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lint learner finish

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This commit is contained in:
tqchen
2015-07-03 19:20:45 -07:00
parent 1581de08da
commit aba41d07cd
8 changed files with 127 additions and 112 deletions
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28
src/learner/objective-inl.hpp
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@@ -1,10 +1,12 @@
#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
/*!
* Copyright 2014 by Contributors
* \file objective-inl.hpp
* \brief objective function implementations
* \author Tianqi Chen, Kailong Chen
*/
#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
#include <vector>
#include <algorithm>
#include <utility>
@@ -176,14 +178,14 @@ class RegLossObj : public IObjFunction {
// poisson regression for count
class PoissonRegression : public IObjFunction {
public:
explicit PoissonRegression(void) {
PoissonRegression(void) {
max_delta_step = 0.0f;
}
virtual ~PoissonRegression(void) {}
virtual void SetParam(const char *name, const char *val) {
using namespace std;
if (!strcmp( "max_delta_step", name )) {
if (!strcmp("max_delta_step", name)) {
max_delta_step = static_cast<float>(atof(val));
}
}
@@ -201,9 +203,9 @@ class PoissonRegression : public IObjFunction {
// check if label in range
bool label_correct = true;
// start calculating gradient
const long ndata = static_cast<bst_omp_uint>(preds.size());
const long ndata = static_cast<bst_omp_uint>(preds.size()); // NOLINT(*)
#pragma omp parallel for schedule(static)
for (long i = 0; i < ndata; ++i) {
for (long i = 0; i < ndata; ++i) { // NOLINT(*)
float p = preds[i];
float w = info.GetWeight(i);
float y = info.labels[i];
@@ -219,9 +221,9 @@ class PoissonRegression : public IObjFunction {
}
virtual void PredTransform(std::vector<float> *io_preds) {
std::vector<float> &preds = *io_preds;
const long ndata = static_cast<long>(preds.size());
const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
#pragma omp parallel for schedule(static)
for (long j = 0; j < ndata; ++j) {
for (long j = 0; j < ndata; ++j) { // NOLINT(*)
preds[j] = std::exp(preds[j]);
}
}
@@ -234,7 +236,7 @@ class PoissonRegression : public IObjFunction {
virtual const char* DefaultEvalMetric(void) const {
return "poisson-nloglik";
}
private:
float max_delta_step;
};
@@ -467,7 +469,7 @@ class LambdaRankObj : public IObjFunction {
: pos_index(pos_index), neg_index(neg_index), weight(1.0f) {}
};
/*!
* \brief get lambda weight for existing pairs
* \brief get lambda weight for existing pairs
* \param list a list that is sorted by pred score
* \param io_pairs record of pairs, containing the pairs to fill in weights
*/
@@ -555,10 +557,10 @@ class LambdaRankObjMAP : public LambdaRankObj {
float ap_acc;
/*!
* \brief the accumulated precision,
* assuming a positive instance is missing
* assuming a positive instance is missing
*/
float ap_acc_miss;
/*!
/*!
* \brief the accumulated precision,
* assuming that one more positive instance is inserted ahead
*/