// μ、∑计算
private void mvButton_Click(object sender, EventArgs e)
{
ArrayList samples = FeatureHelper.GetFeaturesList();
MVHelper.SetSampleList(samples);
ResetResultView();
}
private void featureExtractButton_Click(object sender, EventArgs e)
{
//FeatureHelper.FeatureExtract();
#region 检查前提条件
//无论开测试还是闭测试，都要计算训练样本列表，因此要求训练样本文件列表不为空
#endregion
FeatureHelper.ds = new DataSet();
FeatureHelper.dt = new DataTable();
GridviewInit();
featureDataGridView.DataSource = null;
featureDataGridView.Rows.Clear();
featureDataGridView.Refresh();
DataSet ds = new DataSet();
DataTable dt = new DataTable();
ArrayList arr = new ArrayList();
// 或者直接将arr作为参数传入
FeatureHelper.GetSamplesFeatures(); //初始化训练样本
arr = FeatureHelper.GetFeaturesList();
#region 列初始化
dt.Columns.Add("文件夹", typeof(string));
dt.Columns.Add("类别", typeof(string));
string colname = string.Empty;
for (int i = 2, j = 1; i < 26; i++, j++)
{
colname = string.Format("ET1({0})", j.ToString());
dt.Columns.Add(colname, typeof(string));
}
for (int i = 26, j = 1; i < 50; i++, j++)
{
colname = string.Format("DT12({0})", j.ToString());
dt.Columns.Add(colname, typeof(string));
}
#endregion
#region Datatable行设值
for (int i = 0; i < arr.Count; i++)
{
DataRow row = dt.NewRow();
Features f = (Features)arr[i];//装箱
row[0] = f.Filepath;
row[1] = f.classID;
for (int j = 2; j < 50; j++)
{
row[j] = f.feature_vector[j - 2];
}
dt.Rows.Add(row);
}
ds.Tables.Add(dt);
this.featureDataGridView.DataSource = ds.Tables[0];
#endregion
}
private void ControlInit()
{
this.classifyButton.Enabled = false;
if (FeatureHelper.GetFeaturesList().Count == FeatureHelper.GetTestFeaturesList().Count)
{
this.rbKn.Enabled = false;
this.rbnearest.Enabled = false;
}
}
// 最近邻是否为开测试测试
public bool NearestCheck()
{
// 判断测试样本与训练样本是否一样多，或者判断关于开闭测试的radioButton选择状态
if (FeatureHelper.GetFeaturesList().Count == FeatureHelper.GetTestFeaturesList().Count)
{
MessageBox.Show(this, "最近近邻法首先进行开测试进行样本提取", "提示信息", MessageBoxButtons.OK);
return(false);
}
return(true);
}
private void KFunctionForm_Load(object sender, EventArgs e)
{
#region 数据初始化
IList testSampleList = FeatureHelper.GetTestFeaturesList(); //获取原始测试
KnNear kn = new KnNear();
//每个元素都存储在 KeyValuePair<TKey, TValue> 对象中
IDictionary <int, double> map = new Dictionary <int, double>();
//每个元素都存储在DictionaryEntry
IDictionary dictionary = new Hashtable();
//DictionaryEntry进行循环遍历
ArrayList klist = new ArrayList();
ArrayList ratelist = new ArrayList();
#endregion
#region 获取K和识别率
for (int k = 1; k < 50; k += 2)
{
int testResult = 0;
double correctCount = 0;
double correctRate = 0.0;
foreach (Features feature in testSampleList)
{
testResult = kn.DoK_nearest(feature, FeatureHelper.GetFeaturesList(), k);
if (testResult > 0)
{
//检查结果是否正确
if (testResult == feature.classID)
{
correctCount++;
}
}
}
correctRate = (correctCount / Convert.ToDouble(testSampleList.Count)) * 100.0;
map.Add(k, correctRate / 10);
klist.Add(k);
ratelist.Add(correctRate * 2);
}
#endregion
#region 显示点图
PreviewFunctionImage(klist, ratelist);
#endregion
}
// 重置相关结果视图
private void ResetResultView()
{
classChooseComboBox.Items.Clear();
classFeatureList = MVHelper.GetClassFeatureList(FeatureHelper.GetFeaturesList());
//按照类列表中的内容，生成类选择下拉列表的项目
for (int i = 0; i < classFeatureList.Count; i++)
{
Sample sample = (Sample)classFeatureList[i];
string classIDStr = "000" + sample.ClassID;
classChooseComboBox.Items.Add(classIDStr.Substring(classIDStr.Length - 3));
}
//初始选择
classChooseComboBox.SelectedIndex = 0;
//计算初始选择的结果
ShowResultView(Int32.Parse((string)this.classChooseComboBox.Items[0]));
}
//均值u和协方差∑计算
private void meanToolStripMenuItem_Click(object sender, EventArgs e)
{
this.sampleArray = FeatureHelper.GetFeaturesList();
if (sampleArray == null || sampleArray.Count == 1)
{
MessageBox.Show(this, "还没有提取特征值，请按步骤来", "提示信息", MessageBoxButtons.OK);
}
else
{
MeanCalculateForm mcform = new MeanCalculateForm();
mcform.ShowDialog();
}
#region 测试
//MeanCalculateForm mcform = new MeanCalculateForm();
//mcform.ShowDialog();
#endregion
}
// 判断K值的合法性
public bool KCheck(int k)
{
if (FeatureHelper.GetFeaturesList().Count == FeatureHelper.GetTestFeaturesList().Count)
{
MessageBox.Show(this, "Kn近邻法首先进行开测试进行样本提取", "提示信息", MessageBoxButtons.OK);
return(false);
}
if (k % 2 == 0)
{
MessageBox.Show(this, "K 值必须是奇数！", "提示信息", MessageBoxButtons.OK);
return(false);
}
if (k < 1 || k > 49)
{
MessageBox.Show(this, "K 值必须在 1 到 49 之间！", "提示信息", MessageBoxButtons.OK);
return(false);
}
return(true);
}
// 分类运算
private void classifyButton_Click(object sender, EventArgs e)
{
//前提检验
if (SelectedPCXHelper.GetSelPCXFromLB().Count == 0 || SelectedPCXHelper.GetUnselPCXList().Count == 0)
{
MessageBox.Show(this, "您还未提取样本特征，或者还未设置测试样本集！", "提示信息", MessageBoxButtons.OK);
}
else
{
string correctRate = null; //正确率
string myfilepath = filepathText.Text.ToString();
Features feature;
#region 这里做一个文件名为unknown.pcx的判断
string filename = FeatureHelper.GetUnknownName(myfilepath);
if (filename.ToLower().Equals("unknown"))
{
feature = new Features(myfilepath);
}
else
{
int classID = Convert.ToInt32(FeatureHelper.GetUpperFoldername(myfilepath));
feature = new Features(myfilepath, classID);
}
#endregion
#region Bayes分类法
if (rbBayes.Checked)
{
#region 数据初始化
CheckInit();
double correctCount = 0.0;
#endregion
IList sampleList = FeatureHelper.GetFeaturesList(); //获取原始训练样本
//从降维器获取降维后的新样本
IList newSampleList = MDAHelper.GetMDSampleList();
MVHelper.SetSampleList((ArrayList)newSampleList);
Bayes bayes = Bayes.GetInstance();
bayes.TrainSampleList = newSampleList; //向贝叶斯分类器注入降维后的训练样本
//int classID = Convert.ToInt32(FeatureHelper.GetUpperFoldername(myfilepath));
//Features feature = new Features(myfilepath, classID);
feature = MDAHelper.MDSample(feature); //测试样本降维
int testClassID = bayes.DecisionFunction(feature); //用贝叶斯决策进行测试样本分类
//结果显示
lblunknownclassify.Text = testClassID.ToString("000");
if (feature.classID == testClassID)
{
lblerrorinfo.Text = "Bayes分类法分类正确";
lblerrorinfo.ForeColor = Color.Green;
}
//unknown.pcx处理
else if (feature.classID == -1)
{
}
else
{
lblerrorinfo.Text = "Bayes分类法分类失败";
lblerrorinfo.ForeColor = Color.Green;
}
}
#endregion
#region Kn近邻法
if (rbKn.Checked)
{
#region 相关数据初始化
CheckInit();
int testResult = -1;
double correctCount = 0.0;
int kvalue = Constant.kvalue;
#endregion
#region 效的情况下进行计算
if (KCheck(kvalue))
{
KnNear my_knearest = new KnNear();
//int classID = Convert.ToInt32(FeatureHelper.GetUpperFoldername(myfilepath));
//Features currfeature = new Features(myfilepath, classID);
testResult = my_knearest.DoK_nearest(feature, FeatureHelper.GetFeaturesList(), kvalue);
//testResult为K近邻的分类结果
// 其实testResult的结果直接就是result求的值
string result = testResult.ToString("000");
lblunknownclassify.Text = result;
result = ResultConvert(result);
int testID = Convert.ToInt32(result);
if (testID > 0 && testID == feature.classID)
{
//correctRate = "分类正确率： " + Constant.kn_Rate;
lblerrorinfo.Text = "Kn近邻法分类正确";
lblerrorinfo.ForeColor = Color.Green;
}
//unknown.pcx处理
else if (feature.classID == -1)
{
}
else
{
lblerrorinfo.Text = "Kn近邻法分类失败！";
lblerrorinfo.ForeColor = Color.Green;
}
}
#endregion
}
#endregion
#region 最近邻法
if (rbnearest.Checked)
{
#region 初始化
CheckInit();
int testResult = -1;
double correctCount = 0.0;
#endregion
#region 最近邻分类
if (NearestCheck())
{
Nearest nearest = new Nearest();
//int classID = Convert.ToInt32(FeatureHelper.GetUpperFoldername(myfilepath));
//Features currfeature = new Features(myfilepath, classID);
testResult = nearest.Do_Nearest(feature, FeatureHelper.GetFeaturesList());
string result = testResult.ToString("000");
lblunknownclassify.Text = result;
if (testResult > 0 && testResult == feature.classID)
{
lblerrorinfo.Text = "最近邻法分类正确";
lblerrorinfo.ForeColor = Color.Green;
}
//unknown.pcx处理
else if (feature.classID == -1)
{
}
else
{
lblerrorinfo.Text = "最近邻法分类失败！";
lblerrorinfo.ForeColor = Color.Green;
}
}
#endregion
}
#endregion
}
}
private void testButton_Click(object sender, EventArgs e)
{
#region 数据初始化
double correctCount = 0.0;
double correctRate = 0.0;
IList sampleList = FeatureHelper.GetFeaturesList(); //获取原始训练样本
//从降维器获取降维后的新样本
IList newSampleList = MDAHelper.GetMDSampleList();
MVHelper.SetSampleList((ArrayList)newSampleList);
Bayes bayes = Bayes.GetInstance();
bayes.TrainSampleList = newSampleList; //向贝叶斯分类器注入降维后的训练样本
IList testSampleList = FeatureHelper.GetTestFeaturesList(); //获取测试样本
#endregion
#region DataGridView操作
bayesDataGridView.DataSource = null;
bayesDataGridView.Rows.Clear();
bayesDataGridView.Refresh();
DataSet ds = new DataSet();
DataTable dt = new DataTable();
// 或者直接将arr作为参数传入
//FeatureHelper.GetSamplesFeatures(); //初始化训练和测试样本
dt.Columns.Add("文件夹", typeof(string));
dt.Columns.Add("所属类别", typeof(string));
dt.Columns.Add("测试类别", typeof(string));
dt.Columns.Add("正误判断", typeof(string));
for (int i = 0; i < testSampleList.Count; i++)
{
DataRow row = dt.NewRow();
string rightOrWrong = "×";
Features feature = (Features)testSampleList[i];
row[0] = feature.Filepath;
row[1] = feature.classID;
feature = MDAHelper.MDSample(feature); //测试样本降维
int testClassID = bayes.DecisionFunction(feature); //用贝叶斯决策进行测试样本分类
// 用StringBuilder加快字符串处理速度。【值类型和堆类型】
StringBuilder sb = new StringBuilder();
sb.Append("类");
sb.Append(testClassID.ToString());
sb.ToString();
row[2] = sb;
if (feature.classID == testClassID)
{
correctCount++;
row[3] = " ";
}
else
{
row[3] = rightOrWrong;
//this.bayesDataGridView.DefaultCellStyle.ForeColor = Color.Red;
}
dt.Rows.Add(row);
}
ds.Tables.Add(dt);
this.bayesDataGridView.DataSource = ds.Tables[0];
#endregion
#region Bayes分类性能显示
correctRate = (correctCount / Convert.ToDouble(testSampleList.Count)) * 100.0;
Constant.bayes_Rate = correctRate.ToString("0.000") + "%";
dataShowLabel.Text = "测试样本总数 " + testSampleList.Count + " ，Bayes判断正确 " + correctCount + " 个，正确率为：" + Constant.bayes_Rate;
#endregion
}
// 获取Dataset数据
public DataSet GetViewDS(out double ccount, out double crate, out int count)
{
#region 数据初始化
double correctCount = 0;
double correctRate = 0.0;
int testResult = 0;
Nearest nearest = new Nearest();
IList testSampleList = FeatureHelper.GetTestFeaturesList(); //获取原始测试
#endregion
#region DataGridView操作
dgv_result.DataSource = null;
dgv_result.Rows.Clear();
dgv_result.Refresh();
DataSet ds = new DataSet();
DataTable dt = new DataTable();
dt.Columns.Add("样本路径", typeof(string));
dt.Columns.Add("样本类", typeof(string));
dt.Columns.Add("样本测试结果类", typeof(string));
dt.Columns.Add("正误判断", typeof(string));
foreach (Features feature in testSampleList)
{
testResult = nearest.Do_Nearest(feature, FeatureHelper.GetFeaturesList());
if (testResult > 0)
{
DataRow row = dt.NewRow();
string rightOrWrong = "×";
row[0] = feature.Filepath;
row[1] = feature.classID;
row[2] = string.Format("类{0}", testResult);
//检查结果是否正确
if (testResult == feature.classID)
{
correctCount++;
row[3] = " ";
}
else
{
row[3] = rightOrWrong;
//this.knDataGridView.DefaultCellStyle.ForeColor = Color.Red;
}
dt.Rows.Add(row);
}
}
ds.Tables.Add(dt);
//this.dgv_result.DataSource = ds.Tables[0];
//SetControlPropertyValue(dgv_result, "DataSource", ds.Tables[0]);
#endregion
#region Kn近邻法性能显示
//correctRate = (correctCount / Convert.ToDouble(testSampleList.Count)) * 100.0;
//Constant.kn_Rate = correctRate.ToString("0.000") + "%";
//lbl_result.Text = "测试样本总数 " + testSampleList.Count + " ，Kn近邻法判断正确 "
// + correctCount + " 个，正确率为：" + Constant.kn_Rate;
#endregion
ccount = correctCount;
crate = correctRate;
count = testSampleList.Count;
return(ds);
}
// 测试启动
private void knTestButton_Click(object sender, EventArgs e)
{
int kvalue = Convert.ToInt32(kValueText.Text);
Constant.kvalue = kvalue;
#region K有效的情况下进行计算
if (KCheck(kvalue))
{
#region 数据初始化
double correctCount = 0;
double correctRate = 0.0;
int testResult = 0;
KnNear my_knearest = new KnNear();
IList testSampleList = FeatureHelper.GetTestFeaturesList(); //获取原始测试
#endregion
#region DataGridView操作
knDataGridView.DataSource = null;
knDataGridView.Rows.Clear();
knDataGridView.Refresh();
DataSet ds = new DataSet();
DataTable dt = new DataTable();
dt.Columns.Add("样本路径", typeof(string));
dt.Columns.Add("样本测试结果类", typeof(string));
dt.Columns.Add("所取K值", typeof(string));
dt.Columns.Add("正误判断", typeof(string));
foreach (Features feature in testSampleList)
{
testResult = my_knearest.DoK_nearest(feature, FeatureHelper.GetFeaturesList(), kvalue);
if (testResult > 0)
{
DataRow row = dt.NewRow();
string rightOrWrong = "×";
row[0] = feature.Filepath;
row[1] = string.Format("类{0}", testResult);
row[2] = kvalue;
//检查结果是否正确
if (testResult == feature.classID)
{
correctCount++;
row[3] = " ";
}
else
{
row[3] = rightOrWrong;
//this.knDataGridView.DefaultCellStyle.ForeColor = Color.Red;
}
dt.Rows.Add(row);
}
}
ds.Tables.Add(dt);
this.knDataGridView.DataSource = ds.Tables[0];
#endregion
#region Kn近邻法性能显示
correctRate = (correctCount / Convert.ToDouble(testSampleList.Count)) * 100.0;
Constant.kn_Rate = correctRate.ToString("0.000") + "%";
resultLabel.Text = "测试样本总数 " + testSampleList.Count + " ，Kn近邻法判断正确 "
+ correctCount + " 个，正确率为：" + Constant.kn_Rate;
#endregion
}
#endregion
}
