Creating Distance-Minimization-Based Clustering Targeting Model

Hi guys! In this tutorial, we will demonstrate on how to create cluster-based targeting model for your weapon systems.

For best results, please use:

  • K-Means

    • This model sets up X number of clusters and finds the center of data.

  • Agglomerative Hierarchical

    • This model sets up number of clusters that are equal to number of data and merge them together until it forms X number of clusters.

  • MeanShift

    • This model sets up number of clusters that are equal to number of data and merge them together until it forms X number of clusters.

    • Trickier to set up.

Initializing The Clustering Model

Before we can produce ourselves a targeting model, we first need to construct a model, which is shown below. Ensure that the distance function is not “CosineDistance”.


local TargetingModel = DataPredict.Models.KMeans.new({numberOfClusters = 3, distanceFunction = "Euclidean"}) -- For this tutorial, we will assume that we have three missiles, so only three locations it can land.

Collecting The Players’ Locations

In order to find the center of the clusters, we first need all the players’ location data and put them into a matrix.


local playerLocationDataMatrix = {

  {player1LocationX, player1LocationY, player1LocationZ},
  {player2LocationX, player2LocationY, player2LocationZ},
  {player3LocationX, player3LocationY, player3LocationZ},
  {player4LocationX, player4LocationY, player4LocationZ},
  {player5LocationX, player5LocationY, player5LocationZ},
  {player6LocationX, player6LocationY, player6LocationZ},
  {player7LocationX, player7LocationY, player7LocationZ},

}

Getting The Center Of Clusters

Once you collected the players’ location data, you must call model’s train() function. This will generate the center of clusters to the model parameters.


TargetingModel:train(playerLocationDataMatrix)

Once train() is called, call the getModelParameters() function to get the center of cluster location data.


local centroidMatrix = TargetingModel:getModelParameters()

centroidMatrix = centroidMatrix[1] -- This is a must if you're using K-Means or Meanshift because they store the ModelParameters as a table of matrices.

Interacting With The Center Of Clusters

Since we have three clusters, we can expect three rows for our matrix. As such we can process our game logic here.


for clusterIndex, unwrappedClusterVector in ipairs(ModelParameters) do

  local x = unwrappedClusterVector[1]
  
  local y = unwrappedClusterVector[2]
  
  local z = unwrappedClusterVector[3]

  landMissileAt(x, y, z)

end

Resetting Our Targeting System

By default, when you reuse the machine learning models from DataPredict, it will interact with the existing model parameters. As such, we need to reset the model parameters by calling the setModelParameters() function and set it to “nil”.


TargetingModel:setModelParameters(nil)

That’s all for today!