Restoring authorship annotation for <[email protected]>. Commit 1 of 2.

1 files changed, 112 insertions, 112 deletions

diff --git a/library/cpp/linear_regression/benchmark/main.cpp b/library/cpp/linear_regression/benchmark/main.cpp
index 735d41e9888..f68b365d4eb 100644
--- a/library/cpp/linear_regression/benchmark/main.cpp
+++ b/library/cpp/linear_regression/benchmark/main.cpp
@@ -1,114 +1,114 @@
-#include "pool.h"
-
+#include "pool.h" 
+ 
 #include <library/cpp/linear_regression/linear_regression.h>
-
-#include <util/datetime/base.h>
-#include <util/datetime/cputimer.h>
-
+ 
+#include <util/datetime/base.h> 
+#include <util/datetime/cputimer.h> 
+ 
 #include <util/system/type_name.h>
-
-#include <util/string/printf.h>
-
-template <typename TLRSolver>
-void QualityBenchmark(const TPool& originalPool) {
-    auto measure = [&](const double injureFactor, const double injureOffset) {
-        TPool injuredPool = originalPool.InjurePool(injureFactor, injureOffset);
-
-        static const size_t runsCount = 10;
-        static const size_t foldsCount = 10;
-
-        TMeanCalculator determinationCoefficientCalculator;
-
-        TPool::TCVIterator learnIterator = injuredPool.CrossValidationIterator(foldsCount, TPool::LearnIterator);
-        TPool::TCVIterator testIterator = injuredPool.CrossValidationIterator(foldsCount, TPool::TestIterator);
-
-        for (size_t runNumber = 0; runNumber < runsCount; ++runNumber) {
-            for (size_t foldNumber = 0; foldNumber < foldsCount; ++foldNumber) {
-                learnIterator.ResetShuffle();
-                learnIterator.SetTestFold(foldNumber);
-                testIterator.ResetShuffle();
-                testIterator.SetTestFold(foldNumber);
-
-                TLRSolver solver;
-                for (; learnIterator.IsValid(); ++learnIterator) {
-                    solver.Add(learnIterator->Features, learnIterator->Goal, learnIterator->Weight);
-                }
-                TLinearModel model = solver.Solve();
-
-                TDeviationCalculator goalsCalculator;
-                TKahanAccumulator<double> errorsCalculator;
-                for (; testIterator.IsValid(); ++testIterator) {
-                    const double prediction = model.Prediction(testIterator->Features);
-                    const double goal = testIterator->Goal;
-                    const double weight = testIterator->Weight;
-                    const double error = goal - prediction;
-
-                    goalsCalculator.Add(goal, weight);
-                    errorsCalculator += error * error * weight;
-                }
-
-                const double determinationCoefficient = 1 - errorsCalculator.Get() / goalsCalculator.GetDeviation();
-                determinationCoefficientCalculator.Add(determinationCoefficient);
-            }
-        }
-
-        return determinationCoefficientCalculator.GetMean();
-    };
-
-    Cout << TypeName<TLRSolver>() << ":\n";
-    Cout << "\t" << Sprintf("base    : %.10lf\n", measure(1., 0.));
-    Cout << "\t" << Sprintf("injure1 : %.10lf\n", measure(1e-1, 1e+1));
-    Cout << "\t" << Sprintf("injure2 : %.10lf\n", measure(1e-3, 1e+4));
-    Cout << "\t" << Sprintf("injure3 : %.10lf\n", measure(1e-3, 1e+5));
-    Cout << "\t" << Sprintf("injure4 : %.10lf\n", measure(1e-3, 1e+6));
-    Cout << "\t" << Sprintf("injure5 : %.10lf\n", measure(1e-4, 1e+6));
-    Cout << "\t" << Sprintf("injure6 : %.10lf\n", measure(1e-4, 1e+7));
-    Cout << Endl;
-}
-
-template <typename TLRSolver>
-void SpeedBenchmark(const TPool& originalPool) {
-    TDeviationCalculator speedTest;
-
-    static const size_t runsCount = 1000;
-    for (size_t runNumber = 0; runNumber < runsCount; ++runNumber) {
-        TLRSolver solver;
-        TLinearModel model;
-        {
-            TSimpleTimer timer;
-            for (const TInstance& instance : originalPool) {
-                solver.Add(instance.Features, instance.Goal, instance.Weight);
-            }
-            model = solver.Solve();
-
-            speedTest.Add(timer.Get().MicroSeconds());
-        }
-    }
-
-    const double multiplier = 1e-6;
-    Cout << Sprintf("%.5lf +/- %.5lf: ", speedTest.GetMean() * multiplier, speedTest.GetStdDev() * multiplier) << TypeName<TLRSolver>() << Endl;
-}
-
-int main(int argc, const char** argv) {
-    for (int taskNumber = 1; taskNumber < argc; ++taskNumber) {
-        TPool pool;
-        pool.ReadFromFeatures(argv[taskNumber]);
-
-        Cout << argv[taskNumber] << ":" << Endl;
-        QualityBenchmark<TFastBestSLRSolver>(pool);
-        QualityBenchmark<TKahanBestSLRSolver>(pool);
-        QualityBenchmark<TBestSLRSolver>(pool);
-
-        QualityBenchmark<TLinearRegressionSolver>(pool);
-        QualityBenchmark<TFastLinearRegressionSolver>(pool);
-
-        SpeedBenchmark<TFastBestSLRSolver>(pool);
-        SpeedBenchmark<TKahanBestSLRSolver>(pool);
-        SpeedBenchmark<TBestSLRSolver>(pool);
-
-        SpeedBenchmark<TLinearRegressionSolver>(pool);
-        SpeedBenchmark<TFastLinearRegressionSolver>(pool);
-    }
-
-    return 0;
-}
+ 
+#include <util/string/printf.h> 
+ 
+template <typename TLRSolver> 
+void QualityBenchmark(const TPool& originalPool) { 
+    auto measure = [&](const double injureFactor, const double injureOffset) { 
+        TPool injuredPool = originalPool.InjurePool(injureFactor, injureOffset); 
+ 
+        static const size_t runsCount = 10; 
+        static const size_t foldsCount = 10; 
+ 
+        TMeanCalculator determinationCoefficientCalculator; 
+ 
+        TPool::TCVIterator learnIterator = injuredPool.CrossValidationIterator(foldsCount, TPool::LearnIterator); 
+        TPool::TCVIterator testIterator = injuredPool.CrossValidationIterator(foldsCount, TPool::TestIterator); 
+ 
+        for (size_t runNumber = 0; runNumber < runsCount; ++runNumber) { 
+            for (size_t foldNumber = 0; foldNumber < foldsCount; ++foldNumber) { 
+                learnIterator.ResetShuffle(); 
+                learnIterator.SetTestFold(foldNumber); 
+                testIterator.ResetShuffle(); 
+                testIterator.SetTestFold(foldNumber); 
+ 
+                TLRSolver solver; 
+                for (; learnIterator.IsValid(); ++learnIterator) { 
+                    solver.Add(learnIterator->Features, learnIterator->Goal, learnIterator->Weight); 
+                } 
+                TLinearModel model = solver.Solve(); 
+ 
+                TDeviationCalculator goalsCalculator; 
+                TKahanAccumulator<double> errorsCalculator; 
+                for (; testIterator.IsValid(); ++testIterator) { 
+                    const double prediction = model.Prediction(testIterator->Features); 
+                    const double goal = testIterator->Goal; 
+                    const double weight = testIterator->Weight; 
+                    const double error = goal - prediction; 
+ 
+                    goalsCalculator.Add(goal, weight); 
+                    errorsCalculator += error * error * weight; 
+                } 
+ 
+                const double determinationCoefficient = 1 - errorsCalculator.Get() / goalsCalculator.GetDeviation(); 
+                determinationCoefficientCalculator.Add(determinationCoefficient); 
+            } 
+        } 
+ 
+        return determinationCoefficientCalculator.GetMean(); 
+    }; 
+ 
+    Cout << TypeName<TLRSolver>() << ":\n"; 
+    Cout << "\t" << Sprintf("base    : %.10lf\n", measure(1., 0.)); 
+    Cout << "\t" << Sprintf("injure1 : %.10lf\n", measure(1e-1, 1e+1)); 
+    Cout << "\t" << Sprintf("injure2 : %.10lf\n", measure(1e-3, 1e+4)); 
+    Cout << "\t" << Sprintf("injure3 : %.10lf\n", measure(1e-3, 1e+5)); 
+    Cout << "\t" << Sprintf("injure4 : %.10lf\n", measure(1e-3, 1e+6)); 
+    Cout << "\t" << Sprintf("injure5 : %.10lf\n", measure(1e-4, 1e+6)); 
+    Cout << "\t" << Sprintf("injure6 : %.10lf\n", measure(1e-4, 1e+7)); 
+    Cout << Endl; 
+} 
+ 
+template <typename TLRSolver> 
+void SpeedBenchmark(const TPool& originalPool) { 
+    TDeviationCalculator speedTest; 
+ 
+    static const size_t runsCount = 1000; 
+    for (size_t runNumber = 0; runNumber < runsCount; ++runNumber) { 
+        TLRSolver solver; 
+        TLinearModel model; 
+        { 
+            TSimpleTimer timer; 
+            for (const TInstance& instance : originalPool) { 
+                solver.Add(instance.Features, instance.Goal, instance.Weight); 
+            } 
+            model = solver.Solve(); 
+ 
+            speedTest.Add(timer.Get().MicroSeconds()); 
+        } 
+    } 
+ 
+    const double multiplier = 1e-6; 
+    Cout << Sprintf("%.5lf +/- %.5lf: ", speedTest.GetMean() * multiplier, speedTest.GetStdDev() * multiplier) << TypeName<TLRSolver>() << Endl; 
+} 
+ 
+int main(int argc, const char** argv) { 
+    for (int taskNumber = 1; taskNumber < argc; ++taskNumber) { 
+        TPool pool; 
+        pool.ReadFromFeatures(argv[taskNumber]); 
+ 
+        Cout << argv[taskNumber] << ":" << Endl; 
+        QualityBenchmark<TFastBestSLRSolver>(pool); 
+        QualityBenchmark<TKahanBestSLRSolver>(pool); 
+        QualityBenchmark<TBestSLRSolver>(pool); 
+ 
+        QualityBenchmark<TLinearRegressionSolver>(pool); 
+        QualityBenchmark<TFastLinearRegressionSolver>(pool); 
+ 
+        SpeedBenchmark<TFastBestSLRSolver>(pool); 
+        SpeedBenchmark<TKahanBestSLRSolver>(pool); 
+        SpeedBenchmark<TBestSLRSolver>(pool); 
+ 
+        SpeedBenchmark<TLinearRegressionSolver>(pool); 
+        SpeedBenchmark<TFastLinearRegressionSolver>(pool); 
+    } 
+ 
+    return 0; 
+}