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# 

# LSST Data Management System 

# 

# Copyright 2008-2016 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# 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 LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <https://www.lsstcorp.org/LegalNotices/>. 

# 

import unittest 

import numpy 

 

import lsst.utils.tests 

import lsst.log 

import lsst.log.utils 

import lsst.meas.modelfit 

 

# Set trace to 0-5 to view debug messages. Level 5 enables all traces. 

lsst.log.utils.traceSetAt("meas.modelfit.optimizer", 5) 

log = lsst.log.Log.getLogger("meas.modelfit.optimizer") 

 

 

class OptimizerTestCase(lsst.utils.tests.TestCase): 

 

def setUp(self): 

numpy.random.seed(500) 

 

def testTrustRegionSolver(self): 

tolerance = 1E-6 

# start with some positive definite matrices, constructed from random least-squares problems 

log.info("Testing solveTrustRegion with positive-definite matrices") 

m = numpy.random.randn(30, 5) 

y = numpy.random.randn(30) 

f = numpy.dot(m.transpose(), m) 

g = numpy.dot(m.transpose(), y) 

x = numpy.zeros(5) 

for r in numpy.linspace(1E-3, 0.8, 5): 

lsst.meas.modelfit.solveTrustRegion(x, f, g, r, tolerance) 

self.assertLessEqual(numpy.linalg.norm(x), r * (1.0 + tolerance)) 

# now we try some matrices with zero eigenvalues due to model degeneracies 

log.info("Testing solveTrustRegion with positive-semidefinite matrices") 

m[:, -1] = m[:, 0] 

f = numpy.dot(m.transpose(), m) 

g = numpy.dot(m.transpose(), y) 

for r in numpy.linspace(1E-3, 0.8, 5): 

lsst.meas.modelfit.solveTrustRegion(x, f, g, r, tolerance) 

self.assertLessEqual(numpy.linalg.norm(x), r * (1.0 + tolerance)) 

m[:, -2] = m[:, 1] 

f = numpy.dot(m.transpose(), m) 

g = numpy.dot(m.transpose(), y) 

for r in numpy.linspace(1E-3, 0.8, 5): 

lsst.meas.modelfit.solveTrustRegion(x, f, g, r, tolerance) 

self.assertLessEqual(numpy.linalg.norm(x), r * (1.0 + tolerance)) 

log.info("Testing solveTrustRegion with indefinite matrices") 

for i in range(3): 

m = numpy.random.randn(5, 5) 

f = m + m.transpose() 

g = numpy.random.randn(5) 

for r in numpy.linspace(1E-3, 0.8, 5): 

lsst.meas.modelfit.solveTrustRegion(x, f, g, r, tolerance) 

self.assertLessEqual(numpy.linalg.norm(x), r * (1.0 + tolerance)) 

 

 

class TestMemory(lsst.utils.tests.MemoryTestCase): 

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

85 ↛ 86line 85 didn't jump to line 86, because the condition on line 85 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()