public string generateSentence()
{
StringBuilder s = new StringBuilder();
DoubleChain currentString = head.getNextWord();
if (currentString == null)
{
return(generateSentence());
}
DoubleChain nextString = head.getNextNextWord(currentString);
DoubleChain nextNextString = currentString.getNextNextWord(nextString);
s.Append(currentString.text);
s.Append(" ");
s.Append(nextString.text);
while (nextNextString.text != "!" && nextNextString.text != "?" && nextNextString.text != ".")
{
s.Append(" ");
s.Append(nextNextString.text);
currentString = nextString;
nextString = nextNextString;
nextNextString = currentString.getNextNextWord(nextString);
if (nextNextString == null)
{
break;
}
}
s.Append(nextNextString.text); //Add punctuation
s[0] = char.ToUpper(s[0]);
return(s.ToString());
}
public DeepMarkovChain()
{
chains = new Dictionary <string, DoubleChain>();
head = new DoubleChain()
{
text = "[]"
};
chains.Add("[]", head);
}
public bool readyToGenerate()
{
DoubleChain next = head.getNextWord();
if (next == null)
{
return(false);
}
return(head.getNextNextWord(next) != null);
}
public DoubleChain Push(string title)
{
DoubleChain link = new DoubleChain(title);
link.NextChain = first;
if (first != null)
{
first.PreviousChain = link;
}
first = link;
return(link);
}
public override string ToString()
{
DoubleChain currentLink = first;
StringBuilder builder = new StringBuilder();
while (currentLink != null)
{
builder.Append(currentLink);
currentLink = currentLink.NextChain;
}
return(builder.ToString());
}
public void addWord(DoubleChain c)
{
fullCount++;
if (nextNodes.ContainsKey(c.text))
{
nextNodes[c.text].count++;
}
else
{
nextNodes.Add(c.text, new ChainProbability(c, 1));
nextNextNodes.Add(c.text, new Dictionary <string, ChainProbability>());
}
}
public DoubleChain Pop()
{
DoubleChain temp = first;
if (first != null)
{
first = first.NextChain;
if (first != null)
{
first.PreviousChain = null;
}
}
return(temp);
}
public void addNextWord(DoubleChain n, DoubleChain nn)
{
Dictionary <string, ChainProbability> d = nextNextNodes[n.text];
if (d.ContainsKey(nn.text))
{
d[nn.text].count++;
}
else
{
d.Add(nn.text, new ChainProbability(nn, 1));
}
//Add to n as a normal word
n.addWord(nn);
}
public DoubleChain getNextNextWord(DoubleChain c)
{
Dictionary <string, ChainProbability> d = nextNextNodes[c.text];
int fullCount = 0;
foreach (string key in d.Keys)
{
fullCount += d[key].count;
}
int currentCount = RandomHandler.random.Next(fullCount);
foreach (string key in d.Keys)
{
for (int i = 0; i < d[key].count; i++)
{
if (currentCount == 0)
{
return(d[key].chain);
}
currentCount--;
}
}
return(null);
}
public ChainProbability(DoubleChain c, int co)
{
chain = c;
count = co;
}
public DoubleChainedList()
{
first = null;
}
public DeepMarkovChain()
{
chains = new Dictionary<string, DoubleChain>();
head = new DoubleChain() { text = "[]" };
chains.Add("[]", head);
}
public DoubleChain getNextNextWord(DoubleChain c)
{
Dictionary<string, ChainProbability> d = nextNextNodes[c.text];
int fullCount = 0;
foreach (string key in d.Keys)
fullCount += d[key].count;
int currentCount = RandomHandler.random.Next(fullCount);
foreach (string key in d.Keys)
{
for (int i = 0; i < d[key].count; i++)
{
if (currentCount == 0)
return d[key].chain;
currentCount--;
}
}
return null;
}
public void addWord(DoubleChain c)
{
fullCount++;
if (nextNodes.ContainsKey(c.text))
nextNodes[c.text].count++;
else
{
nextNodes.Add(c.text, new ChainProbability(c, 1));
nextNextNodes.Add(c.text, new Dictionary<string, ChainProbability>());
}
}
public void addNextWord(DoubleChain n, DoubleChain nn)
{
Dictionary<string, ChainProbability> d = nextNextNodes[n.text];
if(d.ContainsKey(nn.text))
d[nn.text].count++;
else
d.Add(nn.text, new ChainProbability(nn, 1));
//Add to n as a normal word
n.addWord(nn);
}
public ChainProbability(DoubleChain c, int co)
{
chain = c;
count = co;
}
