public Form_SlopeOne()
{
InitializeComponent();
// 默认选择第一个数据集
this.comboBox1.SelectedIndex = 0;
// 读取数据，得到训练用户集合以及测试用户集合
obj_ReadData = new cReadinData(this.comboBox1.SelectedIndex);
obj_AssStrategy = new cAssStrategy();
testUsers = cReadinData.getTestUser();
trainUsers = cReadinData.getBaseUser();
this.dataGridView1.RowHeadersVisible = false;
this.dataGridView2.RowHeadersVisible = false;
for (int i = 0; i < count_Num.Length; i++)
{
count_Num[i] = 0;
stat_Info[i] = 0;
}
// 根据选择的数据集，填充用户ID的下拉列表
for (int i = 0; i < testUsers.Length - 1; i++)
{
this.comboBox2.Items.Add(testUsers[i + 1].id);
}
// 用户ID默认选择第一个
this.comboBox2.SelectedIndex = 0;
obj_SlopeOne = new SlopeOne();
for (int i = 1; i < trainUsers.Length; i++)
{
userRating = new Dictionary <int, float>();
//count_Num[trainUsers[i].RatingNums/20]++;
for (int j = 1; j < trainUsers[i].Ratings.Length; j++)
{
if (trainUsers[i].Ratings[j] != 0)
{
userRating.Add(j, (float)trainUsers[i].Ratings[j]);
}
}
obj_SlopeOne.AddUserRatings(userRating);
}
this.comboBox3.Items.Clear();
// 填充 连续运行用户数目 下拉列表
for (int i = 15; i < testUsers.Length; i++)
{
comboBox3.Items.Add(i);
}
this.comboBox3.SelectedIndex = 0;
}
public Form_SlopeOne()
{
InitializeComponent();
// 默认选择第一个数据集
this.comboBox1.SelectedIndex = 0;
// 读取数据，得到训练用户集合以及测试用户集合
obj_ReadData = new cReadinData(this.comboBox1.SelectedIndex);
obj_AssStrategy = new cAssStrategy();
testUsers = cReadinData.getTestUser();
trainUsers = cReadinData.getBaseUser();
this.dataGridView1.RowHeadersVisible = false;
this.dataGridView2.RowHeadersVisible = false;
for (int i = 0; i < count_Num.Length; i++)
{
count_Num[i] = 0;
stat_Info[i] = 0;
}
// 根据选择的数据集，填充用户ID的下拉列表
for (int i = 0; i < testUsers.Length - 1; i++)
{
this.comboBox2.Items.Add(testUsers[i + 1].id);
}
// 用户ID默认选择第一个
this.comboBox2.SelectedIndex = 0;
obj_SlopeOne = new SlopeOne();
for (int i = 1; i < trainUsers.Length; i++)
{
userRating = new Dictionary<int, float>();
//count_Num[trainUsers[i].RatingNums/20]++;
for (int j = 1; j < trainUsers[i].Ratings.Length; j++)
{
if (trainUsers[i].Ratings[j] != 0)
{
userRating.Add(j, (float)trainUsers[i].Ratings[j]);
}
}
obj_SlopeOne.AddUserRatings(userRating);
}
this.comboBox3.Items.Clear();
// 填充 连续运行用户数目 下拉列表
for (int i = 15; i < testUsers.Length; i++)
{
comboBox3.Items.Add(i);
}
this.comboBox3.SelectedIndex = 0;
}
public void TestNewUserInTestSet()
{
var recommender = new SlopeOne();
var training_data = new Ratings();
training_data.Add(0, 0, 1.0f);
training_data.Add(1, 1, 5.0f);
training_data.InitScale();
recommender.Ratings = training_data;
recommender.Train();
Assert.AreEqual(3.0, recommender.Predict(2, 1));
}
public void TestNewItemInTestSet()
{
var recommender = new SlopeOne();
recommender.MinRating = 1;
recommender.MaxRating = 5;
var training_data = new Ratings();
training_data.Add(0, 0, 1.0);
training_data.Add(1, 1, 5.0);
recommender.Ratings = training_data;
recommender.Train();
Assert.AreEqual(3.0, recommender.Predict(0, 2));
}
public void Check_Slope_One_Method()
{
MyEntityContext ctx = Application.Context;
List <IAgent> agents = new List <IAgent>();
IAgent a = null;
agents.Add(a = ctx.Agents.Create()); a.Name = "John"; a.Role = RoleEnum.Buyer;
agents.Add(a = ctx.Agents.Create()); a.Name = "Mark"; a.Role = RoleEnum.Buyer;
agents.Add(a = ctx.Agents.Create()); a.Name = "Lucy"; a.Role = RoleEnum.Buyer;
List <IObject> objs = new List <IObject>();
IObject o = null;
objs.Add(o = ctx.Objects.Create()); o.Name = "Subj 1";
objs.Add(o = ctx.Objects.Create()); o.Name = "Subj 2";
objs.Add(o = ctx.Objects.Create()); o.Name = "Subj 3";
List <ILikes> likes = new List <ILikes>();
ILikes l = null;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[0]; l.Object = objs[0]; l.Value = 5;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[0]; l.Object = objs[1]; l.Value = 3;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[0]; l.Object = objs[2]; l.Value = 2;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[1]; l.Object = objs[0]; l.Value = 3;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[1]; l.Object = objs[1]; l.Value = 4;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[2]; l.Object = objs[1]; l.Value = 2;
likes.Add(l = ctx.Likess.Create()); l.Agent = agents[2]; l.Object = objs[2]; l.Value = 5;
SlopeOne so = new SlopeOne();
so.Likes = likes;
so.DEBUG = true;
so.Process();
Console.WriteLine("------------ Estimations -------------");
Console.WriteLine("Estimation for John and Subj 3 is " + so.Estimate(agents[1], objs[2]));
Console.WriteLine("Estimation for Marc and Subj 3 is " + so.Estimate(agents[1], objs[2]));
Console.WriteLine("Estimation for Lucy and Subj 1 is " + so.Estimate(agents[2], objs[0]));
Assert.True(true);
}
static void Main(string[] args)
{
SlopeOne slope = new SlopeOne();
slope.ReadData(@"C:\Users\Admin\Desktop\Assignment7.csv");
//slope.Run();
var rate = slope.PredictFor(1, 1);
//NBC nbc = new NBC();
//nbc.Verdict();
Console.WriteLine("Rate: {0}", rate);
//Console.WriteLine
//var x = nbc.Probability(true);
//var y = nbc.Probability(false);
Console.ReadKey();
//var reddit = new Reddit(@"C:\Users\col403\Desktop\reddit.csv");
//reddit.ReadData();
//reddit.RankNews();
//reddit.Print();
//Task3 task = new Task3(@"C:\Users\col403\Desktop\ml-latest-small\movies.csv",
// @"C:\Users\col403\Desktop\ml-latest-small\ratings.csv");
//var dm = task.DampedMeanCollection();
//var dampedTop10 = dm.OrderByDescending(x => x.Value).Take(10);
//foreach(var d in dampedTop10)
//{
// string similar = task.GetMovieName(task.GetMostSimilar(d.Key));
// Console.WriteLine("{0} damped mean: {1}. Similar to {2}",task.GetMovieName(d.Key), d.Value, similar);
//}
//var fdsx = task.SimilarityValue(4324, 4234);
//string similar = task.GetMovieName(task.GetMostSimilar(8));
//Console.ReadKey();
}
