protected void Button1_Click(object sender, EventArgs e)
{
double calyxLength = double.Parse(CalyxLength.Text);
double calyxWidth = double.Parse(CalyxWidth.Text);
double petalLength = double.Parse(PetalLength.Text);
double petalWidth = double.Parse(PetalWidth.Text);
string sql = "select Label, CalyxLength, CalyxWidth, PetalLength, PetalWidth from tblIris";
SQLHelper sh = new SQLHelper();
DataTable dt = new DataTable();
DataSet ds = new DataSet();
try
{
sh.RunSQL(sql, ref ds);
if (ds.Tables[0] != null)
{
dt = ds.Tables[0];
if (dt.Rows.Count > 0)
{
ClassNaiveBayes classifier = new ClassNaiveBayes();
classifier.TrainClassifier(dt);
Response.Write(classifier.Classify(new double[] { calyxLength, calyxWidth, petalLength, petalWidth }));
}
}
}
catch (Exception ex)
{
Response.Write(ex.Message);
}
finally
{
sh.Close();
}
}
protected void btnOut_Click(object sender, EventArgs e)
{
double Symptom = double.Parse(ddlSymptom.SelectedValue);
double Job = double.Parse(ddlJob.SelectedValue);
/*
* 打喷嚏--1
* 头痛--2
*
* 护士--1
* 农夫--2
* 建筑工人--3
* 教师--4
*/
//training data
DataTable table = new DataTable();
table.Columns.Add("disease");
table.Columns.Add("symptom", typeof(double));
table.Columns.Add("job", typeof(double));
table.Rows.Add("感冒", 1, 1);
table.Rows.Add("过敏", 1, 2);
table.Rows.Add("脑震荡", 2, 3);
table.Rows.Add("感冒", 2, 3);
table.Rows.Add("感冒", 1, 4);
table.Rows.Add("脑震荡", 2, 4);
ClassNaiveBayes classifier = new ClassNaiveBayes();
classifier.TrainClassifier(table);
Response.Write(classifier.Classify(new double[] { Symptom, Job }));//新来了打喷嚏的建筑工人，问可能得什么病
}
protected void btnOut_Click(object sender, EventArgs e)
{
double Symptom = double.Parse(ddlSymptom.SelectedValue);
double Job = double.Parse(ddlJob.SelectedValue);
DataTable table = new DataTable();
table.Columns.Add("disease");
table.Columns.Add("symptom", typeof(double));
table.Columns.Add("job", typeof(double));
table.Rows.Add("感冒", 1, 1);
table.Rows.Add("过敏", 1, 2);
table.Rows.Add("脑震荡", 2, 3);
table.Rows.Add("感冒", 2, 3);
table.Rows.Add("感冒", 1, 4);
table.Rows.Add("脑震荡", 2, 4);
ClassNaiveBayes classifier = new ClassNaiveBayes();
classifier.TrainClassifier(table);
Response.Write(classifier.Classify(new double[] { Symptom, Job }));
}
protected void Page_Load(object sender, EventArgs e)
{
string sql = "select (case gender when 1 then '男性' else '女性' end) as Gender, Footsize, Weight, Height from tblBodies";
SQLHelper sh = new SQLHelper();
DataTable dt = new DataTable();
DataSet ds = new DataSet();
try
{
sh.RunSQL(sql, ref ds);
if (ds.Tables[0] != null)
{
dt = ds.Tables[0];
if (dt.Rows.Count > 0)
{
DataTable table = new DataTable();
table.Columns.Add("gender");
table.Columns.Add("footsize", typeof(double));
table.Columns.Add("height", typeof(double));
table.Columns.Add("weight", typeof(double));
for (int i = 0; i < Math.Ceiling(dt.Rows.Count * 0.7); i++)
{
table.Rows.Add(dt.Rows[i][0], dt.Rows[i][1], dt.Rows[i][2], dt.Rows[i][3]);
}
ClassNaiveBayes classifier = new ClassNaiveBayes();
classifier.TrainClassifier(table);
int times = 0;
int people = 1;
for (int j = int.Parse(Math.Ceiling(dt.Rows.Count * 0.7).ToString()); j < dt.Rows.Count; j++)
{
double footsize = double.Parse(dt.Rows[j]["footsize"].ToString());
double height = double.Parse(dt.Rows[j]["height"].ToString());
double weight = double.Parse(dt.Rows[j]["weight"].ToString());
string possiblegender = classifier.Classify(new double[] { footsize, height, weight });
string realgender = dt.Rows[j]["gender"].ToString();
Response.Write(string.Format("第{0}人脚长{1} cm，身高{2} cm，体重{3} kg，预测性别为：{4}；", people, footsize, height, weight, possiblegender));
Response.Write("实际性别为：" + realgender + "。");
people++;
if (possiblegender == realgender)
{
Response.Write("预测成功！" + "</br>");
times++;
}
else
{
Response.Write("预测失败！" + "</br>");
}
}
Response.Write(string.Format("共预测成功{0}次，成功率为{1}%。", times, 100 * times / people));
}
}
}
catch (Exception ex)
{
Response.Write(ex.Message);
}
finally
{
sh.Close();
}
}
