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-rw-r--r--dtrain/test/logreg_cd/bin_class.cc4
-rw-r--r--dtrain/test/logreg_cd/bin_class.h22
-rw-r--r--dtrain/test/logreg_cd/log_reg.cc39
-rw-r--r--dtrain/test/logreg_cd/log_reg.h14
4 files changed, 0 insertions, 79 deletions
diff --git a/dtrain/test/logreg_cd/bin_class.cc b/dtrain/test/logreg_cd/bin_class.cc
deleted file mode 100644
index 19bcde25..00000000
--- a/dtrain/test/logreg_cd/bin_class.cc
+++ /dev/null
@@ -1,4 +0,0 @@
-#include "bin_class.h"
-
-Objective::~Objective() {}
-
diff --git a/dtrain/test/logreg_cd/bin_class.h b/dtrain/test/logreg_cd/bin_class.h
deleted file mode 100644
index 3466109a..00000000
--- a/dtrain/test/logreg_cd/bin_class.h
+++ /dev/null
@@ -1,22 +0,0 @@
-#ifndef _BIN_CLASS_H_
-#define _BIN_CLASS_H_
-
-#include <vector>
-#include "sparse_vector.h"
-
-struct TrainingInstance {
- // TODO add other info? loss for MIRA-type updates?
- SparseVector<double> x_feature_map;
- bool y;
-};
-
-struct Objective {
- virtual ~Objective();
-
- // returns f(x) and f'(x)
- virtual double ObjectiveAndGradient(const SparseVector<double>& x,
- const std::vector<TrainingInstance>& training_instances,
- SparseVector<double>* g) const = 0;
-};
-
-#endif
diff --git a/dtrain/test/logreg_cd/log_reg.cc b/dtrain/test/logreg_cd/log_reg.cc
deleted file mode 100644
index ec2331fe..00000000
--- a/dtrain/test/logreg_cd/log_reg.cc
+++ /dev/null
@@ -1,39 +0,0 @@
-#include "log_reg.h"
-
-#include <vector>
-#include <cmath>
-
-#include "sparse_vector.h"
-
-using namespace std;
-
-double LogisticRegression::ObjectiveAndGradient(const SparseVector<double>& x,
- const vector<TrainingInstance>& training_instances,
- SparseVector<double>* g) const {
- double cll = 0;
- for (int i = 0; i < training_instances.size(); ++i) {
- const double dotprod = training_instances[i].x_feature_map.dot(x); // TODO no bias, if bias, add x[0]
- double lp_false = dotprod;
- double lp_true = -dotprod;
- if (0 < lp_true) {
- lp_true += log1p(exp(-lp_true));
- lp_false = log1p(exp(lp_false));
- } else {
- lp_true = log1p(exp(lp_true));
- lp_false += log1p(exp(-lp_false));
- }
- lp_true *= -1;
- lp_false *= -1;
- if (training_instances[i].y) { // true label
- cll -= lp_true;
- (*g) -= training_instances[i].x_feature_map * exp(lp_false);
- // (*g)[0] -= exp(lp_false); // bias
- } else { // false label
- cll -= lp_false;
- (*g) += training_instances[i].x_feature_map * exp(lp_true);
- // g += corpus[i].second * exp(lp_true);
- }
- }
- return cll;
-}
-
diff --git a/dtrain/test/logreg_cd/log_reg.h b/dtrain/test/logreg_cd/log_reg.h
deleted file mode 100644
index ecc560b8..00000000
--- a/dtrain/test/logreg_cd/log_reg.h
+++ /dev/null
@@ -1,14 +0,0 @@
-#ifndef _LOG_REG_H_
-#define _LOG_REG_H_
-
-#include <vector>
-#include "sparse_vector.h"
-#include "bin_class.h"
-
-struct LogisticRegression : public Objective {
- double ObjectiveAndGradient(const SparseVector<double>& x,
- const std::vector<TrainingInstance>& training_instances,
- SparseVector<double>* g) const;
-};
-
-#endif