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
}
// 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);
}
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
}