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author | Edoardo Pasca <edo.paskino@gmail.com> | 2019-04-01 17:40:08 +0100 |
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committer | Edoardo Pasca <edo.paskino@gmail.com> | 2019-04-01 17:40:08 +0100 |
commit | 47e743cf3ff3474b516d492b0c5b3d47d4b73848 (patch) | |
tree | 1af4b3dff5305a2699421637d895cea0eb6d0456 | |
parent | aaa4eae2f43df1a1ed3c15ba2dacdc4dce5a43d6 (diff) | |
download | framework-47e743cf3ff3474b516d492b0c5b3d47d4b73848.tar.gz framework-47e743cf3ff3474b516d492b0c5b3d47d4b73848.tar.bz2 framework-47e743cf3ff3474b516d492b0c5b3d47d4b73848.tar.xz framework-47e743cf3ff3474b516d492b0c5b3d47d4b73848.zip |
python2.7 fixes
-rw-r--r-- | Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py | 25 | ||||
-rw-r--r-- | Wrappers/Python/test/test_functions.py | 6 |
2 files changed, 20 insertions, 11 deletions
diff --git a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py index 5489d92..597d4d8 100644 --- a/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py +++ b/Wrappers/Python/ccpi/optimisation/functions/L2NormSquared.py @@ -1,12 +1,21 @@ # -*- coding: utf-8 -*- +# This work is part of the Core Imaging Library developed by +# Visual Analytics and Imaging System Group of the Science Technology +# Facilities Council, STFC -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 7 13:10:56 2019 +# Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca + +# 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 -@author: evangelos -""" +# 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. import numpy from ccpi.optimisation.functions import Function @@ -75,10 +84,10 @@ class L2NormSquared(Function): if out is None: # FIXME: this is a number - return (1/4) * x.squared_norm() + tmp + return (1./4.) * x.squared_norm() + tmp else: # FIXME: this is a DataContainer - out.fill((1/4) * x.squared_norm() + tmp) + out.fill((1./4.) * x.squared_norm() + tmp) def proximal(self, x, tau, out = None): diff --git a/Wrappers/Python/test/test_functions.py b/Wrappers/Python/test/test_functions.py index 3e5f26f..54dfa57 100644 --- a/Wrappers/Python/test/test_functions.py +++ b/Wrappers/Python/test/test_functions.py @@ -62,7 +62,7 @@ class TestFunction(unittest.TestCase): self.assertEqual(a2, g(d)) # Compare convex conjugate of g - a3 = 0.5 * d.power(2).sum() + (d*noisy_data).sum() + a3 = 0.5 * d.squared_norm() + d.dot(noisy_data) self.assertEqual(a3, g.convex_conjugate(d)) #print( a3, g.convex_conjugate(d)) @@ -91,12 +91,12 @@ class TestFunction(unittest.TestCase): #check convex conjuagate no data c1 = f.convex_conjugate(u) - c2 = 1/4 * u.squared_norm() + c2 = 1/4. * u.squared_norm() numpy.testing.assert_equal(c1, c2) #check convex conjuagate with data d1 = f1.convex_conjugate(u) - d2 = (1/4) * u.squared_norm() + (u*b).sum() + d2 = (1./4.) * u.squared_norm() + (u*b).sum() numpy.testing.assert_equal(d1, d2) # check proximal no data |