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This commit is contained in:
Ricardo Montañana Gómez
2024-01-09 17:45:06 +01:00
parent 73cf64d8c2
commit 455d9f3330
87 changed files with 41694 additions and 1 deletions
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15
tests/CMakeLists.txt Normal file
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if(ENABLE_TESTING)
set(TEST_PLATFORM "unit_tests_platform")
include_directories(
${BayesNet_SOURCE_DIR}/src/BayesNet
${BayesNet_SOURCE_DIR}/src/Platform
${BayesNet_SOURCE_DIR}/lib/Files
${BayesNet_SOURCE_DIR}/lib/mdlp
${BayesNet_SOURCE_DIR}/lib/json/include
${BayesNet_SOURCE_DIR}/lib/argparse/include
)
set(TEST_SOURCES_PLATFORM TestUtils.cc)
add_executable(${TEST_PLATFORM} ${TEST_SOURCES_PLATFORM})
target_link_libraries(${TEST_PLATFORM} PUBLIC "${TORCH_LIBRARIES}" ArffFiles mdlp Catch2::Catch2WithMain)
add_test(NAME ${TEST_PLATFORM} COMMAND ${TEST_PLATFORM})
endif(ENABLE_TESTING)

104
tests/TestUtils.cc Normal file
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#include "TestUtils.h"
class Paths {
public:
static std::string datasets()
{
return "../../data/";
}
};
pair<std::vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<std::string> features)
{
std::vector<mdlp::labels_t> Xd;
map<std::string, int> maxes;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
mdlp::labels_t& xd = fimdlp.transform(X[i]);
maxes[features[i]] = *max_element(xd.begin(), xd.end()) + 1;
Xd.push_back(xd);
}
return { Xd, maxes };
}
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y)
{
std::vector<mdlp::labels_t> Xd;
auto fimdlp = mdlp::CPPFImdlp();
for (int i = 0; i < X.size(); i++) {
fimdlp.fit(X[i], y);
mdlp::labels_t& xd = fimdlp.transform(X[i]);
Xd.push_back(xd);
}
return Xd;
}
bool file_exists(const std::std::string& name)
{
if (FILE* file = fopen(name.c_str(), "r")) {
fclose(file);
return true;
} else {
return false;
}
}
tuple<torch::Tensor, torch::Tensor, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::std::string& name, bool class_last, bool discretize_dataset)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff", class_last);
// Get Dataset X, y
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<std::string, std::vector<int>>();
if (discretize_dataset) {
auto Xr = discretizeDataset(X, y);
Xd = torch::zeros({ static_cast<int>(Xr.size()), static_cast<int>(Xr[0].size()) }, torch::kInt32);
for (int i = 0; i < features.size(); ++i) {
states[features[i]] = std::vector<int>(*max_element(Xr[i].begin(), Xr[i].end()) + 1);
auto item = states.at(features[i]);
iota(begin(item), end(item), 0);
Xd.index_put_({ i, "..." }, torch::tensor(Xr[i], torch::kInt32));
}
states[className] = std::vector<int>(*max_element(y.begin(), y.end()) + 1);
iota(begin(states.at(className)), end(states.at(className)), 0);
} else {
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i]));
}
}
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
}
tuple<std::vector<std::vector<int>>, std::vector<int>, std::vector<std::string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::std::string& name)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<std::string>(name) + ".arff");
// Get Dataset X, y
std::vector<mdlp::samples_t>& X = handler.getX();
mdlp::labels_t& y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
std::vector<std::string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
// Discretize Dataset
std::vector<mdlp::labels_t> Xd;
map<std::string, int> maxes;
tie(Xd, maxes) = discretize(X, y, features);
maxes[className] = *max_element(y.begin(), y.end()) + 1;
map<std::string, std::vector<int>> states;
for (auto feature : features) {
states[feature] = std::vector<int>(maxes[feature]);
}
states[className] = std::vector<int>(maxes[className]);
return { Xd, y, features, className, states };
}

43
tests/TestUtils.h Normal file
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#ifndef TEST_UTILS_H
#define TEST_UTILS_H
#include <torch/torch.h>
#include <string>
#include <vector>
#include <map>
#include <std::tuple>
#include "ArffFiles.h"
#include "CPPFImdlp.h"
bool file_exists(const std::std::string& name);
std::pair<vector<mdlp::labels_t>, map<std::string, int>> discretize(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y, std::vector<string> features);
std::vector<mdlp::labels_t> discretizeDataset(std::vector<mdlp::samples_t>& X, mdlp::labels_t& y);
std::tuple<vector<vector<int>>, std::vector<int>, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadFile(const std::string& name);
std::tuple<torch::Tensor, torch::Tensor, std::vector<string>, std::string, map<std::string, std::vector<int>>> loadDataset(const std::string& name, bool class_last, bool discretize_dataset);
class RawDatasets {
public:
RawDatasets(const std::string& file_name, bool discretize)
{
// Xt can be either discretized or not
tie(Xt, yt, featurest, classNamet, statest) = loadDataset(file_name, true, discretize);
// Xv is always discretized
tie(Xv, yv, featuresv, classNamev, statesv) = loadFile(file_name);
auto yresized = torch::transpose(yt.view({ yt.size(0), 1 }), 0, 1);
dataset = torch::cat({ Xt, yresized }, 0);
nSamples = dataset.size(1);
weights = torch::full({ nSamples }, 1.0 / nSamples, torch::kDouble);
weightsv = std::vector<double>(nSamples, 1.0 / nSamples);
classNumStates = discretize ? statest.at(classNamet).size() : 0;
}
torch::Tensor Xt, yt, dataset, weights;
std::vector<vector<int>> Xv;
std::vector<double> weightsv;
std::vector<int> yv;
std::vector<string> featurest, featuresv;
map<std::string, std::vector<int>> statest, statesv;
std::string classNamet, classNamev;
int nSamples, classNumStates;
double epsilon = 1e-5;
};
#endif //TEST_UTILS_H

863
tests/data/diabetes.arff Executable file
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% 1. Title: Pima Indians Diabetes Database
%
% 2. Sources:
% (a) Original owners: National Institute of Diabetes and Digestive and
% Kidney Diseases
% (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu)
% Research Center, RMI Group Leader
% Applied Physics Laboratory
% The Johns Hopkins University
% Johns Hopkins Road
% Laurel, MD 20707
% (301) 953-6231
% (c) Date received: 9 May 1990
%
% 3. Past Usage:
% 1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \&
% Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast
% the onset of diabetes mellitus. In {\it Proceedings of the Symposium
% on Computer Applications and Medical Care} (pp. 261--265). IEEE
% Computer Society Press.
%
% The diagnostic, binary-valued variable investigated is whether the
% patient shows signs of diabetes according to World Health Organization
% criteria (i.e., if the 2 hour post-load plasma glucose was at least
% 200 mg/dl at any survey examination or if found during routine medical
% care). The population lives near Phoenix, Arizona, USA.
%
% Results: Their ADAP algorithm makes a real-valued prediction between
% 0 and 1. This was transformed into a binary decision using a cutoff of
% 0.448. Using 576 training instances, the sensitivity and specificity
% of their algorithm was 76% on the remaining 192 instances.
%
% 4. Relevant Information:
% Several constraints were placed on the selection of these instances from
% a larger database. In particular, all patients here are females at
% least 21 years old of Pima Indian heritage. ADAP is an adaptive learning
% routine that generates and executes digital analogs of perceptron-like
% devices. It is a unique algorithm; see the paper for details.
%
% 5. Number of Instances: 768
%
% 6. Number of Attributes: 8 plus class
%
% 7. For Each Attribute: (all numeric-valued)
% 1. Number of times pregnant
% 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
% 3. Diastolic blood pressure (mm Hg)
% 4. Triceps skin fold thickness (mm)
% 5. 2-Hour serum insulin (mu U/ml)
% 6. Body mass index (weight in kg/(height in m)^2)
% 7. Diabetes pedigree function
% 8. Age (years)
% 9. Class variable (0 or 1)
%
% 8. Missing Attribute Values: None
%
% 9. Class Distribution: (class value 1 is interpreted as "tested positive for
% diabetes")
%
% Class Value Number of instances
% 0 500
% 1 268
%
% 10. Brief statistical analysis:
%
% Attribute number: Mean: Standard Deviation:
% 1. 3.8 3.4
% 2. 120.9 32.0
% 3. 69.1 19.4
% 4. 20.5 16.0
% 5. 79.8 115.2
% 6. 32.0 7.9
% 7. 0.5 0.3
% 8. 33.2 11.8
%
%
%
%
%
%
% Relabeled values in attribute 'class'
% From: 0 To: tested_negative
% From: 1 To: tested_positive
%
@relation pima_diabetes
@attribute 'preg' real
@attribute 'plas' real
@attribute 'pres' real
@attribute 'skin' real
@attribute 'insu' real
@attribute 'mass' real
@attribute 'pedi' real
@attribute 'age' real
@attribute 'class' { tested_negative, tested_positive}
@data
6,148,72,35,0,33.6,0.627,50,tested_positive
1,85,66,29,0,26.6,0.351,31,tested_negative
8,183,64,0,0,23.3,0.672,32,tested_positive
1,89,66,23,94,28.1,0.167,21,tested_negative
0,137,40,35,168,43.1,2.288,33,tested_positive
5,116,74,0,0,25.6,0.201,30,tested_negative
3,78,50,32,88,31,0.248,26,tested_positive
10,115,0,0,0,35.3,0.134,29,tested_negative
2,197,70,45,543,30.5,0.158,53,tested_positive
8,125,96,0,0,0,0.232,54,tested_positive
4,110,92,0,0,37.6,0.191,30,tested_negative
10,168,74,0,0,38,0.537,34,tested_positive
10,139,80,0,0,27.1,1.441,57,tested_negative
1,189,60,23,846,30.1,0.398,59,tested_positive
5,166,72,19,175,25.8,0.587,51,tested_positive
7,100,0,0,0,30,0.484,32,tested_positive
0,118,84,47,230,45.8,0.551,31,tested_positive
7,107,74,0,0,29.6,0.254,31,tested_positive
1,103,30,38,83,43.3,0.183,33,tested_negative
1,115,70,30,96,34.6,0.529,32,tested_positive
3,126,88,41,235,39.3,0.704,27,tested_negative
8,99,84,0,0,35.4,0.388,50,tested_negative
7,196,90,0,0,39.8,0.451,41,tested_positive
9,119,80,35,0,29,0.263,29,tested_positive
11,143,94,33,146,36.6,0.254,51,tested_positive
10,125,70,26,115,31.1,0.205,41,tested_positive
7,147,76,0,0,39.4,0.257,43,tested_positive
1,97,66,15,140,23.2,0.487,22,tested_negative
13,145,82,19,110,22.2,0.245,57,tested_negative
5,117,92,0,0,34.1,0.337,38,tested_negative
5,109,75,26,0,36,0.546,60,tested_negative
3,158,76,36,245,31.6,0.851,28,tested_positive
3,88,58,11,54,24.8,0.267,22,tested_negative
6,92,92,0,0,19.9,0.188,28,tested_negative
10,122,78,31,0,27.6,0.512,45,tested_negative
4,103,60,33,192,24,0.966,33,tested_negative
11,138,76,0,0,33.2,0.42,35,tested_negative
9,102,76,37,0,32.9,0.665,46,tested_positive
2,90,68,42,0,38.2,0.503,27,tested_positive
4,111,72,47,207,37.1,1.39,56,tested_positive
3,180,64,25,70,34,0.271,26,tested_negative
7,133,84,0,0,40.2,0.696,37,tested_negative
7,106,92,18,0,22.7,0.235,48,tested_negative
9,171,110,24,240,45.4,0.721,54,tested_positive
7,159,64,0,0,27.4,0.294,40,tested_negative
0,180,66,39,0,42,1.893,25,tested_positive
1,146,56,0,0,29.7,0.564,29,tested_negative
2,71,70,27,0,28,0.586,22,tested_negative
7,103,66,32,0,39.1,0.344,31,tested_positive
7,105,0,0,0,0,0.305,24,tested_negative
1,103,80,11,82,19.4,0.491,22,tested_negative
1,101,50,15,36,24.2,0.526,26,tested_negative
5,88,66,21,23,24.4,0.342,30,tested_negative
8,176,90,34,300,33.7,0.467,58,tested_positive
7,150,66,42,342,34.7,0.718,42,tested_negative
1,73,50,10,0,23,0.248,21,tested_negative
7,187,68,39,304,37.7,0.254,41,tested_positive
0,100,88,60,110,46.8,0.962,31,tested_negative
0,146,82,0,0,40.5,1.781,44,tested_negative
0,105,64,41,142,41.5,0.173,22,tested_negative
2,84,0,0,0,0,0.304,21,tested_negative
8,133,72,0,0,32.9,0.27,39,tested_positive
5,44,62,0,0,25,0.587,36,tested_negative
2,141,58,34,128,25.4,0.699,24,tested_negative
7,114,66,0,0,32.8,0.258,42,tested_positive
5,99,74,27,0,29,0.203,32,tested_negative
0,109,88,30,0,32.5,0.855,38,tested_positive
2,109,92,0,0,42.7,0.845,54,tested_negative
1,95,66,13,38,19.6,0.334,25,tested_negative
4,146,85,27,100,28.9,0.189,27,tested_negative
2,100,66,20,90,32.9,0.867,28,tested_positive
5,139,64,35,140,28.6,0.411,26,tested_negative
13,126,90,0,0,43.4,0.583,42,tested_positive
4,129,86,20,270,35.1,0.231,23,tested_negative
1,79,75,30,0,32,0.396,22,tested_negative
1,0,48,20,0,24.7,0.14,22,tested_negative
7,62,78,0,0,32.6,0.391,41,tested_negative
5,95,72,33,0,37.7,0.37,27,tested_negative
0,131,0,0,0,43.2,0.27,26,tested_positive
2,112,66,22,0,25,0.307,24,tested_negative
3,113,44,13,0,22.4,0.14,22,tested_negative
2,74,0,0,0,0,0.102,22,tested_negative
7,83,78,26,71,29.3,0.767,36,tested_negative
0,101,65,28,0,24.6,0.237,22,tested_negative
5,137,108,0,0,48.8,0.227,37,tested_positive
2,110,74,29,125,32.4,0.698,27,tested_negative
13,106,72,54,0,36.6,0.178,45,tested_negative
2,100,68,25,71,38.5,0.324,26,tested_negative
15,136,70,32,110,37.1,0.153,43,tested_positive
1,107,68,19,0,26.5,0.165,24,tested_negative
1,80,55,0,0,19.1,0.258,21,tested_negative
4,123,80,15,176,32,0.443,34,tested_negative
7,81,78,40,48,46.7,0.261,42,tested_negative
4,134,72,0,0,23.8,0.277,60,tested_positive
2,142,82,18,64,24.7,0.761,21,tested_negative
6,144,72,27,228,33.9,0.255,40,tested_negative
2,92,62,28,0,31.6,0.13,24,tested_negative
1,71,48,18,76,20.4,0.323,22,tested_negative
6,93,50,30,64,28.7,0.356,23,tested_negative
1,122,90,51,220,49.7,0.325,31,tested_positive
1,163,72,0,0,39,1.222,33,tested_positive
1,151,60,0,0,26.1,0.179,22,tested_negative
0,125,96,0,0,22.5,0.262,21,tested_negative
1,81,72,18,40,26.6,0.283,24,tested_negative
2,85,65,0,0,39.6,0.93,27,tested_negative
1,126,56,29,152,28.7,0.801,21,tested_negative
1,96,122,0,0,22.4,0.207,27,tested_negative
4,144,58,28,140,29.5,0.287,37,tested_negative
3,83,58,31,18,34.3,0.336,25,tested_negative
0,95,85,25,36,37.4,0.247,24,tested_positive
3,171,72,33,135,33.3,0.199,24,tested_positive
8,155,62,26,495,34,0.543,46,tested_positive
1,89,76,34,37,31.2,0.192,23,tested_negative
4,76,62,0,0,34,0.391,25,tested_negative
7,160,54,32,175,30.5,0.588,39,tested_positive
4,146,92,0,0,31.2,0.539,61,tested_positive
5,124,74,0,0,34,0.22,38,tested_positive
5,78,48,0,0,33.7,0.654,25,tested_negative
4,97,60,23,0,28.2,0.443,22,tested_negative
4,99,76,15,51,23.2,0.223,21,tested_negative
0,162,76,56,100,53.2,0.759,25,tested_positive
6,111,64,39,0,34.2,0.26,24,tested_negative
2,107,74,30,100,33.6,0.404,23,tested_negative
5,132,80,0,0,26.8,0.186,69,tested_negative
0,113,76,0,0,33.3,0.278,23,tested_positive
1,88,30,42,99,55,0.496,26,tested_positive
3,120,70,30,135,42.9,0.452,30,tested_negative
1,118,58,36,94,33.3,0.261,23,tested_negative
1,117,88,24,145,34.5,0.403,40,tested_positive
0,105,84,0,0,27.9,0.741,62,tested_positive
4,173,70,14,168,29.7,0.361,33,tested_positive
9,122,56,0,0,33.3,1.114,33,tested_positive
3,170,64,37,225,34.5,0.356,30,tested_positive
8,84,74,31,0,38.3,0.457,39,tested_negative
2,96,68,13,49,21.1,0.647,26,tested_negative
2,125,60,20,140,33.8,0.088,31,tested_negative
0,100,70,26,50,30.8,0.597,21,tested_negative
0,93,60,25,92,28.7,0.532,22,tested_negative
0,129,80,0,0,31.2,0.703,29,tested_negative
5,105,72,29,325,36.9,0.159,28,tested_negative
3,128,78,0,0,21.1,0.268,55,tested_negative
5,106,82,30,0,39.5,0.286,38,tested_negative
2,108,52,26,63,32.5,0.318,22,tested_negative
10,108,66,0,0,32.4,0.272,42,tested_positive
4,154,62,31,284,32.8,0.237,23,tested_negative
0,102,75,23,0,0,0.572,21,tested_negative
9,57,80,37,0,32.8,0.096,41,tested_negative
2,106,64,35,119,30.5,1.4,34,tested_negative
5,147,78,0,0,33.7,0.218,65,tested_negative
2,90,70,17,0,27.3,0.085,22,tested_negative
1,136,74,50,204,37.4,0.399,24,tested_negative
4,114,65,0,0,21.9,0.432,37,tested_negative
9,156,86,28,155,34.3,1.189,42,tested_positive
1,153,82,42,485,40.6,0.687,23,tested_negative
8,188,78,0,0,47.9,0.137,43,tested_positive
7,152,88,44,0,50,0.337,36,tested_positive
2,99,52,15,94,24.6,0.637,21,tested_negative
1,109,56,21,135,25.2,0.833,23,tested_negative
2,88,74,19,53,29,0.229,22,tested_negative
17,163,72,41,114,40.9,0.817,47,tested_positive
4,151,90,38,0,29.7,0.294,36,tested_negative
7,102,74,40,105,37.2,0.204,45,tested_negative
0,114,80,34,285,44.2,0.167,27,tested_negative
2,100,64,23,0,29.7,0.368,21,tested_negative
0,131,88,0,0,31.6,0.743,32,tested_positive
6,104,74,18,156,29.9,0.722,41,tested_positive
3,148,66,25,0,32.5,0.256,22,tested_negative
4,120,68,0,0,29.6,0.709,34,tested_negative
4,110,66,0,0,31.9,0.471,29,tested_negative
3,111,90,12,78,28.4,0.495,29,tested_negative
6,102,82,0,0,30.8,0.18,36,tested_positive
6,134,70,23,130,35.4,0.542,29,tested_positive
2,87,0,23,0,28.9,0.773,25,tested_negative
1,79,60,42,48,43.5,0.678,23,tested_negative
2,75,64,24,55,29.7,0.37,33,tested_negative
8,179,72,42,130,32.7,0.719,36,tested_positive
6,85,78,0,0,31.2,0.382,42,tested_negative
0,129,110,46,130,67.1,0.319,26,tested_positive
5,143,78,0,0,45,0.19,47,tested_negative
5,130,82,0,0,39.1,0.956,37,tested_positive
6,87,80,0,0,23.2,0.084,32,tested_negative
0,119,64,18,92,34.9,0.725,23,tested_negative
1,0,74,20,23,27.7,0.299,21,tested_negative
5,73,60,0,0,26.8,0.268,27,tested_negative
4,141,74,0,0,27.6,0.244,40,tested_negative
7,194,68,28,0,35.9,0.745,41,tested_positive
8,181,68,36,495,30.1,0.615,60,tested_positive
1,128,98,41,58,32,1.321,33,tested_positive
8,109,76,39,114,27.9,0.64,31,tested_positive
5,139,80,35,160,31.6,0.361,25,tested_positive
3,111,62,0,0,22.6,0.142,21,tested_negative
9,123,70,44,94,33.1,0.374,40,tested_negative
7,159,66,0,0,30.4,0.383,36,tested_positive
11,135,0,0,0,52.3,0.578,40,tested_positive
8,85,55,20,0,24.4,0.136,42,tested_negative
5,158,84,41,210,39.4,0.395,29,tested_positive
1,105,58,0,0,24.3,0.187,21,tested_negative
3,107,62,13,48,22.9,0.678,23,tested_positive
4,109,64,44,99,34.8,0.905,26,tested_positive
4,148,60,27,318,30.9,0.15,29,tested_positive
0,113,80,16,0,31,0.874,21,tested_negative
1,138,82,0,0,40.1,0.236,28,tested_negative
0,108,68,20,0,27.3,0.787,32,tested_negative
2,99,70,16,44,20.4,0.235,27,tested_negative
6,103,72,32,190,37.7,0.324,55,tested_negative
5,111,72,28,0,23.9,0.407,27,tested_negative
8,196,76,29,280,37.5,0.605,57,tested_positive
5,162,104,0,0,37.7,0.151,52,tested_positive
1,96,64,27,87,33.2,0.289,21,tested_negative
7,184,84,33,0,35.5,0.355,41,tested_positive
2,81,60,22,0,27.7,0.29,25,tested_negative
0,147,85,54,0,42.8,0.375,24,tested_negative
7,179,95,31,0,34.2,0.164,60,tested_negative
0,140,65,26,130,42.6,0.431,24,tested_positive
9,112,82,32,175,34.2,0.26,36,tested_positive
12,151,70,40,271,41.8,0.742,38,tested_positive
5,109,62,41,129,35.8,0.514,25,tested_positive
6,125,68,30,120,30,0.464,32,tested_negative
5,85,74,22,0,29,1.224,32,tested_positive
5,112,66,0,0,37.8,0.261,41,tested_positive
0,177,60,29,478,34.6,1.072,21,tested_positive
2,158,90,0,0,31.6,0.805,66,tested_positive
7,119,0,0,0,25.2,0.209,37,tested_negative
7,142,60,33,190,28.8,0.687,61,tested_negative
1,100,66,15,56,23.6,0.666,26,tested_negative
1,87,78,27,32,34.6,0.101,22,tested_negative
0,101,76,0,0,35.7,0.198,26,tested_negative
3,162,52,38,0,37.2,0.652,24,tested_positive
4,197,70,39,744,36.7,2.329,31,tested_negative
0,117,80,31,53,45.2,0.089,24,tested_negative
4,142,86,0,0,44,0.645,22,tested_positive
6,134,80,37,370,46.2,0.238,46,tested_positive
1,79,80,25,37,25.4,0.583,22,tested_negative
4,122,68,0,0,35,0.394,29,tested_negative
3,74,68,28,45,29.7,0.293,23,tested_negative
4,171,72,0,0,43.6,0.479,26,tested_positive
7,181,84,21,192,35.9,0.586,51,tested_positive
0,179,90,27,0,44.1,0.686,23,tested_positive
9,164,84,21,0,30.8,0.831,32,tested_positive
0,104,76,0,0,18.4,0.582,27,tested_negative
1,91,64,24,0,29.2,0.192,21,tested_negative
4,91,70,32,88,33.1,0.446,22,tested_negative
3,139,54,0,0,25.6,0.402,22,tested_positive
6,119,50,22,176,27.1,1.318,33,tested_positive
2,146,76,35,194,38.2,0.329,29,tested_negative
9,184,85,15,0,30,1.213,49,tested_positive
10,122,68,0,0,31.2,0.258,41,tested_negative
0,165,90,33,680,52.3,0.427,23,tested_negative
9,124,70,33,402,35.4,0.282,34,tested_negative
1,111,86,19,0,30.1,0.143,23,tested_negative
9,106,52,0,0,31.2,0.38,42,tested_negative
2,129,84,0,0,28,0.284,27,tested_negative
2,90,80,14,55,24.4,0.249,24,tested_negative
0,86,68,32,0,35.8,0.238,25,tested_negative
12,92,62,7,258,27.6,0.926,44,tested_positive
1,113,64,35,0,33.6,0.543,21,tested_positive
3,111,56,39,0,30.1,0.557,30,tested_negative
2,114,68,22,0,28.7,0.092,25,tested_negative
1,193,50,16,375,25.9,0.655,24,tested_negative
11,155,76,28,150,33.3,1.353,51,tested_positive
3,191,68,15,130,30.9,0.299,34,tested_negative
3,141,0,0,0,30,0.761,27,tested_positive
4,95,70,32,0,32.1,0.612,24,tested_negative
3,142,80,15,0,32.4,0.2,63,tested_negative
4,123,62,0,0,32,0.226,35,tested_positive
5,96,74,18,67,33.6,0.997,43,tested_negative
0,138,0,0,0,36.3,0.933,25,tested_positive
2,128,64,42,0,40,1.101,24,tested_negative
0,102,52,0,0,25.1,0.078,21,tested_negative
2,146,0,0,0,27.5,0.24,28,tested_positive
10,101,86,37,0,45.6,1.136,38,tested_positive
2,108,62,32,56,25.2,0.128,21,tested_negative
3,122,78,0,0,23,0.254,40,tested_negative
1,71,78,50,45,33.2,0.422,21,tested_negative
13,106,70,0,0,34.2,0.251,52,tested_negative
2,100,70,52,57,40.5,0.677,25,tested_negative
7,106,60,24,0,26.5,0.296,29,tested_positive
0,104,64,23,116,27.8,0.454,23,tested_negative
5,114,74,0,0,24.9,0.744,57,tested_negative
2,108,62,10,278,25.3,0.881,22,tested_negative
0,146,70,0,0,37.9,0.334,28,tested_positive
10,129,76,28,122,35.9,0.28,39,tested_negative
7,133,88,15,155,32.4,0.262,37,tested_negative
7,161,86,0,0,30.4,0.165,47,tested_positive
2,108,80,0,0,27,0.259,52,tested_positive
7,136,74,26,135,26,0.647,51,tested_negative
5,155,84,44,545,38.7,0.619,34,tested_negative
1,119,86,39,220,45.6,0.808,29,tested_positive
4,96,56,17,49,20.8,0.34,26,tested_negative
5,108,72,43,75,36.1,0.263,33,tested_negative
0,78,88,29,40,36.9,0.434,21,tested_negative
0,107,62,30,74,36.6,0.757,25,tested_positive
2,128,78,37,182,43.3,1.224,31,tested_positive
1,128,48,45,194,40.5,0.613,24,tested_positive
0,161,50,0,0,21.9,0.254,65,tested_negative
6,151,62,31,120,35.5,0.692,28,tested_negative
2,146,70,38,360,28,0.337,29,tested_positive
0,126,84,29,215,30.7,0.52,24,tested_negative
14,100,78,25,184,36.6,0.412,46,tested_positive
8,112,72,0,0,23.6,0.84,58,tested_negative
0,167,0,0,0,32.3,0.839,30,tested_positive
2,144,58,33,135,31.6,0.422,25,tested_positive
5,77,82,41,42,35.8,0.156,35,tested_negative
5,115,98,0,0,52.9,0.209,28,tested_positive
3,150,76,0,0,21,0.207,37,tested_negative
2,120,76,37,105,39.7,0.215,29,tested_negative
10,161,68,23,132,25.5,0.326,47,tested_positive
0,137,68,14,148,24.8,0.143,21,tested_negative
0,128,68,19,180,30.5,1.391,25,tested_positive
2,124,68,28,205,32.9,0.875,30,tested_positive
6,80,66,30,0,26.2,0.313,41,tested_negative
0,106,70,37,148,39.4,0.605,22,tested_negative
2,155,74,17,96,26.6,0.433,27,tested_positive
3,113,50,10,85,29.5,0.626,25,tested_negative
7,109,80,31,0,35.9,1.127,43,tested_positive
2,112,68,22,94,34.1,0.315,26,tested_negative
3,99,80,11,64,19.3,0.284,30,tested_negative
3,182,74,0,0,30.5,0.345,29,tested_positive
3,115,66,39,140,38.1,0.15,28,tested_negative
6,194,78,0,0,23.5,0.129,59,tested_positive
4,129,60,12,231,27.5,0.527,31,tested_negative
3,112,74,30,0,31.6,0.197,25,tested_positive
0,124,70,20,0,27.4,0.254,36,tested_positive
13,152,90,33,29,26.8,0.731,43,tested_positive
2,112,75,32,0,35.7,0.148,21,tested_negative
1,157,72,21,168,25.6,0.123,24,tested_negative
1,122,64,32,156,35.1,0.692,30,tested_positive
10,179,70,0,0,35.1,0.2,37,tested_negative
2,102,86,36,120,45.5,0.127,23,tested_positive
6,105,70,32,68,30.8,0.122,37,tested_negative
8,118,72,19,0,23.1,1.476,46,tested_negative
2,87,58,16,52,32.7,0.166,25,tested_negative
1,180,0,0,0,43.3,0.282,41,tested_positive
12,106,80,0,0,23.6,0.137,44,tested_negative
1,95,60,18,58,23.9,0.26,22,tested_negative
0,165,76,43,255,47.9,0.259,26,tested_negative
0,117,0,0,0,33.8,0.932,44,tested_negative
5,115,76,0,0,31.2,0.343,44,tested_positive
9,152,78,34,171,34.2,0.893,33,tested_positive
7,178,84,0,0,39.9,0.331,41,tested_positive
1,130,70,13,105,25.9,0.472,22,tested_negative
1,95,74,21,73,25.9,0.673,36,tested_negative
1,0,68,35,0,32,0.389,22,tested_negative
5,122,86,0,0,34.7,0.29,33,tested_negative
8,95,72,0,0,36.8,0.485,57,tested_negative
8,126,88,36,108,38.5,0.349,49,tested_negative
1,139,46,19,83,28.7,0.654,22,tested_negative
3,116,0,0,0,23.5,0.187,23,tested_negative
3,99,62,19,74,21.8,0.279,26,tested_negative
5,0,80,32,0,41,0.346,37,tested_positive
4,92,80,0,0,42.2,0.237,29,tested_negative
4,137,84,0,0,31.2,0.252,30,tested_negative
3,61,82,28,0,34.4,0.243,46,tested_negative
1,90,62,12,43,27.2,0.58,24,tested_negative
3,90,78,0,0,42.7,0.559,21,tested_negative
9,165,88,0,0,30.4,0.302,49,tested_positive
1,125,50,40,167,33.3,0.962,28,tested_positive
13,129,0,30,0,39.9,0.569,44,tested_positive
12,88,74,40,54,35.3,0.378,48,tested_negative
1,196,76,36,249,36.5,0.875,29,tested_positive
5,189,64,33,325,31.2,0.583,29,tested_positive
5,158,70,0,0,29.8,0.207,63,tested_negative
5,103,108,37,0,39.2,0.305,65,tested_negative
4,146,78,0,0,38.5,0.52,67,tested_positive
4,147,74,25,293,34.9,0.385,30,tested_negative
5,99,54,28,83,34,0.499,30,tested_negative
6,124,72,0,0,27.6,0.368,29,tested_positive
0,101,64,17,0,21,0.252,21,tested_negative
3,81,86,16,66,27.5,0.306,22,tested_negative
1,133,102,28,140,32.8,0.234,45,tested_positive
3,173,82,48,465,38.4,2.137,25,tested_positive
0,118,64,23,89,0,1.731,21,tested_negative
0,84,64,22,66,35.8,0.545,21,tested_negative
2,105,58,40,94,34.9,0.225,25,tested_negative
2,122,52,43,158,36.2,0.816,28,tested_negative
12,140,82,43,325,39.2,0.528,58,tested_positive
0,98,82,15,84,25.2,0.299,22,tested_negative
1,87,60,37,75,37.2,0.509,22,tested_negative
4,156,75,0,0,48.3,0.238,32,tested_positive
0,93,100,39,72,43.4,1.021,35,tested_negative
1,107,72,30,82,30.8,0.821,24,tested_negative
0,105,68,22,0,20,0.236,22,tested_negative
1,109,60,8,182,25.4,0.947,21,tested_negative
1,90,62,18,59,25.1,1.268,25,tested_negative
1,125,70,24,110,24.3,0.221,25,tested_negative
1,119,54,13,50,22.3,0.205,24,tested_negative
5,116,74,29,0,32.3,0.66,35,tested_positive
8,105,100,36,0,43.3,0.239,45,tested_positive
5,144,82,26,285,32,0.452,58,tested_positive
3,100,68,23,81,31.6,0.949,28,tested_negative
1,100,66,29,196,32,0.444,42,tested_negative
5,166,76,0,0,45.7,0.34,27,tested_positive
1,131,64,14,415,23.7,0.389,21,tested_negative
4,116,72,12,87,22.1,0.463,37,tested_negative
4,158,78,0,0,32.9,0.803,31,tested_positive
2,127,58,24,275,27.7,1.6,25,tested_negative
3,96,56,34,115,24.7,0.944,39,tested_negative
0,131,66,40,0,34.3,0.196,22,tested_positive
3,82,70,0,0,21.1,0.389,25,tested_negative
3,193,70,31,0,34.9,0.241,25,tested_positive
4,95,64,0,0,32,0.161,31,tested_positive
6,137,61,0,0,24.2,0.151,55,tested_negative
5,136,84,41,88,35,0.286,35,tested_positive
9,72,78,25,0,31.6,0.28,38,tested_negative
5,168,64,0,0,32.9,0.135,41,tested_positive
2,123,48,32,165,42.1,0.52,26,tested_negative
4,115,72,0,0,28.9,0.376,46,tested_positive
0,101,62,0,0,21.9,0.336,25,tested_negative
8,197,74,0,0,25.9,1.191,39,tested_positive
1,172,68,49,579,42.4,0.702,28,tested_positive
6,102,90,39,0,35.7,0.674,28,tested_negative
1,112,72,30,176,34.4,0.528,25,tested_negative
1,143,84,23,310,42.4,1.076,22,tested_negative
1,143,74,22,61,26.2,0.256,21,tested_negative
0,138,60,35,167,34.6,0.534,21,tested_positive
3,173,84,33,474,35.7,0.258,22,tested_positive
1,97,68,21,0,27.2,1.095,22,tested_negative
4,144,82,32,0,38.5,0.554,37,tested_positive
1,83,68,0,0,18.2,0.624,27,tested_negative
3,129,64,29,115,26.4,0.219,28,tested_positive
1,119,88,41,170,45.3,0.507,26,tested_negative
2,94,68,18,76,26,0.561,21,tested_negative
0,102,64,46,78,40.6,0.496,21,tested_negative
2,115,64,22,0,30.8,0.421,21,tested_negative
8,151,78,32,210,42.9,0.516,36,tested_positive
4,184,78,39,277,37,0.264,31,tested_positive
0,94,0,0,0,0,0.256,25,tested_negative
1,181,64,30,180,34.1,0.328,38,tested_positive
0,135,94,46,145,40.6,0.284,26,tested_negative
1,95,82,25,180,35,0.233,43,tested_positive
2,99,0,0,0,22.2,0.108,23,tested_negative
3,89,74,16,85,30.4,0.551,38,tested_negative
1,80,74,11,60,30,0.527,22,tested_negative
2,139,75,0,0,25.6,0.167,29,tested_negative
1,90,68,8,0,24.5,1.138,36,tested_negative
0,141,0,0,0,42.4,0.205,29,tested_positive
12,140,85,33,0,37.4,0.244,41,tested_negative
5,147,75,0,0,29.9,0.434,28,tested_negative
1,97,70,15,0,18.2,0.147,21,tested_negative
6,107,88,0,0,36.8,0.727,31,tested_negative
0,189,104,25,0,34.3,0.435,41,tested_positive
2,83,66,23,50,32.2,0.497,22,tested_negative
4,117,64,27,120,33.2,0.23,24,tested_negative
8,108,70,0,0,30.5,0.955,33,tested_positive
4,117,62,12,0,29.7,0.38,30,tested_positive
0,180,78,63,14,59.4,2.42,25,tested_positive
1,100,72,12,70,25.3,0.658,28,tested_negative
0,95,80,45,92,36.5,0.33,26,tested_negative
0,104,64,37,64,33.6,0.51,22,tested_positive
0,120,74,18,63,30.5,0.285,26,tested_negative
1,82,64,13,95,21.2,0.415,23,tested_negative
2,134,70,0,0,28.9,0.542,23,tested_positive
0,91,68,32,210,39.9,0.381,25,tested_negative
2,119,0,0,0,19.6,0.832,72,tested_negative
2,100,54,28,105,37.8,0.498,24,tested_negative
14,175,62,30,0,33.6,0.212,38,tested_positive
1,135,54,0,0,26.7,0.687,62,tested_negative
5,86,68,28,71,30.2,0.364,24,tested_negative
10,148,84,48,237,37.6,1.001,51,tested_positive
9,134,74,33,60,25.9,0.46,81,tested_negative
9,120,72,22,56,20.8,0.733,48,tested_negative
1,71,62,0,0,21.8,0.416,26,tested_negative
8,74,70,40,49,35.3,0.705,39,tested_negative
5,88,78,30,0,27.6,0.258,37,tested_negative
10,115,98,0,0,24,1.022,34,tested_negative
0,124,56,13,105,21.8,0.452,21,tested_negative
0,74,52,10,36,27.8,0.269,22,tested_negative
0,97,64,36,100,36.8,0.6,25,tested_negative
8,120,0,0,0,30,0.183,38,tested_positive
6,154,78,41,140,46.1,0.571,27,tested_negative
1,144,82,40,0,41.3,0.607,28,tested_negative
0,137,70,38,0,33.2,0.17,22,tested_negative
0,119,66,27,0,38.8,0.259,22,tested_negative
7,136,90,0,0,29.9,0.21,50,tested_negative
4,114,64,0,0,28.9,0.126,24,tested_negative
0,137,84,27,0,27.3,0.231,59,tested_negative
2,105,80,45,191,33.7,0.711,29,tested_positive
7,114,76,17,110,23.8,0.466,31,tested_negative
8,126,74,38,75,25.9,0.162,39,tested_negative
4,132,86,31,0,28,0.419,63,tested_negative
3,158,70,30,328,35.5,0.344,35,tested_positive
0,123,88,37,0,35.2,0.197,29,tested_negative
4,85,58,22,49,27.8,0.306,28,tested_negative
0,84,82,31,125,38.2,0.233,23,tested_negative
0,145,0,0,0,44.2,0.63,31,tested_positive
0,135,68,42,250,42.3,0.365,24,tested_positive
1,139,62,41,480,40.7,0.536,21,tested_negative
0,173,78,32,265,46.5,1.159,58,tested_negative
4,99,72,17,0,25.6,0.294,28,tested_negative
8,194,80,0,0,26.1,0.551,67,tested_negative
2,83,65,28,66,36.8,0.629,24,tested_negative
2,89,90,30,0,33.5,0.292,42,tested_negative
4,99,68,38,0,32.8,0.145,33,tested_negative
4,125,70,18,122,28.9,1.144,45,tested_positive
3,80,0,0,0,0,0.174,22,tested_negative
6,166,74,0,0,26.6,0.304,66,tested_negative
5,110,68,0,0,26,0.292,30,tested_negative
2,81,72,15,76,30.1,0.547,25,tested_negative
7,195,70,33,145,25.1,0.163,55,tested_positive
6,154,74,32,193,29.3,0.839,39,tested_negative
2,117,90,19,71,25.2,0.313,21,tested_negative
3,84,72,32,0,37.2,0.267,28,tested_negative
6,0,68,41,0,39,0.727,41,tested_positive
7,94,64,25,79,33.3,0.738,41,tested_negative
3,96,78,39,0,37.3,0.238,40,tested_negative
10,75,82,0,0,33.3,0.263,38,tested_negative
0,180,90,26,90,36.5,0.314,35,tested_positive
1,130,60,23,170,28.6,0.692,21,tested_negative
2,84,50,23,76,30.4,0.968,21,tested_negative
8,120,78,0,0,25,0.409,64,tested_negative
12,84,72,31,0,29.7,0.297,46,tested_positive
0,139,62,17,210,22.1,0.207,21,tested_negative
9,91,68,0,0,24.2,0.2,58,tested_negative
2,91,62,0,0,27.3,0.525,22,tested_negative
3,99,54,19,86,25.6,0.154,24,tested_negative
3,163,70,18,105,31.6,0.268,28,tested_positive
9,145,88,34,165,30.3,0.771,53,tested_positive
7,125,86,0,0,37.6,0.304,51,tested_negative
13,76,60,0,0,32.8,0.18,41,tested_negative
6,129,90,7,326,19.6,0.582,60,tested_negative
2,68,70,32,66,25,0.187,25,tested_negative
3,124,80,33,130,33.2,0.305,26,tested_negative
6,114,0,0,0,0,0.189,26,tested_negative
9,130,70,0,0,34.2,0.652,45,tested_positive
3,125,58,0,0,31.6,0.151,24,tested_negative
3,87,60,18,0,21.8,0.444,21,tested_negative
1,97,64,19,82,18.2,0.299,21,tested_negative
3,116,74,15,105,26.3,0.107,24,tested_negative
0,117,66,31,188,30.8,0.493,22,tested_negative
0,111,65,0,0,24.6,0.66,31,tested_negative
2,122,60,18,106,29.8,0.717,22,tested_negative
0,107,76,0,0,45.3,0.686,24,tested_negative
1,86,66,52,65,41.3,0.917,29,tested_negative
6,91,0,0,0,29.8,0.501,31,tested_negative
1,77,56,30,56,33.3,1.251,24,tested_negative
4,132,0,0,0,32.9,0.302,23,tested_positive
0,105,90,0,0,29.6,0.197,46,tested_negative
0,57,60,0,0,21.7,0.735,67,tested_negative
0,127,80,37,210,36.3,0.804,23,tested_negative
3,129,92,49,155,36.4,0.968,32,tested_positive
8,100,74,40,215,39.4,0.661,43,tested_positive
3,128,72,25,190,32.4,0.549,27,tested_positive
10,90,85,32,0,34.9,0.825,56,tested_positive
4,84,90,23,56,39.5,0.159,25,tested_negative
1,88,78,29,76,32,0.365,29,tested_negative
8,186,90,35,225,34.5,0.423,37,tested_positive
5,187,76,27,207,43.6,1.034,53,tested_positive
4,131,68,21,166,33.1,0.16,28,tested_negative
1,164,82,43,67,32.8,0.341,50,tested_negative
4,189,110,31,0,28.5,0.68,37,tested_negative
1,116,70,28,0,27.4,0.204,21,tested_negative
3,84,68,30,106,31.9,0.591,25,tested_negative
6,114,88,0,0,27.8,0.247,66,tested_negative
1,88,62,24,44,29.9,0.422,23,tested_negative
1,84,64,23,115,36.9,0.471,28,tested_negative
7,124,70,33,215,25.5,0.161,37,tested_negative
1,97,70,40,0,38.1,0.218,30,tested_negative
8,110,76,0,0,27.8,0.237,58,tested_negative
11,103,68,40,0,46.2,0.126,42,tested_negative
11,85,74,0,0,30.1,0.3,35,tested_negative
6,125,76,0,0,33.8,0.121,54,tested_positive
0,198,66,32,274,41.3,0.502,28,tested_positive
1,87,68,34,77,37.6,0.401,24,tested_negative
6,99,60,19,54,26.9,0.497,32,tested_negative
0,91,80,0,0,32.4,0.601,27,tested_negative
2,95,54,14,88,26.1,0.748,22,tested_negative
1,99,72,30,18,38.6,0.412,21,tested_negative
6,92,62,32,126,32,0.085,46,tested_negative
4,154,72,29,126,31.3,0.338,37,tested_negative
0,121,66,30,165,34.3,0.203,33,tested_positive
3,78,70,0,0,32.5,0.27,39,tested_negative
2,130,96,0,0,22.6,0.268,21,tested_negative
3,111,58,31,44,29.5,0.43,22,tested_negative
2,98,60,17,120,34.7,0.198,22,tested_negative
1,143,86,30,330,30.1,0.892,23,tested_negative
1,119,44,47,63,35.5,0.28,25,tested_negative
6,108,44,20,130,24,0.813,35,tested_negative
2,118,80,0,0,42.9,0.693,21,tested_positive
10,133,68,0,0,27,0.245,36,tested_negative
2,197,70,99,0,34.7,0.575,62,tested_positive
0,151,90,46,0,42.1,0.371,21,tested_positive
6,109,60,27,0,25,0.206,27,tested_negative
12,121,78,17,0,26.5,0.259,62,tested_negative
8,100,76,0,0,38.7,0.19,42,tested_negative
8,124,76,24,600,28.7,0.687,52,tested_positive
1,93,56,11,0,22.5,0.417,22,tested_negative
8,143,66,0,0,34.9,0.129,41,tested_positive
6,103,66,0,0,24.3,0.249,29,tested_negative
3,176,86,27,156,33.3,1.154,52,tested_positive
0,73,0,0,0,21.1,0.342,25,tested_negative
11,111,84,40,0,46.8,0.925,45,tested_positive
2,112,78,50,140,39.4,0.175,24,tested_negative
3,132,80,0,0,34.4,0.402,44,tested_positive
2,82,52,22,115,28.5,1.699,25,tested_negative
6,123,72,45,230,33.6,0.733,34,tested_negative
0,188,82,14,185,32,0.682,22,tested_positive
0,67,76,0,0,45.3,0.194,46,tested_negative
1,89,24,19,25,27.8,0.559,21,tested_negative
1,173,74,0,0,36.8,0.088,38,tested_positive
1,109,38,18,120,23.1,0.407,26,tested_negative
1,108,88,19,0,27.1,0.4,24,tested_negative
6,96,0,0,0,23.7,0.19,28,tested_negative
1,124,74,36,0,27.8,0.1,30,tested_negative
7,150,78,29,126,35.2,0.692,54,tested_positive
4,183,0,0,0,28.4,0.212,36,tested_positive
1,124,60,32,0,35.8,0.514,21,tested_negative
1,181,78,42,293,40,1.258,22,tested_positive
1,92,62,25,41,19.5,0.482,25,tested_negative
0,152,82,39,272,41.5,0.27,27,tested_negative
1,111,62,13,182,24,0.138,23,tested_negative
3,106,54,21,158,30.9,0.292,24,tested_negative
3,174,58,22,194,32.9,0.593,36,tested_positive
7,168,88,42,321,38.2,0.787,40,tested_positive
6,105,80,28,0,32.5,0.878,26,tested_negative
11,138,74,26,144,36.1,0.557,50,tested_positive
3,106,72,0,0,25.8,0.207,27,tested_negative
6,117,96,0,0,28.7,0.157,30,tested_negative
2,68,62,13,15,20.1,0.257,23,tested_negative
9,112,82,24,0,28.2,1.282,50,tested_positive
0,119,0,0,0,32.4,0.141,24,tested_positive
2,112,86,42,160,38.4,0.246,28,tested_negative
2,92,76,20,0,24.2,1.698,28,tested_negative
6,183,94,0,0,40.8,1.461,45,tested_negative
0,94,70,27,115,43.5,0.347,21,tested_negative
2,108,64,0,0,30.8,0.158,21,tested_negative
4,90,88,47,54,37.7,0.362,29,tested_negative
0,125,68,0,0,24.7,0.206,21,tested_negative
0,132,78,0,0,32.4,0.393,21,tested_negative
5,128,80,0,0,34.6,0.144,45,tested_negative
4,94,65,22,0,24.7,0.148,21,tested_negative
7,114,64,0,0,27.4,0.732,34,tested_positive
0,102,78,40,90,34.5,0.238,24,tested_negative
2,111,60,0,0,26.2,0.343,23,tested_negative
1,128,82,17,183,27.5,0.115,22,tested_negative
10,92,62,0,0,25.9,0.167,31,tested_negative
13,104,72,0,0,31.2,0.465,38,tested_positive
5,104,74,0,0,28.8,0.153,48,tested_negative
2,94,76,18,66,31.6,0.649,23,tested_negative
7,97,76,32,91,40.9,0.871,32,tested_positive
1,100,74,12,46,19.5,0.149,28,tested_negative
0,102,86,17,105,29.3,0.695,27,tested_negative
4,128,70,0,0,34.3,0.303,24,tested_negative
6,147,80,0,0,29.5,0.178,50,tested_positive
4,90,0,0,0,28,0.61,31,tested_negative
3,103,72,30,152,27.6,0.73,27,tested_negative
2,157,74,35,440,39.4,0.134,30,tested_negative
1,167,74,17,144,23.4,0.447,33,tested_positive
0,179,50,36,159,37.8,0.455,22,tested_positive
11,136,84,35,130,28.3,0.26,42,tested_positive
0,107,60,25,0,26.4,0.133,23,tested_negative
1,91,54,25,100,25.2,0.234,23,tested_negative
1,117,60,23,106,33.8,0.466,27,tested_negative
5,123,74,40,77,34.1,0.269,28,tested_negative
2,120,54,0,0,26.8,0.455,27,tested_negative
1,106,70,28,135,34.2,0.142,22,tested_negative
2,155,52,27,540,38.7,0.24,25,tested_positive
2,101,58,35,90,21.8,0.155,22,tested_negative
1,120,80,48,200,38.9,1.162,41,tested_negative
11,127,106,0,0,39,0.19,51,tested_negative
3,80,82,31,70,34.2,1.292,27,tested_positive
10,162,84,0,0,27.7,0.182,54,tested_negative
1,199,76,43,0,42.9,1.394,22,tested_positive
8,167,106,46,231,37.6,0.165,43,tested_positive
9,145,80,46,130,37.9,0.637,40,tested_positive
6,115,60,39,0,33.7,0.245,40,tested_positive
1,112,80,45,132,34.8,0.217,24,tested_negative
4,145,82,18,0,32.5,0.235,70,tested_positive
10,111,70,27,0,27.5,0.141,40,tested_positive
6,98,58,33,190,34,0.43,43,tested_negative
9,154,78,30,100,30.9,0.164,45,tested_negative
6,165,68,26,168,33.6,0.631,49,tested_negative
1,99,58,10,0,25.4,0.551,21,tested_negative
10,68,106,23,49,35.5,0.285,47,tested_negative
3,123,100,35,240,57.3,0.88,22,tested_negative
8,91,82,0,0,35.6,0.587,68,tested_negative
6,195,70,0,0,30.9,0.328,31,tested_positive
9,156,86,0,0,24.8,0.23,53,tested_positive
0,93,60,0,0,35.3,0.263,25,tested_negative
3,121,52,0,0,36,0.127,25,tested_positive
2,101,58,17,265,24.2,0.614,23,tested_negative
2,56,56,28,45,24.2,0.332,22,tested_negative
0,162,76,36,0,49.6,0.364,26,tested_positive
0,95,64,39,105,44.6,0.366,22,tested_negative
4,125,80,0,0,32.3,0.536,27,tested_positive
5,136,82,0,0,0,0.64,69,tested_negative
2,129,74,26,205,33.2,0.591,25,tested_negative
3,130,64,0,0,23.1,0.314,22,tested_negative
1,107,50,19,0,28.3,0.181,29,tested_negative
1,140,74,26,180,24.1,0.828,23,tested_negative
1,144,82,46,180,46.1,0.335,46,tested_positive
8,107,80,0,0,24.6,0.856,34,tested_negative
13,158,114,0,0,42.3,0.257,44,tested_positive
2,121,70,32,95,39.1,0.886,23,tested_negative
7,129,68,49,125,38.5,0.439,43,tested_positive
2,90,60,0,0,23.5,0.191,25,tested_negative
7,142,90,24,480,30.4,0.128,43,tested_positive
3,169,74,19,125,29.9,0.268,31,tested_positive
0,99,0,0,0,25,0.253,22,tested_negative
4,127,88,11,155,34.5,0.598,28,tested_negative
4,118,70,0,0,44.5,0.904,26,tested_negative
2,122,76,27,200,35.9,0.483,26,tested_negative
6,125,78,31,0,27.6,0.565,49,tested_positive
1,168,88,29,0,35,0.905,52,tested_positive
2,129,0,0,0,38.5,0.304,41,tested_negative
4,110,76,20,100,28.4,0.118,27,tested_negative
6,80,80,36,0,39.8,0.177,28,tested_negative
10,115,0,0,0,0,0.261,30,tested_positive
2,127,46,21,335,34.4,0.176,22,tested_negative
9,164,78,0,0,32.8,0.148,45,tested_positive
2,93,64,32,160,38,0.674,23,tested_positive
3,158,64,13,387,31.2,0.295,24,tested_negative
5,126,78,27,22,29.6,0.439,40,tested_negative
10,129,62,36,0,41.2,0.441,38,tested_positive
0,134,58,20,291,26.4,0.352,21,tested_negative
3,102,74,0,0,29.5,0.121,32,tested_negative
7,187,50,33,392,33.9,0.826,34,tested_positive
3,173,78,39,185,33.8,0.97,31,tested_positive
10,94,72,18,0,23.1,0.595,56,tested_negative
1,108,60,46,178,35.5,0.415,24,tested_negative
5,97,76,27,0,35.6,0.378,52,tested_positive
4,83,86,19,0,29.3,0.317,34,tested_negative
1,114,66,36,200,38.1,0.289,21,tested_negative
1,149,68,29,127,29.3,0.349,42,tested_positive
5,117,86,30,105,39.1,0.251,42,tested_negative
1,111,94,0,0,32.8,0.265,45,tested_negative
4,112,78,40,0,39.4,0.236,38,tested_negative
1,116,78,29,180,36.1,0.496,25,tested_negative
0,141,84,26,0,32.4,0.433,22,tested_negative
2,175,88,0,0,22.9,0.326,22,tested_negative
2,92,52,0,0,30.1,0.141,22,tested_negative
3,130,78,23,79,28.4,0.323,34,tested_positive
8,120,86,0,0,28.4,0.259,22,tested_positive
2,174,88,37,120,44.5,0.646,24,tested_positive
2,106,56,27,165,29,0.426,22,tested_negative
2,105,75,0,0,23.3,0.56,53,tested_negative
4,95,60,32,0,35.4,0.284,28,tested_negative
0,126,86,27,120,27.4,0.515,21,tested_negative
8,65,72,23,0,32,0.6,42,tested_negative
2,99,60,17,160,36.6,0.453,21,tested_negative
1,102,74,0,0,39.5,0.293,42,tested_positive
11,120,80,37,150,42.3,0.785,48,tested_positive
3,102,44,20,94,30.8,0.4,26,tested_negative
1,109,58,18,116,28.5,0.219,22,tested_negative
9,140,94,0,0,32.7,0.734,45,tested_positive
13,153,88,37,140,40.6,1.174,39,tested_negative
12,100,84,33,105,30,0.488,46,tested_negative
1,147,94,41,0,49.3,0.358,27,tested_positive
1,81,74,41,57,46.3,1.096,32,tested_negative
3,187,70,22,200,36.4,0.408,36,tested_positive
6,162,62,0,0,24.3,0.178,50,tested_positive
4,136,70,0,0,31.2,1.182,22,tested_positive
1,121,78,39,74,39,0.261,28,tested_negative
3,108,62,24,0,26,0.223,25,tested_negative
0,181,88,44,510,43.3,0.222,26,tested_positive
8,154,78,32,0,32.4,0.443,45,tested_positive
1,128,88,39,110,36.5,1.057,37,tested_positive
7,137,90,41,0,32,0.391,39,tested_negative
0,123,72,0,0,36.3,0.258,52,tested_positive
1,106,76,0,0,37.5,0.197,26,tested_negative
6,190,92,0,0,35.5,0.278,66,tested_positive
2,88,58,26,16,28.4,0.766,22,tested_negative
9,170,74,31,0,44,0.403,43,tested_positive
9,89,62,0,0,22.5,0.142,33,tested_negative
10,101,76,48,180,32.9,0.171,63,tested_negative
2,122,70,27,0,36.8,0.34,27,tested_negative
5,121,72,23,112,26.2,0.245,30,tested_negative
1,126,60,0,0,30.1,0.349,47,tested_positive
1,93,70,31,0,30.4,0.315,23,tested_negative

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%
% 1. Title: Protein Localization Sites
%
%
% 2. Creator and Maintainer:
% Kenta Nakai
% Institue of Molecular and Cellular Biology
% Osaka, University
% 1-3 Yamada-oka, Suita 565 Japan
% nakai@imcb.osaka-u.ac.jp
% http://www.imcb.osaka-u.ac.jp/nakai/psort.html
% Donor: Paul Horton (paulh@cs.berkeley.edu)
% Date: September, 1996
% See also: yeast database
%
% 3. Past Usage.
% Reference: "A Probablistic Classification System for Predicting the Cellular
% Localization Sites of Proteins", Paul Horton & Kenta Nakai,
% Intelligent Systems in Molecular Biology, 109-115.
% St. Louis, USA 1996.
% Results: 81% for E.coli with an ad hoc structured
% probability model. Also similar accuracy for Binary Decision Tree and
% Bayesian Classifier methods applied by the same authors in
% unpublished results.
%
% Predicted Attribute: Localization site of protein. ( non-numeric ).
%
%
% 4. The references below describe a predecessor to this dataset and its
% development. They also give results (not cross-validated) for classification
% by a rule-based expert system with that version of the dataset.
%
% Reference: "Expert Sytem for Predicting Protein Localization Sites in
% Gram-Negative Bacteria", Kenta Nakai & Minoru Kanehisa,
% PROTEINS: Structure, Function, and Genetics 11:95-110, 1991.
%
% Reference: "A Knowledge Base for Predicting Protein Localization Sites in
% Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa,
% Genomics 14:897-911, 1992.
%
%
% 5. Number of Instances: 336 for the E.coli dataset and
%
%
% 6. Number of Attributes.
% for E.coli dataset: 8 ( 7 predictive, 1 name )
%
% 7. Attribute Information.
%
% 1. Sequence Name: Accession number for the SWISS-PROT database
% 2. mcg: McGeoch's method for signal sequence recognition.
% 3. gvh: von Heijne's method for signal sequence recognition.
% 4. lip: von Heijne's Signal Peptidase II consensus sequence score.
% Binary attribute.
% 5. chg: Presence of charge on N-terminus of predicted lipoproteins.
% Binary attribute.
% 6. aac: score of discriminant analysis of the amino acid content of
% outer membrane and periplasmic proteins.
% 7. alm1: score of the ALOM membrane spanning region prediction program.
% 8. alm2: score of ALOM program after excluding putative cleavable signal
% regions from the sequence.
%
% NOTE - the sequence name has been removed
%
% 8. Missing Attribute Values: None.
%
%
% 9. Class Distribution. The class is the localization site. Please see Nakai &
% Kanehisa referenced above for more details.
%
% cp (cytoplasm) 143
% im (inner membrane without signal sequence) 77
% pp (perisplasm) 52
% imU (inner membrane, uncleavable signal sequence) 35
% om (outer membrane) 20
% omL (outer membrane lipoprotein) 5
% imL (inner membrane lipoprotein) 2
% imS (inner membrane, cleavable signal sequence) 2
@relation ecoli
@attribute mcg numeric
@attribute gvh numeric
@attribute lip numeric
@attribute chg numeric
@attribute aac numeric
@attribute alm1 numeric
@attribute alm2 numeric
@attribute class {cp,im,pp,imU,om,omL,imL,imS}
@data
0.49,0.29,0.48,0.5,0.56,0.24,0.35,cp
0.07,0.4,0.48,0.5,0.54,0.35,0.44,cp
0.56,0.4,0.48,0.5,0.49,0.37,0.46,cp
0.59,0.49,0.48,0.5,0.52,0.45,0.36,cp
0.23,0.32,0.48,0.5,0.55,0.25,0.35,cp
0.67,0.39,0.48,0.5,0.36,0.38,0.46,cp
0.29,0.28,0.48,0.5,0.44,0.23,0.34,cp
0.21,0.34,0.48,0.5,0.51,0.28,0.39,cp
0.2,0.44,0.48,0.5,0.46,0.51,0.57,cp
0.42,0.4,0.48,0.5,0.56,0.18,0.3,cp
0.42,0.24,0.48,0.5,0.57,0.27,0.37,cp
0.25,0.48,0.48,0.5,0.44,0.17,0.29,cp
0.39,0.32,0.48,0.5,0.46,0.24,0.35,cp
0.51,0.5,0.48,0.5,0.46,0.32,0.35,cp
0.22,0.43,0.48,0.5,0.48,0.16,0.28,cp
0.25,0.4,0.48,0.5,0.46,0.44,0.52,cp
0.34,0.45,0.48,0.5,0.38,0.24,0.35,cp
0.44,0.27,0.48,0.5,0.55,0.52,0.58,cp
0.23,0.4,0.48,0.5,0.39,0.28,0.38,cp
0.41,0.57,0.48,0.5,0.39,0.21,0.32,cp
0.4,0.45,0.48,0.5,0.38,0.22,0,cp
0.31,0.23,0.48,0.5,0.73,0.05,0.14,cp
0.51,0.54,0.48,0.5,0.41,0.34,0.43,cp
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0.74,0.49,0.48,0.5,0.42,0.54,0.36,pp
0.7,0.61,0.48,0.5,0.56,0.52,0.43,pp
0.66,0.86,0.48,0.5,0.34,0.41,0.36,pp
0.73,0.78,0.48,0.5,0.58,0.51,0.31,pp
0.65,0.57,0.48,0.5,0.47,0.47,0.51,pp
0.72,0.86,0.48,0.5,0.17,0.55,0.21,pp
0.67,0.7,0.48,0.5,0.46,0.45,0.33,pp
0.67,0.81,0.48,0.5,0.54,0.49,0.23,pp
0.67,0.61,0.48,0.5,0.51,0.37,0.38,pp
0.63,1,0.48,0.5,0.35,0.51,0.49,pp
0.57,0.59,0.48,0.5,0.39,0.47,0.33,pp
0.71,0.71,0.48,0.5,0.4,0.54,0.39,pp
0.66,0.74,0.48,0.5,0.31,0.38,0.43,pp
0.67,0.81,0.48,0.5,0.25,0.42,0.25,pp
0.64,0.72,0.48,0.5,0.49,0.42,0.19,pp
0.68,0.82,0.48,0.5,0.38,0.65,0.56,pp
0.32,0.39,0.48,0.5,0.53,0.28,0.38,pp
0.7,0.64,0.48,0.5,0.47,0.51,0.47,pp
0.63,0.57,0.48,0.5,0.49,0.7,0.2,pp
0.74,0.82,0.48,0.5,0.49,0.49,0.41,pp
0.63,0.86,0.48,0.5,0.39,0.47,0.34,pp
0.63,0.83,0.48,0.5,0.4,0.39,0.19,pp
0.63,0.71,0.48,0.5,0.6,0.4,0.39,pp
0.71,0.86,0.48,0.5,0.4,0.54,0.32,pp
0.68,0.78,0.48,0.5,0.43,0.44,0.42,pp
0.64,0.84,0.48,0.5,0.37,0.45,0.4,pp
0.74,0.47,0.48,0.5,0.5,0.57,0.42,pp
0.75,0.84,0.48,0.5,0.35,0.52,0.33,pp
0.63,0.65,0.48,0.5,0.39,0.44,0.35,pp
0.69,0.67,0.48,0.5,0.3,0.39,0.24,pp
0.7,0.71,0.48,0.5,0.42,0.84,0.85,pp
0.69,0.8,0.48,0.5,0.46,0.57,0.26,pp
0.64,0.66,0.48,0.5,0.41,0.39,0.2,pp
0.63,0.8,0.48,0.5,0.46,0.31,0.29,pp
0.66,0.71,0.48,0.5,0.41,0.5,0.35,pp
0.69,0.59,0.48,0.5,0.46,0.44,0.52,pp
0.68,0.67,0.48,0.5,0.49,0.4,0.34,pp
0.64,0.78,0.48,0.5,0.5,0.36,0.38,pp
0.62,0.78,0.48,0.5,0.47,0.49,0.54,pp
0.76,0.73,0.48,0.5,0.44,0.39,0.39,pp
0.64,0.81,0.48,0.5,0.37,0.39,0.44,pp
0.29,0.39,0.48,0.5,0.52,0.4,0.48,pp
0.62,0.83,0.48,0.5,0.46,0.36,0.4,pp
0.56,0.54,0.48,0.5,0.43,0.37,0.3,pp
0.69,0.66,0.48,0.5,0.41,0.5,0.25,pp
0.69,0.65,0.48,0.5,0.63,0.48,0.41,pp
0.43,0.59,0.48,0.5,0.52,0.49,0.56,pp
0.74,0.56,0.48,0.5,0.47,0.68,0.3,pp
0.71,0.57,0.48,0.5,0.48,0.35,0.32,pp
0.61,0.6,0.48,0.5,0.44,0.39,0.38,pp
0.59,0.61,0.48,0.5,0.42,0.42,0.37,pp
0.74,0.74,0.48,0.5,0.31,0.53,0.52,pp

332
tests/data/glass.arff Executable file
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% 1. Title: Glass Identification Database
%
% 2. Sources:
% (a) Creator: B. German
% -- Central Research Establishment
% Home Office Forensic Science Service
% Aldermaston, Reading, Berkshire RG7 4PN
% (b) Donor: Vina Spiehler, Ph.D., DABFT
% Diagnostic Products Corporation
% (213) 776-0180 (ext 3014)
% (c) Date: September, 1987
%
% 3. Past Usage:
% -- Rule Induction in Forensic Science
% -- Ian W. Evett and Ernest J. Spiehler
% -- Central Research Establishment
% Home Office Forensic Science Service
% Aldermaston, Reading, Berkshire RG7 4PN
% -- Unknown technical note number (sorry, not listed here)
% -- General Results: nearest neighbor held its own with respect to the
% rule-based system
%
% 4. Relevant Information:n
% Vina conducted a comparison test of her rule-based system, BEAGLE, the
% nearest-neighbor algorithm, and discriminant analysis. BEAGLE is
% a product available through VRS Consulting, Inc.; 4676 Admiralty Way,
% Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189.
% In determining whether the glass was a type of "float" glass or not,
% the following results were obtained (# incorrect answers):
%
% Type of Sample Beagle NN DA
% Windows that were float processed (87) 10 12 21
% Windows that were not: (76) 19 16 22
%
% The study of classification of types of glass was motivated by
% criminological investigation. At the scene of the crime, the glass left
% can be used as evidence...if it is correctly identified!
%
% 5. Number of Instances: 214
%
% 6. Number of Attributes: 10 (including an Id#) plus the class attribute
% -- all attributes are continuously valued
%
% 7. Attribute Information:
% 1. Id number: 1 to 214
% 2. RI: refractive index
% 3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as
% are attributes 4-10)
% 4. Mg: Magnesium
% 5. Al: Aluminum
% 6. Si: Silicon
% 7. K: Potassium
% 8. Ca: Calcium
% 9. Ba: Barium
% 10. Fe: Iron
% 11. Type of glass: (class attribute)
% -- 1 building_windows_float_processed
% -- 2 building_windows_non_float_processed
% -- 3 vehicle_windows_float_processed
% -- 4 vehicle_windows_non_float_processed (none in this database)
% -- 5 containers
% -- 6 tableware
% -- 7 headlamps
%
% 8. Missing Attribute Values: None
%
% Summary Statistics:
% Attribute: Min Max Mean SD Correlation with class
% 2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642
% 3. Na: 10.73 17.38 13.4079 0.8166 0.5030
% 4. Mg: 0 4.49 2.6845 1.4424 -0.7447
% 5. Al: 0.29 3.5 1.4449 0.4993 0.5988
% 6. Si: 69.81 75.41 72.6509 0.7745 0.1515
% 7. K: 0 6.21 0.4971 0.6522 -0.0100
% 8. Ca: 5.43 16.19 8.9570 1.4232 0.0007
% 9. Ba: 0 3.15 0.1750 0.4972 0.5751
% 10. Fe: 0 0.51 0.0570 0.0974 -0.1879
%
% 9. Class Distribution: (out of 214 total instances)
% -- 163 Window glass (building windows and vehicle windows)
% -- 87 float processed
% -- 70 building windows
% -- 17 vehicle windows
% -- 76 non-float processed
% -- 76 building windows
% -- 0 vehicle windows
% -- 51 Non-window glass
% -- 13 containers
% -- 9 tableware
% -- 29 headlamps
%
%
%
%
%
%
%
% Relabeled values in attribute 'Type'
% From: '1' To: 'build wind float'
% From: '2' To: 'build wind non-float'
% From: '3' To: 'vehic wind float'
% From: '4' To: 'vehic wind non-float'
% From: '5' To: containers
% From: '6' To: tableware
% From: '7' To: headlamps
%
@relation Glass
@attribute 'RI' real
@attribute 'Na' real
@attribute 'Mg' real
@attribute 'Al' real
@attribute 'Si' real
@attribute 'K' real
@attribute 'Ca' real
@attribute 'Ba' real
@attribute 'Fe' real
@attribute 'Type' {'build wind float', 'build wind non-float', 'vehic wind float', 'vehic wind non-float', containers, tableware, headlamps}
@data
1.51793,12.79,3.5,1.12,73.03,0.64,8.77,0,0,'build wind float'
1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0,0,'vehic wind float'
1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0,0,'build wind float'
1.51299,14.4,1.74,1.54,74.55,0,7.59,0,0,tableware
1.53393,12.3,0,1,70.16,0.12,16.19,0,0.24,'build wind non-float'
1.51655,12.75,2.85,1.44,73.27,0.57,8.79,0.11,0.22,'build wind non-float'
1.51779,13.64,3.65,0.65,73,0.06,8.93,0,0,'vehic wind float'
1.51837,13.14,2.84,1.28,72.85,0.55,9.07,0,0,'build wind float'
1.51545,14.14,0,2.68,73.39,0.08,9.07,0.61,0.05,headlamps
1.51789,13.19,3.9,1.3,72.33,0.55,8.44,0,0.28,'build wind non-float'
1.51625,13.36,3.58,1.49,72.72,0.45,8.21,0,0,'build wind non-float'
1.51743,12.2,3.25,1.16,73.55,0.62,8.9,0,0.24,'build wind non-float'
1.52223,13.21,3.77,0.79,71.99,0.13,10.02,0,0,'build wind float'
1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0,0,'vehic wind float'
1.51665,13.14,3.45,1.76,72.48,0.6,8.38,0,0.17,'vehic wind float'
1.51707,13.48,3.48,1.71,72.52,0.62,7.99,0,0,'build wind non-float'
1.51719,14.75,0,2,73.02,0,8.53,1.59,0.08,headlamps
1.51629,12.71,3.33,1.49,73.28,0.67,8.24,0,0,'build wind non-float'
1.51994,13.27,0,1.76,73.03,0.47,11.32,0,0,containers
1.51811,12.96,2.96,1.43,72.92,0.6,8.79,0.14,0,'build wind non-float'
1.52152,13.05,3.65,0.87,72.22,0.19,9.85,0,0.17,'build wind float'
1.52475,11.45,0,1.88,72.19,0.81,13.24,0,0.34,'build wind non-float'
1.51841,12.93,3.74,1.11,72.28,0.64,8.96,0,0.22,'build wind non-float'
1.51754,13.39,3.66,1.19,72.79,0.57,8.27,0,0.11,'build wind float'
1.52058,12.85,1.61,2.17,72.18,0.76,9.7,0.24,0.51,containers
1.51569,13.24,3.49,1.47,73.25,0.38,8.03,0,0,'build wind non-float'
1.5159,12.82,3.52,1.9,72.86,0.69,7.97,0,0,'build wind non-float'
1.51683,14.56,0,1.98,73.29,0,8.52,1.57,0.07,headlamps
1.51687,13.23,3.54,1.48,72.84,0.56,8.1,0,0,'build wind non-float'
1.5161,13.33,3.53,1.34,72.67,0.56,8.33,0,0,'vehic wind float'
1.51674,12.87,3.56,1.64,73.14,0.65,7.99,0,0,'build wind non-float'
1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0,0,'vehic wind float'
1.51115,17.38,0,0.34,75.41,0,6.65,0,0,tableware
1.51645,13.44,3.61,1.54,72.39,0.66,8.03,0,0,'build wind non-float'
1.51755,13,3.6,1.36,72.99,0.57,8.4,0,0.11,'build wind float'
1.51571,12.72,3.46,1.56,73.2,0.67,8.09,0,0.24,'build wind float'
1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0,0.26,'build wind float'
1.5173,12.35,2.72,1.63,72.87,0.7,9.23,0,0,'build wind non-float'
1.51662,12.85,3.51,1.44,73.01,0.68,8.23,0.06,0.25,'build wind non-float'
1.51409,14.25,3.09,2.08,72.28,1.1,7.08,0,0,'build wind non-float'
1.51797,12.74,3.48,1.35,72.96,0.64,8.68,0,0,'build wind float'
1.51806,13,3.8,1.08,73.07,0.56,8.38,0,0.12,'build wind non-float'
1.51627,13,3.58,1.54,72.83,0.61,8.04,0,0,'build wind non-float'
1.5159,13.24,3.34,1.47,73.1,0.39,8.22,0,0,'build wind non-float'
1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,'vehic wind float'
1.51755,12.71,3.42,1.2,73.2,0.59,8.64,0,0,'build wind float'
1.51514,14.01,2.68,3.5,69.89,1.68,5.87,2.2,0,containers
1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0,0,'build wind float'
1.51784,13.08,3.49,1.28,72.86,0.6,8.49,0,0,'build wind float'
1.52177,13.2,3.68,1.15,72.75,0.54,8.52,0,0,'build wind non-float'
1.51753,12.57,3.47,1.38,73.39,0.6,8.55,0,0.06,'build wind float'
1.51851,13.2,3.63,1.07,72.83,0.57,8.41,0.09,0.17,'build wind non-float'
1.51743,13.3,3.6,1.14,73.09,0.58,8.17,0,0,'build wind float'
1.51593,13.09,3.59,1.52,73.1,0.67,7.83,0,0,'build wind non-float'
1.5164,14.37,0,2.74,72.85,0,9.45,0.54,0,headlamps
1.51735,13.02,3.54,1.69,72.73,0.54,8.44,0,0.07,'build wind float'
1.52247,14.86,2.2,2.06,70.26,0.76,9.76,0,0,headlamps
1.52099,13.69,3.59,1.12,71.96,0.09,9.4,0,0,'build wind float'
1.51769,13.65,3.66,1.11,72.77,0.11,8.6,0,0,'vehic wind float'
1.51846,13.41,3.89,1.33,72.38,0.51,8.28,0,0,'build wind non-float'
1.51848,13.64,3.87,1.27,71.96,0.54,8.32,0,0.32,'build wind non-float'
1.51905,13.6,3.62,1.11,72.64,0.14,8.76,0,0,'build wind float'
1.51567,13.29,3.45,1.21,72.74,0.56,8.57,0,0,'build wind float'
1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0,0,'build wind float'
1.5232,13.72,3.72,0.51,71.75,0.09,10.06,0,0.16,'build wind float'
1.51556,13.87,0,2.54,73.23,0.14,9.41,0.81,0.01,headlamps
1.51926,13.2,3.33,1.28,72.36,0.6,9.14,0,0.11,'build wind float'
1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0,0.37,'vehic wind float'
1.53125,10.73,0,2.1,69.81,0.58,13.3,3.15,0.28,'build wind non-float'
1.52152,13.05,3.65,0.87,72.32,0.19,9.85,0,0.17,'build wind float'
1.51829,14.46,2.24,1.62,72.38,0,9.26,0,0,tableware
1.51892,13.46,3.83,1.26,72.55,0.57,8.21,0,0.14,'build wind non-float'
1.51888,14.99,0.78,1.74,72.5,0,9.95,0,0,tableware
1.51829,13.24,3.9,1.41,72.33,0.55,8.31,0,0.1,'build wind non-float'
1.523,13.31,3.58,0.82,71.99,0.12,10.17,0,0.03,'build wind float'
1.51652,13.56,3.57,1.47,72.45,0.64,7.96,0,0,'build wind non-float'
1.51768,12.56,3.52,1.43,73.15,0.57,8.54,0,0,'build wind float'
1.51215,12.99,3.47,1.12,72.98,0.62,8.35,0,0.31,'build wind float'
1.51646,13.04,3.4,1.26,73.01,0.52,8.58,0,0,'vehic wind float'
1.51721,12.87,3.48,1.33,73.04,0.56,8.43,0,0,'build wind float'
1.51763,12.8,3.66,1.27,73.01,0.6,8.56,0,0,'build wind float'
1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0,0,'build wind float'
1.52127,14.32,3.9,0.83,71.5,0,9.49,0,0,'vehic wind float'
1.51779,13.21,3.39,1.33,72.76,0.59,8.59,0,0,'build wind float'
1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0,0,containers
1.518,13.71,3.93,1.54,71.81,0.54,8.21,0,0.15,'build wind non-float'
1.52777,12.64,0,0.67,72.02,0.06,14.4,0,0,'build wind non-float'
1.5175,12.82,3.55,1.49,72.75,0.54,8.52,0,0.19,'build wind float'
1.51764,12.98,3.54,1.21,73,0.65,8.53,0,0,'build wind float'
1.52177,13.75,1.01,1.36,72.19,0.33,11.14,0,0,'build wind non-float'
1.51645,14.94,0,1.87,73.11,0,8.67,1.38,0,headlamps
1.51786,12.73,3.43,1.19,72.95,0.62,8.76,0,0.3,'build wind float'
1.52152,13.12,3.58,0.9,72.2,0.23,9.82,0,0.16,'build wind float'
1.51937,13.79,2.41,1.19,72.76,0,9.77,0,0,tableware
1.51514,14.85,0,2.42,73.72,0,8.39,0.56,0,headlamps
1.52172,13.48,3.74,0.9,72.01,0.18,9.61,0,0.07,'build wind float'
1.51732,14.95,0,1.8,72.99,0,8.61,1.55,0,headlamps
1.5202,13.98,1.35,1.63,71.76,0.39,10.56,0,0.18,'build wind non-float'
1.51605,12.9,3.44,1.45,73.06,0.44,8.27,0,0,'build wind non-float'
1.51847,13.1,3.97,1.19,72.44,0.6,8.43,0,0,'build wind non-float'
1.51761,13.89,3.6,1.36,72.73,0.48,7.83,0,0,'build wind float'
1.51673,13.3,3.64,1.53,72.53,0.65,8.03,0,0.29,'build wind non-float'
1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0,headlamps
1.51685,14.92,0,1.99,73.06,0,8.4,1.59,0,headlamps
1.51658,14.8,0,1.99,73.11,0,8.28,1.71,0,headlamps
1.51316,13.02,0,3.04,70.48,6.21,6.96,0,0,containers
1.51709,13,3.47,1.79,72.72,0.66,8.18,0,0,'build wind non-float'
1.51727,14.7,0,2.34,73.28,0,8.95,0.66,0,headlamps
1.51898,13.58,3.35,1.23,72.08,0.59,8.91,0,0,'build wind float'
1.51969,12.64,0,1.65,73.75,0.38,11.53,0,0,containers
1.5182,12.62,2.76,0.83,73.81,0.35,9.42,0,0.2,'build wind non-float'
1.51617,14.95,0,2.27,73.3,0,8.71,0.67,0,headlamps
1.51911,13.9,3.73,1.18,72.12,0.06,8.89,0,0,'build wind float'
1.51651,14.38,0,1.94,73.61,0,8.48,1.57,0,headlamps
1.51694,12.86,3.58,1.31,72.61,0.61,8.79,0,0,'vehic wind float'
1.52315,13.44,3.34,1.23,72.38,0.6,8.83,0,0,headlamps
1.52068,13.55,2.09,1.67,72.18,0.53,9.57,0.27,0.17,'build wind non-float'
1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0,headlamps
1.51818,13.72,0,0.56,74.45,0,10.99,0,0,'build wind non-float'
1.51769,12.45,2.71,1.29,73.7,0.56,9.06,0,0.24,'build wind float'
1.5166,12.99,3.18,1.23,72.97,0.58,8.81,0,0.24,'build wind non-float'
1.51589,12.88,3.43,1.4,73.28,0.69,8.05,0,0.24,'build wind float'
1.5241,13.83,2.9,1.17,71.15,0.08,10.79,0,0,'build wind non-float'
1.52725,13.8,3.15,0.66,70.57,0.08,11.64,0,0,'build wind non-float'
1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0,0.28,containers
1.51748,12.86,3.56,1.27,73.21,0.54,8.38,0,0.17,'build wind float'
1.51653,11.95,0,1.19,75.18,2.7,8.93,0,0,headlamps
1.51623,14.14,0,2.88,72.61,0.08,9.18,1.06,0,headlamps
1.52101,13.64,4.49,1.1,71.78,0.06,8.75,0,0,'build wind float'
1.51763,12.61,3.59,1.31,73.29,0.58,8.5,0,0,'build wind float'
1.51596,13.02,3.56,1.54,73.11,0.72,7.9,0,0,'build wind non-float'
1.51674,12.79,3.52,1.54,73.36,0.66,7.9,0,0,'build wind non-float'
1.52065,14.36,0,2.02,73.42,0,8.44,1.64,0,headlamps
1.51768,12.65,3.56,1.3,73.08,0.61,8.69,0,0.14,'build wind float'
1.52369,13.44,0,1.58,72.22,0.32,12.24,0,0,containers
1.51756,13.15,3.61,1.05,73.24,0.57,8.24,0,0,'build wind float'
1.51754,13.48,3.74,1.17,72.99,0.59,8.03,0,0,'build wind float'
1.51711,12.89,3.62,1.57,72.96,0.61,8.11,0,0,'build wind non-float'
1.5221,13.73,3.84,0.72,71.76,0.17,9.74,0,0,'build wind float'
1.51594,13.09,3.52,1.55,72.87,0.68,8.05,0,0.09,'build wind non-float'
1.51784,12.68,3.67,1.16,73.11,0.61,8.7,0,0,'build wind float'
1.51909,13.89,3.53,1.32,71.81,0.51,8.78,0.11,0,'build wind float'
1.51977,13.81,3.58,1.32,71.72,0.12,8.67,0.69,0,'build wind float'
1.51666,12.86,0,1.83,73.88,0.97,10.17,0,0,containers
1.51631,13.34,3.57,1.57,72.87,0.61,7.89,0,0,'build wind non-float'
1.51872,12.93,3.66,1.56,72.51,0.58,8.55,0,0.12,'build wind non-float'
1.51708,13.72,3.68,1.81,72.06,0.64,7.88,0,0,'build wind non-float'
1.52081,13.78,2.28,1.43,71.99,0.49,9.85,0,0.17,'build wind non-float'
1.51574,14.86,3.67,1.74,71.87,0.16,7.36,0,0.12,'build wind non-float'
1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0,0,'build wind non-float'
1.51131,13.69,3.2,1.81,72.81,1.76,5.43,1.19,0,headlamps
1.52227,14.17,3.81,0.78,71.35,0,9.69,0,0,'build wind float'
1.52614,13.7,0,1.36,71.24,0.19,13.44,0,0.1,'build wind non-float'
1.51811,13.33,3.85,1.25,72.78,0.52,8.12,0,0,'build wind non-float'
1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0,0,'vehic wind float'
1.51751,12.81,3.57,1.35,73.02,0.62,8.59,0,0,'build wind float'
1.51508,15.15,0,2.25,73.5,0,8.34,0.63,0,headlamps
1.51915,12.73,1.85,1.86,72.69,0.6,10.09,0,0,containers
1.51966,14.77,3.75,0.29,72.02,0.03,9,0,0,'build wind float'
1.51844,13.25,3.76,1.32,72.4,0.58,8.42,0,0,'build wind non-float'
1.52664,11.23,0,0.77,73.21,0,14.68,0,0,'build wind non-float'
1.52172,13.51,3.86,0.88,71.79,0.23,9.54,0,0.11,'build wind float'
1.51602,14.85,0,2.38,73.28,0,8.76,0.64,0.09,headlamps
1.51321,13,0,3.02,70.7,6.21,6.93,0,0,containers
1.52739,11.02,0,0.75,73.08,0,14.96,0,0,'build wind non-float'
1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0,0,'build wind float'
1.51747,12.84,3.5,1.14,73.27,0.56,8.55,0,0,'build wind float'
1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0,0.35,'build wind non-float'
1.51646,13.41,3.55,1.25,72.81,0.68,8.1,0,0,'build wind non-float'
1.51609,15.01,0,2.51,73.05,0.05,8.83,0.53,0,headlamps
1.51667,12.94,3.61,1.26,72.75,0.56,8.6,0,0,'build wind non-float'
1.51588,13.12,3.41,1.58,73.26,0.07,8.39,0,0.19,'build wind non-float'
1.52667,13.99,3.7,0.71,71.57,0.02,9.82,0,0.1,'build wind float'
1.51831,14.39,0,1.82,72.86,1.41,6.47,2.88,0,headlamps
1.51918,14.04,3.58,1.37,72.08,0.56,8.3,0,0,'build wind float'
1.51613,13.88,1.78,1.79,73.1,0,8.67,0.76,0,headlamps
1.52196,14.36,3.85,0.89,71.36,0.15,9.15,0,0,'build wind float'
1.51824,12.87,3.48,1.29,72.95,0.6,8.43,0,0,'build wind float'
1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0,0,containers
1.51969,14.56,0,0.56,73.48,0,11.22,0,0,tableware
1.51618,13.01,3.5,1.48,72.89,0.6,8.12,0,0,'build wind non-float'
1.51645,13.4,3.49,1.52,72.65,0.67,8.08,0,0.1,'build wind non-float'
1.51796,13.5,3.36,1.63,71.94,0.57,8.81,0,0.09,'vehic wind float'
1.52222,14.43,0,1,72.67,0.1,11.52,0,0.08,'build wind non-float'
1.51783,12.69,3.54,1.34,72.95,0.57,8.75,0,0,'build wind float'
1.51711,14.23,0,2.08,73.36,0,8.62,1.67,0,headlamps
1.51736,12.78,3.62,1.29,72.79,0.59,8.7,0,0,'build wind float'
1.51808,13.43,2.87,1.19,72.84,0.55,9.03,0,0,'build wind float'
1.5167,13.24,3.57,1.38,72.7,0.56,8.44,0,0.1,'vehic wind float'
1.52043,13.38,0,1.4,72.25,0.33,12.5,0,0,containers
1.519,13.49,3.48,1.35,71.95,0.55,9,0,0,'build wind float'
1.51778,13.21,2.81,1.29,72.98,0.51,9.02,0,0.09,'build wind float'
1.51905,14,2.39,1.56,72.37,0,9.57,0,0,tableware
1.51531,14.38,0,2.66,73.1,0.04,9.08,0.64,0,headlamps
1.51916,14.15,0,2.09,72.74,0,10.88,0,0,tableware
1.51841,13.02,3.62,1.06,72.34,0.64,9.13,0,0.15,'build wind non-float'
1.5159,13.02,3.58,1.51,73.12,0.69,7.96,0,0,'build wind non-float'
1.51593,13.25,3.45,1.43,73.17,0.61,7.86,0,0,'build wind non-float'
1.5164,12.55,3.48,1.87,73.23,0.63,8.08,0,0.09,'build wind non-float'
1.51663,12.93,3.54,1.62,72.96,0.64,8.03,0,0.21,'build wind non-float'
1.5169,13.33,3.54,1.61,72.54,0.68,8.11,0,0,'build wind non-float'
1.51869,13.19,3.37,1.18,72.72,0.57,8.83,0,0.16,'build wind float'
1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0,0,'vehic wind float'
1.51775,12.85,3.48,1.23,72.97,0.61,8.56,0.09,0.22,'build wind float'
1.5186,13.36,3.43,1.43,72.26,0.51,8.6,0,0,'build wind non-float'
1.5172,13.38,3.5,1.15,72.85,0.5,8.43,0,0,'build wind float'
1.51623,14.2,0,2.79,73.46,0.04,9.04,0.4,0.09,headlamps
1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0,0,'build wind float'
1.51761,12.81,3.54,1.23,73.24,0.58,8.39,0,0,'build wind float'
1.5161,13.42,3.4,1.22,72.69,0.59,8.32,0,0,'vehic wind float'
1.51592,12.86,3.52,2.12,72.66,0.69,7.97,0,0,'build wind non-float'
1.51613,13.92,3.52,1.25,72.88,0.37,7.94,0,0.14,'build wind non-float'
1.51689,12.67,2.88,1.71,73.21,0.73,8.54,0,0,'build wind non-float'
1.51852,14.09,2.19,1.66,72.67,0,9.32,0,0,tableware

225
tests/data/iris.arff Executable file
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% 1. Title: Iris Plants Database
%
% 2. Sources:
% (a) Creator: R.A. Fisher
% (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
% (c) Date: July, 1988
%
% 3. Past Usage:
% - Publications: too many to mention!!! Here are a few.
% 1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
% Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
% to Mathematical Statistics" (John Wiley, NY, 1950).
% 2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
% (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
% 3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
% Structure and Classification Rule for Recognition in Partially Exposed
% Environments". IEEE Transactions on Pattern Analysis and Machine
% Intelligence, Vol. PAMI-2, No. 1, 67-71.
% -- Results:
% -- very low misclassification rates (0% for the setosa class)
% 4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
% Transactions on Information Theory, May 1972, 431-433.
% -- Results:
% -- very low misclassification rates again
% 5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
% conceptual clustering system finds 3 classes in the data.
%
% 4. Relevant Information:
% --- This is perhaps the best known database to be found in the pattern
% recognition literature. Fisher's paper is a classic in the field
% and is referenced frequently to this day. (See Duda & Hart, for
% example.) The data set contains 3 classes of 50 instances each,
% where each class refers to a type of iris plant. One class is
% linearly separable from the other 2; the latter are NOT linearly
% separable from each other.
% --- Predicted attribute: class of iris plant.
% --- This is an exceedingly simple domain.
%
% 5. Number of Instances: 150 (50 in each of three classes)
%
% 6. Number of Attributes: 4 numeric, predictive attributes and the class
%
% 7. Attribute Information:
% 1. sepal length in cm
% 2. sepal width in cm
% 3. petal length in cm
% 4. petal width in cm
% 5. class:
% -- Iris Setosa
% -- Iris Versicolour
% -- Iris Virginica
%
% 8. Missing Attribute Values: None
%
% Summary Statistics:
% Min Max Mean SD Class Correlation
% sepal length: 4.3 7.9 5.84 0.83 0.7826
% sepal width: 2.0 4.4 3.05 0.43 -0.4194
% petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
% petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
%
% 9. Class Distribution: 33.3% for each of 3 classes.
@RELATION iris
@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength REAL
@ATTRIBUTE petalwidth REAL
@ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica}
@DATA
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
%
%
%

10177
tests/data/kdd_JapaneseVowels.arff Executable file
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20191
tests/data/letter.arff Executable file
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399
tests/data/liver-disorders.arff Executable file
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% 1. Title: BUPA liver disorders
%
% 2. Source information:
% -- Creators: BUPA Medical Research Ltd.
% -- Donor: Richard S. Forsyth
% 8 Grosvenor Avenue
% Mapperley Park
% Nottingham NG3 5DX
% 0602-621676
% -- Date: 5/15/1990
%
% 3. Past usage:
% -- None known other than what is shown in the PC/BEAGLE User's Guide
% (written by Richard S. Forsyth).
%
% 4. Relevant information:
% -- The first 5 variables are all blood tests which are thought
% to be sensitive to liver disorders that might arise from
% excessive alcohol consumption. Each line in the bupa.data file
% constitutes the record of a single male individual.
% -- It appears that drinks>5 is some sort of a selector on this database.
% See the PC/BEAGLE User's Guide for more information.
%
% 5. Number of instances: 345
%
% 6. Number of attributes: 7 overall
%
% 7. Attribute information:
% 1. mcv mean corpuscular volume
% 2. alkphos alkaline phosphotase
% 3. sgpt alamine aminotransferase
% 4. sgot aspartate aminotransferase
% 5. gammagt gamma-glutamyl transpeptidase
% 6. drinks number of half-pint equivalents of alcoholic beverages
% drunk per day
% 7. selector field used to split data into two sets
%
% 8. Missing values: none%
% Information about the dataset
% CLASSTYPE: nominal
% CLASSINDEX: last
%
@relation liver-disorders
@attribute mcv INTEGER
@attribute alkphos INTEGER
@attribute sgpt INTEGER
@attribute sgot INTEGER
@attribute gammagt INTEGER
@attribute drinks REAL
@attribute selector {1,2}
@data
85,92,45,27,31,0.0,1
85,64,59,32,23,0.0,2
86,54,33,16,54,0.0,2
91,78,34,24,36,0.0,2
87,70,12,28,10,0.0,2
98,55,13,17,17,0.0,2
88,62,20,17,9,0.5,1
88,67,21,11,11,0.5,1
92,54,22,20,7,0.5,1
90,60,25,19,5,0.5,1
89,52,13,24,15,0.5,1
82,62,17,17,15,0.5,1
90,64,61,32,13,0.5,1
86,77,25,19,18,0.5,1
96,67,29,20,11,0.5,1
91,78,20,31,18,0.5,1
89,67,23,16,10,0.5,1
89,79,17,17,16,0.5,1
91,107,20,20,56,0.5,1
94,116,11,33,11,0.5,1
92,59,35,13,19,0.5,1
93,23,35,20,20,0.5,1
90,60,23,27,5,0.5,1
96,68,18,19,19,0.5,1
84,80,47,33,97,0.5,1
92,70,24,13,26,0.5,1
90,47,28,15,18,0.5,1
88,66,20,21,10,0.5,1
91,102,17,13,19,0.5,1
87,41,31,19,16,0.5,1
86,79,28,16,17,0.5,1
91,57,31,23,42,0.5,1
93,77,32,18,29,0.5,1
88,96,28,21,40,0.5,1
94,65,22,18,11,0.5,1
91,72,155,68,82,0.5,2
85,54,47,33,22,0.5,2
79,39,14,19,9,0.5,2
85,85,25,26,30,0.5,2
89,63,24,20,38,0.5,2
84,92,68,37,44,0.5,2
89,68,26,39,42,0.5,2
89,101,18,25,13,0.5,2
86,84,18,14,16,0.5,2
85,65,25,14,18,0.5,2
88,61,19,21,13,0.5,2
92,56,14,16,10,0.5,2
95,50,29,25,50,0.5,2
91,75,24,22,11,0.5,2
83,40,29,25,38,0.5,2
89,74,19,23,16,0.5,2
85,64,24,22,11,0.5,2
92,57,64,36,90,0.5,2
94,48,11,23,43,0.5,2
87,52,21,19,30,0.5,2
85,65,23,29,15,0.5,2
84,82,21,21,19,0.5,2
88,49,20,22,19,0.5,2
96,67,26,26,36,0.5,2
90,63,24,24,24,0.5,2
90,45,33,34,27,0.5,2
90,72,14,15,18,0.5,2
91,55,4,8,13,0.5,2
91,52,15,22,11,0.5,2
87,71,32,19,27,1.0,1
89,77,26,20,19,1.0,1
89,67,5,17,14,1.0,2
85,51,26,24,23,1.0,2
103,75,19,30,13,1.0,2
90,63,16,21,14,1.0,2
90,63,29,23,57,2.0,1
90,67,35,19,35,2.0,1
87,66,27,22,9,2.0,1
90,73,34,21,22,2.0,1
86,54,20,21,16,2.0,1
90,80,19,14,42,2.0,1
87,90,43,28,156,2.0,2
96,72,28,19,30,2.0,2
91,55,9,25,16,2.0,2
95,78,27,25,30,2.0,2
92,101,34,30,64,2.0,2
89,51,41,22,48,2.0,2
91,99,42,33,16,2.0,2
94,58,21,18,26,2.0,2
92,60,30,27,297,2.0,2
94,58,21,18,26,2.0,2
88,47,33,26,29,2.0,2
92,65,17,25,9,2.0,2
92,79,22,20,11,3.0,1
84,83,20,25,7,3.0,1
88,68,27,21,26,3.0,1
86,48,20,20,6,3.0,1
99,69,45,32,30,3.0,1
88,66,23,12,15,3.0,1
89,62,42,30,20,3.0,1
90,51,23,17,27,3.0,1
81,61,32,37,53,3.0,2
89,89,23,18,104,3.0,2
89,65,26,18,36,3.0,2
92,75,26,26,24,3.0,2
85,59,25,20,25,3.0,2
92,61,18,13,81,3.0,2
89,63,22,27,10,4.0,1
90,84,18,23,13,4.0,1
88,95,25,19,14,4.0,1
89,35,27,29,17,4.0,1
91,80,37,23,27,4.0,1
91,109,33,15,18,4.0,1
91,65,17,5,7,4.0,1
88,107,29,20,50,4.0,2
87,76,22,55,9,4.0,2
87,86,28,23,21,4.0,2
87,42,26,23,17,4.0,2
88,80,24,25,17,4.0,2
90,96,34,49,169,4.0,2
86,67,11,15,8,4.0,2
92,40,19,20,21,4.0,2
85,60,17,21,14,4.0,2
89,90,15,17,25,4.0,2
91,57,15,16,16,4.0,2
96,55,48,39,42,4.0,2
79,101,17,27,23,4.0,2
90,134,14,20,14,4.0,2
89,76,14,21,24,4.0,2
88,93,29,27,31,4.0,2
90,67,10,16,16,4.0,2
92,73,24,21,48,4.0,2
91,55,28,28,82,4.0,2
83,45,19,21,13,4.0,2
90,74,19,14,22,4.0,2
92,66,21,16,33,5.0,1
93,63,26,18,18,5.0,1
86,78,47,39,107,5.0,2
97,44,113,45,150,5.0,2
87,59,15,19,12,5.0,2
86,44,21,11,15,5.0,2
87,64,16,20,24,5.0,2
92,57,21,23,22,5.0,2
90,70,25,23,112,5.0,2
99,59,17,19,11,5.0,2
92,80,10,26,20,6.0,1
95,60,26,22,28,6.0,1
91,63,25,26,15,6.0,1
92,62,37,21,36,6.0,1
95,50,13,14,15,6.0,1
90,76,37,19,50,6.0,1
96,70,70,26,36,6.0,1
95,62,64,42,76,6.0,1
92,62,20,23,20,6.0,1
91,63,25,26,15,6.0,1
82,56,67,38,92,6.0,2
92,82,27,24,37,6.0,2
90,63,12,26,21,6.0,2
88,37,9,15,16,6.0,2
100,60,29,23,76,6.0,2
98,43,35,23,69,6.0,2
91,74,87,50,67,6.0,2
92,87,57,25,44,6.0,2
93,99,36,34,48,6.0,2
90,72,17,19,19,6.0,2
97,93,21,20,68,6.0,2
93,50,18,25,17,6.0,2
90,57,20,26,33,6.0,2
92,76,31,28,41,6.0,2
88,55,19,17,14,6.0,2
89,63,24,29,29,6.0,2
92,79,70,32,84,7.0,1
92,93,58,35,120,7.0,1
93,84,58,47,62,7.0,2
97,71,29,22,52,8.0,1
84,99,33,19,26,8.0,1
96,44,42,23,73,8.0,1
90,62,22,21,21,8.0,1
92,94,18,17,6,8.0,1
90,67,77,39,114,8.0,1
97,71,29,22,52,8.0,1
91,69,25,25,66,8.0,2
93,59,17,20,14,8.0,2
92,95,85,48,200,8.0,2
90,50,26,22,53,8.0,2
91,62,59,47,60,8.0,2
92,93,22,28,123,9.0,1
92,77,86,41,31,10.0,1
86,66,22,24,26,10.0,2
98,57,31,34,73,10.0,2
95,80,50,64,55,10.0,2
92,108,53,33,94,12.0,2
97,92,22,28,49,12.0,2
93,77,39,37,108,16.0,1
94,83,81,34,201,20.0,1
87,75,25,21,14,0.0,1
88,56,23,18,12,0.0,1
84,97,41,20,32,0.0,2
94,91,27,20,15,0.5,1
97,62,17,13,5,0.5,1
92,85,25,20,12,0.5,1
82,48,27,15,12,0.5,1
88,74,31,25,15,0.5,1
95,77,30,14,21,0.5,1
88,94,26,18,8,0.5,1
91,70,19,19,22,0.5,1
83,54,27,15,12,0.5,1
91,105,40,26,56,0.5,1
86,79,37,28,14,0.5,1
91,96,35,22,135,0.5,1
89,82,23,14,35,0.5,1
90,73,24,23,11,0.5,1
90,87,19,25,19,0.5,1
89,82,33,32,18,0.5,1
85,79,17,8,9,0.5,1
85,119,30,26,17,0.5,1
78,69,24,18,31,0.5,1
88,107,34,21,27,0.5,1
89,115,17,27,7,0.5,1
92,67,23,15,12,0.5,1
89,101,27,34,14,0.5,1
91,84,11,12,10,0.5,1
94,101,41,20,53,0.5,2
88,46,29,22,18,0.5,2
88,122,35,29,42,0.5,2
84,88,28,25,35,0.5,2
90,79,18,15,24,0.5,2
87,69,22,26,11,0.5,2
65,63,19,20,14,0.5,2
90,64,12,17,14,0.5,2
85,58,18,24,16,0.5,2
88,81,41,27,36,0.5,2
86,78,52,29,62,0.5,2
82,74,38,28,48,0.5,2
86,58,36,27,59,0.5,2
94,56,30,18,27,0.5,2
87,57,30,30,22,0.5,2
98,74,148,75,159,0.5,2
94,75,20,25,38,0.5,2
83,68,17,20,71,0.5,2
93,56,25,21,33,0.5,2
101,65,18,21,22,0.5,2
92,65,25,20,31,0.5,2
92,58,14,16,13,0.5,2
86,58,16,23,23,0.5,2
85,62,15,13,22,0.5,2
86,57,13,20,13,0.5,2
86,54,26,30,13,0.5,2
81,41,33,27,34,1.0,1
91,67,32,26,13,1.0,1
91,80,21,19,14,1.0,1
92,60,23,15,19,1.0,1
91,60,32,14,8,1.0,1
93,65,28,22,10,1.0,1
90,63,45,24,85,1.0,2
87,92,21,22,37,1.0,2
83,78,31,19,115,1.0,2
95,62,24,23,14,1.0,2
93,59,41,30,48,1.0,2
84,82,43,32,38,2.0,1
87,71,33,20,22,2.0,1
86,44,24,15,18,2.0,1
86,66,28,24,21,2.0,1
88,58,31,17,17,2.0,1
90,61,28,29,31,2.0,1
88,69,70,24,64,2.0,1
93,87,18,17,26,2.0,1
98,58,33,21,28,2.0,1
91,44,18,18,23,2.0,2
87,75,37,19,70,2.0,2
94,91,30,26,25,2.0,2
88,85,14,15,10,2.0,2
89,109,26,25,27,2.0,2
87,59,37,27,34,2.0,2
93,58,20,23,18,2.0,2
88,57,9,15,16,2.0,2
94,65,38,27,17,3.0,1
91,71,12,22,11,3.0,1
90,55,20,20,16,3.0,1
91,64,21,17,26,3.0,2
88,47,35,26,33,3.0,2
82,72,31,20,84,3.0,2
85,58,83,49,51,3.0,2
91,54,25,22,35,4.0,1
98,50,27,25,53,4.0,2
86,62,29,21,26,4.0,2
89,48,32,22,14,4.0,2
82,68,20,22,9,4.0,2
83,70,17,19,23,4.0,2
96,70,21,26,21,4.0,2
94,117,77,56,52,4.0,2
93,45,11,14,21,4.0,2
93,49,27,21,29,4.0,2
84,73,46,32,39,4.0,2
91,63,17,17,46,4.0,2
90,57,31,18,37,4.0,2
87,45,19,13,16,4.0,2
91,68,14,20,19,4.0,2
86,55,29,35,108,4.0,2
91,86,52,47,52,4.0,2
88,46,15,33,55,4.0,2
85,52,22,23,34,4.0,2
89,72,33,27,55,4.0,2
95,59,23,18,19,4.0,2
94,43,154,82,121,4.0,2
96,56,38,26,23,5.0,2
90,52,10,17,12,5.0,2
94,45,20,16,12,5.0,2
99,42,14,21,49,5.0,2
93,102,47,23,37,5.0,2
94,71,25,26,31,5.0,2
92,73,33,34,115,5.0,2
87,54,41,29,23,6.0,1
92,67,15,14,14,6.0,1
98,101,31,26,32,6.0,1
92,53,51,33,92,6.0,1
97,94,43,43,82,6.0,1
93,43,11,16,54,6.0,1
93,68,24,18,19,6.0,1
95,36,38,19,15,6.0,1
99,86,58,42,203,6.0,1
98,66,103,57,114,6.0,1
92,80,10,26,20,6.0,1
96,74,27,25,43,6.0,2
95,93,21,27,47,6.0,2
86,109,16,22,28,6.0,2
91,46,30,24,39,7.0,2
102,82,34,78,203,7.0,2
85,50,12,18,14,7.0,2
91,57,33,23,12,8.0,1
91,52,76,32,24,8.0,1
93,70,46,30,33,8.0,1
87,55,36,19,25,8.0,1
98,123,28,24,31,8.0,1
82,55,18,23,44,8.0,2
95,73,20,25,225,8.0,2
97,80,17,20,53,8.0,2
100,83,25,24,28,8.0,2
88,91,56,35,126,9.0,2
91,138,45,21,48,10.0,1
92,41,37,22,37,10.0,1
86,123,20,25,23,10.0,2
91,93,35,34,37,10.0,2
87,87,15,23,11,10.0,2
87,56,52,43,55,10.0,2
99,75,26,24,41,12.0,1
96,69,53,43,203,12.0,2
98,77,55,35,89,15.0,1
91,68,27,26,14,16.0,1
98,99,57,45,65,20.0,1

2306
tests/data/mfeat-factors.arff Executable file
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