// 特征提取
private void fetureToolStripMenuItem_Click(object sender, EventArgs e)
{
// 首先 如果16个训练样本没进行选择的话
pcxlist = SelectedPCXHelper.GetSelPCXFromLB();
if (pcxlist == null || pcxlist.Count == 0)
{
MessageBox.Show(this, "还没选择训练样本，请按步骤来", "提示信息", MessageBoxButtons.OK);
}
else
{
OutlineFeatureForm ofform = new OutlineFeatureForm();
ofform.ShowDialog();
}
}
private void browerDirButton_Click(object sender, EventArgs e)
{
FolderBrowserDialog fbdlg = new FolderBrowserDialog();
fbdlg.Description = "选择要进行计算的目录";
fbdlg.RootFolder = Environment.SpecialFolder.MyComputer;
fbdlg.ShowNewFolderButton = true;
DialogResult result = fbdlg.ShowDialog(this);
if (result == DialogResult.OK)
{
this.textDirPath.Text = fbdlg.SelectedPath;
SelectedPCXHelper.pcxlist = new List <PCXImage>(); //每次单击浏览的时候都初始化
SelectedPCXHelper.SetSelectedPCXImage(this.textDirPath.Text);
//this.selPcximage = SelectedPCXHelper.GetSelectedPCXImage(this.textDirPath.Text);
LoadLB(this.textDirPath.Text); //加载ListBox的数据
}
}
private void confirmButton_Click(object sender, EventArgs e)
{
if (selectedSampleListBox.Items.Count == 0)
{
MessageBox.Show(this, "你没有选择训练样本，请先选择", "提示信息", MessageBoxButtons.OK);
}
else
{
List <PCXImage> pcxList = new List <PCXImage>();
int count = selectedSampleListBox.Items.Count;
IList list = selectedSampleListBox.Items;
List <string> strlist = (List <string>)SelectedPCXHelper.ListConvert(list);
foreach (string str in strlist)
{
string filename = string.Format(@"{0}", str);
Image image = PCXHelper.LoadPCX(filename);
PCXImage pcximage = PCXHelper.GetPCXImage(filename);
PCXImage pImage = PCXHelper.GetPCXImage(pcximage);
pcxList.Add(pImage);
}
pcxList = pcxList.ToList();
SelectedPCXHelper.SetSelPCXFromLB(pcxList, textDirPath.Text);
List <PCXImage> unselpcxList = new List <PCXImage>();
IList unsellist = unSelSampleListBox.Items;
List <string> unselstrlist = (List <string>)SelectedPCXHelper.ListConvert(unsellist);
foreach (string str in unselstrlist)
{
string filename = string.Format(@"{0}", str);
Image image = PCXHelper.LoadPCX(filename);
PCXImage pcximage = PCXHelper.GetPCXImage(filename);
PCXImage pImage = PCXHelper.GetPCXImage(pcximage);
unselpcxList.Add(pImage);
}
unselpcxList = unselpcxList.ToList();
SelectedPCXHelper.SetUnselPCXList(unselpcxList, textDirPath.Text);
this.Close();
}
}
// 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
}
}
// 对测试样本和训练样本在ListBox中的初始化
private void LoadLB(string path)
{
this.pcxlist = SelectedPCXHelper.GetPCXImageList(path);// 这是最初开始加载的pcxlist，不是移动变化后的
selectedSampleListBox.Items.Clear();
unSelSampleListBox.Items.Clear();
// 在这里做一个radiobutton进行开闭测试的选择
if (this.closeRadioButton.Checked)
{
Constant.openchecked = false;
foreach (PCXImage pcx in pcxlist)
{
selectedSampleListBox.Items.Add(pcx.OldFilename);
unSelSampleListBox.Items.Add(pcx.OldFilename);
}
}
else
{
Constant.openchecked = true;
foreach (PCXImage pcx in pcxlist)
{
selectedSampleListBox.Items.Add(pcx.OldFilename);
}
Random ran = new Random();
int count = pcxlist.Count;
int testnum = (int)(count * Constant.testRate);
#region 随机在每个类里面选择80%做训练样本，20%做测试样本
#region 循环里面最好不要进行数值的初始化
int p = count;
int k = 0;
int m = k + 20;
#endregion
for (int i = 0; i < testnum; i++)
{
// 注意题意是：对每个人的手写签名，用其中80%的图像作为训练样本进行训练，用余下的20%的图像进行测试,所以这里的处理有个技巧性。
//int _index = ran.Next(0, count); //产生0到pcx总数的随机数，且count的数必须每次循环后减一，即count--。
int _index = ran.Next(k, m);
#region 数据移除
// 如果从训练样本中已经移除，则会出现_index大于剩余训练样本总和的情况，会报错。注意添加删除的顺序
//if (_index >= selectedSampleListBox.Items.Count)
//{
// int number = selectedSampleListBox.Items.Count - 2;
// selectedSampleListBox.Items.RemoveAt(number); //At 15
// unSelSampleListBox.Items.Add(selectedSampleListBox.Items[number - 1]); //添加的是 14
//}
//else
//{
// selectedSampleListBox.Items.RemoveAt(_index);
// unSelSampleListBox.Items.Add(selectedSampleListBox.Items[_index - 1]);
//}
#endregion
int number = selectedSampleListBox.Items.Count;
// 顺序不能乱 先添加后删除
unSelSampleListBox.Items.Add(selectedSampleListBox.Items[_index]);
selectedSampleListBox.Items.RemoveAt(_index);
//count--;
// 20%做测试样本，80%做训练样本
if (selectedSampleListBox.Items.Count == p - 4) //说明移除四个
{
k += 16; //20-4
p -= 4;
m = k + 20;
}
else
{
m--;
}
}
#endregion
}
}
