// Construye un bloque a partir de los últimos 5 minutos
public DbTimeBlock(DateTime start, TimeSpan length, AnalysisResults globalAR, Dictionary<DbTopic, AnalysisResults> topicAR)
{
this.Start = start;
this.Length = length;
this.GlobalAR = globalAR;
this.TopicAR = topicAR;
this.Used = false;
}
// Construye bloques medianos o grandes, basados en bloques chicos o medianos respectivamente.
public void BuildTimeBlockFromBlocks(TimeSpan length)
{
TimeSpan childBlocksLength = TimeSpan.FromSeconds(0);
if (length == TS_medium)
childBlocksLength = TS_short;
else if (length == TS_long)
childBlocksLength = TS_medium;
IEnumerable<DbTimeBlock> ch = from DbTimeBlock t in db
where t.Length == childBlocksLength && t.Used == false
orderby t.Start descending
select t;
int cant = ch.Count();
AnalysisResults GlobalAR = new AnalysisResults();
Dictionary<DbTopic, AnalysisResults> TopicAR = new Dictionary<DbTopic, AnalysisResults>();
foreach (DbTopic t in db.Query<DbTopic>())
TopicAR.Add(t, new AnalysisResults());
if (cant > this.TimeBlockConsolidationThreshold)
{
foreach (DbTimeBlock cht in ch)
{
GlobalAR.Popularity += cht.GlobalAR.Popularity;
GlobalAR.PosVal += cht.GlobalAR.PosVal;
GlobalAR.NegVal += cht.GlobalAR.NegVal;
GlobalAR.Ambiguity += cht.GlobalAR.Ambiguity;
// Para poder comparar las palabras importantes de un subbloque con otro, ya que cada uno fue calculado con tfidf distintos.
double normalizationFactor = 0;
foreach(KeyValuePair<string,double> kv in cht.GlobalAR.relevantList)
normalizationFactor += kv.Value;
foreach(KeyValuePair<string,double> kv in cht.GlobalAR.relevantList)
if(GlobalAR.RelevantTerms.ContainsKey(kv.Key))
GlobalAR.RelevantTerms[kv.Key] += kv.Value/normalizationFactor;
else
GlobalAR.RelevantTerms[kv.Key] = kv.Value/normalizationFactor;
// ídem a lo anterior, para cada topic
foreach (KeyValuePair<DbTopic, AnalysisResults> tar in cht.TopicAR)
{
TopicAR[tar.Key].Popularity += tar.Value.Popularity;
TopicAR[tar.Key].PosVal += tar.Value.PosVal;
TopicAR[tar.Key].NegVal += tar.Value.NegVal;
TopicAR[tar.Key].Ambiguity += tar.Value.Ambiguity;
normalizationFactor = 0;
foreach(KeyValuePair<string,double> kv in cht.TopicAR[tar.Key].relevantList)
normalizationFactor += kv.Value;
foreach(KeyValuePair<string,double> kv in cht.TopicAR[tar.Key].relevantList)
if(TopicAR[tar.Key].RelevantTerms.ContainsKey(kv.Key))
TopicAR[tar.Key].RelevantTerms[kv.Key] += kv.Value/normalizationFactor;
else
TopicAR[tar.Key].RelevantTerms[kv.Key] = kv.Value / normalizationFactor;
}
cht.Used = true;
db.Store(cht);
}
GlobalAR.Ambiguity /= cant;
GlobalAR.DictionaryToList();
foreach (DbTopic t in db.Query<DbTopic>())
{
TopicAR[t].Ambiguity /= cant;
TopicAR[t].DictionaryToList();
}
DbTimeBlock toAdd = new DbTimeBlock(DateTime.Now.Add(-length), length, GlobalAR, TopicAR);
db.Store(toAdd);
Console.Out.WriteLine("\n\n[ CONSTRUIDO TIME BLOCK (desde otros bloques), tamaño: " + length +"+ ]\n");
}
}
private void UpdateTable()
{
if (sortByCol == 2)
{
resList.Sort((pair1, pair2) => { return(-pair1.Value.Popularity.CompareTo(pair2.Value.Popularity)); }); // Pop desc
}
if (sortByCol == 3)
{
resList.Sort((pair1, pair2) => { return(-pair1.Value.PosVal.CompareTo(pair2.Value.PosVal)); }); // Pop desc
}
if (sortByCol == 4)
{
resList.Sort((pair1, pair2) => { return(-pair1.Value.NegVal.CompareTo(pair2.Value.NegVal)); }); // Pop desc
}
if (sortByCol == 5)
{
resList.Sort((pair1, pair2) => { return(-pair1.Value.Ambiguity.CompareTo(pair2.Value.Ambiguity)); }); // Pop desc
}
tableLayoutPanel1.Controls.Clear();
tableLayoutPanel1.RowCount = 0;
tableLayoutPanel1.Controls.Add(new Label()
{
Text = "Nombre", Width = 200
}, 1, 0);
Button bPop = new Button()
{
Text = "Pop.", Width = 200
};
bPop.Click += (sender, args) =>
{
sortByCol = 2;
UpdateTable();
UpdateChart();
};
tableLayoutPanel1.Controls.Add(bPop, 2, 0);
Button bPos = new Button()
{
Text = "+", Width = 200
};
bPos.Click += (sender, args) =>
{
sortByCol = 3;
UpdateTable();
UpdateChart();
};
tableLayoutPanel1.Controls.Add(bPos, 3, 0);
Button bNeg = new Button()
{
Text = "-", Width = 200
};
bNeg.Click += (sender, args) =>
{
sortByCol = 4;
UpdateTable();
UpdateChart();
};
tableLayoutPanel1.Controls.Add(bNeg, 4, 0);
Button bAmb = new Button()
{
Text = "Ambig.", Width = 200
};
bAmb.Click += (sender, args) =>
{
sortByCol = 5;
UpdateTable();
UpdateChart();
};
tableLayoutPanel1.Controls.Add(bAmb, 5, 0);
int row = 1;
foreach (var item in resList)
{
PictureBox bDominio = new PictureBox();
bDominio.Image = Image.FromFile("Dom" + item.Key.Domain + ".png");
bDominio.Size = new Size(20, 20);
bDominio.Anchor = AnchorStyles.None;
tableLayoutPanel1.Controls.Add(bDominio, 0, row);
Button bName = new Button()
{
Text = item.Key.Alias[1], Width = 200
};
bName.Click += (sender, args) => {
buildingCloud = true;
ActiveTopic = item.Key;
cloud.Enabled = false;
cloudWorker.Dispose();
cloudWorker = new BackgroundWorker();
cloudWorker.DoWork += (e, a) =>
{
try
{
List <Gma.CodeCloud.Controls.TextAnalyses.Processing.IWord> iwords = new List <Gma.CodeCloud.Controls.TextAnalyses.Processing.IWord>();
int cantCloudWords = 0;
// Tomo las palabras relevantes de un tópico porque al armar la tabla sólo había pedido un análisis simple.
List <KeyValuePair <string, double> > relevantList = core.GetTopicTermIntersectionAnalysis(item.Key, "", true).relevantList;
foreach (var i in relevantList)
{
if (cantCloudWords > 30)
{
break;
}
// Si la palabra es el topic de la nube, la saltea.
bool cont = false;
foreach (String al in item.Key.Alias)
{
if (al.Contains(i.Key))
{
cont = true;
}
}
if (cont)
{
continue;
}
AnalysisResults intersection = core.GetTopicTermIntersectionAnalysis(item.Key, i.Key, false);
int neg;
if (intersection == null)
{
neg = 0;
}
else
{
neg = intersection.NegVal;
}
int pos;
if (intersection == null)
{
pos = 0;
}
else
{
pos = intersection.PosVal;
}
double rr = (double)(neg - pos) / (neg + pos + 1); int r = (int)(rr * 100) + 127;
double gg = (double)(pos - neg) / (neg + pos + 1); int g = (int)(gg * 100) + 127;
int b;
if (neg == 0 && pos == 0)
{
b = 150;
}
else
{
b = 50;
}
Color c = Color.FromArgb(255, r, g, b);
iwords.Add(new Gma.CodeCloud.Controls.TextAnalyses.Processing.Word(i.Key, (int)i.Value, c));
cantCloudWords++;
}
if (iwords.Count > 0)
{
this.cloud.WeightedWords = iwords;
}
}
catch (Exception ee)
{
Console.Out.WriteLine("No se pudo crear nube de palabras. Probablemente no sean suficientes.");
}
pictureBox1.Image = core.GetTopicImage(2, item.Key);
};
cloudWorker.RunWorkerCompleted += (e, a) => {
cloud.Update();
cloud.Enabled = true;
buildingCloud = false;
};
cloudWorker.RunWorkerAsync();
};
tableLayoutPanel1.Controls.Add(bName, 1, row);
Button bTopicPop = new Button()
{
Text = "" + item.Value.Popularity
};
bTopicPop.Click += (a, e) =>
{
IEnumerable <DbTweet> tweetSet = core.GetTopicTermIntersectionTweets(item.Key, ""); // Funciona intersección con nada
FormTweets tweetsWindow = new FormTweets(core, tweetSet, item.Key, FormTweets.SelectionFav, null);
tweetsWindow.Show();
};
tableLayoutPanel1.Controls.Add(bTopicPop, 2, row);
Button bTopicPos = new Button()
{
Text = "" + item.Value.PosVal
};
bTopicPos.Click += (a, e) =>
{
IEnumerable <DbTweet> tweetSet = core.GetTopicTermIntersectionTweets(item.Key, "");
FormTweets tweetsWindow = new FormTweets(core, tweetSet, item.Key, FormTweets.SelectionPos, null);
tweetsWindow.Show();
};
tableLayoutPanel1.Controls.Add(bTopicPos, 3, row);
Button bTopicNeg = new Button()
{
Text = "" + item.Value.NegVal
};
bTopicNeg.Click += (a, e) =>
{
IEnumerable <DbTweet> tweetSet = core.GetTopicTermIntersectionTweets(item.Key, "");
FormTweets tweetsWindow = new FormTweets(core, tweetSet, item.Key, FormTweets.SelectionNeg, null);
tweetsWindow.Show();
};
tableLayoutPanel1.Controls.Add(bTopicNeg, 4, row);
Button bTopicAmb = new Button()
{
Text = "" + (int)(item.Value.Ambiguity * 100) + "%"
};
bTopicAmb.Click += (a, e) =>
{
IEnumerable <DbTweet> tweetSet = core.GetTopicTermIntersectionTweets(item.Key, "");
FormTweets tweetsWindow = new FormTweets(core, tweetSet, item.Key, FormTweets.SelectionAmb, null);
tweetsWindow.Show();
};
tableLayoutPanel1.Controls.Add(bTopicAmb, 5, row);
row++;
}
tableLayoutPanel1.AutoSize = true;
tableLayoutPanel1.Update();
//graphTime++;
}
// Construye bloques medianos o grandes, basados en bloques chicos o medianos respectivamente.
public void BuildTimeBlockFromBlocks(TimeSpan length)
{
TimeSpan childBlocksLength = TimeSpan.FromSeconds(0);
if (length == TS_medium)
{
childBlocksLength = TS_short;
}
else if (length == TS_long)
{
childBlocksLength = TS_medium;
}
IEnumerable <DbTimeBlock> ch = from DbTimeBlock t in db
where t.Length == childBlocksLength && t.Used == false
orderby t.Start descending
select t;
int cant = ch.Count();
AnalysisResults GlobalAR = new AnalysisResults();
Dictionary <DbTopic, AnalysisResults> TopicAR = new Dictionary <DbTopic, AnalysisResults>();
foreach (DbTopic t in db.Query <DbTopic>())
{
TopicAR.Add(t, new AnalysisResults());
}
if (cant > this.TimeBlockConsolidationThreshold)
{
foreach (DbTimeBlock cht in ch)
{
GlobalAR.Popularity += cht.GlobalAR.Popularity;
GlobalAR.PosVal += cht.GlobalAR.PosVal;
GlobalAR.NegVal += cht.GlobalAR.NegVal;
GlobalAR.Ambiguity += cht.GlobalAR.Ambiguity;
// Para poder comparar las palabras importantes de un subbloque con otro, ya que cada uno fue calculado con tfidf distintos.
double normalizationFactor = 0;
foreach (KeyValuePair <string, double> kv in cht.GlobalAR.relevantList)
{
normalizationFactor += kv.Value;
}
foreach (KeyValuePair <string, double> kv in cht.GlobalAR.relevantList)
{
if (GlobalAR.RelevantTerms.ContainsKey(kv.Key))
{
GlobalAR.RelevantTerms[kv.Key] += kv.Value / normalizationFactor;
}
else
{
GlobalAR.RelevantTerms[kv.Key] = kv.Value / normalizationFactor;
}
}
// ídem a lo anterior, para cada topic
foreach (KeyValuePair <DbTopic, AnalysisResults> tar in cht.TopicAR)
{
TopicAR[tar.Key].Popularity += tar.Value.Popularity;
TopicAR[tar.Key].PosVal += tar.Value.PosVal;
TopicAR[tar.Key].NegVal += tar.Value.NegVal;
TopicAR[tar.Key].Ambiguity += tar.Value.Ambiguity;
normalizationFactor = 0;
foreach (KeyValuePair <string, double> kv in cht.TopicAR[tar.Key].relevantList)
{
normalizationFactor += kv.Value;
}
foreach (KeyValuePair <string, double> kv in cht.TopicAR[tar.Key].relevantList)
{
if (TopicAR[tar.Key].RelevantTerms.ContainsKey(kv.Key))
{
TopicAR[tar.Key].RelevantTerms[kv.Key] += kv.Value / normalizationFactor;
}
else
{
TopicAR[tar.Key].RelevantTerms[kv.Key] = kv.Value / normalizationFactor;
}
}
}
cht.Used = true;
db.Store(cht);
}
GlobalAR.Ambiguity /= cant;
GlobalAR.DictionaryToList();
foreach (DbTopic t in db.Query <DbTopic>())
{
TopicAR[t].Ambiguity /= cant;
TopicAR[t].DictionaryToList();
}
DbTimeBlock toAdd = new DbTimeBlock(DateTime.Now.Add(-length), length, GlobalAR, TopicAR);
db.Store(toAdd);
Console.Out.WriteLine("\n\n[ CONSTRUIDO TIME BLOCK (desde otros bloques), tamaño: " + length + "+ ]\n");
}
}
