Hendrik/xgboost
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some fix to make it more c++

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tqchen
2014-09-01 22:06:10 -07:00
parent 50f1b5d903
commit 42fb7b4d9d
7 changed files with 26 additions and 21 deletions
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2
src/learner/evaluation-inl.hpp
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@@ -8,8 +8,8 @@
#include <vector>
#include <utility>
#include <string>
#include <climits>
#include <cmath>
#include <climits>
#include <algorithm>
#include "./evaluation.h"
#include "./helper_utils.h"

1
src/learner/helper_utils.h
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@@ -7,6 +7,7 @@
*/
#include <utility>
#include <vector>
#include <cmath>
#include <algorithm>
namespace xgboost {
namespace learner {

16
src/learner/objective-inl.hpp
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@@ -6,9 +6,9 @@
* \author Tianqi Chen, Kailong Chen
*/
#include <vector>
#include <cmath>
#include <algorithm>
#include <utility>
#include <cmath>
#include <functional>
#include "../data.h"
#include "./objective.h"
@@ -37,7 +37,7 @@ struct LossType {
case kLogisticRaw:
case kLinearSquare: return x;
case kLogisticClassify:
case kLogisticNeglik: return 1.0f / (1.0f + expf(-x));
case kLogisticNeglik: return 1.0f / (1.0f + std::exp(-x));
default: utils::Error("unknown loss_type"); return 0.0f;
}
}
@@ -50,7 +50,7 @@ struct LossType {
inline float FirstOrderGradient(float predt, float label) const {
switch (loss_type) {
case kLinearSquare: return predt - label;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticRaw: predt = 1.0f / (1.0f + std::exp(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt - label;
default: utils::Error("unknown loss_type"); return 0.0f;
@@ -65,7 +65,7 @@ struct LossType {
inline float SecondOrderGradient(float predt, float label) const {
switch (loss_type) {
case kLinearSquare: return 1.0f;
case kLogisticRaw: predt = 1.0f / (1.0f + expf(-predt));
case kLogisticRaw: predt = 1.0f / (1.0f + std::exp(-predt));
case kLogisticClassify:
case kLogisticNeglik: return predt * (1 - predt);
default: utils::Error("unknown loss_type"); return 0.0f;
@@ -80,7 +80,7 @@ struct LossType {
loss_type == kLogisticNeglik ) {
utils::Check(base_score > 0.0f && base_score < 1.0f,
"base_score must be in (0,1) for logistic loss");
base_score = -logf(1.0f / base_score - 1.0f);
base_score = -std::log(1.0f / base_score - 1.0f);
}
return base_score;
}
@@ -419,8 +419,8 @@ class LambdaRankObjNDCG : public LambdaRankObj {
for (size_t i = 0; i < pairs.size(); ++i) {
unsigned pos_idx = pairs[i].pos_index;
unsigned neg_idx = pairs[i].neg_index;
float pos_loginv = 1.0f / logf(pos_idx + 2.0f);
float neg_loginv = 1.0f / logf(neg_idx + 2.0f);
float pos_loginv = 1.0f / std::log(pos_idx + 2.0f);
float neg_loginv = 1.0f / std::log(neg_idx + 2.0f);
int pos_label = static_cast<int>(sorted_list[pos_idx].label);
int neg_label = static_cast<int>(sorted_list[neg_idx].label);
float original =
@@ -438,7 +438,7 @@ class LambdaRankObjNDCG : public LambdaRankObj {
for (size_t i = 0; i < labels.size(); ++i) {
const unsigned rel = static_cast<unsigned>(labels[i]);
if (rel != 0) {
sumdcg += ((1 << rel) - 1) / logf(static_cast<float>(i + 2));
sumdcg += ((1 << rel) - 1) / std::log(static_cast<float>(i + 2));
}
}
return static_cast<float>(sumdcg);