energy_operators.py 6.34 KB
Newer Older
Martin Reinecke's avatar
Martin Reinecke committed
1
2
3
4
5
6
7
8
9
10
11
12
13
# 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/>.
#
14
# Copyright(C) 2013-2019 Max-Planck-Society
Martin Reinecke's avatar
Martin Reinecke committed
15
#
16
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
Martin Reinecke's avatar
Martin Reinecke committed
17

Philipp Arras's avatar
Philipp Arras committed
18
from .. import utilities
Martin Reinecke's avatar
Martin Reinecke committed
19
from ..domain_tuple import DomainTuple
Philipp Arras's avatar
Philipp Arras committed
20
21
from ..field import Field
from ..linearization import Linearization
Martin Reinecke's avatar
Martin Reinecke committed
22
from ..sugar import makeOp, makeDomain
Martin Reinecke's avatar
Martin Reinecke committed
23
from .operator import Operator
Martin Reinecke's avatar
fix    
Martin Reinecke committed
24
from .sampling_enabler import SamplingEnabler
Philipp Arras's avatar
Philipp Arras committed
25
from .sandwich_operator import SandwichOperator
Martin Reinecke's avatar
Martin Reinecke committed
26
from .simple_linear_operators import VdotOperator
Martin Reinecke's avatar
Martin Reinecke committed
27
28
29


class EnergyOperator(Operator):
Philipp Arras's avatar
Philipp Arras committed
30
    """Operator which has a scalar domain as target domain."""
Martin Reinecke's avatar
Martin Reinecke committed
31
32
33
34
35
36
37
38
    _target = DomainTuple.scalar_domain()


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

    def apply(self, x):
39
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
40
        if isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
41
            val = Field.scalar(x.val.vdot(x.val))
Martin Reinecke's avatar
Martin Reinecke committed
42
            jac = VdotOperator(2*x.val)(x.jac)
43
            return x.new(val, jac)
Martin Reinecke's avatar
Martin Reinecke committed
44
        return Field.scalar(x.vdot(x))
Martin Reinecke's avatar
Martin Reinecke committed
45
46
47
48
49
50
51
52


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
53
        self._domain = op.domain
Martin Reinecke's avatar
Martin Reinecke committed
54
55

    def apply(self, x):
56
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
57
        if isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
58
59
            t1 = self._op(x.val)
            jac = VdotOperator(t1)(x.jac)
Martin Reinecke's avatar
Martin Reinecke committed
60
            val = Field.scalar(0.5*x.val.vdot(t1))
61
            return x.new(val, jac)
Martin Reinecke's avatar
Martin Reinecke committed
62
        return Field.scalar(0.5*x.vdot(self._op(x)))
Martin Reinecke's avatar
Martin Reinecke committed
63
64
65
66
67
68
69
70
71
72
73
74
75
76


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
77
78
79
80
        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
81
82
83
        self._icov = None if covariance is None else covariance.inverse

    def _checkEquivalence(self, newdom):
Martin Reinecke's avatar
fix    
Martin Reinecke committed
84
        newdom = makeDomain(newdom)
Martin Reinecke's avatar
Martin Reinecke committed
85
        if self._domain is None:
Philipp Arras's avatar
Philipp Arras committed
86
            self._domain = newdom
Martin Reinecke's avatar
Martin Reinecke committed
87
        else:
Philipp Arras's avatar
Philipp Arras committed
88
            if self._domain != newdom:
Martin Reinecke's avatar
Martin Reinecke committed
89
90
91
                raise ValueError("domain mismatch")

    def apply(self, x):
92
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
93
        residual = x if self._mean is None else x-self._mean
Philipp Arras's avatar
Changes    
Philipp Arras committed
94
        res = self._op(residual).real
95
        if not isinstance(x, Linearization) or not x.want_metric:
Martin Reinecke's avatar
Martin Reinecke committed
96
97
98
99
100
101
            return res
        metric = SandwichOperator.make(x.jac, self._icov)
        return res.add_metric(metric)


class PoissonianEnergy(EnergyOperator):
102
103
104
    def __init__(self, d):
        self._d = d
        self._domain = DomainTuple.make(d.domain)
Martin Reinecke's avatar
Martin Reinecke committed
105
106

    def apply(self, x):
107
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
108
109
        res = x.sum() - x.log().vdot(self._d)
        if not isinstance(x, Linearization):
Martin Reinecke's avatar
Martin Reinecke committed
110
            return Field.scalar(res)
111
112
        if not x.want_metric:
            return res
Martin Reinecke's avatar
Martin Reinecke committed
113
114
115
        metric = SandwichOperator.make(x.jac, makeOp(1./x.val))
        return res.add_metric(metric)

116

117
class InverseGammaLikelihood(EnergyOperator):
118
119
120
    def __init__(self, d):
        self._d = d
        self._domain = DomainTuple.make(d.domain)
121
122

    def apply(self, x):
123
        self._check_input(x)
Philipp Frank's avatar
Philipp Frank committed
124
        res = 0.5*(x.log().sum() + (1./x).vdot(self._d))
125
126
        if not isinstance(x, Linearization):
            return Field.scalar(res)
127
128
        if not x.want_metric:
            return res
129
130
131
132
        metric = SandwichOperator.make(x.jac, makeOp(0.5/(x.val**2)))
        return res.add_metric(metric)


Martin Reinecke's avatar
Martin Reinecke committed
133
class BernoulliEnergy(EnergyOperator):
134
    def __init__(self, d):
Martin Reinecke's avatar
Martin Reinecke committed
135
        self._d = d
136
        self._domain = DomainTuple.make(d.domain)
Martin Reinecke's avatar
Martin Reinecke committed
137
138

    def apply(self, x):
139
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
140
141
        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
142
            return Field.scalar(v)
143
144
        if not x.want_metric:
            return v
Martin Reinecke's avatar
Martin Reinecke committed
145
146
147
148
149
150
151
152
153
154
        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
155
        self._domain = lh.domain
Martin Reinecke's avatar
Martin Reinecke committed
156
157

    def apply(self, x):
158
        self._check_input(x)
159
160
        if (self._ic_samp is None or not isinstance(x, Linearization) or
                not x.want_metric):
Martin Reinecke's avatar
cleanup    
Martin Reinecke committed
161
            return self._lh(x)+self._prior(x)
Martin Reinecke's avatar
Martin Reinecke committed
162
        else:
163
            lhx, prx = self._lh(x), self._prior(x)
Martin Reinecke's avatar
Martin Reinecke committed
164
165
166
167
            mtr = SamplingEnabler(lhx.metric, prx.metric.inverse,
                                  self._ic_samp, prx.metric.inverse)
            return (lhx+prx).add_metric(mtr)

Philipp Arras's avatar
Philipp Arras committed
168
169
170
171
172
    def __repr__(self):
        subs = 'Likelihood:\n{}'.format(utilities.indent(self._lh.__repr__()))
        subs += '\nPrior: Quadratic{}'.format(self._lh.domain.keys())
        return 'Hamiltonian:\n' + utilities.indent(subs)

Martin Reinecke's avatar
Martin Reinecke committed
173
174
175
176
177
178
179
180
181

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
182
        self._domain = h.domain
Martin Reinecke's avatar
Martin Reinecke committed
183
184
185
        self._res_samples = tuple(res_samples)

    def apply(self, x):
186
        self._check_input(x)
Martin Reinecke's avatar
Martin Reinecke committed
187
188
        mymap = map(lambda v: self._h(x+v), self._res_samples)
        return utilities.my_sum(mymap) * (1./len(self._res_samples))