Creating Probability-Based Clustering Placement Model

Hi guys! In this tutorial, we will demonstrate on how to create cluster-based placement model.

You must use Expectation-Maximization model.

Initializing The Clustering Model

-- For this tutorial, we will let the model decide how many clusters it will produce based on player / asset spread.

-- Note, we're setting math.huge here, but that doesn't mean we will begin producing an infinite amount of clusters! It will start at 1 and increases it until the model finds a suitable number of clusters.

local PlacementModel = DataPredict.Models.ExpectationMaximization.new({numberOfClusters = math.huge})

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.

PlacementModel:train(playerLocationDataMatrix)

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

local centroidMatrix = PlacementModel:getModelParameters()

centroidMatrix = centroidMatrix[1]

Interacting With The Center Of Clusters

Since we have dynamic number of clusters, we can expect multiple rows for our matrix. As such we can process our game logic here.

Asset Placement

local function placeAssetAtRandomLocation(Asset)

  local ModelParameters = PlacementModel:getModelParameters()

  local meanMatrix = ModelParameters[1]
  
  local varianceMatrix = ModelParameters[2]
  
  local numberOfClusters = #meanMatrix
  
  local randomClusterIndex = math.random(1, numberOfClusters)

  local randomUnwrappedMeanVector = meanMatrix[randomClusterIndex]

  local randomUnwrappedVarianceVector = varianceMatrix[randomClusterIndex]

  local x = randomUnwrappedMeanVector[1] + ((math.random() * 2 - 1) * randomUnwrappedVarianceVector[1])

  local y = randomUnwrappedMeanVector[2] + ((math.random() * 2 - 1) * randomUnwrappedVarianceVector[2])

  local z = randomUnwrappedMeanVector[3] + ((math.random() * 2 - 1) * randomUnwrappedVarianceVector[3])

  placeAsset(asset, x, y, z)

end

Asset Removal

local AssetFolder = workspace.AssetFolder

local removeAssetDistanceThreshold = 10

local function removeAllAssetsWithLowInteractionProbability()

  local ModelParameters = PlacementModel:getModelParameters()

  local meanMatrix = ModelParameters[1]

  local assetFolderChildren = AssetFolder:getChildren()

  for _, child in ipairs(assetFolderChildren) do

    local keepChild = false

    local childPosition = child.Position

    local childPositionX = childPosition.X

    local childPositionY = childPosition.Y

    local childPositionZ = childPosition.Z

    for _, unwrappedMeanVector in ipairs(meanMatrix) do

     local distanceVector = Vector3.new(childPositionX - unwrappedMeanVector[1], childPositionY - unwrappedMeanVector[2], childPositionZ - unwrappedMeanVector[3])

     local distance = distanceVector.Magnitude

     if (distance > removeAssetDistanceThreshold) then continue end

     keepChild = true

     break

    end

    if (not keepChild) then child:Destroy() end

  end

end

Resetting Our Placement 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”.

PlacementModel:setModelParameters(nil)

That’s all for today!