R9490/plot.py
54e99150734emaster

plot.py
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			import numpy as np
			import matplotlib.pyplot as plt
			from matplotlib import cm
			from mpl_toolkits.mplot3d import Axes3D


			def plotminimizer(f,points,method):
			"""

			Plot the function f and the successive approximations of the minimizer


			Parameters
			----------
			f : scalar function of a vector of 2 variables.

			points : numpy ndarray
			2-D array of shape (number of iterations + 1, 2) containing the successive approximations
			for the solution obtained with the iterations

			method : strig
			e.g. "CG" for Conjigate gradient, "BFGS" for quasi-Newton method of Broyden,
			Fletcher, Goldfarb, and Shanno

			"""

			fig = plt.figure(1)
			ax = fig.gca(projection='3d')

			# Remove grid lines (dotted lines inside plot)
			ax.grid(False)




			# evaluating the functions at the points
			fp = np.zeros(points.shape[0])
			i = 0
			for p in points:
			fp[i] = f(p)
			i = i + 1

			# plotting points and lines for each point pair
			ax.plot(points[:,0], points[:,1], fp, 'ro--', linewidth=2, markersize=5)


			# Plot the surface
			X = np.linspace(-3, 3, 50)
			Y = np.linspace(-3, 3, 50)
			X, Y = np.meshgrid(X, Y)
			Z = np.zeros((X.shape[0], X.shape[1]))
			for i in range(X.shape[0]):
			for j in range(X.shape[1]):
			Z[i, j] = f(np.array([X[i, j], Y[i, j]]))

			ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,linewidth=0, antialiased=False, alpha=0.2)


			# Plot the contour lines
			ax.contour(X, Y, Z, colors="black")


			# Add title and axis names
			plt.title('Convergence of '+method+' method')
			plt.xlabel('x')
			plt.ylabel('y')

			plt.show()