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# -*- coding: utf-8 -*-
#========================================================================
# Copyright 2019 Science Technology Facilities Council
# Copyright 2019 University of Manchester
#
# This work is part of the Core Imaging Library developed by Science Technology
# Facilities Council and University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#=========================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from ccpi.optimisation.algorithms import Algorithm
class GradientDescent(Algorithm):
'''
Gradient Descent algorithm
'''
def __init__(self, **kwargs):
'''initialisation can be done at creation time if all
proper variables are passed or later with set_up'''
super(GradientDescent, self).__init__()
x_init = kwargs.get('x_init', None)
objective_function = kwargs.get('objective_function', None)
rate = kwargs.get('rate', None)
if x_init is not None and objective_function is not None and rate is not None:
print(self.__class__.__name__, "set_up called from creator")
self.set_up(x_init=x_init, objective_function=objective_function, rate=rate)
def should_stop(self):
'''stopping criterion, currently only based on number of iterations'''
return self.iteration >= self.max_iteration
def set_up(self, x_init, objective_function, rate):
'''initialisation of the algorithm'''
self.x = x_init.copy()
self.objective_function = objective_function
self.rate = rate
self.loss.append(objective_function(x_init))
self.iteration = 0
try:
self.memopt = self.objective_function.memopt
except AttributeError as ae:
self.memopt = False
if self.memopt:
self.x_update = x_init.copy()
self.configured = True
def update(self):
'''Single iteration'''
if self.memopt:
self.objective_function.gradient(self.x, out=self.x_update)
self.x_update *= -self.rate
self.x += self.x_update
else:
self.x += -self.rate * self.objective_function.gradient(self.x)
def update_objective(self):
self.loss.append(self.objective_function(self.x))
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