def test(): import numpy as np import plot as p; reload(p) data = np.random.rand(100,200); p.plot(data, 'test') reload(p) pts = np.random.rand(10000,3); p.plot3d(pts);
def test():
import numpy as np
import plot as p
reload(p)
data = np.random.rand(100, 200)
p.plot(data, 'test')
reload(p)
pts = np.random.rand(10000, 3)
p.plot3d(pts)
x += alpha * p
xs.append(x.copy())
z.append(S(x))
r -= alpha * Ap
rr1 = np.einsum('i,i', r, r)
if np.sqrt(rr1) < tol:
print('Found minimum, |r| = ', np.sqrt(rr1))
print('Minimum found in ', i, ' steps')
print('Minimum = ', x)
break
p = r + (rr1 / rr0) * p
rr0 = rr1
if i >= (max_steps + 1):
print('Reached maximum number of steps; exiting minimization')
print('|r| = ', np.sqrt(rr1))
print('x = ', x)
return np.stack(xs, axis=0), np.asarray(z)
# Build function to minimize
A = np.array([[4.0, 1.0], [1.0, 3.0]])
b = np.array([1.0, 2.0])
x0 = np.array([-3.0, -3.0])
# Minimize the function
xs, z = cg(A, b, x0)
# Plot solution and minimization steps
plot3d(xs[:, 0], xs[:, 1], z + 6, S2, grid_range=[-3.0, 3.0])
# Function to minimize
def f_to_minimize(coords):
x, y = coords
return 2 * x**2 + (3 / 2) * y**2 + x * y - x - 2 * y + 6
# Function to pass as callback
def getIterationSteps(solution):
global step
step += 1
x, y = solution
z = f_to_minimize(solution)
print("At step {:d}: x = {:f}, y = {:f}, f(x,y) = {:f}".format(
step, x, y, z))
x_step.append(x)
y_step.append(y)
z_step.append(z)
# Minimize
step = 0
x_step = []
y_step = []
z_step = []
OptRes = minimize(f_to_minimize, (-3., -3.),
method='CG',
tol=1.e-10,
callback=getIterationSteps)
# Plot
plot.plot3d(x_step, y_step, z_step, f_to_minimize)