public RandomBlockCreator(int count, int maxRow, int maxCol)
{
var matrix = new RandomMatrix(maxRow, maxCol, count);
_info = matrix.Normalize().Select(x => new BlockInfo {
X = x.X, Y = x.Y
});
}
public ANEWEmotionSensor(string datadirectory)
{
// Data files contained in [datadrectory]/metrics
string metricsdir = datadirectory + Path.DirectorySeparatorChar + "metrics" + Path.DirectorySeparatorChar;
source = new MemoizedSource <string, ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> >(new ANEWFileSource(metricsdir + "anew.csv"));
stemmer = new PorterStemmer();
// These matrices are used in G emotion = vad
// positives are mean > 5; negatives are mean < 5
double[,] positives = new double[, ] {
{ .890, -.020, -.110, .116, -.035 },
{ .649, .139, -.287, .441, .051 },
{ .601, .153, -.305, .125, .042 }
};
double[,] positiveTs = new double[, ] {
{ 45.40, 0.73, 4.24, 4.95, 1.55 },
{ 19.75, 2.984, 6.57, 11.26, 1.36 },
{ 16.60, 2.98, 6.34, 2.88, 1.00 }
};
double[,] positiveSEs = Matrix.ElementwiseDivide(positives, positiveTs);
double[,] negatives = new double[, ] {
{ .291, -.044, -.515, .020, -.243 },
{ .050, .492, -.309, .670, -.042 },
{ .136, .369, -.625, -.144, .041 }
};
double[,] negativeTs = new double[, ] {
{ 8.91, 1.27, 13.80, 0.58, 8.27 },
{ 1.36, 12.59, 7.33, 17.11, 1.27 },
{ 2.93, 7.49, 11.75, 2.91, 0.98 }
};
double[,] negativeSEs = Matrix.ElementwiseDivide(negatives, negativeTs);
ContinuousDistribution[,] randomPositives = RandomMatrix.MakeGaussians(positives, positiveSEs);
ContinuousDistribution[,] randomNegatives = RandomMatrix.MakeGaussians(negatives, negativeSEs);
positiveProduct = RandomMatrix.Multiply(RandomMatrix.Transpose(randomPositives),
RandomMatrix.Inverse(RandomMatrix.Multiply(randomPositives, RandomMatrix.Transpose(randomPositives))));
negativeProduct = RandomMatrix.Multiply(RandomMatrix.Transpose(randomNegatives),
RandomMatrix.Inverse(RandomMatrix.Multiply(randomNegatives, RandomMatrix.Transpose(randomNegatives))));
positiveMatrix = Matrix.Multiply(Matrix.Transpose(positives), Matrix.Inverse(Matrix.Multiply(positives, Matrix.Transpose(positives))));
negativeMatrix = Matrix.Multiply(Matrix.Transpose(negatives), Matrix.Inverse(Matrix.Multiply(negatives, Matrix.Transpose(negatives))));
}
public double[] EstimateEmotions(string text)
{
List <string> words = StringUtilities.SplitWords(text.ToLower(), true);
// 3. Look up each word in ANEWFileSource
double[] numer = new double[(int)Emotions.COUNT], denom = new double[(int)Emotions.COUNT];
for (int ii = 0; ii < (int)Emotions.COUNT; ii++)
{
numer[ii] = denom[ii] = 0;
}
foreach (string word in words)
{
if (word.StartsWith(" ") || word.Length <= 2)
{
continue;
}
ThreeTuple <ContinuousDistribution, ContinuousDistribution, ContinuousDistribution> vad;
if (!TryGetWordOrStem(source, word, out vad))
{
continue;
}
numer[(int)Emotions.Valence] += vad.one.Mean / vad.one.Variance;
denom[(int)Emotions.Valence] += 1 / vad.one.Variance;
numer[(int)Emotions.Arousal] += vad.two.Mean / vad.two.Variance;
denom[(int)Emotions.Arousal] += 1 / vad.two.Variance;
numer[(int)Emotions.Dominance] += vad.three.Mean / vad.three.Variance;
denom[(int)Emotions.Dominance] += 1 / vad.three.Variance;
// 4. Apply regressions from other paper
ContinuousDistribution[,] vector = new ContinuousDistribution[, ] {
{ vad.one }, { vad.two }, { vad.three }
};
ContinuousDistribution[,] emotions;
if (vad.one.Mean >= .5)
{
emotions = RandomMatrix.Multiply(positiveProduct, vector);
}
else
{
emotions = RandomMatrix.Multiply(negativeProduct, vector);
}
// 5. Take mean within bounds and sum weighted by variance
for (int ii = 3; ii < (int)Emotions.COUNT; ii++)
{
ContinuousDistribution clipped = emotions[ii - 3, 0].Transform(0, .1).Clip(0, 1);
numer[ii] += clipped.Mean / clipped.Variance;
denom[ii] += 1 / clipped.Variance;
}
}
for (int ii = 0; ii < (int)Emotions.COUNT; ii++)
{
numer[ii] /= denom[ii];
}
return(numer);
}
