//更新权重方法
func (fs *FtrlSolver) Update(x util.Pvector, y float64) float64 {
if !fs.Init {
return 0
}
var weights util.Pvector = make(util.Pvector, fs.Featnum)
var gradients []float64 = make([]float64, fs.Featnum)
var wTx float64 = 0.
for i := 0; i < len(x); i++ {
item := x[i]
if util.UtilGreater(fs.Dropout, 0.0) {
rand_prob := util.UniformDistribution()
if rand_prob < fs.Dropout {
continue
}
}
var idx int = item.Index
if idx >= fs.Featnum {
continue
}
//获取w权重值
var val float64 = fs.GetWeight(idx)
//建立w权重数组
weights = append(weights, util.Pair{idx, val})
//每个样本梯度值默认赋值为样本x本身
gradients = append(gradients, item.Value)
//计算仿射函数wT*x的值
wTx += val * item.Value
}
//计算模型预估值
var pred float64 = util.Sigmoid(wTx)
//计算p_t-y_t值，为计算每个样本的梯度做准备
var grad float64 = pred - y
//计算g_i = (p_t-y_t)*x_i
util.VectorMultiplies(gradients, grad)
for k := 0; k < len(weights); k++ {
var i int = weights[k].Index
var w_i float64 = weights[k].Value
var grad_i float64 = gradients[k]
var sigma float64 = (math.Sqrt(fs.N[i]+grad_i*grad_i) - math.Sqrt(fs.N[i])) / fs.Alpha
//z_i=z_i+g_i-sigma_i*w_(t,i)
fs.Z[i] += grad_i - sigma*w_i
//n_i=n_i+g_i*g_i
fs.N[i] += grad_i * grad_i
}
return pred
}
func (lr *LRModel) Predict(x util.Pvector) float64 {
if !lr.Init {
return 0
}
var wTx float64 = 0.
for i := 0; i < len(x); i++ {
item := x[i]
wTx += lr.Model[item.Index] * item.Value
}
var pred float64 = util.Sigmoid(wTx)
return pred
}
func (fs *FtrlSolver) Predict(x util.Pvector) float64 {
if !fs.Init {
return 0
}
var wTx float64 = 0.
for i := 0; i < len(x); i++ {
idx := x[i].Index
val := fs.GetWeight(idx)
wTx += val * x[i].Value
}
pred := util.Sigmoid(wTx)
return pred
}
func (fw *FtrlWorker) Update(
x util.Pvector,
y float64,
param_server *FtrlParamServer) float64 {
if !fw.FtrlSolver.Init {
return 0.
}
var weights util.Pvector = make(util.Pvector, fw.FtrlSolver.Featnum)
var gradients []float64 = make([]float64, fw.FtrlSolver.Featnum)
var wTx float64 = 0.
for i := 0; i < len(x); i++ {
item := x[i]
if util.UtilGreater(fw.FtrlSolver.Dropout, 0.0) {
rand_prob := util.UniformDistribution()
if rand_prob < fw.FtrlSolver.Dropout {
continue
}
}
var idx int = item.Index
if idx >= fw.FtrlSolver.Featnum {
continue
}
//获取w权重值
var val float64 = fw.FtrlSolver.GetWeight(idx)
//建立w权重数组
weights = append(weights, util.Pair{idx, val})
//每个样本梯度值默认赋值为样本x本身
gradients = append(gradients, item.Value)
//计算仿射函数wT*x的值
wTx += val * item.Value
}
//计算模型预估值
var pred float64 = util.Sigmoid(wTx)
//计算p_t-y_t值，为计算每个样本的梯度做准备
var grad float64 = pred - y
//计算g_i = (p_t-y_t)*x_i
util.VectorMultiplies(gradients, grad)
for k := 0; k < len(weights); k++ {
var i int = weights[k].Index
var g int = i / ParamGroupSize
if fw.ParamGroupStep[g]%fw.FetchStep == 0 {
param_server.FetchParamGroup(
fw.FtrlSolver.N,
fw.FtrlSolver.Z,
g)
}
var w_i float64 = weights[k].Value
var grad_i float64 = gradients[k]
var sigma float64 = (math.Sqrt(fw.FtrlSolver.N[i]+grad_i*grad_i) - math.Sqrt(fw.FtrlSolver.N[i])) / fw.FtrlSolver.Alpha
fw.FtrlSolver.Z[i] += grad_i - sigma*w_i
fw.FtrlSolver.N[i] += grad_i * grad_i
fw.ZUpdate[i] += grad_i - sigma*w_i
fw.NUpdate[i] += grad_i * grad_i
if fw.ParamGroupStep[g]%fw.PushStep == 0 {
param_server.PushParamGroup(fw.NUpdate, fw.ZUpdate, g)
}
fw.ParamGroupStep[g] += 1
}
return pred
}