Commit 85d62337 authored by Davis King's avatar Davis King

Minor tweak

parent f22d6736
...@@ -138,7 +138,7 @@ template <typename T> static auto go(T&& f, const matrix<double, 0, 1>& a) -> de ...@@ -138,7 +138,7 @@ template <typename T> static auto go(T&& f, const matrix<double, 0, 1>& a) -> de
{ {
for (long j = 0; j < specs[i].lower.size(); ++j) for (long j = 0; j < specs[i].lower.size(); ++j)
{ {
if (!specs[i].is_integer_variable[j] && specs[i].lower(j) > 0 && specs[i].upper(j)/specs[i].lower(j) > 1000) if (!specs[i].is_integer_variable[j] && specs[i].lower(j) > 0 && specs[i].upper(j)/specs[i].lower(j) >= 1000)
{ {
log_scale[i].push_back(true); log_scale[i].push_back(true);
specs[i].lower(j) = std::log(specs[i].lower(j)); specs[i].lower(j) = std::log(specs[i].lower(j));
......
...@@ -124,7 +124,7 @@ namespace dlib ...@@ -124,7 +124,7 @@ namespace dlib
- More than max_runtime time has elapsed since the start of this function. - More than max_runtime time has elapsed since the start of this function.
- Any variables that satisfy the following conditions are optimized on a log-scale: - Any variables that satisfy the following conditions are optimized on a log-scale:
- The lower bound on the variable is > 0 - The lower bound on the variable is > 0
- The ratio of the upper bound to lower bound is > 1000 - The ratio of the upper bound to lower bound is >= 1000
- The variable is not an integer variable - The variable is not an integer variable
We do this because it's common to optimize machine learning models that have We do this because it's common to optimize machine learning models that have
parameters with bounds in a range such as [1e-5 to 1e10] (e.g. the SVM C parameters with bounds in a range such as [1e-5 to 1e10] (e.g. the SVM C
...@@ -207,7 +207,7 @@ namespace dlib ...@@ -207,7 +207,7 @@ namespace dlib
- More than max_runtime time has elapsed since the start of this function. - More than max_runtime time has elapsed since the start of this function.
- Any variables that satisfy the following conditions are optimized on a log-scale: - Any variables that satisfy the following conditions are optimized on a log-scale:
- The lower bound on the variable is > 0 - The lower bound on the variable is > 0
- The ratio of the upper bound to lower bound is > 1000 - The ratio of the upper bound to lower bound is >= 1000
- The variable is not an integer variable - The variable is not an integer variable
We do this because it's common to optimize machine learning models that have We do this because it's common to optimize machine learning models that have
parameters with bounds in a range such as [1e-5 to 1e10] (e.g. the SVM C parameters with bounds in a range such as [1e-5 to 1e10] (e.g. the SVM C
......
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