Added a range parameter to calculate impostors after some particular iterations

src/mlpack/methods/lmnn/lmnn.hpp

@@ -75,6 +75,11 @@ class LMNN
   //! Modify the regularization value.
   double& Regularization() { return objFunction.Regularization(); }
 
+  //! Access the range value.
+  const size_t& Range() const { return objFunction.Range(); }
+  //! Modify the range value.
+  size_t& Range() { return objFunction.Range(); }
+
   //! Access the value of k.
   const size_t& K() const { return k; }
 

src/mlpack/methods/lmnn/lmnn_function.hpp

@@ -157,6 +157,11 @@ class LMNNFunction
   //! Modify the value of k.
   size_t& K() { return k; }
 
+  //! Access the value of range.
+  const size_t& Range() const { return range; }
+  //! Modify the value of k.
+  size_t& Range() { return range; }
+
  private:
   //! data.  This will be an alias until Shuffle() is called.
   arma::mat dataset;
@@ -176,6 +181,10 @@ class LMNNFunction
   MetricType metric;
   //! Regularization value.
   double regularization;
+  //! Keep iterations count.
+  size_t iteration;
+  //! Range after which impostors need to be recalculated.
+  size_t range;
   //! Holds pre-calculated cij.
   arma::mat p_cij;
   //! False if nothing has ever been precalculated.

src/mlpack/methods/lmnn/lmnn_function_impl.hpp

@@ -32,6 +32,8 @@ LMNNFunction<MetricType>::LMNNFunction(const arma::mat& dataset,
     k(k),
     metric(metric),
     regularization(regularization),
+    iteration(0),
+    range(1),
     precalculated(false)
 {
   // Initialize the initial learning point.
@@ -74,9 +76,12 @@ double LMNNFunction<MetricType>::Evaluate(const arma::mat& coordinates)
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  // Re-calculate impostors on transformed dataset.
-  Constraints constraint(transformedDataset, labels, k);
-  constraint.Impostors(impostors);
+  if (iteration++ % range == 0)
+  {
+    // Re-calculate impostors on transformed dataset.
+    Constraints constraint(transformedDataset, labels, k);
+    constraint.Impostors(impostors);
+  }
 
   for (size_t i = 0; i < dataset.n_cols; i++)
   {
@@ -128,8 +133,12 @@ double LMNNFunction<MetricType>::Evaluate(const arma::mat& coordinates,
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  Constraints constraint(transformedDataset, labels, k);
-  constraint.Impostors(impostors, begin, batchSize);
+  if (iteration++ % range == 0)
+  {
+    // Re-calculate impostors on transformed dataset.
+    Constraints constraint(transformedDataset, labels, k);
+    constraint.Impostors(impostors, begin, batchSize);
+  }
 
   for (size_t i = begin; i < begin + batchSize; i++)
   {
@@ -286,9 +295,12 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  // Calculate impostors.
-  Constraints constraint(transformedDataset, labels, k);
-  constraint.Impostors(impostors);
+  if (iteration++ % range == 0)
+  {
+    // Re-calculate impostors on transformed dataset.
+    Constraints constraint(transformedDataset, labels, k);
+    constraint.Impostors(impostors);
+  }
 
   gradient.zeros(coordinates.n_rows, coordinates.n_cols);
 
@@ -361,9 +373,12 @@ double LMNNFunction<MetricType>::EvaluateWithGradient(
   // Apply metric over dataset.
   transformedDataset = coordinates * dataset;
 
-  // Calculate impostors.
-  Constraints constraint(transformedDataset, labels, k);
-  constraint.Impostors(impostors, begin, batchSize);
+  if (iteration++ % range == 0)
+  {
+    // Re-calculate impostors on transformed dataset.
+    Constraints constraint(transformedDataset, labels, k);
+    constraint.Impostors(impostors, begin, batchSize);
+  }
 
   gradient.zeros(coordinates.n_rows, coordinates.n_cols);
 

src/mlpack/methods/lmnn/lmnn_main.cpp

@@ -134,6 +134,8 @@ PARAM_DOUBLE_IN("min_step", "Minimum step of line search for L-BFGS.", "m",
     1e-20);
 PARAM_DOUBLE_IN("max_step", "Maximum step of line search for L-BFGS.", "M",
     1e20);
+PARAM_INT_IN("range", "Number of iterations after which impostors needs to be "
+    "recalculated", "R", 1);
 PARAM_INT_IN("seed", "Random seed.  If 0, 'std::time(NULL)' is used.", "s", 0);
 
 using namespace mlpack;
@@ -234,6 +236,7 @@ static void mlpackMain()
   const double beta2 = CLI::GetParam<double>("beta2");
   const double epsilon = CLI::GetParam<double>("epsilon");
   const double batchDelta = CLI::GetParam<double>("batch_delta");
+  const size_t range = (size_t) CLI::GetParam<int>("range");
 
   // Load data.
   arma::mat data = std::move(CLI::GetParam<arma::mat>("input"));
@@ -283,6 +286,7 @@ static void mlpackMain()
   {
     LMNN<LMetric<2>> lmnn(data, labels, numTargets);
     lmnn.Regularization() = regularization;
+    lmnn.Range() = range;
     lmnn.Optimizer().StepSize() = stepSize;
     lmnn.Optimizer().MaxIterations() = passes * data.n_cols;
     lmnn.Optimizer().Beta1() = beta1;
@@ -298,6 +302,7 @@ static void mlpackMain()
   {
     LMNN<LMetric<2>, BBS_BB> lmnn(data, labels, numTargets);
     lmnn.Regularization() = regularization;
+    lmnn.Range() = range;
     lmnn.Optimizer().StepSize() = stepSize;
     lmnn.Optimizer().BatchDelta() = batchDelta;
     lmnn.Optimizer().MaxIterations() = passes * data.n_cols;
@@ -313,6 +318,7 @@ static void mlpackMain()
     // diverge to inf causing serious memory problems.
     LMNN<LMetric<2>, StandardSGD> lmnn(data, labels, numTargets);
     lmnn.Regularization() = regularization;
+    lmnn.Range() = range;
     lmnn.Optimizer().StepSize() = stepSize;
     lmnn.Optimizer().MaxIterations() = passes * data.n_cols;
     lmnn.Optimizer().Tolerance() = tolerance;
@@ -325,6 +331,7 @@ static void mlpackMain()
   {
     LMNN<LMetric<2>, L_BFGS> lmnn(data, labels, numTargets);
     lmnn.Regularization() = regularization;
+    lmnn.Range() = range;
     lmnn.Optimizer().NumBasis() = numBasis;
     lmnn.Optimizer().MaxIterations() = maxIterations;
     lmnn.Optimizer().ArmijoConstant() = armijoConstant;