public override int[] ComputeModel(double[][] inputs) { int[] predicted = new int[inputs.Length]; DiabetesDido.DAL.DiabetesDataSetTableAdapters.NewDataSetTempTableAdapter newDataSetTempTA = new DAL.DiabetesDataSetTableAdapters.NewDataSetTempTableAdapter(); var query = newDataSetTempTA.GetData().AsEnumerable().Skip(newDataSetTempTA.GetData().Rows.Count - inputs.Length); DataTable testData = query.CopyToDataTable<DataRow>(); DataTable predictData = NaiveBayes(testData); for (int i = 0; i < predictData.Rows.Count - 1; i++) { String expectValue = testData.Rows[i][TableMetaData.ClassAttribute].ToString(); String predictValue = predictData.Rows[i][TableMetaData.ClassAttribute].ToString(); if (predictValue == TableMetaData.PositiveString) predicted[i] = possiveValue; else predicted[i] = negativeValue; } return predicted; }
public override int[] ComputeModel(double[][] inputs) { int[] predicted = new int[inputs.Length]; DiabetesDido.DAL.DiabetesDataSetTableAdapters.NewDataSetTempTableAdapter newDataSetTempTA = new DAL.DiabetesDataSetTableAdapters.NewDataSetTempTableAdapter(); var query = newDataSetTempTA.GetData().AsEnumerable().Skip(newDataSetTempTA.GetData().Rows.Count - inputs.Length); DataTable testData = query.CopyToDataTable <DataRow>(); DataTable predictData = NaiveBayes(testData); for (int i = 0; i < predictData.Rows.Count - 1; i++) { String expectValue = testData.Rows[i][TableMetaData.ClassAttribute].ToString(); String predictValue = predictData.Rows[i][TableMetaData.ClassAttribute].ToString(); if (predictValue == TableMetaData.PositiveString) { predicted[i] = possiveValue; } else { predicted[i] = negativeValue; } } return(predicted); }