private void SetFileName()
{
_analogy = new WordAnalogy(_fileName)
{
MinimumDistance = 0.0
};
_distance = new Distance(_fileName);
}
public void TestWordAnalogy()
{
var analogy = new WordAnalogy("faust1.bin")
{
MinimumDistance = 0
};
var result = analogy.Search(new[] { "Natur", "Gott" });
Assert.IsNotNull(result);
var top = result.OrderByDescending(x => x.Value).Take(10).ToArray();
Console.WriteLine(top.First().Key);
}
private void SetFileName()
{
_analogy = new WordAnalogy(_fileName);
_distance = new Distance(_fileName);
}
static void Main(string[] args)
{
// -train <file> Use text data from <file> to train the model
string train = "Corpus.txt";
// -output <file> Use <file> to save the resulting word vectors / word clusters
string output = "Vectors.bin";
// -save-vocab <file> The vocabulary will be saved to <file>
string savevocab = "";
// -read-vocab <file> The vocabulary will be read from <file>, not constructed from the training data
string readvocab = "";
// -size <int> Set size of word vectors; default is 100
int size = 100;
// -debug <int> Set the debug mode (default = 2 = more info during training)
int debug = 2;
// -binary <int> Save the resulting vectors in binary moded; default is 0 (off)
int binary = 1;
// -cbow <int> Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)
int cbow = 1;
// -alpha <float> Set the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW
float alpha = 0.05f;
// -sample <float> Set threshold for occurrence of words. Those that appear with higher frequency in the training data
float sample = 1e-4f;
// -hs <int> Use Hierarchical Softmax; default is 0 (not used)
int hs = 0;
// -negative <int> Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)
int negative = 25;
// -threads <int> Use <int> threads (default 12)
int threads = 12;
// -iter <int> Run more training iterations (default 5)
long iter = 15;
// -min-count <int> This will discard words that appear less than <int> times; default is 5
int mincount = 5;
// -classes <int> Output word classes rather than word vectors; default number of classes is 0 (vectors are written)
long classes = 0;
// -window <int> Set max skip length between words; default is 5
int window = 12;
Word2Vec word2Vec = new Word2Vec(train, output, savevocab, readvocab, size, debug, binary, cbow, alpha, sample, hs, negative, threads, iter, mincount, classes, window);
var totalTime = Stopwatch.StartNew();
var highRes = Stopwatch.IsHighResolution;
word2Vec.TrainModel();
totalTime.Stop();
var trainingTime = totalTime.ElapsedMilliseconds;
Console.WriteLine("Training took {0}ms", trainingTime);
path = @"Vectors.bin";
distance = new Distance(path);
wordAnalogy = new WordAnalogy(path);
string[] wordList = new string[] { "paris france madrid" };
var searchTime = Stopwatch.StartNew();
foreach (string word in wordList)
{
distance.Search(word);
wordAnalogy.Search(word);
}
searchTime.Stop();
var firstSearchTime = searchTime.ElapsedMilliseconds;
Console.WriteLine("Search took {0}ms", firstSearchTime);
int outerN = 5;
for (int outer = 0; outer < outerN; outer++)
{
foreach (string word in wordList)
{
int N = 11;
var minSearchTime = long.MaxValue;
var maxSearchTime = long.MinValue;
long[] searchTimes = new long[N];
Console.WriteLine($"Batch {outer}, searching {word}: running {N} searches");
for (int inner = 0; inner < N; inner++)
{
searchTime.Restart();
distance.Search(word);
BestWord[] result = wordAnalogy.Search(word);
searchTime.Stop();
/*foreach (var bestWord in result)
* {
* Console.WriteLine("{0}\t\t{1}", bestWord.Word, bestWord.Distance);
* }*/
long interval = highRes ? searchTime.ElapsedTicks : searchTime.ElapsedMilliseconds;
searchTimes[inner] = interval;
if (interval < minSearchTime)
{
minSearchTime = interval;
}
if (interval > maxSearchTime)
{
maxSearchTime = interval;
}
}
if (highRes)
{
double averageSearch = 1000 * ((double)searchTimes.Sum() / N / Stopwatch.Frequency);
double medianSearch = 1000 * ((double)searchTimes.OrderBy(t => t).ElementAt(N / 2) / Stopwatch.Frequency);
Console.WriteLine("Steadystate min search time: {0:F2}ms", (1000 * minSearchTime) / Stopwatch.Frequency);
Console.WriteLine("Steadystate max search time: {0:F2}ms", (1000 * maxSearchTime) / Stopwatch.Frequency);
Console.WriteLine("Steadystate average search time: {0:F2}ms", averageSearch);
Console.WriteLine("Steadystate median search time: {0:F2}ms", medianSearch);
}
else
{
long averageSearch = searchTimes.Sum() / N;
long medianSearch = searchTimes.OrderBy(t => t).ElementAt(N / 2);
Console.WriteLine("Steadystate min search time: {0}ms", minSearchTime);
Console.WriteLine("Steadystate max search time: {0}ms", maxSearchTime);
Console.WriteLine("Steadystate average search time: {0}ms", (int)averageSearch);
Console.WriteLine("Steadystate median search time: {0}ms", (int)medianSearch);
}
Console.WriteLine("");
}
}
}
static void Main(string[] args)
{
// -train <file> Use text data from <file> to train the model
string train = "Corpus.txt";
// -output <file> Use <file> to save the resulting word vectors / word clusters
string output = "Vectors.bin";
// -save-vocab <file> The vocabulary will be saved to <file>
string savevocab = "";
// -read-vocab <file> The vocabulary will be read from <file>, not constructed from the training data
string readvocab = "";
// -size <int> Set size of word vectors; default is 100
int size = 100;
// -debug <int> Set the debug mode (default = 2 = more info during training)
int debug = 2;
// -binary <int> Save the resulting vectors in binary moded; default is 0 (off)
int binary = 1;
// -cbow <int> Use the continuous bag of words model; default is 1 (use 0 for skip-gram model)
int cbow = 1;
// -alpha <float> Set the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW
float alpha = 0.05f;
// -sample <float> Set threshold for occurrence of words. Those that appear with higher frequency in the training data
float sample = 1e-4f;
// -hs <int> Use Hierarchical Softmax; default is 0 (not used)
int hs = 0;
// -negative <int> Number of negative examples; default is 5, common values are 3 - 10 (0 = not used)
int negative = 25;
// -threads <int> Use <int> threads (default 12)
int threads = 12;
// -iter <int> Run more training iterations (default 5)
long iter = 15;
// -min-count <int> This will discard words that appear less than <int> times; default is 5
int mincount = 5;
// -classes <int> Output word classes rather than word vectors; default number of classes is 0 (vectors are written)
long classes = 0;
// -window <int> Set max skip length between words; default is 5
int window = 12;
Word2Vec.Word2Vec word2Vec = new Word2Vec.Word2Vec(train, output, savevocab, readvocab, size, debug, binary, cbow, alpha, sample, hs, negative, threads, iter, mincount, classes, window);
Console.WriteLine("Train Model: Y/N");
string usr = Console.ReadLine().ToLower();
if (usr == "y")
{
word2Vec.TrainModel();
}
path = @"Vectors.bin";
distance = new Distance(path);
wordAnalogy = new WordAnalogy(path);
while (true)
{
Console.Clear();
Console.WriteLine("{0}\nPlease enter Word: ", new string('-', 50));
string input = Console.ReadLine();
Console.Clear();
distance.Search(input, 10);
wordAnalogy.Search(input, 10);
Console.ReadLine();
}
}
