fixed result struct

francescoalemanno · Apr 22, 2024 · 7c8c0a7 · 7c8c0a7
1 parent a5a4130
commit 7c8c0a7
Show file tree

Hide file tree

Showing 5 changed files with 62 additions and 29 deletions.
diff --git a/_examples/other.go b/_examples/other.go
@@ -18,8 +18,8 @@ func main() {
 	mu := []float64{1.0, 2.0}    // Initial mean vector
 	sigma := []float64{0.5, 0.5} // Initial standard deviation vector
 
-	optimizedMu, optimizedSigma := GoES.DefaultOpt(myCustomFunction, mu, sigma)
+	res, _ := GoES.DefaultOpt(myCustomFunction, mu, sigma)
 
-	fmt.Println("Optimized mean:", optimizedMu)
-	fmt.Println("Optimized standard deviation:", optimizedSigma)
+	fmt.Println("Optimized mean:", res.Mu)
+	fmt.Println("Optimized standard deviation:", res.Sigma)
 }
diff --git a/_examples/sphere.go b/_examples/sphere.go
@@ -29,7 +29,7 @@ func main() {
 	cfg.Verbose = false
 
 	// Perform optimization
-	optimizedMu, _ := GoES.Opt(sphere, mu, sigma, cfg)
+	res, _ := GoES.Opt(sphere, mu, sigma, cfg)
 
-	fmt.Println("Optimum:", optimizedMu) // should be close to vector [0, 1, 2, ..., dim-1]
+	fmt.Println("Optimum:", res.Mu) // should be close to vector [0, 1, 2, ..., dim-1]
 }
diff --git a/main.go b/main.go
@@ -37,9 +37,10 @@ This code implements a specific algorithm called CMA-ES (Covariance Matrix Adapt
 
 Overall, this code provides an implementation of CMA-ES for optimizing a black-box function in Go. It allows you to specify your own objective function and configure various parameters for the optimization process.
 */
-package goes
+package GoES
 
 import (
+	"fmt"
 	"log"
 	"math"
 	"slices"
@@ -69,12 +70,17 @@ func Defaults() Config {
 	return cfg
 }
 
+type Result struct {
+	Mu    []float64
+	Sigma []float64
+}
+
 const const_Ez0 = 0.7978845608028661 // mean(abs(randn()))
-func Opt(fn func([]float64) float64, mu []float64, sigma []float64, cfg Config) ([]float64, []float64) {
+func Opt(fn func([]float64) float64, mu []float64, sigma []float64, cfg Config) (Result, error) {
 	pop_n := cfg.PopSize
 	n := len(mu)
 	if len(sigma) != n {
-		log.Panic("mu and sigma must have the same length.")
+		return Result{}, fmt.Errorf("mu (len %d) and sigma (len %d) must have the same length", len(mu), len(sigma))
 	}
 	for pop_n*pop_n <= 144*n {
 		pop_n++
@@ -149,10 +155,10 @@ func Opt(fn func([]float64) float64, mu []float64, sigma []float64, cfg Config)
 			log.Println("GoES: ", runs, mu, sigma, pop[pop_n/2].C)
 		}
 	}
-	return mu, sigma
+	return Result{Mu: mu, Sigma: sigma}, nil
 }
 
-func DefaultOpt(fn func([]float64) float64, mu []float64, sigma []float64) ([]float64, []float64) {
+func DefaultOpt(fn func([]float64) float64, mu []float64, sigma []float64) (Result, error) {
 	cfg := Defaults()
 	cfg.Generations = int(math.Ceil(math.Sqrt(float64(len(mu)*2+1)) * 300))
 	return Opt(fn, mu, sigma, cfg)
@@ -174,3 +180,19 @@ func makeWeights(pop_size int) []float64 {
 	}
 	return W
 }
+
+func Positive(z float64) float64 {
+	if z < 0 {
+		return 1 / (1 - z)
+	}
+	return z + 1
+}
+
+func Probability(z float64) float64 {
+	p := Positive(z)
+	return p / (1 + p)
+}
+
+func Bounded(x, a, b float64) float64 {
+	return Probability(x)*(b-a) + a
+}
diff --git a/main_test.go b/main_test.go
@@ -1,4 +1,4 @@
-package goes
+package GoES
 
 import (
 	"bytes"
@@ -22,8 +22,11 @@ func cost_test(ince float64, iva_detratta float64) float64 {
 		pen := impo*perc_pay - ince_l
 		return abs2(pen)
 	}
-	mu, _ := DefaultOpt(cost, []float64{2 * ince}, []float64{ince / 10})
-	return mu[0]
+	sol, err := DefaultOpt(cost, []float64{ince * 0.9}, []float64{ince / 10})
+	if err != nil {
+		return math.NaN()
+	}
+	return sol.Mu[0]
 }
 
 func TestUni(t *testing.T) {
@@ -42,9 +45,14 @@ func TestUni(t *testing.T) {
 
 func TestBi(t *testing.T) {
 	muw := []float64{4, -3}
-	mu, sig := DefaultOpt(func(f []float64) float64 {
+	sol, err_opt := DefaultOpt(func(f []float64) float64 {
 		return abs2(f[0]-muw[0]) + 100.0*abs2(f[0]+f[1]-muw[0]-muw[1])
 	}, []float64{0.0, 0.0}, []float64{1.0, 1.0})
+	if err_opt != nil {
+		t.Error(err_opt)
+	}
+	mu := sol.Mu
+	sig := sol.Sigma
 	err := math.Sqrt(abs2((mu[0]-muw[0])/muw[0]) + abs2((mu[1]-muw[1])/muw[1]))
 	if err > 1e-6 {
 		t.Error("got: ", mu, sig, " wanted:", muw, " error:", err)
@@ -66,17 +74,21 @@ func TestVerbose(t *testing.T) {
 	}
 }
 
-func TestPanic(t *testing.T) {
-	defer func() {
-		if r := recover(); r == nil {
-			t.Errorf("The code did not panic")
-		}
-	}()
-
-	// The following is the code under test for panicking
-	DefaultOpt(
+func TestError(t *testing.T) {
+	_, err := DefaultOpt(
 		func(f []float64) float64 { return 0.0 },
 		[]float64{0.0, 0.0},
 		[]float64{1.0}, // here there is a missing element
 	)
+	if err == nil {
+		t.Error("Expected error")
+	}
+	_, err = DefaultOpt(
+		func(f []float64) float64 { return 0.0 },
+		[]float64{0.0, 0.0},
+		[]float64{1.0, 1.0}, // here there is a missing element
+	)
+	if err != nil {
+		t.Error("Expected success")
+	}
 }
diff --git a/readme.md b/readme.md
@@ -66,11 +66,10 @@ func main() {
 	cfg.Verbose = false
 
 	// Perform optimization
-	optimizedMu, _ := GoES.Opt(sphere, mu, sigma, cfg)
+	res, _ := GoES.Opt(sphere, mu, sigma, cfg)
 
-	fmt.Println("Optimum:", optimizedMu) // should be close to vector [0, 1, 2, ..., dim-1]
+	fmt.Println("Optimum:", res.Mu) // should be close to vector [0, 1, 2, ..., dim-1]
 }
-
 ```
 
 **Example 2: Default Optimization Config**
@@ -98,9 +97,9 @@ func main() {
 	mu := []float64{1.0, 2.0}    // Initial mean vector
 	sigma := []float64{0.5, 0.5} // Initial standard deviation vector
 
-	optimizedMu, optimizedSigma := GoES.DefaultOpt(myCustomFunction, mu, sigma)
+	res, _ := GoES.DefaultOpt(myCustomFunction, mu, sigma)
 
-	fmt.Println("Optimized mean:", optimizedMu)
-	fmt.Println("Optimized standard deviation:", optimizedSigma)
+	fmt.Println("Optimized mean:", res.Mu)
+	fmt.Println("Optimized standard deviation:", res.Sigma)
 }
 ```