[mlpack-svn] r16755 - mlpack/trunk/src/mlpack/core/optimizers/sa
fastlab-svn at coffeetalk-1.cc.gatech.edu
fastlab-svn at coffeetalk-1.cc.gatech.edu
Thu Jul 3 10:10:14 EDT 2014
Author: rcurtin
Date: Thu Jul 3 10:10:14 2014
New Revision: 16755
Log:
Minor cleanups; no functionality changes. Make some things const where
possible.
Modified:
mlpack/trunk/src/mlpack/core/optimizers/sa/exponential_schedule.hpp
mlpack/trunk/src/mlpack/core/optimizers/sa/sa.hpp
mlpack/trunk/src/mlpack/core/optimizers/sa/sa_impl.hpp
Modified: mlpack/trunk/src/mlpack/core/optimizers/sa/exponential_schedule.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/core/optimizers/sa/exponential_schedule.hpp (original)
+++ mlpack/trunk/src/mlpack/core/optimizers/sa/exponential_schedule.hpp Thu Jul 3 10:10:14 2014
@@ -1,49 +1,61 @@
-/*
+/**
* @file exponential_schedule.hpp
* @author Zhihao Lou
*
- * Exponential (geometric) cooling schedule used in SA
+ * Exponential (geometric) cooling schedule used in SA.
*/
-
#ifndef __MLPACK_CORE_OPTIMIZERS_SA_EXPONENTIAL_SCHEDULE_HPP
#define __MLPACK_CORE_OPTIMIZERS_SA_EXPONENTIAL_SCHEDULE_HPP
namespace mlpack {
namespace optimization {
-/*
+/**
* The exponential cooling schedule cools the temperature T at every step
+ * according to the equation
+ *
* \f[
- * T_{n+1}=(1-\lambda)T_{n}
+ * T_{n+1} = (1-\lambda) T_{n}
* \f]
+ *
* where \f$ 0<\lambda<1 \f$ is the cooling speed. The smaller \f$ \lambda \f$
* is, the slower the cooling speed, and better the final result will be. Some
- * literature uses \f$ \alpha=(-1\lambda) \f$ instead. In practice, \f$ \alpha \f$
- * is very close to 1 and will be awkward to input (e.g. alpha=0.999999 vs
- * lambda=1e-6).
+ * literature uses \f$ \alpha = (-1 \lambda) \f$ instead. In practice,
+ * \f$ \alpha \f$ is very close to 1 and will be awkward to input (e.g.
+ * alpha = 0.999999 vs lambda = 1e-6).
*/
class ExponentialSchedule
{
public:
- /*
- * Construct the ExponentialSchedule with the given parameter
+ /*
+ * Construct the ExponentialSchedule with the given parameter.
+ *
+ * @param lambda Cooling speed.
+ */
+ ExponentialSchedule(const double lambda = 0.001) : lambda(lambda) { }
+
+ /**
+ * Returns the next temperature given current status. The current system's
+ * energy is not used in this calculation.
*
- * @param lambda Cooling speed
+ * @param currentTemperature Current temperature of system.
+ * @param currentEnergy Current energy of system (not used).
*/
- ExponentialSchedule(const double lambda = 0.001) : lambda(lambda){};
+ double NextTemperature(
+ const double currentTemperature,
+ const double /* currentEnergy */)
+ {
+ return (1 - lambda) * currentTemperature;
+ }
+
+ //! Get the cooling speed, lambda.
+ double Lambda() const { return lambda; }
+ //! Modify the cooling speed, lambda.
+ double& Lambda() { return lambda; }
- //! returns the next temperature given current status
- double nextTemperature(const double currentTemperate, const double)
- {return (1-lambda) * currentTemperate;}
-
- //! Get the cooling speed lambda
- double Lambda() const {return lambda;}
- //! Modify the cooling speed lambda
- double& Lambda() {return lambda;}
private:
+ //! The cooling speed.
double lambda;
-
-
};
}; // namespace optimization
Modified: mlpack/trunk/src/mlpack/core/optimizers/sa/sa.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/core/optimizers/sa/sa.hpp (original)
+++ mlpack/trunk/src/mlpack/core/optimizers/sa/sa.hpp Thu Jul 3 10:10:14 2014
@@ -19,18 +19,18 @@
* schedule can be changed via a template parameter.
*
* The algorithm keeps the temperature at initial temperature for initMove
- * steps to get rid of the dependency of initial condition. After that, it
+ * steps to get rid of the dependency on the initial condition. After that, it
* cools every step until the system is considered frozen or maxIterations is
* reached.
*
- * At each step, SA only perturbs one parameter at a time. The process that SA
- * perturbed all parameters in a problem is called a sweep. Every moveCtrlSweep
- * the algorithm does feedback move control to change the average move size
- * depending on the responsiveness of each parameter. Parameter gain controls
- * the proportion of the feedback control.
+ * At each step, SA only perturbs one parameter at a time. When SA has perturbed
+ * all parameters in a problem, a sweep has been completed. Every moveCtrlSweep
+ * sweeps, the algorithm does feedback move control to change the average move
+ * size depending on the responsiveness of each parameter. Parameter gain
+ * controls the proportion of the feedback control.
*
- * The system is considered "frozen" when its score failed to change more then
- * tolerance for consecutive maxToleranceSweep sweeps.
+ * The system is considered "frozen" when its score fails to change more then
+ * tolerance for maxToleranceSweep consecutive sweeps.
*
* For SA to work, the FunctionType parameter must implement the following
* two methods:
@@ -53,7 +53,7 @@
class SA
{
public:
- /*
+ /**
* Construct the SA optimizer with the given function and parameters.
*
* @param function Function to be minimized.
@@ -81,8 +81,7 @@
const double initMoveCoef = 0.3,
const double gain = 0.3);
-
- /*&
+ /**
* Optimize the given function using simulated annealing. The given starting
* point will be modified to store the finishing point of the algorithm, and
* the final objective value is returned.
Modified: mlpack/trunk/src/mlpack/core/optimizers/sa/sa_impl.hpp
==============================================================================
--- mlpack/trunk/src/mlpack/core/optimizers/sa/sa_impl.hpp (original)
+++ mlpack/trunk/src/mlpack/core/optimizers/sa/sa_impl.hpp Thu Jul 3 10:10:14 2014
@@ -1,4 +1,4 @@
-/*
+/**
* @file sa_impl.hpp
* @auther Zhihao Lou
*
@@ -57,7 +57,6 @@
const size_t rows = function.GetInitialPoint().n_rows;
const size_t cols = function.GetInitialPoint().n_cols;
- size_t i;
size_t frozenCount = 0;
double energy = function.Evaluate(iterate);
size_t oldEnergy = energy;
@@ -69,16 +68,16 @@
arma::mat accept(rows, cols);
accept.zeros();
- // Initial Moves to get rid of dependency of initial states.
- for (i = 0; i < initMoves; ++i)
+ // Initial moves to get rid of dependency of initial states.
+ for (size_t i = 0; i < initMoves; ++i)
GenerateMove(iterate, accept, energy, idx, sweepCounter);
// Iterating and cooling.
- for (i = 0; i != maxIterations; ++i)
+ for (size_t i = 0; i != maxIterations; ++i)
{
oldEnergy = energy;
GenerateMove(iterate, accept, energy, idx, sweepCounter);
- temperature = coolingSchedule.nextTemperature(temperature, energy);
+ temperature = coolingSchedule.NextTemperature(temperature, energy);
// Determine if the optimization has entered (or continues to be in) a
// frozen state.
@@ -121,8 +120,8 @@
size_t& idx,
size_t& sweepCounter)
{
- double prevEnergy = energy;
- double prevValue = iterate(idx);
+ const double prevEnergy = energy;
+ const double prevValue = iterate(idx);
// It is possible to use a non-Laplace distribution here, but it is difficult
// because the acceptance ratio should be as close to 0.44 as possible, and
@@ -135,11 +134,11 @@
iterate(idx) += move;
energy = function.Evaluate(iterate);
- // According to Metropolis criterion, accept the move with probability
+ // According to the Metropolis criterion, accept the move with probability
// min{1, exp(-(E_new - E_old) / T)}.
- double xi = math::Random();
- double delta = energy - prevEnergy;
- double criterion = std::exp(-delta / temperature);
+ const double xi = math::Random();
+ const double delta = energy - prevEnergy;
+ const double criterion = std::exp(-delta / temperature);
if (delta <= 0. || criterion > xi)
{
accept(idx) += 1.;
@@ -164,7 +163,7 @@
}
}
-/*
+/**
* MoveControl() uses a proportional feedback control to determine the size
* parameter to pass to the move generation distribution. The target of such
* move control is to make the acceptance ratio, accept/nMoves, be as close to
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