public Dictionary<int,float>[] GetReposCorrs(TrainingData td, int[][] uod, List<int>[] mod)
{
Dictionary<int, float>[] retVal = new Dictionary<int, float>[td.Repositories.Count];
for (int i = 0; i < retVal.Length; i++) retVal[i] = new Dictionary<int, float>();
int[] matches = new int[td.Repositories.Count];
for (int reposA = 0; reposA < td.Repositories.Count; reposA++)
{
List<int> users = mod[reposA];
foreach (int userA in users)
{
int[] reposBs = uod[userA];
foreach(int reposB in reposBs)
{
if(reposB == reposA) continue;
matches[reposB]++;
}
}
for (int i = 0; i < td.Repositories.Count; i++)
{
int matchCount = matches[i];
if (matchCount > 0)
{
float weight = matches[i] / (float)(50 + Math.Sqrt(mod[reposA].Count) * Math.Sqrt(mod[i].Count));
retVal[reposA][i] = weight;
matches[i] = 0;
}
}
}
return retVal;
}
public static int[][] GetUserOrderRepositories(TrainingData td)
{
int usrCount = td.Users.Count;
int[][] retVal = new int[usrCount][];
foreach (User u in td.Users)
{
Repository[] reps = u.Repo.GetList();
retVal[u.ID] = new int[reps.Length];
for (int i = 0; i < reps.Length; i++)
retVal[u.ID][i] = reps[i].ID;
}
return retVal;
}
static void Main(string[] args)
{
string repos = @"C:\Users\Aron\Github\download\repos.txt";
string data = @"C:\Users\Aron\Github\download\data.txt";
string lang = @"C:\Users\Aron\Github\download\lang.txt";
string test = @"C:\Users\Aron\Github\download\test.txt";
string results = @"C:\Users\Aron\Github\GithubContest\results.txt";
TrainingData trainData = new TrainingData();
trainData.Load(repos, lang, data);
TestData testData = new TestData();
testData.Load(test, trainData);
SimpleMovieKnn smknn = new SimpleMovieKnn();
int[][] predictions1 = smknn.Run2(trainData, testData, results);
SimpleCounterModel scm = new SimpleCounterModel();
int[][] predictions2 = scm.Run(trainData, testData, results);
// combine
int[][] blend = new int[testData.Users.Count][];
for (int i = 0; i < blend.Length; i++)
{
blend[i] = new int[10];
for (int j = 0; j < 5; j++)
{
blend[i][j] = predictions1[i][j];
}
int cnt = 5;
foreach (int j in predictions2[i])
{
if (!blend[i].Contains<int>(j))
{
blend[i][cnt] = j;
cnt++;
if (cnt == 10) break;
}
}
}
DataFormatter.OutputPredictions(results, trainData, testData, blend);
//QuickFix(trainData);
/*
LogisticSVD svd = new LogisticSVD();
svd.Setup(trainData, testData, results);
for(int epoch = 0; epoch < 500; epoch++)
svd.Train(1);
svd.Predict();*/
}
private static void QuickFix(TrainingData trainData)
{
string results = @"C:\Users\Aron\Github\GithubContest\results.txt";
string[] allLines = File.ReadAllLines(results);
StreamWriter sw = new StreamWriter(File.OpenWrite(results + ".bak"));
foreach (string s in allLines)
{
int usrID = int.Parse(s.Substring(0, s.IndexOf(':')));
User u = trainData.Users[usrID];
string outStr = u.ExternalID.ToString() + s.Substring(s.IndexOf(':'));
sw.WriteLine(outStr);
}
sw.Close();
}
public static void OutputPredictions(string outputPath, TrainingData train, TestData test, int[][] predictions)
{
if (File.Exists(outputPath)) File.Delete(outputPath);
StreamWriter sw = new StreamWriter(File.OpenWrite(outputPath));
for (int usrIndx = 0; usrIndx < predictions.Length; usrIndx++)
{
string outStr = test.Users[usrIndx].ExternalID + ":";
for (int i = 0; i < predictions[usrIndx].Length; i++)
{
outStr += train.Repositories.GetByInternalID(predictions[usrIndx][i]).ExternalID;
if (i < predictions[usrIndx].Length - 1) outStr += ",";
}
sw.WriteLine(outStr);
}
sw.Close();
}
public int[][] Run(TrainingData td, TestData test, string outPath)
{
int[][] uod = DataFormatter.GetUserOrderRepositories(td);
List<int>[] mod = new List<int>[td.Repositories.Count];
for (int i = 0; i < td.Repositories.Count; i++)
mod[i] = new List<int>();
// populate mod
for (int i = 0; i < uod.Length; i++)
{
for (int j = 0; j < uod[i].Length; j++)
{
mod[uod[i][j]].Add(i);
}
}
// Dictionary<int, float>[] corrs = GetReposCorrs(td, uod, mod);
int[][] predictions = new int[test.Users.Count][];
for (int usrIndx = 0; usrIndx < test.Users.Count; usrIndx++)
{
User userA = test.Users[usrIndx];
int[] repos = uod[userA.ID];
int[] repoMatches = new int[mod.Length];
float[] movieWeights = new float[mod.Length];
int[] repoCountsA = new int[mod.Length];
//int[] repoCountsB = new int[mod.Length];
for (int rIndx = 0; rIndx < repos.Length; rIndx++)
{
int rID = repos[rIndx];
List<int> users = mod[rID];
foreach (int userB in users)
{
if (userB == userA.ID) continue;
// for every movie in user B
foreach(int repo in uod[userB])
{
if (repo == rID) continue;
repoMatches[repo]++;
}
}
}
for (int i = 0; i < movieWeights.Length; i++)
{
if (repoMatches[i] > 0)
movieWeights[i] = repoMatches[i]; // (repoMatches[i] / (1000 + (float)(Math.Sqrt(repoCountsA[i]) * Math.Sqrt(repoCountsB[i]))));
}
// find x highest
predictions[usrIndx] = new int[10];
for (int i = 0; i < 10; i++)
{
int highRepo = 0;
float highWeight = 0;
for (int j = 0; j < repoMatches.Length; j++)
{
float weight = movieWeights[j];
if (weight > highWeight)
{
highRepo = j;
highWeight = weight;
}
}
movieWeights[highRepo] = 0; // zero out to skip next round
predictions[usrIndx][i] = highRepo;
}
}
return predictions;
}
// for each user in test
// find similar users, count up
public int[][] Run2(TrainingData td, TestData test, string outPath)
{
int[][] uod = DataFormatter.GetUserOrderRepositories(td);
List<int>[] mod = new List<int>[td.Repositories.Count];
for (int i = 0; i < td.Repositories.Count; i++)
mod[i] = new List<int>();
// populate mod
for (int i = 0; i < uod.Length; i++)
{
for (int j = 0; j < uod[i].Length; j++)
{
mod[uod[i][j]].Add(i);
}
}
Dictionary<int, float>[] corrs = GetReposCorrs(td, uod, mod);
int[][] predictions = new int[test.Users.Count][];
Repository[] repoList = td.Repositories.GetList();
for (int usrIndx = 0; usrIndx < test.Users.Count; usrIndx++)
{
User userA = test.Users[usrIndx];
float[] weights = new float[td.Repositories.Count];
foreach(int repos in uod[userA.ID])
{
foreach(KeyValuePair<int,float> kvp in corrs[repos])
{
if (kvp.Key == repos) continue;
weights[kvp.Key] += kvp.Value;
}
}
// find x highest
predictions[usrIndx] = new int[10];
for (int i = 0; i < 10; i++)
{
int highRepo = 0;
float highWeight = 0;
for (int j = 0; j < weights.Length; j++)
{
float weight = weights[j];
if (weight > highWeight)
{
highRepo = j;
highWeight = weight;
}
}
weights[highRepo] = 0; // zero out to skip next round
predictions[usrIndx][i] = highRepo;
}
}
return predictions;
}
public void Setup(TrainingData td, TestData test, string results)
{
this.td = td;
this.test = test;
this.results = results;
// model training data
uod = DataFormatter.GetUserOrderRepositories(td);
userCount = td.Users.Count;
repoCount = td.Repositories.Count;
// model hypers
featureCount = 10;
// model params
Random r = new Random();
userBias = new float[userCount];
for (int i = 0; i < userCount; i++)
userBias[i] = -10;
repoBias = new float[repoCount];
for (int i = 0; i < userCount; i++)
repoBias[i] = -10;
userFeatures = new float[userCount][];
for (int i = 0; i < userCount; i++)
{
userFeatures[i] = new float[featureCount];
for (int f = 0; f < featureCount; f++)
{
userFeatures[i][f] = -.1f + .001f * r.Next(200);
}
}
repoFeatures = new float[repoCount][];
for (int i = 0; i < repoCount; i++)
{
repoFeatures[i] = new float[featureCount];
for (int f = 0; f < featureCount; f++)
{
repoFeatures[i][f] = -.1f + .001f * r.Next(200);
}
}
}
