internal void ClickModel(
Gaussian inputScoreMean,
Gamma inputScorePrec,
Gamma inputJudgePrec,
Gamma inputClickPrec,
Gaussian inputLowerThreshMean,
Gaussian inputUpperThreshMean,
Gaussian[] clickObservations) // Click observations for this label
{
int n = clickObservations.Length;
// Add variables outside plate - all variables outside the plate should
// be marked as outputs because we will be communicating their messages
// across labels
double scoreMean = Factor.Random(inputScoreMean);
double scorePrec = Factor.Random(inputScorePrec);
double judgePrec = Factor.Random(inputJudgePrec);
double clickPrec = Factor.Random(inputClickPrec);
double lowerThresh = Factor.Random(inputLowerThreshMean);
double upperThresh = Factor.Random(inputUpperThreshMean);
// Now set up the plate
double[] scores = new double[n];
double[] scoresJ = new double[n];
double[] scoresC = new double[n];
bool b = true;
for (int i = 0; i < n; i++)
{
scores[i] = Factor.Gaussian(scoreMean, 2.0);
// click-based score
scoresC[i] = Factor.Gaussian(scores[i], clickPrec);
Constrain.EqualRandom(scoresC[i], clickObservations[i]);
// judged score
scoresJ[i] = Factor.Gaussian(scores[i], judgePrec);
b = Factor.IsBetween(scoresJ[i], lowerThresh, upperThresh);
}
InferNet.Infer(scoreMean, nameof(scoreMean));
InferNet.Infer(scorePrec, nameof(scorePrec));
InferNet.Infer(judgePrec, nameof(judgePrec));
InferNet.Infer(clickPrec, nameof(clickPrec));
InferNet.Infer(lowerThresh, nameof(lowerThresh));
InferNet.Infer(upperThresh, nameof(upperThresh));
}
internal void Click5LabelModel(
Gaussian inputScoreMean,
Gamma inputScorePrec,
Gamma inputJudgePrec,
Gamma inputClickPrec,
Gaussian[] inputThresh,
Gaussian[][] clickObservations)
{
// Add variables outside plate - all variables outside the plate should
// be marked as outputs because we will be communicating their messages
// across chunks
double scoreMean = Factor.Random(inputScoreMean);
double scorePrec = Factor.Random(inputScorePrec);
double judgePrec = Factor.Random(inputJudgePrec);
double clickPrec = Factor.Random(inputClickPrec);
double thresh0 = Factor.Random(inputThresh[0]);
double thresh1 = Factor.Random(inputThresh[1]);
double thresh2 = Factor.Random(inputThresh[2]);
double thresh3 = Factor.Random(inputThresh[3]);
double thresh4 = Factor.Random(inputThresh[4]);
double thresh5 = Factor.Random(inputThresh[5]);
//Attrib.AllVars(new DivideMessages(false), thresh0, thresh1, thresh2, thresh3, thresh4, thresh5);
//Attrib.AllVars(new DivideMessages(false), scoreMean, scorePrec, judgePrec, clickPrec);
// Plate 1
int n1 = clickObservations[0].Length;
double[] scores1 = new double[n1];
double[] scoresJ1 = new double[n1];
double[] scoresC1 = new double[n1];
for (int i1 = 0; i1 < n1; i1++)
{
Attrib.Var(i1, new Sequential());
scores1[i1] = Factor.Gaussian(scoreMean, scorePrec);
// click-based score
scoresC1[i1] = Factor.Gaussian(scores1[i1], clickPrec);
Constrain.EqualRandom(scoresC1[i1], clickObservations[0][i1]);
// judged score
scoresJ1[i1] = Factor.Gaussian(scores1[i1], judgePrec);
bool h1 = Factor.IsBetween(scoresJ1[i1], thresh0, thresh1);
Constrain.Equal(true, h1);
}
// Plate 2
int n2 = clickObservations[1].Length;
double[] scores2 = new double[n2];
double[] scoresJ2 = new double[n2];
double[] scoresC2 = new double[n2];
for (int i2 = 0; i2 < n2; i2++)
{
Attrib.Var(i2, new Sequential());
scores2[i2] = Factor.Gaussian(scoreMean, scorePrec);
// click-based score
scoresC2[i2] = Factor.Gaussian(scores2[i2], clickPrec);
Constrain.EqualRandom(scoresC2[i2], clickObservations[1][i2]);
// judged score
scoresJ2[i2] = Factor.Gaussian(scores2[i2], judgePrec);
bool h2 = Factor.IsBetween(scoresJ2[i2], thresh1, thresh2);
Constrain.Equal(true, h2);
}
// Plate 3
int n3 = clickObservations[2].Length;
double[] scores3 = new double[n3];
double[] scoresJ3 = new double[n3];
double[] scoresC3 = new double[n3];
for (int i3 = 0; i3 < n3; i3++)
{
Attrib.Var(i3, new Sequential());
scores3[i3] = Factor.Gaussian(scoreMean, scorePrec);
// click-based score
scoresC3[i3] = Factor.Gaussian(scores3[i3], clickPrec);
Constrain.EqualRandom(scoresC3[i3], clickObservations[2][i3]);
// judged score
scoresJ3[i3] = Factor.Gaussian(scores3[i3], judgePrec);
bool h3 = Factor.IsBetween(scoresJ3[i3], thresh2, thresh3);
Constrain.Equal(true, h3);
}
// Plate 4
int n4 = clickObservations[3].Length;
double[] scores4 = new double[n4];
double[] scoresJ4 = new double[n4];
double[] scoresC4 = new double[n4];
for (int i4 = 0; i4 < n4; i4++)
{
Attrib.Var(i4, new Sequential());
scores4[i4] = Factor.Gaussian(scoreMean, scorePrec);
// click-based score
scoresC4[i4] = Factor.Gaussian(scores4[i4], clickPrec);
Constrain.EqualRandom(scoresC4[i4], clickObservations[3][i4]);
// judged score
scoresJ4[i4] = Factor.Gaussian(scores4[i4], judgePrec);
bool h4 = Factor.IsBetween(scoresJ4[i4], thresh3, thresh4);
Constrain.Equal(true, h4);
}
// Plate 5
int n5 = clickObservations[4].Length;
double[] scores5 = new double[n5];
double[] scoresJ5 = new double[n5];
double[] scoresC5 = new double[n5];
for (int i5 = 0; i5 < n5; i5++)
{
Attrib.Var(i5, new Sequential());
scores5[i5] = Factor.Gaussian(scoreMean, scorePrec);
// click-based score
scoresC5[i5] = Factor.Gaussian(scores5[i5], clickPrec);
Constrain.EqualRandom(scoresC5[i5], clickObservations[4][i5]);
// judged score
scoresJ5[i5] = Factor.Gaussian(scores5[i5], judgePrec);
bool h5 = Factor.IsBetween(scoresJ5[i5], thresh4, thresh5);
Constrain.Equal(true, h5);
}
//Attrib.AllVars(new DivideMessages(false), scores1, scores2, scores3, scores4, scores5);
//Attrib.AllVars(new DivideMessages(false), scoresC1, scoresC2, scoresC3, scoresC4, scoresC5);
//Attrib.AllVars(new DivideMessages(false), scoresJ1, scoresJ2, scoresJ3, scoresJ4, scoresJ5);
InferNet.Infer(scoreMean, nameof(scoreMean));
InferNet.Infer(scorePrec, nameof(scorePrec));
InferNet.Infer(judgePrec, nameof(judgePrec));
InferNet.Infer(clickPrec, nameof(clickPrec));
InferNet.Infer(thresh0, nameof(thresh0));
InferNet.Infer(thresh1, nameof(thresh1));
InferNet.Infer(thresh2, nameof(thresh2));
InferNet.Infer(thresh3, nameof(thresh3));
InferNet.Infer(thresh4, nameof(thresh4));
InferNet.Infer(thresh5, nameof(thresh5));
}