Hill-climbing optimisation

import numpy as np
import matplotlib.pyplot as plt
from plotutils import *


x = np.linspace(-2.0, 2.0, 1000)
y = np.linspace(-2.0, 2.0, 1000)
X, Y = np.meshgrid(x, y)

Z = np.exp(-(X**2 + Y**2))

fig,ax=plt.subplots(1,1)
cp = ax.contourf(X, Y, Z, cmap=cmaps[1])

ax.set_title('f(x,y)')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()

from gradient_free_optimizers import HillClimbingOptimizer

search_space = {
  "x": x,
  "y": y,
}

opt = HillClimbingOptimizer(search_space)

def f(pos):
    x = pos["x"]
    y = pos["y"]
    z = np.exp(-(x**2 + y**2))
    return z

result = opt.search(f, 
                    n_iter=30000, 
                    verbosity=['print_times'], 
                    random_state=23)

   Evaluation time   : 0.8271605968475342 sec    [33.25 %]
   Optimization time : 1.6604328155517578 sec    [66.75 %]
   Iteration time    : 2.487593412399292 sec    [12059.85 iter/sec]

opt.results

x = np.linspace(-1.0, 3.0, 1000)
y = np.linspace(-1.0, 3.0, 1000)
X, Y = np.meshgrid(x, y)

Z = np.exp(-(X**2 + Y**2))+2*np.exp(-((X-1.7)**2+(Y-1.7)**2))

fig,ax=plt.subplots(1,1)
cp = ax.contourf(X, Y, Z, cmap=cmaps[1])

ax.set_title('f(x,y)')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()

opt = HillClimbingOptimizer(search_space)

def f(pos):
    x = pos["x"]
    y = pos["y"]
    z = np.exp(-(x**2 + y**2))+2*np.exp(-((x-1.7)**2+(y-1.7)**2))
    return z

result = opt.search(f, 
                    n_iter=30000, 
                    verbosity=['print_times'], 
                    random_state=23)

   Evaluation time   : 0.9191639423370361 sec    [35.42 %]
   Optimization time : 1.6756830215454102 sec    [64.58 %]
   Iteration time    : 2.5948469638824463 sec    [11561.38 iter/sec]

opt.results