d594b11f35
* Implement Transform class. * Add tests for softmax. * Use Transform in regression, softmax and hinge objectives, except for Cox. * Mark old gpu objective functions deprecated. * static_assert for softmax. * Split up multi-gpu tests.
153 lines
3.8 KiB
C++
153 lines
3.8 KiB
C++
/*!
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* Copyright 2015 by Contributors
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* \file math.h
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* \brief additional math utils
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* \author Tianqi Chen
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*/
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#ifndef XGBOOST_COMMON_MATH_H_
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#define XGBOOST_COMMON_MATH_H_
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#include <utility>
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#include <vector>
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#include <cmath>
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#include <algorithm>
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#include <utility>
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#include "avx_helpers.h"
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namespace xgboost {
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namespace common {
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/*!
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* \brief calculate the sigmoid of the input.
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* \param x input parameter
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* \return the transformed value.
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*/
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XGBOOST_DEVICE inline float Sigmoid(float x) {
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return 1.0f / (1.0f + expf(-x));
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}
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inline avx::Float8 Sigmoid(avx::Float8 x) {
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return avx::Sigmoid(x);
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}
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/*!
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* \brief Do inplace softmax transformaton on start to end
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*
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* \tparam Iterator Input iterator type
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*
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* \param start Start iterator of input
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* \param end end iterator of input
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*/
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template <typename Iterator>
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XGBOOST_DEVICE inline void Softmax(Iterator start, Iterator end) {
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static_assert(std::is_same<bst_float,
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typename std::remove_reference<
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decltype(std::declval<Iterator>().operator*())>::type
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>::value,
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"Values should be of type bst_float");
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bst_float wmax = *start;
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for (Iterator i = start+1; i != end; ++i) {
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wmax = fmaxf(*i, wmax);
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}
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double wsum = 0.0f;
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for (Iterator i = start; i != end; ++i) {
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*i = expf(*i - wmax);
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wsum += *i;
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}
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for (Iterator i = start; i != end; ++i) {
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*i /= static_cast<float>(wsum);
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}
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}
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/*!
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* \brief Find the maximum iterator within the iterators
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* \param begin The begining iterator.
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* \param end The end iterator.
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* \return the iterator point to the maximum value.
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* \tparam Iterator The type of the iterator.
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*/
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template<typename Iterator>
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XGBOOST_DEVICE inline Iterator FindMaxIndex(Iterator begin, Iterator end) {
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Iterator maxit = begin;
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for (Iterator it = begin; it != end; ++it) {
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if (*it > *maxit) maxit = it;
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}
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return maxit;
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}
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/*!
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* \brief perform numerically safe logsum
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* \param x left input operand
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* \param y right input operand
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* \return log(exp(x) + exp(y))
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*/
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inline float LogSum(float x, float y) {
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if (x < y) {
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return y + std::log(std::exp(x - y) + 1.0f);
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} else {
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return x + std::log(std::exp(y - x) + 1.0f);
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}
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}
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/*!
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* \brief perform numerically safe logsum
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* \param begin The begining iterator.
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* \param end The end iterator.
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* \return the iterator point to the maximum value.
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* \tparam Iterator The type of the iterator.
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*/
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template<typename Iterator>
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inline float LogSum(Iterator begin, Iterator end) {
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float mx = *begin;
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for (Iterator it = begin; it != end; ++it) {
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mx = std::max(mx, *it);
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}
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float sum = 0.0f;
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for (Iterator it = begin; it != end; ++it) {
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sum += std::exp(*it - mx);
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}
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return mx + std::log(sum);
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}
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// comparator functions for sorting pairs in descending order
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inline static bool CmpFirst(const std::pair<float, unsigned> &a,
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const std::pair<float, unsigned> &b) {
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return a.first > b.first;
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}
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inline static bool CmpSecond(const std::pair<float, unsigned> &a,
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const std::pair<float, unsigned> &b) {
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return a.second > b.second;
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}
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#if XGBOOST_STRICT_R_MODE
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// check nan
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bool CheckNAN(double v);
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double LogGamma(double v);
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#else
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template<typename T>
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inline bool CheckNAN(T v) {
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#ifdef _MSC_VER
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return (_isnan(v) != 0);
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#else
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return std::isnan(v);
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#endif
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}
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template<typename T>
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inline T LogGamma(T v) {
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#ifdef _MSC_VER
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#if _MSC_VER >= 1800
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return lgamma(v);
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#else
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#pragma message("Warning: lgamma function was not available until VS2013"\
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", poisson regression will be disabled")
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utils::Error("lgamma function was not available until VS2013");
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return static_cast<T>(1.0);
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#endif
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#else
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return lgamma(v);
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#endif
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}
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#endif // XGBOOST_STRICT_R_MODE_
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} // namespace common
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} // namespace xgboost
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#endif // XGBOOST_COMMON_MATH_H_
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