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
}
public YTDomain(IRow pRow)
{
_id = pRow.OID;
_name = FeatureHelper.GetRowValue(pRow, "DomainName").ToString();
_description = FeatureHelper.GetRowValue(pRow, "DomainDescription").ToString();
_domainValues = FeatureHelper.GetRowValue(pRow, "DomainValues").ToString();
if (string.IsNullOrEmpty(_domainValues))
{
_valuePairs = new List <YTDomainValue>();
}
else
{
_valuePairs = JsonConvert.DeserializeObject <List <YTDomainValue> >(_domainValues);
}
}
// 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]));
}
public YTField(IRow pRow)
{
_id = pRow.OID;
_name = FeatureHelper.GetRowValue(pRow, "FieldName").ToString();
_aliasName = FeatureHelper.GetRowValue(pRow, "AliasName").ToString();
_length = ConvertHelper.ObjectToInt(FeatureHelper.GetRowValue(pRow, "FieldLength"));
_precision = ConvertHelper.ObjectToInt(FeatureHelper.GetRowValue(pRow, "FieldPrecision"));
_fieldTypeName = FeatureHelper.GetRowValue(pRow, "FieldType").ToString();
_fieldType = FieldHelper.ConvertFromSimpleString(_fieldTypeName);
string allowNullStr = FeatureHelper.GetRowValue(pRow, "AllowNull").ToString();
_allowNull = string.IsNullOrEmpty(allowNullStr) || allowNullStr.ToUpper().StartsWith("T") || allowNullStr == "真";
string keyStr = FeatureHelper.GetRowValue(pRow, "IsKey").ToString();
_isKey = string.IsNullOrEmpty(keyStr) == false || (keyStr.ToUpper().StartsWith("T") == true ? true : false);
_domain = FeatureHelper.GetRowValue(pRow, "FieldDomain").ToString();
_defaultValue = FeatureHelper.GetRowValue(pRow, "DefaultValue").ToString();
}
public void Load(IFeature pFeature, string nameField)
{
_boundary = pFeature.Shape;
string pName = FeatureHelper.GetFeatureStringValue(pFeature, nameField);
PageName = pName;
_autoElements.Clear();
for (int i = 0; i < pFeature.Fields.FieldCount; i++)
{
IField pField = pFeature.Fields.Field[i];
if (pField.Type == esriFieldType.esriFieldTypeGeometry)
{
continue;
}
string pValue = FeatureHelper.GetFeatureStringValue(pFeature, pField.Name);
_autoElements.Add(new PrintPageElement(pField.Name, pField.AliasName, pValue));
}
}
public void GetMedianFeature_Works_As_Expected()
{
var features = new List <Feature>();
features.Add(new Feature("place1", 1, 2));
features.Add(new Feature("place2", 2, 2));
features.Add(new Feature("place3", 3, 2));
features.Add(new Feature("place4", 4, 2));
features.Add(new Feature("place5", 5, 2));
features.Add(new Feature("place6", 6, 2));
features.Add(new Feature("place7", 7, 2));
//sort by X in test but Y is also used during building process
features = features.OrderBy(i => i.X).ToList();
var medianFeature = FeatureHelper.GetMedianFeatureFromList(features);
Assert.Equal("place4", medianFeature.Name);
}
public void Setup()
{
testDbContext = Substitute.For <IDbDataContext>();
testUserService = Substitute.For <IUserService>();
pageService = new PageService(testDbContext, testUserService);
mockedPage = PageHelper.Create();
mockedUser = UserHelper.Create();
mockedUser.Accesses = new List <UserAccess>();
mockedFeatures = FeatureHelper.CreateList(3);
FeatureHelper.CreateFeatureAccess(mockedFeatures, mockedPage, mockedUser, AccessType.None);
mockedPage.Features = mockedFeatures;
testUserService.GetUserByUserName(mockedUser.UserName).Returns(mockedUser);
mockedPage.PageName = "Index";
mockedPages = new List <Page>()
{
mockedPage
}.AsQueryable();
testDbContext.Pages.Returns(mockedPages);
}
public void BuildTree(KDNode currentNode, List <Feature> features, bool useXAxis)
{
if (useXAxis)
{
features = features.OrderBy(i => i.X).ToList();
}
else
{
features = features.OrderBy(i => i.Y).ToList();
}
Feature med = FeatureHelper.GetMedianFeatureFromList(features);
var node = InsertNode(currentNode, med, true);
var splitFeatures = FeatureHelper.SplitFeatures(features, features.IndexOf(med));
List <Feature> leftSplit = splitFeatures[0].ToList();
List <Feature> rightSplit = splitFeatures[1].ToList();
if (leftSplit.Count <= 1)
{
if (leftSplit.Count == 1)
{
InsertNode(node, leftSplit[0], useXAxis);
}
}
else
{
this.BuildTree(node, leftSplit, !useXAxis); //alternate dimension each level we build
}
// Do the same for the right points
if (rightSplit.Count <= 1)
{
if (rightSplit.Count == 1)
{
InsertNode(node, rightSplit[0], useXAxis);
}
}
else
{
BuildTree(node, rightSplit, !useXAxis);
}
}
public void UpdateRow(IRow pRow)
{
pRow.Value[pRow.Fields.FindField("TemplateName")] = _name;
pRow.Value[pRow.Fields.FindField("AliasName")] = _aliasName;
pRow.Value[pRow.Fields.FindField("BaseName")] = _baseName;
pRow.Value[pRow.Fields.FindField("Dataset")] = _datasetName;
pRow.Value[pRow.Fields.FindField("FeatureType")] = FeatureHelper.ConvertTypeToSimpleString(_featureType);
pRow.Value[pRow.Fields.FindField("GeometryType")] = GeometryHelper.ConvertToString(_geometryType);
if (_fields.Count == 0)
{
pRow.Value[pRow.Fields.FindField("FieldDefs")] = "";
}
else
{
pRow.Value[pRow.Fields.FindField("FieldDefs")] = JsonConvert.SerializeObject(_fields);
}
pRow.Store();
_id = pRow.OID;
}
//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);
}
/// <summary>
/// Executes the specified command.
/// </summary>
/// <param name="command">The command.</param>
/// <param name="keyValues">The key values.</param>
/// <param name="output">The output.</param>
/// <returns></returns>
public override int Execute(string command, StringDictionary keyValues, out string output)
{
output = string.Empty;
Logger.Verbose = true;
ActivationScope scope = ActivationScope.Feature;
if (Params["scope"].UserTypedIn)
{
scope = (ActivationScope)Enum.Parse(typeof(ActivationScope), Params["scope"].Value.ToLowerInvariant(), true);
}
bool force = Params["force"].UserTypedIn;
bool ignoreNonActive = Params["ignorenonactive"].UserTypedIn;
if (ignoreNonActive)
{
force = true;
}
string url = null;
if (Params["url"].UserTypedIn)
{
url = Params["url"].Value.TrimEnd('/');
}
try
{
Logger.Write("Started at {0}", DateTime.Now.ToString());
Guid featureId = FeatureHelper.GetFeatureIdFromParams(Params);
FeatureHelper fh = new FeatureHelper();
fh.ActivateDeactivateFeatureAtScope(scope, featureId, false, url, force, ignoreNonActive);
}
finally
{
Logger.Write("Finished at {0}\r\n", DateTime.Now.ToString());
}
return((int)ErrorCodes.NoError);
}
public XmlNode ToXml(XmlDocument doc)
{
XmlNode templateNode = doc.CreateElement("Template");
XmlAttribute nameAttribute = doc.CreateAttribute("Name");
nameAttribute.Value = _name;
templateNode.Attributes.Append(nameAttribute);
nameAttribute = doc.CreateAttribute("AliasName");
nameAttribute.Value = _aliasName;
templateNode.Attributes.Append(nameAttribute);
nameAttribute = doc.CreateAttribute("BaseName");
nameAttribute.Value = _baseName;
templateNode.Attributes.Append(nameAttribute);
nameAttribute = doc.CreateAttribute("Dataset");
nameAttribute.Value = _datasetName;
templateNode.Attributes.Append(nameAttribute);
nameAttribute = doc.CreateAttribute("FeatureType");
nameAttribute.Value = FeatureHelper.ConvertTypeToSimpleString(_featureType);
templateNode.Attributes.Append(nameAttribute);
nameAttribute = doc.CreateAttribute("GeometryType");
nameAttribute.Value = GeometryHelper.ConvertToString(_geometryType);
templateNode.Attributes.Append(nameAttribute);
XmlNode fieldsNode = doc.CreateElement("Fields");
foreach (IYTField ytField in _fields)
{
fieldsNode.AppendChild(ytField.ToXml(doc));
}
templateNode.AppendChild(fieldsNode);
return(templateNode);
}
public (string Prediction, float Score) Classify(TextBlock textBlock, IEnumerable <PdfPath> paths, IEnumerable <IPdfImage> images,
double averagePageFontHeight, List <DocumentBookmarkNode> pageBookmarksNodes)
{
double bboxArea = textBlock.BoundingBox.Area;
var letters = textBlock.TextLines.SelectMany(li => li.Words).SelectMany(w => w.Letters);
var features = FeatureHelper.GetFeatures(
textBlock, paths,
images, averagePageFontHeight,
textBlock.BoundingBox.Area,
pageBookmarksNodes);
BlockFeatures blockFeatures = new BlockFeatures()
{
BlockAspectRatio = features[0],
CharsCount = features[1],
WordsCount = features[2],
LinesCount = features[3],
PctNumericChars = features[4],
PctAlphabeticalChars = features[5],
PctSymbolicChars = features[6],
PctBulletChars = features[7],
DeltaToHeight = features[8],
PathsCount = features[9],
PctBezierPaths = features[10],
PctHorPaths = features[11],
PctVertPaths = features[12],
PctOblPaths = features[13],
ImagesCount = features[14],
ImageAvgProportion = features[15],
BestNormEditDistance = features[16],
};
var result = predEngine.Predict(blockFeatures);
return(FeatureHelper.Categories[(int)result.Prediction], result.Score.Max());
}
protected override void UpdateDataObject()
{
SPFeatureDefinition featureDef = null;
ActivationScope scope = ActivationScope.Feature;
if (ActivateAtScope.HasValue)
{
scope = ActivateAtScope.Value;
}
if (farmLevelFeature)
{
featureDef = SPFarm.Local.FeatureDefinitions[base.DataObject.Id];
}
else
{
featureDef = base.DataObject;
}
try
{
Logger.Write("Started at {0}", DateTime.Now.ToString());
Guid featureId = featureDef.Id;
FeatureHelper fh = new FeatureHelper();
fh.ActivateDeactivateFeatureAtScope(featureDef, scope, true, Url, Force.IsPresent, IgnoreNonActive.IsPresent);
}
finally
{
Logger.Write("Finished at {0}\r\n", DateTime.Now.ToString());
}
if ((featureDef != null) && (PassThru.IsPresent))
{
base.DataObject = featureDef;
base.WriteResult(base.DataObject);
}
}
public void SplitFeatureList_Works_As_Expected()
{
var features = new List <Feature>();
features.Add(new Feature("place1", 1, 2));
features.Add(new Feature("place2", 2, 2));
features.Add(new Feature("place3", 3, 2));
features.Add(new Feature("place4", 4, 2));
features.Add(new Feature("place5", 5, 2));
features.Add(new Feature("place6", 6, 2));
features.Add(new Feature("place7", 7, 2));
var expectedList1 = new List <Feature>();
expectedList1.Add(new Feature("place1", 1, 2));
expectedList1.Add(new Feature("place2", 2, 2));
expectedList1.Add(new Feature("place3", 3, 2));
var expectedList2 = new List <Feature>();
expectedList2.Add(new Feature("place5", 5, 2));
expectedList2.Add(new Feature("place6", 6, 2));
expectedList2.Add(new Feature("place7", 7, 2));
var outcomeLists = FeatureHelper.SplitFeatures(features, 3);
for (int i = 0; i < outcomeLists[0].Count; i++)
{
Assert.Equal(outcomeLists[0][i].Name, expectedList1[i].Name);
}
for (int i = 0; i < outcomeLists[1].Count; i++)
{
Assert.Equal(outcomeLists[1][i].Name, expectedList2[i].Name);
}
}
public List <FeatureNode> Build()
{
var result = new List <FeatureNode>();
var parentFeatures = FeatureHelper.GetFeaturesWithType(FeatureType.Parent);
foreach (var parentFeature in parentFeatures)
{
var parentInfo = FeatureInfo.Builder.Build(parentFeature, FeatureType.Parent);
var parentNode = new FeatureNode(parentInfo);
foreach (var childFeature in FeatureHelper.GetChildFeatures(parentFeature))
{
var childInfo = FeatureInfo.Builder.Build(childFeature, FeatureType.Child);
var childNode = new FeatureNode(childInfo, parentNode);
parentNode.Nodes.Add(childNode);
}
result.Add(parentNode);
}
return(result);
}
public double FindDistanceToNearestNeighbour(KDNode currentNode, Feature goalFeature, KDNode closestNeighbour, double closestNeighbourDistance)
{
if (currentNode == null)
{
return(closestNeighbourDistance);
}
var currentDistance = FeatureHelper.GetDistance(currentNode.Feature, goalFeature);
if (currentDistance < closestNeighbourDistance)
{
if (currentNode.Feature != goalFeature)
{
closestNeighbour = currentNode;
closestNeighbourDistance = currentDistance;
}
}
if (currentNode.UseXAxis)
{
if (goalFeature.X < currentNode.Feature.X) //is goal.x LEFT current node.x? IF so, traverse LEFT, otherwise go RIGHT (easy to understand because this is X comparison node)
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.LeftNode, goalFeature, closestNeighbour, closestNeighbourDistance);
if (Math.Abs(currentNode.Feature.X - goalFeature.X) < closestNeighbourDistance)
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.RightNode, goalFeature, closestNeighbour, closestNeighbourDistance);
}
}
else
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.RightNode, goalFeature, closestNeighbour, closestNeighbourDistance);
if (Math.Abs(goalFeature.X - currentNode.Feature.X) < closestNeighbourDistance)
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.LeftNode, goalFeature, closestNeighbour, closestNeighbourDistance);
}
}
}
else
{
if (goalFeature.Y > currentNode.Feature.Y) //is goal.y ABOVE current node.y? IF so, traverse to RIGHT to go ABOVE (because this is a Y comparison node) otherwise go left to look down
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.RightNode, goalFeature, closestNeighbour, closestNeighbourDistance);
if (Math.Abs(currentNode.Feature.Y - goalFeature.Y) < closestNeighbourDistance)
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.LeftNode, goalFeature, closestNeighbour, closestNeighbourDistance);
}
}
else
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.LeftNode, goalFeature, closestNeighbour, closestNeighbourDistance);
if (Math.Abs(goalFeature.Y - currentNode.Feature.Y) < closestNeighbourDistance)
{
closestNeighbourDistance = FindDistanceToNearestNeighbour(currentNode.RightNode, goalFeature, closestNeighbour, closestNeighbourDistance);
}
}
}
return(closestNeighbourDistance);
}
// 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
}
}
public FeatureWithObserver(string name) : base(FeatureHelper.GetUnnamed(name))
{
FeatureHelper.CollectSystems(this.name, this);
Object.DontDestroyOnLoad(this.gameObject);
}
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);
}
private void Init(string name)
{
FeatureHelper.CollectSystems(name, this);
}
protected override async void OnClick()
{
var success = await QueuedTask.Run(() => FeatureHelper.GetTopologyFeatureLayers().GenerateRandomPolygons());
Debug.WriteLine($"Created topology features? {success}");
}
// 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
}
/// Constructor, name could be empty/null for noname systems
public Feature(string name)
{
name = FeatureHelper.GetUnnamed(name);
FeatureHelper.CollectSystems(name, this);
}
private void exitButton_Click(object sender, EventArgs e)
{
FeatureHelper.Close();
this.Close();
}
