public bool ApplicantTest()
{
try
{
const string fileBase = "D:/Users/Documentos/Desktop/bank-additional/bank-analiser.csv";
const string fileLearning = "D:/Users/Documentos/Desktop/bank-additional/bank-analiser-learning.csv";
///this is data test....
var candidato = new Model.ApplicantDataCluster
{
age = 25,
consconfid = -31,
education = 1,
empratevar = 1,
housing = 0,
loan = 1,
duration = 300
};
var result = new Clusterizing().GetClusterizing(fileBase, fileLearning, candidato);
return(result != null);
}
catch
{
return(false);
}
}
public ActionResult Create([Bind(Include = "Id,Education,Age,Housing,Loan,Duration,EmpRateVar,ConsConfId,SearchData,DeservCredit,Cpf,BornDate,Name")] Applicant applicant)
{
const string fileBase = "D:/Users/Documentos/Desktop/bank-additional/bank-analiser.csv";
const string fileLearning = "D:/Users/Documentos/Desktop/bank-additional/bank-analiser-learning.csv";
if (ModelState.IsValid)
{
var applcant = new ApplicantDataCluster
{
age = applicant.Age,
consconfid = -(applicant.ConsConfId + 400 / 10),
duration = (applicant.Duration * 10) + 500,
education = applicant.Education,
empratevar = applicant.EmpRateVar,
housing = applicant.Housing,
loan = applicant.Loan
};
var analiseResult = new Clusterizing().GetClusterizing(fileBase, fileLearning, applcant);
return(RedirectToAction("Index", "AnaliseResult", new { group1 = analiseResult.Distances[0],
group2 = analiseResult.Distances[1],
resultGroup = analiseResult.PredictedClusterId }));
}
else
{
return(RedirectToAction("index", "AnaliseResult", new { group1 = 0, group2 = 0, resultGroup = 1 }));
}
}
// GET: DataAnalise
public void Index()
{
const string fileBase = "bank-analiser.csv";
const string fileLearning = "bank-analiser-learning.csv";
var dataAnaliser = new Clusterizing();
dataAnaliser.GetClusterizing(fileBase, fileLearning);
}
public ActionResult Input(string Count, string method, string create)
{
int c;
int[] clustering;
Clusterizing cluster = new Clusterizing();
c = int.Parse(Count);
cluster.InitializeTuples(c, create);
if (method == "k-means")
{
clustering = cluster.Cluster();
}
else if (method == "Forel")
{
clustering = cluster.Cluster2();
}
return(View());
}
public ActionResult Contact(string Vertex, string method, string create)
{
int c;
int[] clustering = { };
Clusterizing cluster = new Clusterizing();
if (Vertex.Length != 0)
{
c = int.Parse(Vertex);
cluster.InitializeTuples(c, create);
if (method == "k-means")
{
clustering = cluster.Cluster();
}
else if (method == "Forel")
{
clustering = cluster.Cluster2();
}
ViewBag.Mass = clustering;
}
return(View("Contact"));
}
