private void readType()
{
FreeContainer.FreeTable(MainFormContainers.SoilType);
InputData id = new InputData(); MainFormContainers.SoilType = id.InputCSV(Config.soilhabitat);
changeGrid2(MainFormContainers.SoilType); MessageBox.Show("类型数据更换完成！");
}
public bool MatrixAnalysis(DataTable dt, int level)
{
FreeContainer.FreeTable(HProfile);
string[] column = { "剖面编号", "预测类型" };
for (int i = 0; i < column.Length; i++)
{
DataColumn dc = new DataColumn(column[i]);
HProfile.Columns.Add(dc);
}
for (int i = 0; i < dt.Rows.Count; i++)
{
DataRow dr = HProfile.NewRow();
dr[0] = dt.Rows[i][0].ToString();
double min = 2;
int col = 1;
for (int j = 1; j < dt.Columns.Count; j++)
{
if (Convert.ToDouble(dt.Rows[i][j]) < min && Convert.ToDouble(dt.Rows[i][j]) != 0)
{
min = Convert.ToDouble(dt.Rows[i][j]);
col = j;
}
}
dr[1] = ReadType(level, dt.Columns[col].ColumnName.ToString());
HProfile.Rows.Add(dr);
}
return(true);
}
/// <summary>
/// 计算各类型土壤分类精确度
/// </summary>
/// <param name="mix">混淆矩阵</param>
/// <returns></returns>
bool MixAccuracy(DataTable mix)
{
FreeContainer.FreeTable(AMatrix);
AMatrix.Columns.Add(new DataColumn(" ", typeof(string))); //添加列
AMatrix.Columns.Add(new DataColumn("用户精度", typeof(string)));
AMatrix.Columns.Add(new DataColumn("错分率", typeof(string)));
for (int i = 0; i < mix.Rows.Count; i++)//遍历混淆矩阵计算各类型精确度
{
DataRow dr = AMatrix.NewRow();
dr[0] = mix.Rows[i][0].ToString();
int sum = 0, num = 0;
double ua = 0, cm = 0;
for (int j = 1; j < mix.Columns.Count; j++)
{
sum += Convert.ToInt32(mix.Rows[i][j]);
if (mix.Columns[j].ToString() == mix.Rows[i][0].ToString())
{
num = Convert.ToInt32(mix.Rows[i][j]);
}
}
ua = Convert.ToDouble(num) / Convert.ToDouble(sum);
cm = 1.0 - ua;
dr[1] = (ua * 100).ToString("f2") + "%";
dr[2] = (cm * 100).ToString("f2") + "%";
AMatrix.Rows.Add(dr);
}
return(true);
}
/// <summary>
/// 清空目录树、表格，并且清空测试集数据
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void toolStripButton6_Click(object sender, EventArgs e)
{
FreeContainer.FreeTreeNode(treeView1, 0);
FreeContainer.FreeTable(MainFormContainers.Testset);
FreeContainer.FreeGrid(dataGridView1);
progressBar1.Value = 0;
}
/// <summary>
/// 直方图训练方法
/// </summary>
/// <param name="Test">测试集</param>
/// <param name="Train">训练集</param>
/// <param name="levels">分类级别</param>
/// <param name="equation">距离函数指令</param>
/// <returns></returns>
public bool GetHistAnswer(List <string> Test, List <string> Train, int levels, int equation)
{
FreeContainer.FreeTable(BriefAccu);
string[] column = { "实验序号", "总正确率" };
for (int i = 0; i < column.Length; i++)
{
DataColumn dc = new DataColumn(column[i]);
BriefAccu.Columns.Add(dc);
}
for (int i = 0; i < Test.Count && i < Train.Count; i++)
{
List <string> TestSample = new List <string>();
List <string> TrainSample = new List <string>();
GetSamplelist(Test[i], out TestSample); //从测试集文件中取出剖面ID
GetSamplelist(Train[i], out TrainSample); //从训练集文件中取出剖面ID
HistogramMatrix(TestSample, TrainSample, equation); //计算距离矩阵
AnswerAnalysis(Matrix, levels); //计算匹配剖面
AccuracyAnalysis(Profile, i + 1); //计算匹配剖面类型
MixMatrix(Profile); //计算混淆矩阵
MixAccuracy(MMatrix); //计算该次实验的总精确度
OutputData od = new OutputData(); //输出该次实验结果
od.ExportToCsv(Config.MatrixFolder, "实验" + (i + 1).ToString() + "混淆矩阵", MMatrix);
od.ExportToCsv(Config.MatrixFolder, "实验" + (i + 1).ToString() + "匹配结果", AMatrix);
AuxiliaryFunc.percent += 90 / Train.Count;
}
return(true);
}
/// <summary>
/// 计算正确率
/// </summary>
/// <param name="dt"></param>
/// <param name="num"></param>
/// <returns></returns>
private bool AccuracyAnalysis(DataTable dt, int num)
{
FreeContainer.FreeTable(Accuracy);
string[] column = { "实验序号", "正确率" };
for (int i = 0; i < column.Length; i++)
{
DataColumn dc = new DataColumn(column[i]);
Accuracy.Columns.Add(dc);
}
int sum = 0;
for (int i = 0; i < dt.Rows.Count; i++)
{
if (dt.Rows[i][3].ToString() == "是")
{
sum++;
}
}
double degree = Convert.ToDouble(sum) / Convert.ToDouble(dt.Rows.Count);
DataRow dr = Accuracy.NewRow();
DataRow dr0 = BriefAccu.NewRow();
dr[0] = "实验" + num.ToString(); dr[1] = (degree * 100).ToString("f2") + "%";
dr0[0] = "实验" + num.ToString(); dr0[1] = (degree * 100).ToString("f2") + "%";
Accuracy.Rows.Add(dr);
BriefAccu.Rows.Add(dr0);
return(true);
}
private void button1_Click(object sender, EventArgs e)
{
FreeContainer.FreeGrid(dataGridView1);
FreeContainer.FreeGrid(dataGridView2);
level = Convert.ToInt32(comboBox1.SelectedIndex + 1);
Thread newthread0 = new Thread(new ThreadStart(RunTask));
Thread newthread = new Thread(new ThreadStart(match));
newthread0.Start(); newthread.Start();
}
public bool HistogramSet(List <string> testset, List <string> trainset, int order)
{
if (testset.Count > 0)
{
FreeContainer.FreeTable(HMatrix);
for (int i = 0; i < trainset.Count + 1; i++)
{
if (i == 0)
{
DataColumn dc = new DataColumn(" ");
HMatrix.Columns.Add(dc);
}
else
{
DataColumn dc = new DataColumn(trainset[i - 1]);
HMatrix.Columns.Add(dc);
}
}
for (int i = 0; i < testset.Count; i++)
{
DataRow dr = HMatrix.NewRow();
dr[0] = testset[i];
List <double> l1 = new List <double>();
ReadHistogram(Config.uksoilhistogram, testset[i], out l1);
for (int j = 0; j < trainset.Count; j++)
{
AuxiliaryFunc.percent = Convert.ToInt32((i + 1) * (j + 1) / testset.Count / trainset.Count * 95);
List <double> l2 = new List <double>();
ReadHistogram(Config.soilhistogram, trainset[j], out l2);
switch (order)
{
case 0: dr[j + 1] = Distance_Fomula.Euclidean(l1, l2).ToString(); break;
case 1: dr[j + 1] = Distance_Fomula.X2(l1, l2).ToString(); break;
case 2: dr[j + 1] = Distance_Fomula.Dxy(l1, l2).ToString(); break;
default: break;
}
}
HMatrix.Rows.Add(dr);
}
return(true);
}
else
{
MessageBox.Show("测试集为空！");
return(false);
}
}
public bool MatrixCal(List <string> testset, List <string> trainset, int order)
{
if (testset.Count > 0)
{
FreeContainer.FreeTable(HMatrix);
SpecAngel sa = new SpecAngel();
WaveShape ws = new WaveShape();
InputData id = new InputData();
for (int i = 0; i < trainset.Count + 1; i++)
{
if (i == 0)
{
DataColumn dc = new DataColumn(" ");
HMatrix.Columns.Add(dc);
}
else
{
DataColumn dc = new DataColumn(trainset[i - 1]);
HMatrix.Columns.Add(dc);
}
}
for (int i = 0; i < testset.Count; i++)
{
DataRow dr = HMatrix.NewRow();
dr[0] = testset[i];
DataTable dt1 = id.InputCSV(Config.uksoilfolder + @"\" + testset[i] + ".csv");
for (int j = 0; j < trainset.Count; j++)
{
AuxiliaryFunc.percent = Convert.ToInt32((i + 1) * (j + 1) / testset.Count / trainset.Count * 95);
DataTable dt2 = id.InputCSV(Config.soilfolder + @"\" + trainset[i] + ".csv");
switch (order)
{
case 0: dr[j + 1] = sa.SAcal(dt1, dt2).ToString(); break;
case 1: dr[j + 1] = ws.WaveCal(dt1, dt2).ToString(); break;
default: break;
}
}
AuxiliaryFunc.percent += Convert.ToDouble(i) / Convert.ToDouble(testset.Count) * 90;
HMatrix.Rows.Add(dr);
}
return(true);
}
else
{
MessageBox.Show("测试集为空！");
return(false);
}
}
/// <summary>
/// 计算直方图距离矩阵
/// </summary>
/// <param name="testset"></param>
/// <param name="trainset"></param>
/// <param name="groups"></param>
/// <param name="order"></param>
/// <returns></returns>
public bool HistogramMatrix(List <string> testset, List <string> trainset, int order)
{
FreeContainer.FreeTable(Matrix);
for (int i = 0; i < trainset.Count + 1; i++)
{
if (i == 0)
{
DataColumn dc = new DataColumn();
Matrix.Columns.Add(dc);
}
else
{
DataColumn dc = new DataColumn(trainset[i - 1]);
Matrix.Columns.Add(dc);
}
}
for (int i = 0; i < testset.Count; i++)
{
DataRow dr = Matrix.NewRow();
dr[0] = testset[i];
List <double> l1 = new List <double>();
ReadHistogram(Config.soilhistogram, testset[i], out l1);
for (int j = 0; j < trainset.Count; j++)
{
List <double> l2 = new List <double>();
ReadHistogram(Config.soilhistogram, trainset[j], out l2);
if (l1.Count > 0 && l2.Count > 0)//防止剖面缺失
{
switch (order)
{
case 0: dr[j + 1] = Distance_Fomula.Euclidean(l1, l2).ToString(); break;
case 1: dr[j + 1] = Distance_Fomula.X2(l1, l2).ToString(); break;
case 2: dr[j + 1] = Distance_Fomula.Dxy(l1, l2).ToString(); break;
default: break;
}
}
else
{
dr[j + 1] = 9999.ToString();//9999表示剖面缺失
}
}
Matrix.Rows.Add(dr);
}
return(true);
}
/// <summary>
/// 计算非特征值距离矩阵
/// </summary>
/// <param name="testset">测试集剖面</param>
/// <param name="trainset">训练集剖面</param>
/// <param name="order"></param>
/// <returns></returns>
public bool NormalMatrix(List <string> testset, List <string> trainset, int order)
{
FreeContainer.FreeTable(Matrix);
SpecAngel sa = new SpecAngel();
WaveShape ws = new WaveShape();
InputData id = new InputData();
for (int i = 0; i < trainset.Count + 1; i++)
{
if (i == 0)
{
DataColumn dc = new DataColumn();
Matrix.Columns.Add(dc);
}
else
{
DataColumn dc = new DataColumn(trainset[i - 1]);
Matrix.Columns.Add(dc);
}
}
for (int i = 0; i < testset.Count; i++)
{
DataRow dr = Matrix.NewRow();
dr[0] = testset[i];
DataTable dt1 = id.InputCSV(Config.uksoilfolder + "\\" + testset[i].Substring(1, testset[i].Length - 2) + ".csv");
for (int j = 0; j < trainset.Count; j++)
{
DataTable dt2 = id.InputCSV(Config.soilfolder + "\\" + trainset[j].Substring(1, trainset[j].Length - 2) + ".csv");
if (dt1.Rows.Count > 0 && dt2.Rows.Count > 0)//防止剖面缺失
{
switch (order)
{
case 0: dr[j + 1] = sa.SAcal(dt1, dt2).ToString(); break;
case 1: dr[j + 1] = ws.WaveCal(dt1, dt2).ToString(); break;
default: break;
}
}
else
{
dr[j + 1] = 9999.ToString();//9999表示剖面缺失
}
}
Matrix.Rows.Add(dr);
}
return(true);
}
/// <summary>
/// 根据匹配结果计算混淆矩阵
/// </summary>
/// <param name="dt">匹配结果</param>
/// <returns></returns>
bool MixMatrix(DataTable dt)
{
FreeContainer.FreeTable(MMatrix);
Hashtable h1 = new Hashtable(); //存储预测类型
Hashtable h2 = new Hashtable(); //存储真实类型
for (int i = 0; i < dt.Rows.Count; i++)
{
if (!h1.ContainsKey(dt.Rows[i][1].ToString()))
{
h1.Add(dt.Rows[i][1].ToString(), h1.Count + 1);//预测
}
if (!h2.ContainsKey(dt.Rows[i][2].ToString()))
{
h2.Add(dt.Rows[i][2].ToString(), h2.Count + 1);//真实
}
}
MMatrix.Columns.Add(new DataColumn(" "));
foreach (string key in h1.Keys)
{
MMatrix.Columns.Add(new DataColumn(key, typeof(int)));
}
foreach (string key in h2.Keys)
{
DataRow dr = MMatrix.NewRow();
dr[0] = key;
for (int j = 1; j < MMatrix.Columns.Count; j++)
{
dr[j] = 0;
}
MMatrix.Rows.Add(dr);
}
for (int i = 0; i < dt.Rows.Count; i++)
{
int row = Convert.ToInt32(h2[dt.Rows[i][2].ToString()]);
int col = Convert.ToInt32(h1[dt.Rows[i][1].ToString()]);
MMatrix.Rows[row - 1][col] = Convert.ToInt32(MMatrix.Rows[row - 1][col]) + 1;
}
return(true);
}
/// <summary>
/// 下拉框2，选项更改触发事件
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void comboBox2_SelectedIndexChanged(object sender, EventArgs e)
{
FreeContainer.FreeTable(dt);
InputData id = new InputData();
string fold;
if (comboBox1.SelectedIndex == 0)
{
fold = Config.soilfolder;
}
else
{
fold = Config.uksoilfolder;
}
dt = id.InputCSV(fold + @"\" + comboBox2.SelectedItem.ToString() + ".csv");
comboBox3.Items.Clear();
for (int i = 1; i <= dt.Rows.Count; i++)
{
comboBox3.Items.Add(i.ToString() + "nm");
}
comboBox3.SelectedIndex = 0;
}
/// <summary>
/// 计算匹配矩阵
/// </summary>
/// <param name="dt">匹配矩阵表格</param>
/// <param name="levels">分类级别</param>
/// <returns></returns>
public bool AnswerAnalysis(DataTable dt, int levels)
{
FreeContainer.FreeTable(Profile);
string[] column = { "土壤编号", "预测类型", "真实类型", "是否匹配" };
for (int i = 0; i < column.Length; i++)
{
DataColumn dc = new DataColumn(column[i]);
Profile.Columns.Add(dc);
}
for (int i = 1; i < dt.Rows.Count; i++)
{
DataRow dr = Profile.NewRow();
dr[0] = dt.Rows[i][0].ToString();
double min = 2;
int col = 1;
for (int j = 1; j < dt.Columns.Count; j++)
{
if (Convert.ToDouble(dt.Rows[i][j]) < min && Convert.ToDouble(dt.Rows[i][j]) != 0)
{
min = Convert.ToDouble(dt.Rows[i][j]);
col = j;
}
}
dr[1] = ReadType(levels, dt.Columns[col].ColumnName.ToString());
dr[2] = ReadType(levels, dt.Rows[i][0].ToString());
if (dr[1].ToString() == dr[2].ToString())
{
dr[3] = "是";
}
else
{
dr[3] = "否";
}
Profile.Rows.Add(dr);
}
return(true);
}
/// <summary>
/// 每五秒清理一次
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void timer1_Tick(object sender, EventArgs e)
{
FreeContainer.ClearMemory();
}
