Perceptron.Rmd

Perceptron <- function(data, learningRate) {
  #Make the weights vector with 0s for each data coloumn
  w <- c(rep(0, ncol(data) -1))
  #The number of rows in the dataframe
  n <- nrow(data)
  #Get the labels from the dataframe
  label <- data[ , 1]
  #Get the data from from the dataframe
  obs <- data[ , 2:ncol(data)]
  #We start assuming that the data is misclassifies
  misclassfied <- TRUE
  #Store the epoc count
  epoc <- 0
  #Vector to store the predicted values
  predictedV <- c(rep(0, n))
  #Store the average error for each Epoc
  avgErr <- 0
  
  while (misclassfied) {
    #Store the sum or errors for each Epoc
    sumErr <- 0
    #Assume that the data isn't misclassified
    misclassfied <- FALSE
    for (i in 1:n) {
      #Get the predicted value from the Classify function (WeightsxData columns)
      predicted <- Classify(as.numeric(obs[i , ]), w)[1,1]
      #Store the predicted value in the vector
      predictedV[i] = predicted
      
      #If we miss classified the observaion
      if(predicted != label[i]){
        misclassfied = TRUE
        #Calculate how we should update the weight vector
        change <- (learningRate * (label[i] - predicted)) * obs[i , ]
        #Update the weight vector
        w <- w + as.numeric(change)
        #Add this error for the total sum of errors for this epoc
        sumErr <- sumErr + (abs(label[i] - predictedV[i]))
      }
    }
    #Calculate the average error for all epocs
    avgErr <- (avgErr + (sumErr/n))/2
    #Update the number of Epocs
    epoc <- epoc + 1
  }
  #Print the average error and the number of EPOCS
  print(paste("Avg Error rate per epoc: ", as.character(avgErr)))
  print(paste("EPOC: ", as.character(epoc)))
  #Return the weight vector
  return(w)
}