/// <summary>
/// Load data from CSV.
/// </summary>
/// <param name="csvFilePath">CSV file path.</param>
/// <param name="hasHeader">CSV has headers.</param>
public virtual void LoadDataFromCsv(string csvFilePath, bool hasHeader = false)
{
if (initialized == false)
{
throw new Exception("ML Model has not been initialized yet.");
}
lock (mlModelCompilation)
{
hasHeaders = hasHeader;
IEnumerable <string> csvLines = File.ReadAllLines(csvFilePath);
if (hasHeader)
{
csvLines = csvLines.Skip(1);
}
foreach (string line in csvLines)
{
IList <double> dict =
new List <double>();
string[] vals = line.Replace(" ", "").Split(",");
for (int i = 0; i < vals.Length - 1; i++)
{
dict.Add(Convert.ToDouble(vals[i]));
}
Validity.Add(Convert.ToBoolean(vals[vals.Length - 1]));
Data.Add(dict.ToArray());
}
// Create random variable w with VectorGuassian (of size 3)
// Features => (Gold | Nickel | Copper) Concentration, IsGoodMaterial?
w = Variable.Random(new VectorGaussian(Vector.Zero(FeatureCount), PositiveDefiniteMatrix.Identity(FeatureCount))).Named("w");
// Create Observable variable based on valid_ui;
y = Variable.Observed(Validity.ToArray());
BayesPointMachine.Run(this, y, w);
}
}