func (rbm *RBM) logistic(v *util.Vector, index int, isRow bool) (output float64) {
output = 0.0
if isRow {
output = util.VecDotProduct(v, rbm.lock.weights.GetValues(index))
} else {
for i := 0; i < rbm.lock.weights.NumLabels(); i++ {
output += v.Get(i) * rbm.lock.weights.Get(i, index)
}
}
output = 1.0 / (1 + math.Exp(-output))
return
}
// 输入和输出都有 bias 项
func (rbm *RBM) SampleHidden(v *util.Vector, n int, binary bool) *util.Vector {
rbm.lock.RLock()
defer rbm.lock.RUnlock()
hiddenDim := rbm.options.NumHiddenUnits + 1
visibleDim := rbm.lock.weights.NumValues()
hiddenUnits := util.NewVector(hiddenDim)
visibleUnits := util.NewVector(visibleDim)
hiddenUnits.Set(0, 1.0)
visibleUnits.Set(0, 1.0)
for j := 1; j < visibleDim; j++ {
visibleUnits.Set(j, v.Get(j))
}
// 更新 hidden units
for i := 1; i < hiddenDim; i++ {
prob := rbm.logistic(visibleUnits, i, true)
if binary {
hiddenUnits.Set(i, rbm.bernoulli(prob))
} else {
hiddenUnits.Set(i, prob)
}
}
// reconstruct n-1 次
for nCD := 0; nCD < n; nCD++ {
for j := 1; j < visibleDim; j++ {
var prob float64
prob = rbm.logistic(hiddenUnits, j, false)
visibleUnits.Set(j, prob)
}
for i := 1; i < hiddenDim; i++ {
prob := rbm.logistic(visibleUnits, i, true)
if binary {
hiddenUnits.Set(i, rbm.bernoulli(prob))
} else {
hiddenUnits.Set(i, prob)
}
}
}
return hiddenUnits
}
// 计算 z = 1 + sum(exp(sum(w_i * x_i)))
//
// 在temp中保存 exp(sum(w_i * x_i))
func ComputeZ(weights *util.Matrix, features *util.Vector, label int, temp *util.Matrix) float64 {
result := float64(1.0)
numLabels := weights.NumLabels() + 1
for iLabel := 1; iLabel < numLabels; iLabel++ {
exp := math.Exp(util.VecDotProduct(features, weights.GetValues(iLabel-1)))
result += exp
tempVec := temp.GetValues(iLabel - 1)
if tempVec.IsSparse() {
for _, k := range features.Keys() {
tempVec.Set(k, exp)
}
} else {
tempVec.SetAll(exp)
}
}
return result
}
