/// <summary>
/// Subsampling test for training
/// </summary>
/// <param name="word">Tested word</param>
/// <returns>True to remove the word</returns>
public bool SubSampling(string word)
{
double fq = Frequencies[word];
return((Math.Sqrt(fq / SubSamplingRate) + 1) * (SubSamplingRate / fq) < UniformRandom.Next());
}
/// <summary>
/// Negative sampling test for training
/// </summary>
/// <param name="word">Tested word</param>
/// <returns>True to sample</returns>
public bool NegativeSampling(string word)
{
double fq = Math.Pow(Frequencies[word], SubSamplingPower);
return(fq / NegativeSamplingSum > UniformRandom.Next());
}
/// <summary>
/// Average embedding learning
/// </summary>
/// <param name="bias"></param>
public void Train(Article.BiasType bias, string path)
{
Corpus corpus = new Corpus("corpus.db");
var articles = corpus.Articles.OrderBy(p => 0.5 > UniformRandom.Next()).ToList();
List <Article> testing = new List <Article>(), training = new List <Article>();
for (int i = 0; i < articles.Count(); i++)
{
if (i < articles.Count() / 4)
{
testing.Add(articles[i]);
}
else
{
training.Add(articles[i]);
}
}
int t_l = training.Where(a => a.Bias == Article.BiasType.Left).Count();
int t_r = training.Where(a => a.Bias == Article.BiasType.Right).Count();
// Counts
foreach (Article a in training)
{
if (a.Bias != bias)
{
continue;
}
CorpusSize++;
var document = a.Document();
foreach (string word in document)
{
string stemmed = Stemmer.Stem(word);
CorpusAppearances[stemmed] = CorpusAppearances.ContainsKey(stemmed) ? CorpusAppearances[stemmed] + 1 : 1;
}
}
// Get results
Matrix <double> X = Matrices.Dense(training.Count, Embedding.EmbeddingSize);
Dictionary <int, int> correct = new Dictionary <int, int>();
int Xi = 0;
foreach (Article a in training)
{
var document = a.Document();
correct[Xi] = (a.Bias == bias) ? 0 : 1;
X.SetRow(Xi++, WeightedAverage(document));
}
double last_loss = double.PositiveInfinity;
for (int iteration = 0; iteration < 100000; iteration++)
{
var hidden_layer = X * W1;
for (int i = 0; i < hidden_layer.RowCount; i++)
{
var row = hidden_layer.Row(i);
hidden_layer.SetRow(i, (row + B1).Map(p => 1 / (1 + Math.Exp(-p))));
}
var scores = hidden_layer * W2;
for (int i = 0; i < scores.RowCount; i++)
{
var row = scores.Row(i);
scores.SetRow(i, row + B2);
}
var probs = scores.Clone().PointwiseExp();
for (int i = 0; i < probs.RowCount; i++)
{
if (Single.IsInfinity((float)probs[i, 0]))
{
probs[i, 0] = 1; probs[i, 1] = 0;
}
else if (Single.IsInfinity((float)probs[i, 1]))
{
probs[i, 1] = 1; probs[i, 0] = 0;
}
else
{
var row = probs.Row(i);
double sum = row.Sum();
probs.SetRow(i, row / sum);
}
}
var dscores = probs.Clone();
for (int i = 0; i < dscores.RowCount; i++)
{
dscores[i, correct[i]] -= 1;
}
dscores /= training.Count;
double data_loss = 0;
for (int i = 0; i < probs.RowCount; i++)
{
double c = probs.Row(i)[correct[i]];
c = -Math.Log(c);
data_loss += c;
}
data_loss /= training.Count;
var reg_loss = 0.5 * Reg * (W1.PointwiseMultiply(W1)).ColumnSums().Sum();
reg_loss += 0.5 * Reg * (W2.PointwiseMultiply(W2)).ColumnSums().Sum();
var loss = data_loss + reg_loss;
if (iteration % 10 == 0)
{
Console.WriteLine(string.Format("Iter. {0}: {1}", iteration, loss));
}
var dW2 = hidden_layer.Transpose() * dscores;
var dB2 = dscores.ColumnSums();
dW2 += Reg * W2;
var dhidden = dscores * W2.Transpose();
dhidden = dhidden.Map(p => p * (1 - p));
var dW1 = X.Transpose() * dhidden;
var dB1 = dhidden.ColumnSums();
dW1 += Reg * W1;
W1 += -StepSize * dW1;
B1 += -StepSize * dB1;
W2 += -StepSize * dW2;
B2 += -StepSize * dB2;
}
Save(path);
int right = 0;
foreach (Article a in testing)
{
double p = Probability(a.Document());
bool ok = (a.Bias == bias && p > 0.5) || (a.Bias != bias && p < 0.5);
if (ok)
{
right++;
}
Console.WriteLine(string.Format("{0:0.00%} Chance of match... {1}", p, ok ? "Ok." : "Wrong!"));
}
double pc = right / (double)testing.Count;
Console.WriteLine(string.Format("\n{0:0.00%} accuracy.", pc));
}