Machine Learning with Swift

Training the logistic regression

The training part is also very similar to linear regression:

public func train(xMat: [[Double]], yVec: [Double], learningRate: Double, maxSteps: Int) { 
  precondition(maxSteps > 0, "The number of learning iterations should be grater then 0.") 
  let sampleCount = xMat.count 
  precondition(sampleCount == yVec.count, "The number of samples in xMat should be equal to the number of labels in yVec.") 
  precondition(sampleCount > 0, "xMat should contain at least one sample.") 
  precondition(xMat.first!.count > 0, "Samples should have at least one feature.") 
  let featureCount = xMat.first!.count 
  let weightsCount = featureCount+1 
     
  weights = [Double](repeating: 1.0, count: weightsCount) 
     
  if normalization {     // Flatten let flattenedXMat ...