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 }));
}
}
public ClusterPrediction GetClusterizing(string pathFile, string pathFileLearning, ApplicantDataCluster applicant)
{
string _dataPath = pathFile;
string _modelPath = pathFileLearning;
var context = new MLContext(seed: 0);
IDataView dataView = context.Data.LoadFromTextFile <Model.ApplicantDataCluster>(_dataPath, separatorChar: ',', hasHeader: true, allowQuoting: true,
trimWhitespace: true, allowSparse: true);
string outputParam = "y";
string[] inputParams = { "age", "education", "housing", "loan", "duration", "empratevar", "consconfid" };
var pipeline = context.Transforms
.Concatenate(outputParam, inputParams)
.Append(context.Clustering.Trainers.KMeans("y", null, 2));
var model = pipeline.Fit(dataView);
using (var fileStream = new FileStream(_modelPath, FileMode.Create, FileAccess.Write, FileShare.Write))
{
context.Model.Save(model, dataView.Schema, fileStream);
}
var predictor = context.Model.CreatePredictionEngine <Model.ApplicantDataCluster, ClusterPrediction>(model);
var result = predictor.Predict(applicant);
return(result);
}
