From 6c9aef3e84965fa66f98386eb5e9316f1c0f07bc Mon Sep 17 00:00:00 2001
From: Tong <st01330@otter85.eps.surrey.ac.uk>
Date: Fri, 7 Feb 2025 11:44:48 +0000
Subject: [PATCH] Test
---
Q1sol-Copy.py | 88 +++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 88 insertions(+)
create mode 100644 Q1sol-Copy.py
diff --git a/Q1sol-Copy.py b/Q1sol-Copy.py
new file mode 100644
index 0000000..2ea69ce
--- /dev/null
+++ b/Q1sol-Copy.py
@@ -0,0 +1,88 @@
+from pylab import *
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+from mpl_toolkits.mplot3d.axes3d import Axes3D
+from numpy import asarray
+from numpy import exp
+from numpy.random import randn
+from numpy.random import rand
+from numpy.random import seed
+xlist=[]
+ylist=[]
+zlist=[]
+xrange = np.linspace(1.1, 5, 100)
+yrange = np.linspace(1.1, 5, 100)
+X,Y = np.meshgrid(xrange, yrange)
+
+# objective function
+def objective(x1,x2):
+ return x1+x2**2/(x1*x2-1)
+
+Z = objective(X, Y)
+
+
+
+# simulated annealing algorithm
+def simulated_annealing(objective, bounds, n_iterations, step_size, temp):
+ # generate an initial point
+ x_cb = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
+ x_cb=[5,5]
+ # evaluate the initial point
+ Jx_cb = objective(x_cb[0],x_cb[1])
+ # current working solution
+ current, Jcurrent = x_cb, Jx_cb
+ scores = list()
+ # run the algorithm
+ for i in range(n_iterations):
+ # calculate temperature for current epoch
+ t = temp / float(i + 1)
+ # perturb x
+ x_per = current + randn(len(bounds)) * step_size
+ # evaluate x_per
+ Jx_per = objective(x_per[0],x_per[1])
+ # check for new x_cb solution
+ if Jx_per < Jx_cb:
+ # store new x_cb point
+ x_cb, Jx_cb = x_per, Jx_per
+ # keep track of scores
+ scores.append(Jx_cb)
+ xlist.append(x_cb[0])
+ ylist.append(x_cb[1])
+ zlist.append(Jx_cb)
+ # report progress
+ print('>%d f(%s) = %.5f' % (i, x_cb, Jx_cb))
+ # difference between x_per and current point evaluation
+ diff = Jx_per - Jcurrent
+ # check if we should keep the new point
+ if diff < 0 or rand() < exp(-diff / t):
+ # store the new current point
+ current, Jcurrent = x_per, Jx_per
+ return [x_cb, Jx_cb, scores]
+
+
+# define range for input
+bounds = asarray([[1.1, 5.0],[1.1,5.0]])
+# define the total iterations
+n_iterations = 1000
+# define the maximum step size
+step_size = 0.1
+# initial temperature
+temp = 000
+# perform the simulated annealing search
+x_cb, score, scores = simulated_annealing(objective, bounds, n_iterations, step_size, temp)
+print('Done!')
+print('f(%s) = %f' % (x_cb, score))
+# line plot of x_cb scores
+pyplot.plot(scores, '.-')
+pyplot.xlabel('Improvement')
+pyplot.ylabel('J(x)')
+pyplot.show()
+
+fig = plt.figure(figsize=(26,6))
+
+# surface_plot with color grading and color bar
+ax = fig.add_subplot(1, 2, 1, projection='3d')
+p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=matplotlib.cm.coolwarm, linewidth=0, antialiased=False, zorder=0)
+ax.plot3D(xlist, ylist,zlist, color="k", marker='o', zorder=10)
+ax.view_init(80,30)
\ No newline at end of file
--
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