def test_initial_guess3():
x0 = [-1, 1]
def f(args):
[x, y] = args
ans = 100 * (y - x**2)**2 + (1 - x)**2
return ans
demo = SteepestDescent(f, x0)
root_est = demo.find_root()
real_root = np.array([1., 1.])
assert real_root.all() == root_est.all()
def __init__(self):
self.current = Board()
self.best = Board()
random.seed()
self.k = 1
self.k_max = None
self.max_score = None
self.N = 0
self.local_search = SteepestDescent()
self.chances = {
Tile.CENTRAL: 0.0,
Tile.BORDER: 0.0,
Tile.CORNER: 0.0,
}
self.prob_map = {
Tile.CENTRAL: 0.0,
Tile.BORDER: 0.0,
Tile.CORNER: 0.0,
}
self.total = 25.0
from modeller import * from modeller.optimizers import actions from modeller.scripts import complete_pdb # Load our custom steepest descent optimizer from steepest_descent import SteepestDescent env = environ() env.io.atom_files_directory = ['../atom_files'] env.libs.topology.read(file='$(LIB)/top_heav.lib') env.libs.parameters.read(file='$(LIB)/par.lib') # Read in the initial structure: code = '1fdn' mdl = complete_pdb(env, code) atmsel = selection(mdl) # Generate the restraints: mdl.restraints.make(atmsel, restraint_type='stereo', spline_on_site=False) # Optimize with our custom optimizer: opt = SteepestDescent(max_iterations=80) opt.optimize(atmsel, actions=actions.trace(5))
import AnnoDomini.AutoDiff as AD
import numpy as np
from matplotlib import pyplot as plt
from scipy import linalg as la
from steepest_descent import SteepestDescent
from matplotlib import pyplot as plt
def f(args):
[x, y] = args
ans = 100 * (y - x**2)**2 + (1 - x)**2
return ans
x0 = [2, 1]
sd = SteepestDescent(f, x0)
root = sd.find_root()
traj = sd.xs
x = [traj[i][0] for i in range(len(traj))]
y = [traj[i][1] for i in range(len(traj))]
traj2 = np.concatenate(
(np.array(x).reshape(-1, 1), np.array(y).reshape(-1, 1)), axis=1)
# print(root)
# print(traj2)
def plot_descent(ans):
X, Y = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-2, 8, 100))
Z = f(np.array([X, Y]))
fig = plt.subplots(1, 1, figsize=(10, 7))
