protected override Float AccumulateOneGradient(ref VBuffer <Float> feat, Float label, Float weight,
ref VBuffer <Float> x, ref VBuffer <Float> grad, ref Float[] scratch)
{
Float bias = 0;
x.GetItemOrDefault(0, ref bias);
Float score = bias + VectorUtils.DotProductWithOffset(ref x, 1, ref feat);
Float s = score / 2;
Float logZ = MathUtils.SoftMax(s, -s);
Float label01 = Math.Min(1, Math.Max(label, 0));
Float label11 = 2 * label01 - 1; //(-1..1) label
Float modelProb1 = MathUtils.ExpSlow(s - logZ);
Float ls = label11 * s;
Float datumLoss = logZ - ls;
//Float loss2 = MathUtil.SoftMax(s-l_s, -s-l_s);
Contracts.Check(!Float.IsNaN(datumLoss), "Unexpected NaN");
Float mult = weight * (modelProb1 - label01);
VectorUtils.AddMultWithOffset(ref feat, mult, ref grad, 1); // Note that 0th L-BFGS weight is for bias.
// Add bias using this strange trick that has advantage of working well for dense and sparse arrays.
// Due to the call to EnsureBiases, we know this region is dense.
Contracts.Assert(grad.Count >= BiasCount && (grad.IsDense || grad.Indices[BiasCount - 1] == BiasCount - 1));
grad.Values[0] += mult;
return(weight * datumLoss);
}
// Poisson: p(y;lambda) = lambda^y * exp(-lambda) / y!
// lambda is the parameter to the Poisson. It is the mean/expected number of occurrences
// p(y;lambda) is the probability that there are y occurences given the expected was lambda
// Our goal is to maximize log-liklihood. Log(p(y;lambda)) = ylog(lambda) - lambda - log(y!)
// lambda = exp(w.x+b)
// then dlog(p(y))/dw_i = x_i*y - x_i*lambda = y*x_i - x_i * lambda
// dp/db = y - lambda
// Goal is to find w that maximizes
// Note: We negate the above in ordrer to minimize
protected override float AccumulateOneGradient(ref VBuffer <float> feat, float label, float weight,
ref VBuffer <float> x, ref VBuffer <float> grad, ref float[] scratch)
{
float bias = 0;
x.GetItemOrDefault(0, ref bias);
float dot = VectorUtils.DotProductWithOffset(ref x, 1, ref feat) + bias;
float lambda = MathUtils.ExpSlow(dot);
float y = label;
float mult = -(y - lambda) * weight;
VectorUtils.AddMultWithOffset(ref feat, mult, ref grad, 1);
// Due to the call to EnsureBiases, we know this region is dense.
Contracts.Assert(grad.Count >= BiasCount && (grad.IsDense || grad.Indices[BiasCount - 1] == BiasCount - 1));
grad.Values[0] += mult;
// From the computer's perspective exp(infinity)==infinity
// so inf-inf=nan, but in reality, infinity is just a large
// number we can't represent, and exp(X)-X for X=inf is just inf.
if (float.IsPositiveInfinity(lambda))
{
return(float.PositiveInfinity);
}
return(-(y * dot - lambda) * weight);
}
protected override float AccumulateOneGradient(ref VBuffer <float> feat, float label, float weight,
ref VBuffer <float> x, ref VBuffer <float> grad, ref float[] scores)
{
if (Utils.Size(scores) < _numClasses)
{
scores = new float[_numClasses];
}
float bias = 0;
for (int c = 0, start = _numClasses; c < _numClasses; c++, start += NumFeatures)
{
x.GetItemOrDefault(c, ref bias);
scores[c] = bias + VectorUtils.DotProductWithOffset(ref x, start, ref feat);
}
float logZ = MathUtils.SoftMax(scores, _numClasses);
float datumLoss = logZ;
int lab = (int)label;
Contracts.Assert(0 <= lab && lab < _numClasses);
for (int c = 0, start = _numClasses; c < _numClasses; c++, start += NumFeatures)
{
float probLabel = lab == c ? 1 : 0;
datumLoss -= probLabel * scores[c];
float modelProb = MathUtils.ExpSlow(scores[c] - logZ);
float mult = weight * (modelProb - probLabel);
VectorUtils.AddMultWithOffset(ref feat, mult, ref grad, start);
// Due to the call to EnsureBiases, we know this region is dense.
Contracts.Assert(grad.Count >= BiasCount && (grad.IsDense || grad.Indices[BiasCount - 1] == BiasCount - 1));
grad.Values[c] += mult;
}
Contracts.Check(FloatUtils.IsFinite(datumLoss), "Data contain bad values.");
return(weight * datumLoss);
}
