Delete Q1sol-Copy.py

Q1sol-Copy.py

-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)
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