energy_operators.py 6.21 KB
Newer Older
Martin Reinecke's avatar
Martin Reinecke committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

from __future__ import absolute_import, division, print_function

Philipp Arras's avatar
Philipp Arras committed
21
from .. import utilities
Martin Reinecke's avatar
Martin Reinecke committed
22
23
from ..compat import *
from ..domain_tuple import DomainTuple
Philipp Arras's avatar
Philipp Arras committed
24
25
from ..field import Field
from ..linearization import Linearization
Martin Reinecke's avatar
Martin Reinecke committed
26
from ..sugar import makeOp, makeDomain
Martin Reinecke's avatar
Martin Reinecke committed
27
from .operator import Operator
Martin Reinecke's avatar
fix    
Martin Reinecke committed
28
from .sampling_enabler import SamplingEnabler
Philipp Arras's avatar
Philipp Arras committed
29
from .sandwich_operator import SandwichOperator
Martin Reinecke's avatar
Martin Reinecke committed
30
from .simple_linear_operators import VdotOperator
Martin Reinecke's avatar
Martin Reinecke committed
31
32
33
34
35
36
37
38
39
40
41


class EnergyOperator(Operator):
    _target = DomainTuple.scalar_domain()


class SquaredNormOperator(EnergyOperator):
    def __init__(self, domain):
        self._domain = domain

    def apply(self, x):
42
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
43
        if isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
44
            val = Field.scalar(x.val.vdot(x.val))
Martin Reinecke's avatar
Martin Reinecke committed
45
            jac = VdotOperator(2*x.val)(x.jac)
46
            return x.new(val, jac)
Martin Reinecke's avatar
Martin Reinecke committed
47
        return Field.scalar(x.vdot(x))
Martin Reinecke's avatar
Martin Reinecke committed
48
49
50
51
52
53
54
55


class QuadraticFormOperator(EnergyOperator):
    def __init__(self, op):
        from .endomorphic_operator import EndomorphicOperator
        if not isinstance(op, EndomorphicOperator):
            raise TypeError("op must be an EndomorphicOperator")
        self._op = op
Martin Reinecke's avatar
Martin Reinecke committed
56
        self._domain = op.domain
Martin Reinecke's avatar
Martin Reinecke committed
57
58

    def apply(self, x):
59
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
60
        if isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
61
62
            t1 = self._op(x.val)
            jac = VdotOperator(t1)(x.jac)
Martin Reinecke's avatar
Martin Reinecke committed
63
            val = Field.scalar(0.5*x.val.vdot(t1))
64
            return x.new(val, jac)
Martin Reinecke's avatar
Martin Reinecke committed
65
        return Field.scalar(0.5*x.vdot(self._op(x)))
Martin Reinecke's avatar
Martin Reinecke committed
66
67
68
69
70
71
72
73
74
75
76
77
78
79


class GaussianEnergy(EnergyOperator):
    def __init__(self, mean=None, covariance=None, domain=None):
        self._domain = None
        if mean is not None:
            self._checkEquivalence(mean.domain)
        if covariance is not None:
            self._checkEquivalence(covariance.domain)
        if domain is not None:
            self._checkEquivalence(domain)
        if self._domain is None:
            raise ValueError("no domain given")
        self._mean = mean
Martin Reinecke's avatar
Martin Reinecke committed
80
81
82
83
        if covariance is None:
            self._op = SquaredNormOperator(self._domain).scale(0.5)
        else:
            self._op = QuadraticFormOperator(covariance.inverse)
Martin Reinecke's avatar
Martin Reinecke committed
84
85
86
        self._icov = None if covariance is None else covariance.inverse

    def _checkEquivalence(self, newdom):
Martin Reinecke's avatar
fix    
Martin Reinecke committed
87
        newdom = makeDomain(newdom)
Martin Reinecke's avatar
Martin Reinecke committed
88
        if self._domain is None:
Philipp Arras's avatar
Philipp Arras committed
89
            self._domain = newdom
Martin Reinecke's avatar
Martin Reinecke committed
90
        else:
Philipp Arras's avatar
Philipp Arras committed
91
            if self._domain != newdom:
Martin Reinecke's avatar
Martin Reinecke committed
92
93
94
                raise ValueError("domain mismatch")

    def apply(self, x):
95
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
96
        residual = x if self._mean is None else x-self._mean
Philipp Arras's avatar
Changes    
Philipp Arras committed
97
        res = self._op(residual).real
98
        if not isinstance(x, Linearization) or not x.want_metric:
Martin Reinecke's avatar
Martin Reinecke committed
99
100
101
102
103
104
            return res
        metric = SandwichOperator.make(x.jac, self._icov)
        return res.add_metric(metric)


class PoissonianEnergy(EnergyOperator):
105
106
107
    def __init__(self, d):
        self._d = d
        self._domain = DomainTuple.make(d.domain)
Martin Reinecke's avatar
Martin Reinecke committed
108
109

    def apply(self, x):
110
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
111
112
        res = x.sum() - x.log().vdot(self._d)
        if not isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
113
            return Field.scalar(res)
114
115
        if not x.want_metric:
            return res
Martin Reinecke's avatar
Martin Reinecke committed
116
117
118
        metric = SandwichOperator.make(x.jac, makeOp(1./x.val))
        return res.add_metric(metric)

119

120
class InverseGammaLikelihood(EnergyOperator):
121
122
123
    def __init__(self, d):
        self._d = d
        self._domain = DomainTuple.make(d.domain)
124
125

    def apply(self, x):
126
        self._check_input(x)
Philipp Frank's avatar
Philipp Frank committed
127
        res = 0.5*(x.log().sum() + (1./x).vdot(self._d))
128
129
        if not isinstance(x, Linearization):
            return Field.scalar(res)
130
131
        if not x.want_metric:
            return res
132
133
134
135
        metric = SandwichOperator.make(x.jac, makeOp(0.5/(x.val**2)))
        return res.add_metric(metric)


Martin Reinecke's avatar
Martin Reinecke committed
136
class BernoulliEnergy(EnergyOperator):
137
    def __init__(self, d):
Martin Reinecke's avatar
Martin Reinecke committed
138
        self._d = d
139
        self._domain = DomainTuple.make(d.domain)
Martin Reinecke's avatar
Martin Reinecke committed
140
141

    def apply(self, x):
142
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
143
144
        v = x.log().vdot(-self._d) - (1.-x).log().vdot(1.-self._d)
        if not isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
145
            return Field.scalar(v)
146
147
        if not x.want_metric:
            return v
Martin Reinecke's avatar
Martin Reinecke committed
148
149
150
151
152
153
154
155
156
157
        met = makeOp(1./(x.val*(1.-x.val)))
        met = SandwichOperator.make(x.jac, met)
        return v.add_metric(met)


class Hamiltonian(EnergyOperator):
    def __init__(self, lh, ic_samp=None):
        self._lh = lh
        self._prior = GaussianEnergy(domain=lh.domain)
        self._ic_samp = ic_samp
Martin Reinecke's avatar
Martin Reinecke committed
158
        self._domain = lh.domain
Martin Reinecke's avatar
Martin Reinecke committed
159
160

    def apply(self, x):
161
        self._check_input(x)
162
163
        if (self._ic_samp is None or not isinstance(x, Linearization) or
                not x.want_metric):
Martin Reinecke's avatar
cleanup    
Martin Reinecke committed
164
            return self._lh(x)+self._prior(x)
Martin Reinecke's avatar
Martin Reinecke committed
165
        else:
166
            lhx, prx = self._lh(x), self._prior(x)
Martin Reinecke's avatar
Martin Reinecke committed
167
168
169
170
171
172
173
174
175
176
177
178
179
            mtr = SamplingEnabler(lhx.metric, prx.metric.inverse,
                                  self._ic_samp, prx.metric.inverse)
            return (lhx+prx).add_metric(mtr)


class SampledKullbachLeiblerDivergence(EnergyOperator):
    def __init__(self, h, res_samples):
        """
        # MR FIXME: does h have to be a Hamiltonian? Couldn't it be any energy?
        h: Hamiltonian
        N: Number of samples to be used
        """
        self._h = h
Martin Reinecke's avatar
Martin Reinecke committed
180
        self._domain = h.domain
Martin Reinecke's avatar
Martin Reinecke committed
181
182
183
        self._res_samples = tuple(res_samples)

    def apply(self, x):
184
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
185
186
        mymap = map(lambda v: self._h(x+v), self._res_samples)
        return utilities.my_sum(mymap) * (1./len(self._res_samples))