private void openFileButton_Click(object sender, EventArgs e)
{
DialogResult result = openFileDialog.ShowDialog();
if (result == DialogResult.OK)
{
if (openFileDialog.SafeFileName.EndsWith(".json"))
{
tweetsPath = openFileDialog.FileName;
tweetDatas = JsonFileController.ReadDataFromJsonFile <TweetData[]>(tweetsPath);
// RegisterUsers() should be called before ParseTweets()
// because we check words array is null or not in RegisterUsers().
RegisterUsers();
ParseTweets();
// save parsed tweets to json file.
JsonFileController.WriteToJsonFile(tweetsPath, tweetDatas);
// Navigate to first tweet.
NavigateLabeledData_Click(null, new EventArgs());
FillTweetInfo();
}
else
{
MessageBox.Show("Please choose a json file.", "Warning", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
}
private void btnLoadAnn_Click(object sender, EventArgs e)
{
trainer = JsonFileController.ReadDataFromJsonFile <Trainer>(Path.Combine(nnParentPath, TrainDataFile));
ShowNeuralNetworkInfo(trainer.Neuralnetwork);
trackbarschanged = false;
btnNNTraining.Enabled = btnNNTesting.Enabled = btnTest.Enabled = btnCMatrix.Enabled = true;
toggle1.Value = trainer.trainingType == TrainingType.WORD_TRAINING ? true : false;
trainedType = trainer.trainingType;
lblsystemType.Text = trainingType.ToString();
}
private void TrainTest(TrainingType type)
{
if (string.IsNullOrEmpty(filePath))
{
MessageBox.Show("you have to choose a file to train....");
return;
}
btnTest.Enabled = true;
progressBar.Value = 0;
TweetData[] twData = JsonFileController.ReadDataFromJsonFile <TweetData[]>(filePath);
ParseTweets(ref twData, type);
Analyser analyser = new Analyser(twData, labels, tbTestCount.Value, chckPickRandomly.Checked);
if (type == TrainingType.WORD_TRAINING)
{
analyser.Analyse1();
}
else
{
analyser.Analyse2();
}
progressBar.Maximum = twData.Length;
trainer = new Trainer(analyser, tbHiddenNeuronCount.Value, double.Parse(lblLearningRate.Text), type);
List <List <Tuple <int, double> > > list;
if (type == TrainingType.WORD_TRAINING)
{
trainer.Train1(progressBar);
list = trainer.Test1(progressBar);
}
else
{
trainer.Train2(progressBar);
list = trainer.Test2(progressBar);
}
Test(type, analyser);
//richtxtAnnResult.Text = "";
//for (int i = 0; i < analyser.TestingTweets.Length; i++)
//{
// richtxtAnnResult.AppendText("TWEET:\n\n");
// richtxtAnnResult.AppendText(analyser.TestingTweets[i].tweet + "\n\n");
// richtxtAnnResult.AppendText("PREDICTION:\n\n");
// for (int j = 0; j < list[i].Count; j++)
// {
// string val = labels[list[i][j].Item1];
// double percentage = list[i][j].Item2 * 100;
// richtxtAnnResult.AppendText(val + ":\t" + percentage.ToString("F2") + "%\n");
// }
// richtxtAnnResult.AppendText("\nANSWER: " + analyser.TestingTweets[i].users[0].labels[0]);
// richtxtAnnResult.AppendText("\n\n");
// richtxtAnnResult.AppendText("#################################################################\n\n");
//}
ShowAnalyserInfo(trainer);
}