seed: Depending on the random policy, user could specify a seed. The same seed should always produce the same random selection.
+ seed: (unsigned int) Depending on the random policy, user could specify a seed. The same seed should always produce the same random selection.
|
diff --git a/include/DataFrame/DataFrame.h b/include/DataFrame/DataFrame.h
index d434c2f7..4455b289 100644
--- a/include/DataFrame/DataFrame.h
+++ b/include/DataFrame/DataFrame.h
@@ -115,6 +115,8 @@ class DataFrame : public ThreadGranularity {
template
using StlVecType = std::vector>;
+ using seed_t = std::random_device::result_type;
+
DataFrame() = default;
// Because of thread safety, these need tender loving care
@@ -937,7 +939,8 @@ class DataFrame : public ThreadGranularity {
template
void
shuffle(const StlVecType &col_names,
- bool also_shuffle_index);
+ bool also_shuffle_index,
+ seed_t seed = seed_t(-1));
// It fills all the "missing values" with the given values, and/or using
// the given method.
@@ -2492,7 +2495,7 @@ class DataFrame : public ThreadGranularity {
//
template
[[nodiscard]] DataFrame
- get_data_by_rand(random_policy spec, double n, size_type seed = 0) const;
+ get_data_by_rand(random_policy spec, double n, seed_t seed = 0) const;
// It behaves like get_data_by_rand(), but it returns a PtrView.
// A view is a DataFrame that is a reference to the original DataFrame.
@@ -2521,11 +2524,11 @@ class DataFrame : public ThreadGranularity {
//
template
[[nodiscard]] PtrView
- get_view_by_rand(random_policy spec, double n, size_type seed = 0);
+ get_view_by_rand(random_policy spec, double n, seed_t seed = 0);
template
[[nodiscard]] ConstPtrView
- get_view_by_rand(random_policy spec, double n, size_type seed = 0) const;
+ get_view_by_rand(random_policy spec, double n, seed_t seed = 0) const;
// This returns a DataFrame with index and col_names copied from the
// original DataFrame
diff --git a/include/DataFrame/DataFrameMLVisitors.h b/include/DataFrame/DataFrameMLVisitors.h
index 551e67c0..eb7191bb 100644
--- a/include/DataFrame/DataFrameMLVisitors.h
+++ b/include/DataFrame/DataFrameMLVisitors.h
@@ -134,11 +134,13 @@ struct KMeansVisitor {
using cluster_type = std::array, K>;
using distance_func =
std::function;
+ using seed_t = std::random_device::result_type;
private:
const size_type iter_num_;
const bool cc_;
+ const seed_t seed_;
distance_func dfunc_;
result_type result_ { }; // K Means
cluster_type clusters_ { }; // K Clusters
@@ -147,10 +149,12 @@ struct KMeansVisitor {
inline void calc_k_means_(const H &column_begin, size_type col_s) {
std::random_device rd;
- std::mt19937 gen(rd());
+ std::mt19937 gen(
+ (seed_ != seed_t(-1)) ? seed_ : rd());
std::uniform_int_distribution rd_gen(0, col_s - 1);
// Pick centroids as random points from the col.
+ //
for (auto &k_mean : result_) [[likely]] {
const value_type &value = *(column_begin + rd_gen(gen));
@@ -280,8 +284,9 @@ struct KMeansVisitor {
distance_func f =
[](const value_type &x, const value_type &y) -> double {
return ((x - y) * (x - y));
- })
- : iter_num_(num_of_iter), cc_(calc_clusters), dfunc_(f) { }
+ },
+ seed_t seed = seed_t(-1))
+ : iter_num_(num_of_iter), cc_(calc_clusters), seed_(seed), dfunc_(f) { }
};
// ----------------------------------------------------------------------------
@@ -316,6 +321,7 @@ struct AffinityPropVisitor {
double min_dist = std::numeric_limits::max();
// Compute similarity between distinct data points i and j
+ //
for (size_type i = 0; i < csize - 1; ++i) [[likely]] {
const value_type &i_val = *(column_begin + i);
@@ -328,6 +334,7 @@ struct AffinityPropVisitor {
}
// Assign min to diagonals
+ //
for (size_type i = 0; i < csize; ++i)
simil[(i * csize) + i - ((i * (i + 1)) >> 1)] = min_dist;
@@ -345,6 +352,7 @@ struct AffinityPropVisitor {
for (size_type m = 0; m < iter_num_; ++m) [[likely]] {
// Update responsibility
+ //
for (size_type i = 0; i < csize; ++i) [[likely]] {
for (size_type j = 0; j < csize; ++j) [[likely]] {
double max_diff = -std::numeric_limits::max();
@@ -370,6 +378,7 @@ struct AffinityPropVisitor {
// Update availability
// Do diagonals first
+ //
for (size_type i = 0; i < csize; ++i) [[likely]] {
const size_type s1 = i * csize;
double sum = 0.0;
diff --git a/include/DataFrame/Internals/DataFrame.tcc b/include/DataFrame/Internals/DataFrame.tcc
index 4aced603..f88f398e 100644
--- a/include/DataFrame/Internals/DataFrame.tcc
+++ b/include/DataFrame/Internals/DataFrame.tcc
@@ -143,17 +143,17 @@ template
template
void
DataFrame::shuffle(const StlVecType &col_names,
- bool also_shuffle_index) {
+ bool also_shuffle_index,
+ seed_t seed) {
- if (also_shuffle_index) {
- std::random_device rd;
- std::mt19937 g(rd());
+ std::random_device rd;
+ std::mt19937 g ((seed != seed_t(-1)) ? seed : rd());
+ if (also_shuffle_index)
std::shuffle(indices_.begin(), indices_.end(), g);
- }
- shuffle_functor_ functor;
- const SpinGuard guard(lock_);
+ shuffle_functor_ functor (g);
+ const SpinGuard guard (lock_);
for (const auto &name_citer : col_names) [[likely]] {
const auto citer = column_tb_.find (name_citer);
diff --git a/include/DataFrame/Internals/DataFrame_functors.h b/include/DataFrame/Internals/DataFrame_functors.h
index 93403618..754c4f05 100644
--- a/include/DataFrame/Internals/DataFrame_functors.h
+++ b/include/DataFrame/Internals/DataFrame_functors.h
@@ -526,7 +526,9 @@ struct sel_remove_functor_ : DataVec::template visitor_base {
template
struct shuffle_functor_ : DataVec::template visitor_base {
- inline shuffle_functor_ () { }
+ inline shuffle_functor_ (std::mt19937 &g) : g_(g) { }
+
+ std::mt19937 &g_;
template
void operator() (T &vec) const;
diff --git a/include/DataFrame/Internals/DataFrame_get.tcc b/include/DataFrame/Internals/DataFrame_get.tcc
index 316752b7..60965636 100644
--- a/include/DataFrame/Internals/DataFrame_get.tcc
+++ b/include/DataFrame/Internals/DataFrame_get.tcc
@@ -2128,7 +2128,7 @@ get_view_by_sel(const char *name1,
template
template
DataFrame DataFrame::
-get_data_by_rand(random_policy spec, double n, size_type seed) const {
+get_data_by_rand(random_policy spec, double n, seed_t seed) const {
bool use_seed = false;
size_type n_rows = static_cast(n);
@@ -2146,13 +2146,10 @@ get_data_by_rand(random_policy spec, double n, size_type seed) const {
}
if (index_s > 0 && n_rows <= index_s) [[likely]] {
- std::random_device rd;
- std::mt19937 gen(rd());
-
- if (use_seed) gen.seed(static_cast(seed));
-
- std::uniform_int_distribution dis(0, index_s - 1);
- StlVecType rand_indices(n_rows);
+ std::random_device rd;
+ std::mt19937 gen(use_seed ? seed : rd());
+ std::uniform_int_distribution dis(0, index_s - 1);
+ StlVecType rand_indices(n_rows);
for (size_type i = 0; i < n_rows; ++i)
rand_indices[i] = dis(gen);
@@ -2204,7 +2201,7 @@ get_data_by_rand(random_policy spec, double n, size_type seed) const {
template
template
typename DataFrame::PtrView DataFrame::
-get_view_by_rand (random_policy spec, double n, size_type seed) {
+get_view_by_rand (random_policy spec, double n, seed_t seed) {
bool use_seed = false;
size_type n_rows = static_cast(n);
@@ -2222,13 +2219,10 @@ get_view_by_rand (random_policy spec, double n, size_type seed) {
}
if (index_s > 0 && n_rows <= index_s) [[likely]] {
- std::random_device rd;
- std::mt19937 gen(rd());
-
- if (use_seed) gen.seed(static_cast(seed));
-
- std::uniform_int_distribution dis(0, index_s - 1);
- StlVecType rand_indices(n_rows);
+ std::random_device rd;
+ std::mt19937 gen(use_seed ? seed : rd());
+ std::uniform_int_distribution dis(0, index_s - 1);
+ StlVecType rand_indices(n_rows);
for (size_type i = 0; i < n_rows; ++i) [[likely]]
rand_indices[i] = dis(gen);
@@ -2281,7 +2275,7 @@ template
template
typename DataFrame::ConstPtrView
DataFrame::
-get_view_by_rand (random_policy spec, double n, size_type seed) const {
+get_view_by_rand (random_policy spec, double n, seed_t seed) const {
bool use_seed = false;
size_type n_rows = static_cast(n);
@@ -2299,13 +2293,10 @@ get_view_by_rand (random_policy spec, double n, size_type seed) const {
}
if (index_s > 0 && n_rows <= index_s) [[likely]] {
- std::random_device rd;
- std::mt19937 gen(rd());
-
- if (use_seed) gen.seed(static_cast(seed));
-
- std::uniform_int_distribution dis(0, index_s - 1);
- StlVecType rand_indices(n_rows);
+ std::random_device rd;
+ std::mt19937 gen(use_seed ? seed : rd());
+ std::uniform_int_distribution dis(0, index_s - 1);
+ StlVecType rand_indices(n_rows);
for (size_type i = 0; i < n_rows; ++i) [[likely]]
rand_indices[i] = dis(gen);
diff --git a/include/DataFrame/Internals/DataFrame_misc.tcc b/include/DataFrame/Internals/DataFrame_misc.tcc
index 5527a64f..2c920315 100644
--- a/include/DataFrame/Internals/DataFrame_misc.tcc
+++ b/include/DataFrame/Internals/DataFrame_misc.tcc
@@ -684,10 +684,7 @@ DataFrame::
shuffle_functor_::
operator() (T &vec) const {
- std::random_device rd;
- std::mt19937 g(rd());
-
- std::shuffle(vec.begin(), vec.end(), g);
+ std::shuffle(vec.begin(), vec.end(), g_);
return;
}
diff --git a/test/dataframe_tester.cc b/test/dataframe_tester.cc
index 2511f94f..13741d72 100644
--- a/test/dataframe_tester.cc
+++ b/test/dataframe_tester.cc
@@ -3719,7 +3719,14 @@ static void test_k_means() {
std::make_pair("col1",
gen_lognormal_dist(item_cnt, p)));
- KMeansVisitor<5, double, unsigned long, 128> km_visitor(1000);
+ KMeansVisitor<5,
+ double,
+ unsigned long,
+ 128> km_visitor(1000, true,
+ [](const double &x, const double &y) {
+ return ((x - y) * (x - y));
+ },
+ 10);
df.single_act_visit("col1", km_visitor);
std::cout << "Means of clusters are: ";
@@ -3797,7 +3804,7 @@ static void test_k_means() {
KMeansVisitor<5,
Point,
unsigned long,
- 128> km_visitor2(1000, true, point_distance);
+ 128> km_visitor2(1000, true, point_distance, 10);
df.single_act_visit("point_col", km_visitor2);
@@ -3883,7 +3890,7 @@ static void test_affinity_propagation() {
StlVecType final_col;
StlVecType col_data;
- p.seed = 3575984165U;
+ p.seed = 10U;
p.min_value = 0;
p.max_value = 10;
@@ -3912,9 +3919,16 @@ static void test_affinity_propagation() {
df.load_data(MyDataFrame::gen_sequence_index(0, item_cnt * 5, 1),
std::make_pair("col1", final_col));
- df.shuffle({"col1"}, false);
+ df.shuffle({"col1"}, false, 10);
- KMeansVisitor<5, double, unsigned long, 128> km_visitor(1000);
+ KMeansVisitor<5,
+ double,
+ unsigned long,
+ 128> km_visitor(1000, true,
+ [](const double &x, const double &y) {
+ return ((x - y) * (x - y));
+ },
+ 10);
AffinityPropVisitor ap_visitor(50);
df.single_act_visit("col1", km_visitor);
diff --git a/test/dataframe_tester_output.txt b/test/dataframe_tester_output.txt
index 142f8f47..86a2ce6a 100644
--- a/test/dataframe_tester_output.txt
+++ b/test/dataframe_tester_output.txt
@@ -744,12 +744,12 @@ col_3:1::15,
col_str:1::11,
col_4:1::22,
-INDEX:1::123450,
-col_1:1::1,
-col_2:1::8,
-col_3:1::15,
-col_str:1::11,
-col_4:1::22,
+INDEX:1::123451,
+col_1:1::2,
+col_2:1::9,
+col_3:1::16,
+col_str:1::22,
+col_4:1::23,
INDEX:1::123452,
col_1:1::3,
@@ -785,17 +785,17 @@ Testing Thread safety ...
Testing View visitors ...
Testing k-means visitor ...
-Means of clusters are: 3.85685151881, 0.606906002983, 19.9820684374, 1.84000146826, 7.56548883355,
-0.94314420035 | 0.981895726286, 0.169963486799 | 0.296020947379, 0.271942650466 | 1.64739221905, 0.326711138057 | 0.863565309388, 1.66146343193 | 0.10325913533, 0.893056486982 | 0.808682871045, 0.282138178835 | 0.371623453277, 0.371895553115 | 0.434925299581, 0.376274486482 | 1.39877606315, 1.0446211384 | 0.571090947977, 0.466324696171 | 0.308323819636, 1.44638826983 | 1.56045441673, 0.320121767249 | 0.501052292835, 0.580846772301 | 1.30830960928, 0.521159294492 | 2.32581712491, 1.36234293815 | 0.289371891003, 0.963647635211 | 1.0261635, 0.637637095911 | 2.04389724471, 1.73748324726 | 0.860053341243, 0.613136565362 | 0.190705662033, 0.701077196961 | 0.193164499076, 0.107195404818 | 0.534806493228, 2.16015060747 | 0.42304329124, 0.922355654532 | 2.02841264143, 1.16959960242 | 2.31610780363, 1.02545023421 | 0.582097280023, 0.494289746101 | 1.28037865741, 0.040977350523 | 0.439638684942, 0.812148875111 | 1.47854501694, 0.569251356464 | 1.15224692006, 0.252754750614 | 0.89989902386, 2.06911336897 | 0.290356054276, 0.305260524931 | 0.990555427867, 0.874265659665 | 2.20241001084, 0.339578116871 | 0.320754547992, 1.93511267746 | 0.587733491711, 1.49362864494 | 1.93197678658, 0.412150967857 | 1.01319160519, 0.902855287476 | 1.07735390865, 1.0106755563 | 0.665572272889, 0.735666970712 | 0.302010636883, 2.07588001793 | 0.794593877907, 0.171995382581 | 1.49726413026, 1.78587441529 | 0.243153154046, 0.378541436116 | 1.31991388346, 0.918472780684 | 2.06758418387, 0.249403107207 | 0.454176981891, 0.583046618517 | 1.94950630499, 0.0628704638461 | 0.387939757822, 2.42609604173 | 1.29214747348, 1.43586493567 | 2.0391938725, 0.417967523855 | 2.22062836794, 1.098242156 | 2.59297376409, 0.480189177144 | 2.10100373728, 1.73565282659 | 1.62926884235, 0.760730091571 | 0.315153143947, 0.866559797058 | 1.76506993856, 0.984499639614 | 0.44249951319, 1.30348326941 | 1.44005990567, 0.13105037532 | 2.63608457803, 0.252311764312 | 1.41263674761, 0.228552899932 | 1.18621757992, 1.0290999953 | 0.390120245762, 0.882866151676 | 1.64057234467, 1.23446216599 | 2.00902296698, 1.71823504911 | 1.66540218242, 0.372278412557 | 1.07397574734, 1.63805067019 | 0.722246090948, 2.57845909391 | 0.640741806074, 1.49235149587 | 1.45715491558, 2.21722378657 | 1.27240410181, 0.326815409914 | 0.485455556687, 0.748585404096 | 0.78756538169, 0.21745467698 | 1.0401079674, 2.21873677412 | 1.02993567713, 1.03597832861 | 0.350879406929, 0.343317792485 | 1.41188975191, 2.51673013034 | 1.01486660235, 1.1147940572 | 1.19178509596, 0.849198242163 | 1.69635984254, 0.758328393524 | 1.4736114257, 0.327236321769 | 0.947598237393, 0.297280087477 | 1.1390147908, 2.18011601789 | 0.297370086524, 0.8865335918 | 1.69415944125, 0.462460808338 | 1.8253858595, 0.960770450807 | 0.280985644803, 0.323387454337 | 0.152741315791, 0.295106002294 | 0.683710648029, 1.56585975281 | 0.828774714947, 1.28799561213 | 0.795433234715, 0.254180636839 | 0.739464244104, 0.486943161095 | 1.03563187104, 0.660394558092 | 0.803358570279, 0.954368451522 | 0.276509523751, 0.406474300541 | 0.827818544391, 0.461464091627 | 0.599869936581, 0.199402475443 | 1.68554411367, 1.20127716327 | 0.216781902792, 0.110914892968 | 0.306856799157, 0.150685481569 | 1.19148169785, 1.83776276952 | 0.469180820593, 1.1195849682 | 2.44610092219, 0.737349207148 | 2.3976699186, 0.333876817449 | 1.37480679256, 1.30679336404 | 1.41567335553, 0.816394091152 | 1.10046765105, 2.29856419478 | 0.485561779197, 0.403110767438 | 1.6268797303, 0.299919940596 | 0.331093402906, 0.483404071006 | 0.290318020102, 0.744873723165 | 1.30272252912, 2.26695861761 | 1.08617294566, 1.50590267295 | 0.433719251144, 1.97273495528 | 2.02645491744, 0.412510467935 | 1.53621178736, 0.340579806797 | 0.953479355234, 0.477579225453 | 1.14369175893, 0.242254482121 | 1.14793354682, 0.812293430821 | 1.56412861853, 0.879667239287 | 0.866309360039, 0.634709228209 | 0.950164048793, 1.76004127096 | 0.755274750165, 1.05606204331 | 2.448576038, 1.76765732635 | 0.983803575974, 0.585782455829 | 0.432310082049, 0.348373922445 | 0.512929947691, 1.60506043565 | 1.07206564725, 0.507838904838 | 1.44461249943, 1.05914192838 | 0.380250398523, 0.896368847415 | 0.299026808915, 1.38264259557 | 0.328637413493, 0.197905188789 | 0.219648997204, 0.610847484075 | 1.378647892, 0.869589558242 | 0.440714327768, 0.783770455788 | 1.06490063493, 0.974629152681 | 2.20910632802, 2.33542719376 | 0.745929386166, 0.315350487304 | 0.354331468356, 0.492928346606 | 0.219002553457, 2.55146883283 | 0.274969047634, 1.40057031621 | 2.27381199684, 1.03864873742 | 0.979007797872, 0.962250524318 | 0.48152121663, 0.386891788541 | 0.483093997597, 1.29666721286 | 1.72165015887, 1.34710948498 | 0.507143795547, 2.18322390256 | 0.172569287983, 1.56301083559 | 0.458743636828, 0.751123742453 | 0.946768432963, 0.690327863639 | 0.992447329403, 0.671823935165 | 2.0978362702, 0.461285118688 | 2.42986142136, 0.250587958715 | 1.29453084627, 2.12937070061 | 1.6383108554, 2.14104233392 | 0.786737857766, 1.66896358889 | 0.377690250523, 2.64777900836 | 0.256778514556, 1.27016157677 | 0.957061170215, 1.48771892078 | 0.616825275551, 0.562017881387 | 1.74590123742, 1.56481864613 | 1.43614555065, 1.10247098536 | 0.377623446533, 0.602776058237 | 0.859106463858, 0.202677292862 | 0.855468120433, 1.82480920271 | 0.496915629909, 0.487247587407 | 0.801326132058, 0.515826294323 | 0.871320724668, 2.19109082809 | 0.644508364546, 0.884618026883 | 1.11758756205, 1.59484914041 | 0.57017322245, 0.498701179507 | 0.933886600228, 0.9693112402 | 1.31405068172, 1.66852970075 | 1.28329575891, 0.579302946369 | 0.151769005917, 0.263279735437 | 2.45623642144, 1.62841230125 | 1.1730311994, 0.307412429659 | 0.676678406507, 0.310302068518 | 0.565438184851, 1.37517051701 | 1.20057856368, 0.252429971783 | 1.69157736474, 0.351928716246 | 2.45380572359, 0.78207486059 | 0.303181203474, 0.818487565897 | 0.0922592753118, 1.30759253408 | 2.28938372681, 0.0365828847014 | 2.65549003031, 0.383402451249 | 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+Means of clusters are: 7.56548883355, 19.9820684374, 1.84000146826, 3.85685151881, 0.606906002983,
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+1.17763942501 | 4.38928183597, 1.28493013344 | 6.35939017311, 0.240869258025 | 7.03164244336, 1.06446976862 | 5.76755888038, 1.85091480661 | 3.56601755836, 0.560800191803 | 3.94172421199, 0.204326020026 | 5.38053457013, 1.06227749902 | 3.32887892782, 0.767676157037 | 8.04083157094, 0.287658429308 | 3.90035765824, 0.549078572277 | 3.46521175036, 1.28557432634 | 4.02652216308, 1.3717432107 | 4.15135683906, 1.1666765887 | 4.21940782793, 1.88126693343 | 3.24503920767, 2.33162638935 | 3.61621194349, 0.88572948374 | 5.62811295615, 1.7815347027 | 6.39802154383, 0.482693103106 | 3.87937362627, 1.02200676131 | 5.82491379951, 2.30702542995 | 3.87702901921, 1.03305362898 | 2.95951458779, 1.32783017054 | 3.73183746736, 2.29782959738 | 2.95475595328, 0.581358895235 | 3.14196042234, 0.289052140622 | 4.21385549394, 0.616046410687 | 2.99549423288, 1.71229618928 | 2.65349923804, 0.0886306586577 | 5.54720096044, 0.542110934517 | 5.27291489362, 0.752293305347 | 2.81746035796, 0.670098941575 | 2.96202919149, 2.47145131742 | 3.79801401632, 0.28133750636 | 4.60208804416, 1.35267797996 | 4.28292648693, 0.364540243969 | 6.3569781481, 2.20812323428 | 5.07805996318, 2.12137568015 | 6.6747098452, 0.166353203758 | 4.03550302661, 0.620417494294 | 4.95164596018, 0.26121846435 | 2.82614274714, 3.22665353121 | 7.54935839024, 3.92625567711 | 6.96830309826, 0.60837176819 | 3.31354097698, 1.64989967559 | 3.30217349962, 0.445023478607 | 3.72288458019, 2.0682043428 | 5.01261948869, 2.63356686713 | 4.72964211396, 0.819647716489 | 3.82130603233, 0.171170256461 | 3.2017898339, 0.524251231048 | 4.09041765945, 0.724765244843 | 3.1598983741, 0.4697885866 | 3.15802002661, 0.52324978008 | 5.96912943032, 0.893823084826 | 5.45683921096, 0.722953832927 | 3.62714869847, 0.704881878481 | 4.02419056834, 4.1602516774 | 6.33314126649, 0.500534521353 | 3.02627453718, 0.583126817415 | 5.96386076994, 0.466048124604 | 3.46294257099, 0.179031759309 | 5.26607584377, 0.45724210253 | 3.11801722202, 0.862164525581 | 2.92851391572, 0.173314113277 | 3.72735933464, 0.538569287115 | 4.92580493638, 0.390680263449 | 2.79462407244, 1.15433944412 | 3.57435442163, 0.0732141894855 | 2.93116741568, 0.263645372967 | 3.57756419709, 0.391982292778 | 4.18362781814, 0.594217196889 | 3.53179940652, 2.14109421305 | 6.55987520748, 3.83639899335 | 4.86797816393, 0.348746070584 | 6.3700253128, 2.12549239953 | 4.98282617037, 2.36752634548 | 3.22792442933, 1.22119375487 | 2.77697171188, 1.00979686636 | 5.29703984899, 0.833895274719 | 6.62909042771, 0.347141870601 | 4.40659287289, 5.08221553469 | 6.10303415513, 0.964895788694 | 5.44053902503, 1.08756545279 | 4.8789513143, 2.23999505903 | 4.3166879081, 0.473521329208 | 2.98710598996, 0.328338724717 | 3.63097391706, 1.23470920872 | 6.7973727924, 1.9035288097 | 3.24610611687, 1.1784536745 | 3.94056489811, 0.411994713837 | 2.91333974149, 0.489683388036 | 3.24695467582, 3.31228444368 | 5.16608807043, 2.15783928424 | 6.64157896073, 0.443948417653 | 3.90620905535, 0.857821288928 | 2.99368138591, 1.49978921496 | 4.82356273722, 0.872133033001 | 3.24174085834, 0.671829924207 | 7.0297101486, 1.05445859205 | 5.57595950789, 2.05900637254 | 6.26457094003, 1.57523003841 | 4.52409713881, 1.86723700435 | 3.43962071765, 1.02665748354 | 2.88684218402, 0.615864835786 | 2.79588577333, 3.19936855967 | 6.28220288061, 2.24047550738 | 5.53008426524, 2.67776787413 | 4.6979181139, 0.691843888188 | 3.21307396541, 0.571058813624 | 2.73530696092, 1.43753751622 | 5.45398911799, 1.24904449588 | 4.59098415211, 1.54683188803 | 6.95696824446, 2.47779777105 | 2.93320405304, 0.781522157919 | 2.91004051631, 0.301246739639 | 5.26895903219, 0.306685834123 | 4.60887518719, 0.265632061612 | 3.26882144595, 1.19966487878 | 3.6535443819, 0.188136710265 | 4.14584814624, 2.33527964251 | 5.80344863563, 1.13784018644 | 3.42992972216, 0.91284645791 | 2.98845131819, 1.0433126427 | 3.16354583136, 1.26296583529 | 3.19865984381, 0.505621332362 | 4.19219777501, 0.931137763214 | 5.40185803252, 2.04991447833 | 4.62938819872, 1.13690449509 | 4.79651539634, 0.351554660116 | 2.8958337638, 1.51567327906 | 4.55829133501, 0.649572699232 | 4.88949726483, 1.33977020783 | 4.82209780777, 2.23734997143 | 3.3258080338, 0.952472446905 | 5.97617136259, 2.57089117035 | 5.69604486922, 1.37084925259 | 5.01281981416, 0.271180017839 | 3.54518484945, 0.856736542786 | 3.53818746945, 1.37977612576 | 4.25200938943, 0.938933657517 | 6.26438414846, 1.17952710259 | 2.88790645751, 0.192888783336 | 5.09273429578, 1.49949839775 | 2.95199691542, 0.84897400017 | 2.91089046258, 1.22377461382 | 4.42589120179, 1.417365782 | 4.06896381627,
1.54081876229 | 12.3779975132, 0.766429423621 | 9.34776193739, 0.455413356751 | 13.183251825, 3.68161078878 | 20.3481237204, 2.35425744809 | 10.9139359587, 0.613231973752 | 12.364624746, 1.94693471203 | 12.8171405043, 0.840922034797 | 9.41115051023, 0.95734730579 | 9.49469931487, 1.97015551877 | 8.89570840834, 0.458238675858 | 19.101984561, 0.869923011972 | 11.646262575, 4.87816630053 | 8.33695792175, 0.921539044558 | 10.9787428056, 1.98427978492 | 10.4619593119, 1.37603007069 | 19.4718669618, 0.569969260551 | 9.76362147103, 1.60606491009 | 8.97144761846, 0.423403937902 | 11.2998140608, 0.472892304832 | 18.3879587203, 4.33486051914 | 10.3958590128, 0.61651596336 | 8.79223302653, 0.709333471028 | 10.1578879873, 4.43210849644 | 11.6260528893, 1.0927858146 | 9.53206042213, 0.188054928458 | 23.7488315602,
@@ -803,18 +803,18 @@ Means of clusters are: 3.85685151881, 0.606906002983, 19.9820684374, 1.840001468
Testing affinity propagation visitor ...
-24.8864674204, 64.8864674204, 84.8864674204, 44.8864674204, 4.88646742035,
+4.698691042, 64.698691042, 44.698691042, 84.698691042, 24.698691042,
-27.7393170613, 21.4526416181, 23.2290856718, 28.1606083177, 26.2709592201, 28.0351069192, 22.7096902721, 20.5427763693, 27.2309887772, 20.3368455564, 22.068028164, 23.6535581541, 21.4048670495, 24.0887403668, 28.7116662964, 25.3735876517, 28.6910053353, 27.4997084472, 21.5840275508, 25.1391063602, 20.792112092, 22.0294774779, 23.1747202459, 26.0443616908, 22.8700032045, 20.507737207, 20.6319322423, 29.0856121755, 29.384575523, 24.7879977908, 26.8811190655, 25.7834812385, 25.533402149, 28.8116955834, 24.7909670742, 28.4147351677, 22.8641158982, 22.6158667776, 21.0856084652, 28.7009633844, 21.831434088, 24.1464065746, 26.3554630222, 25.3504194723, 21.185637531, 22.8714543256, 29.8325726377, 24.9938335144, 29.4442819581, 29.5990682814,
+42.2520045807, 42.193377464, 47.24429173, 42.7757641221, 46.9186390843, 45.6928812986, 49.2432766496, 43.0766002353, 41.7126417006, 42.1982671268, 43.4788238344, 47.5318004395, 42.7901547432, 43.2682485746, 46.0378652093, 40.3607367105, 44.8974351335, 46.034928453, 46.0128310734, 46.0736577489, 42.3261150813, 43.4428973212, 45.1236798139, 44.1005086332, 44.5917265576, 47.0600229553, 40.6189653073, 49.1902139452, 48.9752505523, 49.0569935418, 49.1370436351, 43.6763809821, 41.7248938756, 48.8946164157, 41.1971951939, 41.387824026, 48.1376687354, 45.2267926641, 44.0322445851, 43.9863705151, 43.1343657534, 42.6673194908, 43.7555409884, 40.4663675459, 47.0088268057, 49.6187295396, 49.2006581232, 42.6635007764, 43.0265110822, 41.7111017446,
-1.58402755076, 8.0351069192, 0.631932242255, 5.13910636019, 6.35546302222, 4.99383351443, 9.38457552296, 2.87000320452, 6.88111906546, 1.45264161815, 4.79096707423, 0.542776369347, 5.53340214896, 2.86411589824, 5.37358765167, 0.336845556413, 7.49970844719, 5.78348123847, 1.18563753105, 3.65355815409, 8.69100533529, 8.8116955834, 3.17472024588, 2.0294774779, 7.73931706126, 8.70096338444, 9.83257263767, 9.44428195809, 6.04436169077, 2.87145432564, 1.08560846516, 0.50773720697, 4.08874036683, 4.78799779085, 7.23098877717, 8.41473516768, 1.40486704949, 9.08561217549, 2.70969027208, 2.06802816399, 8.71166629641, 5.35041947229, 6.2709592201, 0.79211209196, 4.14640657457, 8.16060831768, 2.6158667776, 9.59906828139, 1.83143408803, 3.22908567178,
+83.2682485746, 84.1005086332, 82.6635007764, 81.7111017446, 85.2267926641, 83.1343657534, 89.2432766496, 82.1982671268, 89.1370436351, 80.3607367105, 84.5917265576, 89.1902139452, 87.5318004395, 83.6763809821, 89.6187295396, 89.0569935418, 85.6928812986, 89.2006581232, 82.6673194908, 81.7126417006, 82.7757641221, 86.9186390843, 82.193377464, 86.034928453, 83.0265110822, 80.4663675459, 84.8974351335, 87.24429173, 84.0322445851, 82.7901547432, 83.4788238344, 83.0766002353, 87.0088268057, 81.387824026, 83.9863705151, 82.3261150813, 81.7248938756, 86.0378652093, 87.0600229553, 86.0128310734, 83.4428973212, 88.8946164157, 86.0736577489, 83.7555409884, 88.9752505523, 81.1971951939, 80.6189653073, 85.1236798139, 82.2520045807, 88.1376687354,
-48.8116955834, 41.831434088, 40.6319322423, 49.384575523, 43.6535581541, 42.7096902721, 41.5840275508, 44.1464065746, 46.0443616908, 48.1606083177, 45.7834812385, 41.4526416181, 41.4048670495, 43.2290856718, 46.3554630222, 47.2309887772, 48.0351069192, 47.7393170613, 42.6158667776, 48.7009633844, 47.4997084472, 40.3368455564, 42.0294774779, 48.7116662964, 42.8714543256, 42.8641158982, 40.507737207, 44.7879977908, 42.8700032045, 45.3504194723, 49.5990682814, 49.0856121755, 41.185637531, 45.3735876517, 49.4442819581, 40.792112092, 44.0887403668, 40.5427763693, 46.2709592201, 49.8325726377, 45.533402149, 44.9938335144, 42.068028164, 43.1747202459, 45.1391063602, 48.4147351677, 46.8811190655, 44.7909670742, 48.6910053353, 41.0856084652,
+3.75554098836, 2.19337746397, 2.66731949081, 5.69288129863, 9.19021394518, 6.0378652093, 4.59172655755, 0.618965307272, 2.66350077638, 6.07365774893, 0.466367545914, 5.12367981394, 8.13766873538, 4.89743513354, 4.1005086332, 7.24429172995, 1.71110174459, 6.91863908428, 5.22679266414, 2.32611508127, 1.19719519395, 1.71264170064, 1.72489387559, 3.47882383437, 3.07660023531, 2.77576412214, 8.97525055228, 6.03492845303, 0.36073671047, 3.67638098212, 3.13436575339, 1.38782402596, 3.9863705151, 2.25200458072, 9.24327664962, 7.00882680571, 7.5318004395, 4.03224458506, 6.01283107341, 2.19826712681, 2.79015474321, 3.44289732124, 7.0600229553, 9.13704363512, 3.26824857456, 8.89461641573, 9.05699354185, 3.02651108225, 9.20065812318, 9.61872953961,
-82.8700032045, 80.3368455564, 82.6158667776, 88.4147351677, 88.7116662964, 84.9938335144, 81.4526416181, 87.7393170613, 84.0887403668, 81.185637531, 81.831434088, 85.7834812385, 89.4442819581, 81.0856084652, 85.1391063602, 82.8641158982, 80.5427763693, 85.533402149, 87.4997084472, 88.8116955834, 80.507737207, 80.6319322423, 86.2709592201, 84.1464065746, 82.0294774779, 84.7879977908, 86.0443616908, 88.0351069192, 89.384575523, 86.8811190655, 89.0856121755, 82.8714543256, 89.5990682814, 81.5840275508, 88.1606083177, 87.2309887772, 82.7096902721, 82.068028164, 83.6535581541, 83.2290856718, 83.1747202459, 86.3554630222, 84.7909670742, 88.6910053353, 89.8325726377, 80.792112092, 88.7009633844, 85.3735876517, 85.3504194723, 81.4048670495,
+22.193377464, 25.2267926641, 24.5917265576, 22.2520045807, 29.2006581232, 21.387824026, 22.3261150813, 28.1376687354, 21.7111017446, 26.0378652093, 23.4788238344, 28.8946164157, 23.2682485746, 20.3607367105, 23.4428973212, 27.24429173, 25.6928812986, 20.4663675459, 21.7248938756, 26.034928453, 23.7555409884, 27.0088268057, 24.1005086332, 23.0265110822, 26.0128310734, 29.0569935418, 21.7126417006, 23.9863705151, 23.6763809821, 21.1971951939, 26.9186390843, 22.6673194908, 22.6635007764, 29.2432766496, 26.0736577489, 22.7757641221, 22.1982671268, 29.6187295396, 24.0322445851, 27.0600229553, 23.0766002353, 28.9752505523, 25.1236798139, 22.7901547432, 29.1902139452, 29.1370436351, 23.1343657534, 20.6189653073, 24.8974351335, 27.5318004395,
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Testing multi-column sort ...