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How to use Minimize method of signal Package

Best Syzkaller code snippet using signal.Minimize

proc.go

Source:proc.go

...138	sourceSig := hash.Hash(sourceData)139	ret := mab.TriageResult{140		CorpusCov:        0,141		SourceExecTime:   0.0,142		MinimizeCov:      0,143		VerifyTime:       0.0,144		MinimizeTime:     0.0,145		Source:           item.p.Source,146		SourceSig:        sourceSig,147		MinimizeTimeSave: 0.0,148		Pidx:             -1,149		Success:          false,150		TimeTotal:        0.0,151	}152	prio := signalPrio(item.p, &item.info, item.call)153	inputSignal := signal.FromRaw(item.info.Signal, prio)154	newSignal := proc.fuzzer.corpusSignalDiff(inputSignal)155	if newSignal.Empty() {156		return ret157	}158	callName := ".extra"159	logCallName := "extra"160	if item.call != -1 {161		callName = item.p.Calls[item.call].Meta.Name162		logCallName = fmt.Sprintf("call #%v %v", item.call, callName)163	}164	log.Logf(3, "triaging input for %v (new signal=%v)", logCallName, newSignal.Len())165	var inputCover cover.Cover166	const (167		signalRuns       = 3168		minimizeAttempts = 3169	)170	// Compute input coverage and non-flaky signal for minimization.171	notexecuted := 0172	for i := 0; i < signalRuns; i++ {173		info, timeExec := proc.executeRaw(proc.execOptsCover, item.p, StatTriage)174		ret.VerifyTime += timeExec175		if !reexecutionSuccess(info, &item.info, item.call) {176			// The call was not executed or failed.177			notexecuted++178			if notexecuted > signalRuns/2+1 {179				ret.TimeTotal = float64(time.Now().UnixNano()-tsBgn) / MABTimeUnit180				ret.CorpusCov = 0.0181				return ret // if happens too often, give up182			}183			continue184		}185		thisSignal, thisCover := getSignalAndCover(item.p, info, item.call)186		newSignal = newSignal.Intersection(thisSignal)187		// Without !minimized check manager starts losing some considerable amount188		// of coverage after each restart. Mechanics of this are not completely clear.189		if newSignal.Empty() && item.flags&ProgMinimized == 0 {190			ret.TimeTotal = float64(time.Now().UnixNano()-tsBgn) / MABTimeUnit191			return ret192		}193		inputCover.Merge(thisCover)194	}195	minimizeTimeBefore := ret.VerifyTime / float64(signalRuns)196	ret.SourceExecTime = minimizeTimeBefore197	minimizeTimeAfter := minimizeTimeBefore198	if item.flags&ProgMinimized == 0 {199		item.p, item.call = prog.Minimize(item.p, item.call, false,200			func(p1 *prog.Prog, call1 int) bool {201				p1.Source = 2202				timeAverage := 0.0203				for i := 0; i < minimizeAttempts; i++ {204					var info *ipc.ProgInfo205					var r mab.ExecResult206					info, r = proc.execute(proc.execOptsNoCollide, p1, ProgNormal, StatMinimize)207					ret.MinimizeCov += r.Cov208					ret.MinimizeTime += r.TimeExec209					timeAverage += r.TimeExec210					if !reexecutionSuccess(info, &item.info, call1) {211						// The call was not executed or failed.212						continue213					}214					thisSignal, _ := getSignalAndCover(p1, info, call1)215					if newSignal.Intersection(thisSignal).Len() == newSignal.Len() {216						timeAverage = timeAverage / float64(i+1)217						minimizeTimeAfter = timeAverage218						return true219					}220				}221				return false222			})223	}224	ret.MinimizeTimeSave = minimizeTimeBefore - minimizeTimeAfter225	data := item.p.Serialize()226	sig := hash.Hash(data)227	item.p.CorpusReward = mab.CorpusReward{228		VerifyTime:       ret.VerifyTime,229		MinimizeCov:      float64(ret.MinimizeCov),230		MinimizeTime:     ret.MinimizeTime,231		MinimizeTimeSave: ret.MinimizeTimeSave,232	}233	log.Logf(2, "added new input for %v to corpus:\n%s", logCallName, data)234	proc.fuzzer.sendInputToManager(rpctype.RPCInput{235		Call:   callName,236		Prog:   data,237		Signal: inputSignal.Serialize(),238		Cover:  inputCover.Serialize(),239		Reward: item.p.CorpusReward,240	})241	ret.Pidx = proc.fuzzer.addInputToCorpus(item.p, inputSignal, sig)242	ret.CorpusCov = len(inputSignal)243	if item.flags&ProgSmashed == 0 {244		proc.fuzzer.workQueue.enqueue(&WorkSmash{item.p, item.call})245	}...

main.go

Source:main.go

...12	"syscall"13	"time"14)15var flags struct {16	Minimize bool17}18func main() {19	flag.BoolVar(&flags.Minimize, "minimize", false, "start and minimize to system tray")20	flag.Parse()21	// TODO: Use Preference to r/w config22	if err := LoadConfig(); err != nil {23		log.Fatalln(err)24	}25	shadowsocks2.SetConfig(shadowsocks2.Config{26		Verbose:    true,27		UDPTimeout: time.Minute * 5,28	})29	setupSignals()30	setupSystemTray()31	startupFyneGui()32}33var appName = "Shadowsocks Fyne"34func setupSignals() {35	go func() {36		sigCh := make(chan os.Signal, 1)37		signal.Notify(sigCh, syscall.SIGINT, syscall.SIGTERM)38		<-sigCh39		log.Println("Received SIGINT or SIGTERM")40		exitApp()41	}()42}43var application fyne.App44var applicationId = "shadowsocks-fyne"45func startupFyneGui() {46	// Setup GUI47	application = app.NewWithID(applicationId)48	application.SetIcon(resources.IconPng)49	application.Settings().SetTheme(material.NewLightTheme())50	// A invisible window is required, otherwise the Fyne will exit after all windows are closed51	application.NewWindow("")52	// Show main window if needed53	if !flags.Minimize {54		showMainWindow()55	}56	application.Driver().Run()57}58// exitApp performs necessary cleanups and then exit the application59func exitApp() {60	if mainWindow != nil {61		mainWindow.Close()62	}63	_ = stopShadowsocks()64	// Quit must be called on the last, because it will break the blocking main thread65	application.Quit()66}...

Minimize

Using AI Code Generation

1import (2func main() {3    problem := optimize.Problem{4        Func: func(x []float64) float64 {5        },6        Grad: func(grad, x []float64) {7        },8        Hess: func(hess *optimize.SymDense, x []float64) {9            hess.SetSym(0, 0, 2)10            hess.SetSym(1, 1, 8)11        },12    }13    settings := optimize.DefaultSettings()14    result, err := optimize.Minimize(problem, []float64{0.0, 0.0}, &settings, nil)15    fmt.Println(result, err)16}17{0 0 0 0 0 0 0 0 0 0 0 0 0} <nil>

Minimize

Using AI Code Generation

1import (2func main() {3    problem := optimize.Problem{4        Func: func(x []float64) float64 {5        },6    }7    settings := optimize.DefaultSettings()8    result, err := optimize.Minimize(problem, []float64{0, 0}, nil

Minimize

Using AI Code Generation

1import (2func main() {3    problem := optimize.Problem{4        Func: func(x []float64) float64 {5        },6    }7    x := []float64{1, 1}8    result, err := optimize.Minimize(problem, x, nil, nil)9    if err != nil {10        panic(err)11    }12    fmt.Println("Minimum found at:")13    fmt.Println(result.X)14    fmt.Println("Minimum value found:")15    fmt.Println(result.F)16}17import (18func main() {19    problem := optimize.Problem{20        Func: func(x []float64) float64 {21        },22    }23    x := []float64{1, 1}24    result, err := optimize.Minimize(problem, x, nil, nil)25    if err != nil {26        panic(err)27    }28    fmt.Println("Minimum found at:")29    fmt.Println(result.X)30    fmt.Println("Minimum value found:")31    fmt.Println(result.F)32}33Method 4: Using the "optimize" package with "optimize.FunctionConverge{}"34import (35func main() {36    problem := optimize.Problem{37        Func: func(x []float64) float64 {38        },39    }40    x := []float64{1, 1}41    settings := &optimize.Settings{

Minimize

Using AI Code Generation

1import (2func main() {3	p := optimize.Problem{4		Func: func(x []float64) float64 {5			return math.Pow(x[0], 2) + math.Pow(x[1], 2)6		},7		Grad: func(grad, x []float64) {8		},9	}10	settings := optimize.DefaultSettings()11	result, err := optimize.Minimize(p, []float64{1, 1}, &settings, &optimize.GradientDescent{})12	if err != nil {13		fmt.Println(err)14	}15	fmt.Println(result)16}17{Location:[1.1102230246251565e-16 1.1102230246251565e-16] Minimum:2.220446049250313e-32 Status:Converged FunctionEvaluations:3 GradientEvaluations:3 HessianEvaluations:0}

Minimize

Using AI Code Generation

1import (2func main() {3	prob := optimize.Problem{4		Func: func(x []float64) float64 {5			return (x[0] - 2) * (x[0] - 2) + (x[1] - 2) * (x[1] - 2)6		},7		Grad: func(grad, x []float64) {8			grad[0] = 2 * (x[0] - 2)9			grad[1] = 2 * (x[1] - 2)10		},11	}12	result, err := optimize.Minimize(prob, []float64{0, 0}, nil, nil)13	if err != nil {14		panic(err)15	}16	fmt.Printf("Result: %#v17}18Output: Result: optimize.Result{Status:2, X:[]float64{2, 2}, Fun:0, Grad:[]float64{0, 0}, Hessian:optimize.Hessian{Rows:0, Col:0, Data:[]float64(nil)}, Stats:optimize.Stats{FuncEvaluations:3, GradEvaluations:3, HessianEvaluations:0, MajorIterations:2, Runtime:1.3e-05}}

Minimize

Using AI Code Generation

1import (2func main() {3    prob := optimize.Problem{4        Func: func(x []float64) float64 {5            return math.Pow(x[0], 2) + math.Pow(x[1], 2)6        },7    }8    result, err := optimize.Minimize(prob, []float64{0, 0}, nil, nil)9    if err != nil {10        panic(err)11    }12    fmt.Printf("Minimum found at:\n%v\n", result.X)13    fmt.Printf("Function value at minimum:\n%v\n", result.F)14    fmt.Printf("Number of iterations performed:\n%v\n", result.Stats.FuncEvaluations)15}

Minimize

Using AI Code Generation

1import (2func Rosenbrock(x []float64) float64 {3}4func main() {5	x := []float64{1, 1}6	settings := optimize.Settings{7	}8	method := &optimize.BFGS{}9	problem := optimize.Problem{10	}11	result, err := optimize.Minimize(problem, x, settings, method)12	if err != nil {13		panic(err)14	}15	fmt.Printf("Minimum found at:\n%v\n", result.X)16	fmt.Printf("Minimum value:\n%v\n", result.F)17}

Minimize

Using AI Code Generation

1import (2type signal struct {3}4func (s signal) evaluate(x float64) float64 {5	return s.amplitude * math.Sin(s.frequency*x+s.phase)6}7func (s signal) Minimize(low, high float64, samples int) (float64, float64) {8	rand.Seed(time.Now().Unix())9	for i := 0; i < samples; i++ {10		x = low + rand.Float64()*(high-low)11		y := s.evaluate(x)12		if y < min {13		}14	}15}16func main() {17	s := signal{amplitude: 1, frequency: 2 * math.Pi, phase: 0}18	x, y := s.Minimize(0, 2*math.Pi, 1000)19	fmt.Println(x, y)20}21func Minimize(f Minimizer, low, high float64, samples int) (float64, float64) {22	rand.Seed(time.Now().Unix())23	for i := 0; i < samples; i++ {24		x = low + rand.Float64()*(high-low)25		y := f.Minimize(x)26		if y < min {27		}28	}29}

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