func (this *TPAQMixer) get() int {
for this.idx&7 != 0 {
this.buffer[this.ctx+this.idx] = 64
this.idx++
}
// Neural Network dot product (sum weights*inputs)
p := (this.buffer[this.ctx] * this.buffer[this.ctx+8]) +
(this.buffer[this.ctx+1] * this.buffer[this.ctx+9]) +
(this.buffer[this.ctx+2] * this.buffer[this.ctx+10]) +
(this.buffer[this.ctx+3] * this.buffer[this.ctx+11]) +
(this.buffer[this.ctx+4] * this.buffer[this.ctx+12]) +
(this.buffer[this.ctx+5] * this.buffer[this.ctx+13]) +
(this.buffer[this.ctx+6] * this.buffer[this.ctx+14]) +
(this.buffer[this.ctx+7] * this.buffer[this.ctx+15])
this.pr = kanzi.Squash(p >> 15)
return this.pr
}
func newPAQAdaptiveProbMap(n, rate uint) (*PAQAdaptiveProbMap, error) {
this := new(PAQAdaptiveProbMap)
this.data = make([]int, n*33)
this.rate = rate
k := 0
for i := uint(0); i < n; i++ {
for j := 0; j < 33; j++ {
if i == 0 {
this.data[k+j] = kanzi.Squash((j-16)<<7) << 4
} else {
this.data[k+j] = this.data[j]
}
}
k += 33
}
return this, nil
}
