public static void Main(string[] args)
{
ToolArguments parsedArgs = new ToolArguments(args, "None", new MainClass());
PorterStemmer stemmer = new PorterStemmer();
if (parsedArgs["stem"] != null)
Console.WriteLine(parsedArgs["stem"] + " => " + stemmer.stemTerm(parsedArgs["stem"]));
if (parsedArgs["freqrows"] != null) {
DataReader reader = new DataReader(parsedArgs["f"]);
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
TwoTuple<int, int> counts = FrequencyTools.WordCount(parsedArgs["freqrows"], row[1]);
Console.WriteLine(counts.one + "," + counts.two + ",\"" + row[2] + "\"");
}
}
if (parsedArgs["emotion"] != null) {
ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
double[] emotions = sensor.EstimateEmotions(parsedArgs["emotion"]);
for (int ii = 0; ii < (int) ANEWEmotionSensor.Emotions.COUNT; ii++)
Console.WriteLine(((ANEWEmotionSensor.Emotions) ii).ToString() + ": " + emotions[ii]);
}
if (parsedArgs["emorows"] != null) {
int rows = 0, valids = 0;
ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
DataReader reader = new DataReader(parsedArgs["f"]);
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
rows++;
double[] emotions = sensor.EstimateEmotions(row[1]);
Console.WriteLine("\"" + row[0] + "\"," + emotions[0] + "," + emotions[1] + "," + emotions[2] + "," + emotions[3] + "," + emotions[4] + "," + emotions[5] + "," + emotions[6] + "," + emotions[7] + ",\"" + row[2] + "\"");
if (!double.IsNaN(emotions[0]))
valids++;
}
}
}
public static void Main(string[] args)
{
ToolArguments parsedArgs = new ToolArguments(args, "None", new MainClass());
PorterStemmer stemmer = new PorterStemmer();
if (parsedArgs["stem"] != null)
Console.WriteLine(parsedArgs["stem"] + " => " + stemmer.stemTerm(parsedArgs["stem"]));
/*ANEWEmotionSensor sensor2 = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
for (int rr = 0; rr < sensor2.positiveMatrix.GetLength(0); rr++) {
for (int cc = 0; cc < sensor2.positiveMatrix.GetLength(1); cc++)
Console.Write(sensor2.positiveMatrix[rr, cc] + ", ");
Console.WriteLine(" - ");
}
for (int rr = 0; rr < sensor2.negativeMatrix.GetLength(0); rr++) {
for (int cc = 0; cc < sensor2.negativeMatrix.GetLength(1); cc++)
Console.Write(sensor2.negativeMatrix[rr, cc] + ", ");
Console.WriteLine(" - ");
}
return;*/
if (parsedArgs["freqrows"] != null) {
DataReader reader = new DataReader(parsedArgs["f"]);
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
TwoTuple<int, int> counts = FrequencyTools.WordCount(parsedArgs["freqrows"], row[1]);
Console.WriteLine(counts.one + "," + counts.two + ",\"" + row[2] + "\"");
}
}
if (parsedArgs["emotion"] != null) {
ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
double[] emotions = sensor.EstimateEmotions(parsedArgs["emotion"]);
for (int ii = 0; ii < (int) ANEWEmotionSensor.Emotions.COUNT; ii++)
Console.WriteLine(((ANEWEmotionSensor.Emotions) ii).ToString() + ": " + emotions[ii]);
}
if (parsedArgs["emorows"] != null) {
int rows = 0, valids = 0;
ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
DataReader reader = new DataReader(parsedArgs["f"]);
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
rows++;
double[] emotions = sensor.EstimateEmotions(row[1]);
Console.WriteLine("\"" + row[0] + "\"," + emotions[0] + "," + emotions[1] + "," + emotions[2] + "," + emotions[3] + "," + emotions[4] + "," + emotions[5] + "," + emotions[6] + "," + emotions[7] + ",\"" + row[2] + "\"");
if (!double.IsNaN(emotions[0]))
valids++;
}
}
if (parsedArgs["eimpute"] != null) {
ANEWEmotionSensor sensor = new ANEWEmotionSensor("/Users/jrising/projects/virsona/github/data");
// DIAGNOSTIC
/*List<List<string>> rows = new List<List<string>>();
rows.Add(TwitterUtilities.SplitWords("happy aaaa cccc"));
rows.Add(TwitterUtilities.SplitWords("sad bbbb cccc"));
IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> inputed = sensor.ImputeEmotionalContent(rows, 1000);
foreach (KeyValuePair<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> kvp in inputed)
Console.WriteLine(kvp.Key + ": " + kvp.Value.one.Mean + ", " + kvp.Value.two.Mean + ", " + kvp.Value.three.Mean);*/
bool smallFile = false;
if (smallFile) {
DataReader reader = new DataReader(parsedArgs["f"]);
List<List<string>> rows = new List<List<string>>();
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
Console.WriteLine(row);
rows.Add(TwitterUtilities.SplitWords(row[10].ToLower()));
}
reader.Close();
/*IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> inputed = sensor.ImputeEmotionalContent(rows, 10);
double minv = 1, maxv = 0;
foreach (KeyValuePair<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> kvp in inputed) {
minv = Math.Min(minv, kvp.Value.one.Mean);
maxv = Math.Max(maxv, kvp.Value.one.Mean);
Console.WriteLine(kvp.Key + ": " + kvp.Value.one.Mean + " x " + kvp.Value.one.Variance + ", " + kvp.Value.two.Mean + ", " + kvp.Value.three.Mean);
}
Console.WriteLine("Min: " + minv + ", Max: " + maxv);*/
sensor.ImputeEmotionalContent(rows, 10, parsedArgs["f"] + "imputed");
} else {
sensor.ImputeEmotionalContentFromFile(parsedArgs["f"], 11, 0, parsedArgs["f"].Substring(0, parsedArgs["f"].Length - 4) + "imputed.csv");
}
uint jj = 0;
using (var stream = File.CreateText(parsedArgs["f"] + "result")) {
jj++;
if (jj % 1000 == 0)
Console.WriteLine("#" + jj);
DataReader reader = new DataReader(parsedArgs["f"]);
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
double[] emotions = sensor.EstimateEmotions(row[11]);
for (int ii = 0; ii < 11; ii++)
stream.Write(row[ii] + ",");
stream.WriteLine(emotions[0] + "," + emotions[1] + "," + emotions[2] + "," + emotions[3] + "," + emotions[4] + "," + emotions[5] + "," + emotions[6] + "," + emotions[7]);
}
}
}
}
public IDataSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> ImputeEmotionalContentFromFile(string filename, uint column, uint repeats, string imputesave)
{
MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> imputed = new MemorySource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>();
ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>> combo = new ComboSource<string, ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>>(source, imputed);
// Check for existing imputed file
DataReader imputereader = new DataReader(imputesave);
uint kk = 0;
for (string[] row = imputereader.ReadRow(); row != null; row = imputereader.ReadRow()) {
kk++;
if (kk % 1000 == 0)
Console.WriteLine("#" + kk);
double meanv = double.Parse(row[1]), varv = double.Parse(row[2]), meana = double.Parse(row[3]), vara = double.Parse(row[4]), meand = double.Parse(row[5]), vard = double.Parse(row[6]);
ContinuousDistribution valence = new ClippedGaussianDistribution(meanv, varv, 0, 1);
ContinuousDistribution arousal = new ClippedGaussianDistribution(meana, vara, 0, 1);
ContinuousDistribution dominance = new ClippedGaussianDistribution(meand, vard, 0, 1);
imputed[row[0]] = new ThreeTuple<ContinuousDistribution, ContinuousDistribution, ContinuousDistribution>(valence, arousal, dominance);
}
imputereader.Close();
for (uint ii = 0; ii < repeats; ii++) {
Dictionary<string, List<KeyValuePair<double, double>>>
sentencesV = new Dictionary<string, List<KeyValuePair<double, double>>>(),
sentencesA = new Dictionary<string, List<KeyValuePair<double, double>>>(),
sentencesD = new Dictionary<string, List<KeyValuePair<double, double>>>();
DataReader reader = new DataReader(filename);
uint jj = 0;
for (string[] row = reader.ReadRow(); row != null; row = reader.ReadRow()) {
jj++;
if (jj % 1000 == 0)
Console.WriteLine("#" + jj + ": " + sentencesV.Count + ", " + imputed.Count);
List<string> words = TwitterUtilities.SplitWords(row[column].ToLower());
AnalyzeWords(words, combo, sentencesV, sentencesA, sentencesD);
}
reader.Close();
AnalyzeSentences(imputed, sentencesV, sentencesA, sentencesD, imputesave);
}
source = combo;
return imputed;
}
