private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)
{
// 清除用户ID下拉列表
this.comboBox2.Items.Clear();
// 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);
testUsers = cReadinData.getTestUser();
trainUsers = cReadinData.getBaseUser();
// 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;
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);
}
}
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_Apriori()
{
InitializeComponent();
// 读入用户数据
cReadinData obj_readData = new cReadinData(0);
testUsers = cReadinData.getTestUser();
sourceUsers = cReadinData.getBaseUser();
// 初始化支持的关联规则集合
supp_AssRules = new AssociationRule[sourceUsers.Length][];
// 初始化推荐项目集合
recItems = new RecItemid_Degree[sourceUsers.Length][];
// 填充comboBox1
for (int i = 15; i < testUsers.Length; i++)
{
this.comboBox1.Items.Add(i);
}
this.comboBox1.SelectedIndex = 0;
}
// 运行一次算法
private void button1_Click(object sender, EventArgs e)
{
// 记录当前时间
DateTime dt_1 = DateTime.Now;
this.Rec_Items_num = int.Parse(this.textBox13.Text); // Top-N推荐个数
this.progressBar1.Maximum = this.comboBox2.SelectedIndex + 15 + 22;
this.progressBar1.Value = 0;
// 读入数据，生成UI矩阵
this.textBox3.Text = "开始读入数据";
this.progressBar1.Value++;
Application.DoEvents();
cReadinData obj_ReadData = new cReadinData(comboBox1.SelectedIndex);
this.textBox3.Text = "读入数据完成 训练数据：" + obj_ReadData.sTrainFileName[comboBox1.SelectedIndex]
+ " 测试数据：" + obj_ReadData.testfileName[comboBox1.SelectedIndex];
this.progressBar1.Value++;
Application.DoEvents();
int number = int.Parse(textBox2.Text);
this.neigh_num = number;
// 相似度算法的选择
if (this.radioButton1.Checked)
{
sim_alg = 1;
}
else if (this.radioButton2.Checked)
{
sim_alg = 2;
}
else if (this.radioButton3.Checked)
{
sim_alg = 3;
}
// 测试用户数目，最少为15
testUserNum = this.comboBox2.SelectedIndex + 15;
this.textBox3.Text = "初始化最大邻居个数";
// 初始化，最近邻居数量最大为200
obj_ItemBased_CF = new cItemBased_CF(200);
this.progressBar1.Value++;
Application.DoEvents();
// 初始化相关数据
for (int i = 1; i <= 1682; i++)
{
this.textBox3.Text = "初始 " + i + " 个项目数据";
if ((i % 100) == 0)
{
this.progressBar1.Value++;
Application.DoEvents();
}
Application.DoEvents();
obj_ItemBased_CF.generateItemNN(i);
}
this.progressBar1.Value++;
Application.DoEvents();
this.textBox3.Text = "读取最近邻居及其相似值文件";
obj_ItemBased_CF.readFile();
this.progressBar1.Value++;
Application.DoEvents();
// 得到测试用户集合
cUser[] testUsers = cReadinData.getTestUser();
this.textBox3.Text = "读取测试用户集合";
this.progressBar1.Value++;
Application.DoEvents();
double MAE, Precison, Recall, F_Measure;
double total_MAE = 0, total_Precison = 0, total_Recall = 0, total_F_Measure = 0;
double average_MAE, average_Precison, average_Recall, average_F_Measure;
// for循环为每个测试用户产生预测评分以及Top-N推荐，并取得算法评价指标
for (int i = 1; i <= testUserNum; i++)
{
obj_AssStrategy = obj_ItemBased_CF.getPredictRating(testUsers[i], this.sim_alg, neigh_num, Rec_Items_num);
MAE = obj_AssStrategy.MAE;
Precison = obj_AssStrategy.Precison;
Recall = obj_AssStrategy.Recall;
F_Measure = obj_AssStrategy.calculateF_Measure();
// 累计各项指标的和
total_MAE += MAE;
total_Precison += Precison;
total_Recall += Recall;
total_F_Measure += F_Measure;
this.textBox3.Text = "第" + i.ToString() + "个用户 MAE:" + MAE.ToString() + " 查准率:" + Precison
+ " 查全率:" + Recall + " F值:" + F_Measure;
this.progressBar1.Value++;
Application.DoEvents();
}
// 计算各项指标的平均值
average_MAE = total_MAE / this.testUserNum;
average_Precison = total_Precison / this.testUserNum;
average_Recall = total_Recall / this.testUserNum;
average_F_Measure = total_F_Measure / this.testUserNum;
DateTime dt_2 = DateTime.Now;
TimeSpan ts = dt_2.Subtract(dt_1);
this.textBox3.Text = "完成 平均MAE:" + average_MAE.ToString() + " 平均查准率:" + average_Precison +
" 平均查全率:" + average_Recall + " 平均F值:" + average_F_Measure + " 总耗时:" + ts.TotalMilliseconds + "ms";
Application.DoEvents();
this.textBox4.Text = average_MAE.ToString();
this.textBox5.Text = ts.TotalMilliseconds + "ms";
this.textBox6.Text = this.sSimAlg[this.sim_alg - 1];
this.textBox7.Text = this.neigh_num.ToString();
this.textBox9.Text = average_Precison.ToString();
this.textBox10.Text = average_Recall.ToString();
this.textBox11.Text = average_F_Measure.ToString();
this.textBox12.Text = Rec_Items_num.ToString();
string log = this.sSimAlg[this.sim_alg - 1] + " 邻居数:" + this.neigh_num.ToString() + " 平均MAE:" +
average_MAE.ToString() + " 平均查准率:" + average_Precison + " 平均查全率:" + average_Recall +
" 平均F值:" + average_F_Measure + " 总耗时:" + ts.TotalMilliseconds + "ms";
this.dataGridView1.Rows.Add(count_dgv++, sSimAlg[sim_alg - 1], this.neigh_num, this.Rec_Items_num, average_MAE,
average_Precison, average_Recall, average_F_Measure, (ts.TotalMilliseconds / this.testUserNum) + " ms");
}
private void button1_Click(object sender, EventArgs e)
{
DateTime dt_1 = DateTime.Now;
this.Rec_Items_num = int.Parse(this.textBox13.Text); // Top-N推荐个数
this.progressBar1.Maximum = (this.comboBox2.SelectedIndex + 15) * 10 + 5;
this.progressBar1.Value = 0;
// 读入数据，生成UI矩阵
this.textBox3.Text = "开始读入数据";
this.progressBar1.Value++;
Application.DoEvents();
cReadinData obj_ReadData = new cReadinData(comboBox1.SelectedIndex);
this.textBox3.Text = "读入数据完成 训练数据：" + obj_ReadData.sTrainFileName[comboBox1.SelectedIndex]
+ " 测试数据：" + obj_ReadData.testfileName[comboBox1.SelectedIndex];
this.progressBar1.Value += 2;
Application.DoEvents();
// 读取最近邻居个数
int number = int.Parse(textBox2.Text);
this.neigh_num = number;
// 相似度算法的选择
if (this.radioButton1.Checked)
{
sim_alg = 1;
}
else if (this.radioButton2.Checked)
{
sim_alg = 2;
}
else if (this.radioButton3.Checked)
{
sim_alg = 3;
}
// 测试用户数目，最少为15
testUserNum = this.comboBox2.SelectedIndex + 15;
cUserBased_CF obj_UserBased_CF = new cUserBased_CF(this.neigh_num);
cUser[] testUsers = cReadinData.getTestUser();
this.textBox3.Text = "初始化完成 相似度算法:" + sim_alg.ToString() +
" 最近邻居个数:" + this.neigh_num.ToString() + " 测试用户数:" + testUserNum.ToString();
this.progressBar1.Value += 2;
Application.DoEvents();
double MAE_1, Precison, Recall, F_Measure;
double total_MAE = 0, total_Precison = 0, total_Recall = 0, total_F_Measure = 0;
double average_MAE, average_Precison, average_Recall, average_F_Measure;
for (int i = 1; i <= this.testUserNum; i++)
{
this.progressBar1.Value += 5;
obj_AssStrategy = obj_UserBased_CF.getPredictRating(testUsers[i], this.sim_alg, Rec_Items_num);
// 取得各项算法评价指标
MAE_1 = obj_AssStrategy.MAE;
Precison = obj_AssStrategy.Precison;
Recall = obj_AssStrategy.Recall;
F_Measure = obj_AssStrategy.calculateF_Measure();
// 累计各项指标的和
total_MAE += MAE_1;
total_Precison += Precison;
total_Recall += Recall;
total_F_Measure += F_Measure;
this.textBox3.Text = "第 " + i.ToString() + " 个用户计算完成.";
this.progressBar1.Value += 5;
Application.DoEvents();
}
// 计算各个评价准则的平均值
average_MAE = total_MAE / this.testUserNum;
average_Precison = total_Precison / this.testUserNum;
average_Recall = total_Recall / this.testUserNum;
average_F_Measure = total_F_Measure / this.testUserNum;
DateTime dt_2 = DateTime.Now;
TimeSpan ts = dt_2.Subtract(dt_1);
this.textBox3.Text = "所有用户计算完成 总耗时:" + ts.TotalMilliseconds + " ms";
Application.DoEvents();
this.textBox4.Text = average_MAE.ToString();
this.textBox5.Text = ts.TotalMilliseconds + " ms";
this.textBox6.Text = this.sSimAlg[this.sim_alg - 1];
this.textBox7.Text = this.neigh_num.ToString();
this.textBox9.Text = average_Precison.ToString();
this.textBox10.Text = average_Recall.ToString();
this.textBox11.Text = average_F_Measure.ToString();
this.textBox12.Text = "20";
this.dataGridView1.Rows.Add(count_dgv++, sSimAlg[sim_alg - 1], this.neigh_num, this.Rec_Items_num, average_MAE,
average_Precison, average_Recall, average_F_Measure, (ts.TotalMilliseconds / this.testUserNum) + " ms");
}