private void button1_Click(object sender, EventArgs e)
{
// 记录当前时间
int count_predit = 0;
DateTime dt_1 = DateTime.Now;
Rec_Items_num = int.Parse( this.textBox9.Text );
userRating = new Dictionary<int, float>();
// 得到用户
cUser testUser = testUsers[comboBox2.SelectedIndex + 1];
userid = testUser.id;
userRating = new Dictionary<int, float>();
for (int j = 1; j < trainUsers[userid].Ratings.Length; j++)
{
if (trainUsers[userid].Ratings[j] != 0)
{
userRating.Add(j, (float)trainUsers[userid].Ratings[j]);
}
}
// count_predit = trainUsers[userID].Ratings.Length - trainUsers[userID].RatingNums;
// 得到该用户的预测评分
IDictionary<int, float> Predictions = obj_SlopeOne.Predict(userRating);
obj_AssStrategy = obj_SlopeOne.getAssStrategy(userid, Predictions, Rec_Items_num, testUser);
DateTime dt_2 = DateTime.Now;
TimeSpan ts = dt_2.Subtract(dt_1);
this.textBox4.Text = obj_AssStrategy.MAE.ToString(); // MAE
this.textBox5.Text = ts.TotalMilliseconds + "ms"; // 时间
this.textBox6.Text = obj_AssStrategy.Precison.ToString(); // 查准率
this.textBox8.Text = obj_AssStrategy.Recall.ToString(); // 查全率
float F = obj_AssStrategy.calculateF_Measure(); // F1指标
this.textBox7.Text = F.ToString();
this.textBox2.Text = this.Rec_Items_num.ToString(); // Top-N 推荐数
this.textBox3.Text = "MAE:" + obj_AssStrategy.MAE + " 查准率:" + obj_AssStrategy.Precison + " 查全率:" +
obj_AssStrategy.Recall + " F值:" + F + " 总耗时:" + ts.TotalMilliseconds + "ms";
// this.dataGridView1.Rows.Add(count_dgv++, trainUsers[userIndex].id, this.Rec_Items_num, trainUsers[userIndex].RatingNums, obj_AssStrategy.MAE, obj_AssStrategy.Precison,
// obj_AssStrategy.Recall, F, ts.TotalMilliseconds + " ms");
Application.DoEvents();
stat_Info[trainUsers[userid].RatingNums / 50] += obj_AssStrategy.MAE;
count_Num[trainUsers[userid].RatingNums / 50]++;
// 累加相关数据
total_MAE += obj_AssStrategy.MAE;
total_Precison += obj_AssStrategy.Precison;
total_Recall += obj_AssStrategy.Recall;
total_F_Measure += F;
total_Time += ts.TotalMilliseconds;
}
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" );
}
// 开始运行
private void button5_Click(object sender, EventArgs e)
{
int test_num = this.comboBox1.SelectedIndex + 15; // 测试用户数量
int N = int.Parse(this.textBox11.Text); // 推荐数目
cUser curTestUser; // 当前测试用户
cAssStrategy obj_AssStrategy = new cAssStrategy(); // 评价准则与指标
this.textBox7.Text = "测试用户数:" + test_num.ToString()
+ " Top-N 推荐数:" + N;
this.progressBar1.Maximum = (test_num-1) * 3; ; // 进度条最大值
this.progressBar1.Value = 0;
//////////////////////////////////////////////////////////////////////////
double total_N = 0, total_Precison = 0, total_Recall = 0, total_F = 0, total_Time = 0;
double Precison, Recall, F_Measure, Time;
DateTime dt_1, dt_2;
TimeSpan ts;
int real_RecNum = N;
// 对测试集中的用户开始产生推荐
int userid;
for (int i = 0; i < test_num-1; i++)
{
curTestUser = testUsers[i + 1];
userid = curTestUser.id;
dt_1 = DateTime.Now; // 获取当前时间
// 得到ID为userid的用户所支持的关联规则集合
supp_AssRules[userid] = cApriori.getSupport_AssRules(userid);
this.textBox7.Text = "第 " + (i + 1) + " 个用户所支持的关联规则生成.";
this.progressBar1.Value++;
Application.DoEvents();
// 得到推荐电影列表
recItems[userid] = cApriori.getRecItems(supp_AssRules[userid], userid);
real_RecNum = (recItems[userid].Length > N ? N : recItems[userid].Length);
this.textBox7.Text = "第 " + (i + 1) + " 个用户的推荐列表生成.";
this.progressBar1.Value++;
Application.DoEvents();
// 评价准则与指标的计算
obj_AssStrategy = cApriori.getAssStrategy(curTestUser, N);
dt_2 = DateTime.Now;
ts = dt_2.Subtract(dt_1); // 时间间隔
Time = ts.TotalMilliseconds;
Precison = obj_AssStrategy.Precison;
Recall = obj_AssStrategy.Recall;
F_Measure = obj_AssStrategy.calculateF_Measure();
// this.label22.Text = userid.ToString(); // 用户ID
// this.label35.Text = real_RecNum.ToString(); // 实际推荐数目
total_N += real_RecNum;
this.label20.Text = Precison.ToString(); // 查准率
total_Precison += Precison;
this.label23.Text = Recall.ToString(); // 查全率
total_Recall += Recall;
this.label24.Text = F_Measure.ToString(); // F值
total_F += F_Measure;
this.label37.Text = Time + " ms"; // 算法运行时间
total_Time += Time;
this.textBox7.Text = "完成第 " + (i + 1) + " 个用户的结果分析.";
this.progressBar1.Value++;
Application.DoEvents();
}
// 计算平均值
int num = test_num - 1;
double average_N = ((double)total_N / (double)num);
double average_Precison = (double) ( (double)total_Precison / num );
double average_Recall = (double) ( (double)total_Recall / num );
double average_F = (double) ( (double)total_F / num );
double average_Time = (double)total_Time / num;
this.label29.Text = average_N.ToString();
this.label30.Text = average_Precison.ToString();
this.label31.Text = average_Recall.ToString();
this.label32.Text = average_F.ToString();
this.label33.Text = average_Time.ToString() + " ms";
Application.DoEvents();
}
