/// <summary>
/// Creates and returns a new NGramDictionary from a string.<br />
/// This string must be produced by the ToString() method of a NGramDictionary object.
/// </summary>
/// <param name="str">A string produced by the ToString() method of a NGramDictionary object.</param>
/// <returns>A new NGramDictionary object</returns>
public static NGramDictionary DeserializeFrom(string str)
{
string[] lines = str.Split('\n');
int minNGram = Int32.Parse(lines[0].Split('\t')[0]);
int maxNGram = Int32.Parse(lines[0].Split('\t')[1]);
IDictionary <NGram, int> nGrams = new Dictionary <NGram, int>();
var extractor = new NGramExtractor(minNGram, maxNGram);
foreach (string line in lines.Skip(1))
{
string[] row = line.Split('\t');
var nGram = new NGram(row[0].Split(null));
int freq = Int32.Parse(row[1]);
if (!nGrams.ContainsKey(nGram))
{
nGrams.Add(nGram, freq);
}
}
return(new NGramDictionary(extractor, nGrams));
}
public NGramModel(int nGramSize, string modelFilepath)
{
nGramDictionary = new NGramDictionary(extractor);
maxNGramSize = nGramSize;
extractor = new NGramExtractor(1, maxNGramSize);
string[] lines = File.ReadAllLines(modelFilepath);
//AddAll(lines);
}
public double GetSentenceProbability(IList <string> tokens)
{
if (maxNGramSize == 1)
{
return(GetSentenceProbabilityForUnigrams(tokens));
}
List <string> tokenList = tokens.ToList();
AddStartStopSymbols(tokenList);
var ext = new NGramExtractor(maxNGramSize - 1, maxNGramSize);
IList <NGram> nGrams = ext.ExtractAsList(tokenList);
nGrams.RemoveAt(nGrams.Count - 1);
double logP = 0;
for (int i = 0; i < nGrams.Count; i += 2)
{
double p = GetMLE(nGrams[i], nGrams[i + 1]);
logP += Math.Log10(p);
NGram x = nGrams[i];
//Console.WriteLine(nGrams[i + 1] + "/" + nGrams[i] + ":" + Math.Log10(p));
//Console.WriteLine(nGrams[i] + ":" + Math.Log10(GetUnigramMLE(nGrams[i])));
}
//double p = 1;
//for (int i = 0; i < nGrams.Count; i += 2)
//{
// p *= GetMLE(nGrams[i], nGrams[i + 1]);
//}
//return p;
return(logP);
}
public void TestExtractAsDictionary()
{
var extractor = new NGramExtractor(Unigram);
IDictionary<NGram, int> actual = extractor.ExtractAsDictionary(Tokens);
var expected = new Dictionary<NGram, int>
{
{one, 2},
{two, 2},
{three, 2},
{four, 2},
{five, 1}
};
CollectionAssert.AreEquivalent(expected, actual);
extractor = new NGramExtractor(Bigram);
actual = extractor.ExtractAsDictionary(Tokens);
expected = new Dictionary<NGram, int>
{
{one_two, 2},
{two_three, 2},
{three_four, 2},
{four_five, 1},
{five_one, 1}
};
CollectionAssert.AreEquivalent(expected, actual);
extractor = new NGramExtractor(Trigram);
actual = extractor.ExtractAsDictionary(Tokens);
expected = new Dictionary<NGram, int>
{
{one_two_three, 2},
{two_three_four, 2},
{four_five_one, 1},
{three_four_five, 1},
{five_one_two, 1},
};
CollectionAssert.AreEquivalent(expected, actual);
}
private NGramDictionary(NGramExtractor extractor, IDictionary<NGram, int> nGrams)
{
this.extractor = extractor;
this.nGrams = nGrams;
}
public NGramDictionary(NGramExtractor extractor)
{
this.extractor = extractor;
nGrams = new Dictionary<NGram, int>();
}
private NGramDictionary(NGramExtractor extractor, IDictionary <NGram, int> nGrams)
{
this.extractor = extractor;
this.nGrams = nGrams;
}
public NGramDictionary(NGramExtractor extractor)
{
this.extractor = extractor;
nGrams = new Dictionary <NGram, int>();
}
public NGramModel(int nGramSize)
{
maxNGramSize = nGramSize;
extractor = new NGramExtractor(1, maxNGramSize);
nGramDictionary = new NGramDictionary(extractor);
}
public double GetSentenceProbability(IList<string> tokens)
{
if (maxNGramSize == 1)
{
return GetSentenceProbabilityForUnigrams(tokens);
}
List<string> tokenList = tokens.ToList();
AddStartStopSymbols(tokenList);
var ext = new NGramExtractor(maxNGramSize - 1, maxNGramSize);
IList<NGram> nGrams = ext.ExtractAsList(tokenList);
nGrams.RemoveAt(nGrams.Count - 1);
double logP = 0;
for (int i = 0; i < nGrams.Count; i += 2)
{
double p = GetMLE(nGrams[i], nGrams[i + 1]);
logP += Math.Log10(p);
NGram x = nGrams[i];
//Console.WriteLine(nGrams[i + 1] + "/" + nGrams[i] + ":" + Math.Log10(p));
//Console.WriteLine(nGrams[i] + ":" + Math.Log10(GetUnigramMLE(nGrams[i])));
}
//double p = 1;
//for (int i = 0; i < nGrams.Count; i += 2)
//{
// p *= GetMLE(nGrams[i], nGrams[i + 1]);
//}
//return p;
return logP;
}
public NGramModel(int nGramSize)
{
maxNGramSize = nGramSize;
extractor = new NGramExtractor(1, maxNGramSize);
nGramDictionary = new NGramDictionary(extractor);
}
public void TestExtractAsList()
{
var extractor = new NGramExtractor(Unigram);
IList<NGram> actual = extractor.ExtractAsList(Tokens);
var expected = new[] {one, two, three, four, five, one, two, three, four};
CollectionAssert.AreEqual(expected, actual);
extractor = new NGramExtractor(Bigram);
actual = extractor.ExtractAsList(Tokens);
expected = new[] {one_two, two_three, three_four, four_five, five_one, one_two, two_three, three_four};
CollectionAssert.AreEqual(expected, actual);
extractor = new NGramExtractor(Trigram);
actual = extractor.ExtractAsList(Tokens);
expected = new[]
{one_two_three, two_three_four, three_four_five, four_five_one, five_one_two, one_two_three, two_three_four};
CollectionAssert.AreEqual(expected, actual);
extractor = new NGramExtractor(Unigram, Bigram);
actual = extractor.ExtractAsList(Tokens);
expected = new[]
{
one, one_two, two, two_three, three, three_four, four, four_five, five, five_one, one, one_two, two,
two_three, three, three_four, four
};
CollectionAssert.AreEqual(expected, actual);
extractor = new NGramExtractor(Bigram, Trigram);
actual = extractor.ExtractAsList(Tokens);
expected = new[]
{
one_two, one_two_three, two_three, two_three_four, three_four,
three_four_five, four_five, four_five_one, five_one, five_one_two, one_two, one_two_three, two_three,
two_three_four, three_four
};
CollectionAssert.AreEqual(expected, actual);
extractor = new NGramExtractor(Unigram, Trigram);
actual = extractor.ExtractAsList(Tokens);
expected = new[]
{
one, one_two, one_two_three,
two, two_three, two_three_four,
three, three_four, three_four_five,
four, four_five, four_five_one,
five, five_one, five_one_two,
one, one_two, one_two_three,
two, two_three, two_three_four,
three, three_four,
four
};
CollectionAssert.AreEqual(expected, actual);
}
public void TestExtractLetterNGramsAsList()
{
var extractor = new NGramExtractor(Bigram, Trigram);
IEnumerable<string> tokens = "beşiktaş".ToCharArray().Select(x => x.ToString());
IList<NGram> actual = extractor.ExtractAsList(tokens);
}
public void TestExtractAsSet()
{
var extractor = new NGramExtractor(Unigram);
ISet<NGram> actual = extractor.ExtractAsSet(Tokens);
var expected = new[] {one, two, three, four, five};
CollectionAssert.AreEquivalent(expected, actual);
extractor = new NGramExtractor(Bigram);
actual = extractor.ExtractAsSet(Tokens);
expected = new[] {one_two, two_three, three_four, four_five, five_one};
CollectionAssert.AreEquivalent(expected, actual);
extractor = new NGramExtractor(Trigram);
actual = extractor.ExtractAsSet(Tokens);
expected = new[] {one_two_three, two_three_four, three_four_five, four_five_one, five_one_two};
CollectionAssert.AreEquivalent(expected, actual);
}
/// <summary>
/// Creates and returns a new NGramDictionary from a string.<br />
/// This string must be produced by the ToString() method of a NGramDictionary object.
/// </summary>
/// <param name="str">A string produced by the ToString() method of a NGramDictionary object.</param>
/// <returns>A new NGramDictionary object</returns>
public static NGramDictionary DeserializeFrom(string str)
{
string[] lines = str.Split('\n');
int minNGram = Int32.Parse(lines[0].Split('\t')[0]);
int maxNGram = Int32.Parse(lines[0].Split('\t')[1]);
IDictionary<NGram, int> nGrams = new Dictionary<NGram, int>();
var extractor = new NGramExtractor(minNGram, maxNGram);
foreach (string line in lines.Skip(1))
{
string[] row = line.Split('\t');
var nGram = new NGram(row[0].Split(null));
int freq = Int32.Parse(row[1]);
if (!nGrams.ContainsKey(nGram))
{
nGrams.Add(nGram, freq);
}
}
return new NGramDictionary(extractor, nGrams);
}
public NGramModel(int nGramSize, string modelFilepath)
{
nGramDictionary = new NGramDictionary(extractor);
maxNGramSize = nGramSize;
extractor = new NGramExtractor(1, maxNGramSize);
string[] lines = File.ReadAllLines(modelFilepath);
//AddAll(lines);
}
