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diff --git a/Clever-Challenge AMontreuil-Dallaire H2020.R b/Clever-Challenge AMontreuil-Dallaire H2020.R
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+#Part 3
+#The third part of the challenge investigates sequences. In the seq folder, you will find two csv files. 
+#The first file sample.csv contains ~18k events that are classified into two classes: 0 and 1. 
+#All events are uniquely identified by their id and occurs at a precise timestamp. 
+#In addition to the id, the timestamp and the class each event is further categories using 30 metrics (f1, ..., f30).
+
+#Another file, named res.csv contains ids of the ressources used by the events describeb in sample.csv.
+
+#In this part, we are interested in predicting, in advance, what will be the class of the next 1-,5- and 10- events, 
+#given the information known at a precise time. For example, using the all relevent information we know after event 829454 
+#(f1 to f30 and ressources information) can you predict the class of event 829455, 829455@829459 and 829455@829464.
+
+#You are free to use the technics / languages of your choice.
+
+
+
+#Set-up the data
+
+require(Matrix)
+require(ISLR)
+
+#Sample data
+sampleData <- read.csv(file='seq/sample.csv')
+sData <- sampleData
+rownames(sData) <- paste('ID', sData[,1], sep='')
+
+#Ressources data
+resData <- read.csv(file='seq/res.csv')
+resSM <- sparseMatrix(resData[,1],resData[,2])
+
+#Reshape the sparse matrix to the scope of ids and ressources we have
+rownames(resSM) <- paste('ID', 1:dim(resSM)[1], sep='')
+colnames(resSM) <- paste('RES', 1:dim(resSM)[2], sep='')
+res <-resSM[rowSums(resSM)>0,colSums(resSM)>0]
+
+
+##Functions
+
+#Determines the similarities between the events with the cosinus formula
+#v : vector of the event we want to predict
+#m : matrix of all the other events that has at least 1 common ressource with the event we want to predict
+#thr : threshold to determine if an event is similar or not
+#return : a vector of names of the event that are at least similar as the threshold
+cosinusVM <- function(v, m, thr) {
+  n <- sqrt(colSums(m^2))
+  cos <- (v %*% m)/(n * sqrt(sum(v^2)))
+  cos <- as.matrix(cos)
+  colnames(cos)<-colnames(m)
+  simi <- colnames(cos)[cos>thr]
+  return(simi)
+}
+
+
+#Get the similar events based on the common ressources with the given event, evaluated with the cosinus function.
+#res : sparse matrix of the ressources data
+#id : name of the event
+#thr : threshold to determine if an event is similar or not
+#return : a vector of names of the most similar events
+CommonRessources <- function(res, id, thr){
+  resEvent <- res[id,]>0
+  resTF <- t(res>0)
+  resIdEvent <- t(resTF*resEvent)
+  similarIds <- res[rowSums(resIdEvent)>0,]
+  common <- similarIds[,colSums(similarIds)>0]
+  if(!(class(common) == "ngCMatrix")){
+    common <- as.matrix(common)
+    colnames(common) <- colnames(similarIds)[colSums(similarIds)>0]
+  }
+  commonT <- t(common)
+  ordo <- cosinusVM(commonT[,id], commonT, thr)
+  return(ordo)
+}
+
+#Verifies if the predicted class correspond to the real class of the event
+#sData : Samples data with all the features of the events
+#id : name of the event
+#classPredict : predicted class
+#Return : boolean saying if we predicted right
+ResultPrediction <- function(sData, id, classPredict){
+  classReal <- sData[id, 3]
+  return(classReal == classPredict)
+}
+
+#Estimates the class of a given event, based on a trained logistic regression model on the similar events
+#event : number of the event we want to predict
+#sData : Samples data with all the features of the events
+#res : sparse matrix of the ressources data
+#thr : threshold to determine if an event is similar or not
+EstimateClassResult <- function(event, sData, res, thr){
+  id <- paste('ID', event, sep='')
+  # If the event has no ressources in common with any other events, return 0
+  if(sum(res[id,])){
+    if(sum(res[,colnames(res)[res[id,]>0]])==sum(res[id,]>0)){
+      return(0)
+    }
+  }
+  similarID <- CommonRessources(res, id, thr)
+  #If the only id enough similar is itself, we can't predict the class
+  if (sum(res[similarID,]) == sum(res[id,])){
+    return(0)
+  }
+  sampleTrain <- sData[similarID,]
+  sampleTrain <- sampleTrain[-which(rownames(sampleTrain) == id),]
+  sampleTest <- sData[id,]
+  sampleLogistic <- glm(class ~ f1 + f2 + f3 + f3.1 + f4 + f5 + f6 + f7 + f8 + f9 + f10 + f11 + f12 + f13 + f14 + f15 + f16 + f17 + f18 + f19 + f20 + f21 + f21, data = sampleTrain, family = binomial)
+  idProb <- predict(sampleLogistic, newdata = sampleTest, type = "response")
+  classPredict <- (idProb>0.5)*1
+  return(classPredict)
+}
+
+
+#Classifies an array of event
+#sData : Samples data with all the features of the events
+#res : sparse matrix of the ressources data
+#thr : threshold to determine if an event is similar or not
+#Return : a vector of the predicted classes
+Classificator <- function(sData, res, thr, part){
+  classPredict <- rep(0, length(part))
+  i <- 1
+  for (p in part){
+    id <- paste('ID', p, sep='')
+    if (!is.element(id, rownames(res))){
+      classPredict[i] <- 0
+    } else {
+      classPredict[i] = EstimateClassResult(p, sData, res, thr)
+    }
+    i <- i + 1
+  }
+  names(classPredict) <- paste('ID', part, sep='')
+  return(classPredict)
+}
+
+
+#Calculates the results in percentage depending on the threshold of similar event
+#thrs : vector of thresholds to test
+#sData : Samples data with all the features of the events
+#res : sparse matrix of the ressources data
+#part : vector of events randomly sampled
+#return : the results of precisions for each tested threshold
+OptimizeThreshold <- function(thrs, sData, res, part){
+  results <- rep(0, length(thrs))
+  i <- 1
+  for (thr in thrs){
+    classesPredict <- Classificator(sData, res, thr, part)
+    result <- 0
+    j <- 1
+    for (id in names(classesPredict)){
+      result <- result + unname(ResultPrediction(sData, id, classesPredict[id]))
+      j <- j + 1
+    }
+    results[i] = result/length(part)
+    i <- i + 1
+  }
+  return(results)
+}
+
+#Here is how I determined the optimal threshold
+
+#thrs <- c(0.05, 0.1, 0.2, 0.35, 0.5)
+#partThr <- sample(sData[,"event_id"], 2000)
+#thrOpt <- OptimizeThreshold(thrs, sData, res, partThr)
+#thrOpt
+#optResults <- cbind(thrs, thrOpt)
+#plot(optResults)
+
+# I choose thr = 0.35
+thr <- 0.35
+
+
+#Predictes the class of the given event and the next ones
+#event : number of the first event we want to predict
+#sData : Samples data with all the features of the events
+#res : sparse matrix of the ressources data
+#thr : threshold to determine if an event is similar or not
+#f : number of future events we want to predict
+#Return : None. The function prints results on the console
+PredictingFutureEvents <- function(event, sData, res, thr, f){
+  cat("Predictions for the next events :")
+  cat("\n")
+  i <- 1
+  ev <- event
+  predict <- rep(0, f)
+  class <- rep(0, f)
+  while (i<(f+1)){
+    id <- paste('ID', ev, sep='')
+    if (!is.element(id, rownames(res))){
+      predict[i] <- 0
+    } else {
+      predict[i] <- EstimateClassResult(ev, sData, res, thr)*1
+    }
+    names(predict)[i] <- id
+    class[i] <- c(sData[id, 3])
+    names(class)[i] <- id
+    ev <- ev + 1
+    i <- i + 1
+  }
+  print(predict)
+  cat("Real class of the events :")
+  cat("\n")
+  print(class)
+}
+
+options(warn = -1)
+
+#################################################
+# RESULTS
+#################################################
+
+
+# Performance of the algorithm (based on a random sample or on the total events)
+total = dim(sData)[1]
+nbr = 2000
+part <- sample(sData[,"event_id"], nbr)
+
+classPredict <- Classificator(sData, res, thr, part)
+classPredict
+
+classTable <- table(classPredict, sData[paste('ID', part, sep=''), "class"])
+classTable
+
+recall <- classTable[1,1]/sum(classTable[,1])
+recall
+precision <- classTable[1,1]/sum(classTable[1,])
+precision
+
+
+# Recall : 61,0% (based on 2000 random events)
+# Precision : 69,2% (based on 2000 random events)
+
+
+
+#Write the event here for future event predictions
+event <- 811355
+
+#Run these function to see the next predictions
+PredictingFutureEvents(event, sData, res, thr, 1)
+PredictingFutureEvents(event, sData, res, thr, 5)
+PredictingFutureEvents(event, sData, res, thr, 10)
+
+