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
}
// 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
}
}
// 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
}
