/// <summary>
/// Train Bayes Model with some optimization method
/// </summary>
public void ReTrain()
{
var model = BayesTest.LoadModel("bayesStatisticModelFile");
var w = new int[GetLabels().ToArray().Length];
for (int i = 0; i < w.Length; i++) // Initial weight vector
{
w[i] = 1;
}
}
public void Train()
{
this.statisticModel = BayesTest.LoadModel(@"D:\Codes\C#\EntityTyping\Fine-ner\unit test\output\model.txt");
this.developFeatures = LoadBayesData(@"D:\Codes\C#\EntityTyping\Fine-ner\unit test\output\developFeature.txt");
this.labels = GetLabels().ToList();
this.w = new Dictionary <string, double>(this.labels.Count);
foreach (var label in labels) // Initial weight vector
{
w[label] = 1;
}
var learnSpeed = 0.005;
var lastPositive = -1;
this.positive = -1;
int steps = 0;
var lastWeight = new Dictionary <string, double>(w);
while (lastPositive == -1 || ((this.positive - lastPositive) > 0))
{
steps++;
//learnSpeed /= steps;
lastPositive = this.positive;
var diff = GetDiff();
lastWeight = new Dictionary <string, double>(w);
foreach (var label in labels)
{
w[label] -= diff[label] * learnSpeed;
}
Console.WriteLine("positive: " + positive);
foreach (var label in labels)
{
Console.Write(label + "\t" + w[label] + "\t");
}
Console.WriteLine("");
}
FileWriter writer = new LargeFileWriter(@"D:\Codes\C#\EntityTyping\Fine-ner\unit test\output\weight.txt", FileMode.Create);
foreach (var label in labels)
{
writer.WriteLine(label + "\t" + lastWeight[label]);
}
writer.Close();
}
private void Test(HashSet <string> options)
{
if (options == null)
{
options = new HashSet <string>(new string[] { "b" });
}
// test with bayes model
if (options.Contains("b"))
{
var tester = new BayesTest((string)GlobalParameter.Get(DefaultParameter.Field.model_file),
(string)GlobalParameter.Get(DefaultParameter.Field.develop_feature_file),
(string)GlobalParameter.Get(DefaultParameter.Field.test_result_file));
try
{
tester.Test();
}
catch (Exception e)
{
Console.WriteLine("Error occurs during test for " + e.Message);
throw new Exception();
}
}
}
