Parametrisiert

Hansen_ES_CMA.py

@@ -9,15 +9,24 @@ def felli(x):
     return float(np.sum((1e6 ** exponents) * (x ** 2)))
 
 
-def escma():
+def escma(func, *, N=10, xmean=None, sigma=0.5, stopfitness=1e-10, stopeval=None,
-    #Initialization
+          func_args=(), func_kwargs=None, seed=None):
 
-    # User defined input parameters
+    if func_kwargs is None:
-    N = 10                       
+        func_kwargs = {}
-    xmean = np.random.rand(N)
+
-    sigma = 0.5
+    if seed is not None:
-    stopfitness = 1e-10
+        np.random.seed(seed)
-    stopeval = int(1e3 * N**2)
+
+    # Initialization (aus Parametern statt hart verdrahtet)
+    if xmean is None:
+        xmean = np.random.rand(N)
+    else:
+        xmean = np.asarray(xmean, dtype=float)
+        N = xmean.shape[0]
+
+    if stopeval is None:
+        stopeval = int(1e3 * N**2)
 
     # Strategy parameter setting: Selection
     lambda_ = 4 + int(np.floor(3 * np.log(N)))
@@ -46,7 +55,7 @@ def escma():
     eigeneval = 0
     chiN = np.sqrt(N) * (1 - 1/(4*N) + 1/(21 * N**2))
 
-    #Generation Loop
+    # Generation Loop
     counteval = 0
     arx = np.zeros((N, lambda_))
     arz = np.zeros((N, lambda_))
@@ -58,7 +67,7 @@ def escma():
         for k in range(lambda_):
             arz[:, k] = np.random.randn(N)
             arx[:, k] = xmean + sigma * (B @ D @ arz[:, k])
-            arfitness[k] = felli(arx[:, k])
+            arfitness[k] = float(func(arx[:, k], *func_args, **func_kwargs))  # <-- allgemein
             counteval += 1
 
         # Sort by fitness and compute weighted mean into xmean
@@ -107,13 +116,13 @@ def escma():
 
         print(f"{counteval}: {arfitness[0]}")
 
-    #Final Message
+    # Final Message
     print(f"{counteval}: {arfitness[0]}")
     xmin = arx[:, arindex[0]]
     return xmin
 
 
 if __name__ == "__main__":
-    xmin = escma()
+    xmin = escma(felli, N=10)  # <-- Zielfunktion wird übergeben
     print("Bestes gefundenes x:", xmin)
     print("f(xmin) =", felli(xmin))