Coverage for /builds/debichem-team/python-ase/ase/optimize/gpmin/prior.py : 74.29%
Hot-keys on this page
r m x p toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1import numpy as np
4class Prior():
5 """Base class for all priors for the bayesian optimizer.
7 The __init__ method and the prior method are implemented here.
8 Each child class should implement its own potential method, that will be
9 called by the prior method implemented here.
11 When used, the prior should be initialized outside the optimizer and the
12 Prior object should be passed as a function to the optimizer.
13 """
14 def __init__(self):
15 """Basic prior implementation."""
16 pass
18 def prior(self, x):
19 """Actual prior function, common to all Priors"""
20 if len(x.shape) > 1:
21 n = x.shape[0]
22 return np.hstack([self.potential(x[i, :]) for i in range(n)])
23 else:
24 return self.potential(x)
27class ZeroPrior(Prior):
28 """ZeroPrior object, consisting on a constant prior with 0eV energy."""
29 def __init__(self):
30 Prior.__init__(self)
32 def potential(self, x):
33 return np.zeros(x.shape[0]+1)
36class ConstantPrior(Prior):
37 """Constant prior, with energy = constant and zero forces
39 Parameters:
41 constant: energy value for the constant.
43 Example:
45 >>> from ase.optimize import GPMin
46 >>> from ase.optimize.gpmin.prior import ConstantPrior
47 >>> op = GPMin(atoms, Prior = ConstantPrior(10)
48 """
49 def __init__(self, constant):
50 self.constant = constant
51 Prior.__init__(self)
53 def potential(self, x):
54 d = x.shape[0]
55 output = np.zeros(d+1)
56 output[0] = self.constant
57 return output
59 def set_constant(self, constant):
60 self.constant = constant
63class CalculatorPrior(Prior):
64 """CalculatorPrior object, allows the user to
65 use another calculator as prior function instead of the
66 default constant.
68 Parameters:
70 atoms: the Atoms object
71 calculator: one of ASE's calculators
72 """
73 def __init__(self, atoms, calculator):
74 Prior.__init__(self)
75 self.atoms = atoms.copy()
76 self.atoms.calc = calculator
78 def potential(self, x):
79 self.atoms.set_positions(x.reshape(-1, 3))
80 V = self.atoms.get_potential_energy(force_consistent=True)
81 gradV = -self.atoms.get_forces().reshape(-1)
82 return np.append(np.array(V).reshape(-1), gradV)