BayesNet/sample/lib/mdlp/Metrics.cpp

78 lines
2.4 KiB
C++

#include "Metrics.h"
#include <set>
#include <cmath>
using namespace std;
namespace mdlp {
Metrics::Metrics(labels_t& y_, indices_t& indices_): y(y_), indices(indices_),
numClasses(computeNumClasses(0, indices.size()))
{
}
int Metrics::computeNumClasses(size_t start, size_t end)
{
set<int> nClasses;
for (auto i = start; i < end; ++i) {
nClasses.insert(y[indices[i]]);
}
return static_cast<int>(nClasses.size());
}
void Metrics::setData(const labels_t& y_, const indices_t& indices_)
{
indices = indices_;
y = y_;
numClasses = computeNumClasses(0, indices.size());
entropyCache.clear();
igCache.clear();
}
precision_t Metrics::entropy(size_t start, size_t end)
{
precision_t p;
precision_t ventropy = 0;
int nElements = 0;
labels_t counts(numClasses + 1, 0);
if (end - start < 2)
return 0;
if (entropyCache.find({ start, end }) != entropyCache.end()) {
return entropyCache[{start, end}];
}
for (auto i = &indices[start]; i != &indices[end]; ++i) {
counts[y[*i]]++;
nElements++;
}
for (auto count : counts) {
if (count > 0) {
p = static_cast<precision_t>(count) / static_cast<precision_t>(nElements);
ventropy -= p * log2(p);
}
}
entropyCache[{start, end}] = ventropy;
return ventropy;
}
precision_t Metrics::informationGain(size_t start, size_t cut, size_t end)
{
precision_t iGain;
precision_t entropyInterval;
precision_t entropyLeft;
precision_t entropyRight;
size_t nElementsLeft = cut - start;
size_t nElementsRight = end - cut;
size_t nElements = end - start;
if (igCache.find(make_tuple(start, cut, end)) != igCache.end()) {
return igCache[make_tuple(start, cut, end)];
}
entropyInterval = entropy(start, end);
entropyLeft = entropy(start, cut);
entropyRight = entropy(cut, end);
iGain = entropyInterval -
(static_cast<precision_t>(nElementsLeft) * entropyLeft +
static_cast<precision_t>(nElementsRight) * entropyRight) /
static_cast<precision_t>(nElements);
igCache[make_tuple(start, cut, end)] = iGain;
return iGain;
}
}