func TestMaxent(t *testing.T) {
const json = `
[
{
"A": ["a"],
"B": ["b"],
"__label__": "A"
},
{
"A": ["b"],
"B": ["a"],
"__label__": "B"
}
]
`
c := FromJson([]byte(json))
opt := gd.GradientDescent{1, 0.01, -1, 1}
start := make(gd.NDimensionalValue, c.DimCnt())
weights := opt.Optimize(c, start)
if math.Abs(weights[0]+weights[1]) > 0.01 {
t.Errorf("%v=>%v\n%v=>%v", c.FeatureMap[0], weights[0], c.FeatureMap[1], weights[1])
}
}
func main() {
var raw []byte
if len(os.Args) < 3 {
raw, _ = ioutil.ReadAll(os.Stdin)
} else {
raw, _ = ioutil.ReadFile(os.Args[1])
}
// read in the training data
maxent := cloglik.FromJson(raw)
// initialize gradient descent parameters
grad_descent := gd.GradientDescent{}
grad_descent.LearningRate = 0.1
grad_descent.Threshold = 0.01
grad_descent.MaxIter = -1
// parallelism
//threads,_ := strconv.ParseInt(os.Args[3],0,0)
//fmt.Println("Threads: ",threads)
threads := runtime.NumCPU()
runtime.GOMAXPROCS(threads)
grad_descent.Threads = threads
maxent.Threads = threads
// starting point
x0 := make(gd.NDimensionalValue, maxent.DimCnt()) // x0 = [0,...,0]
// minimize
xmin := grad_descent.Optimize(maxent, x0)
fmt.Fprintln(os.Stderr, "Loglikelihood: ", maxent.Val(xmin))
// prepare the result for JSON output
rs := make(map[string]float64)
for i, v := range xmin {
rs[maxent.FeatureMap[i]] = v
}
// output
json_out, _ := json.Marshal(rs)
if len(os.Args) < 3 {
os.Stdout.Write(json_out)
} else {
ioutil.WriteFile(os.Args[2], json_out, 0666)
}
}
