From e83cec371a406ff48903a1fa07f4ba8cbac5f309 Mon Sep 17 00:00:00 2001
From: Michele <m.volpato@cs.ru.nl>
Date: Fri, 8 Jan 2016 15:33:34 +0100
Subject: [PATCH] added figure 2.5 from thesis

---
 CHANGELOG.md                 |   1 +
 README.md                    |   2 +-
 examples/figure2-5_thesis.py | 103 +++++++++++++++++++++++++++++++++++
 3 files changed, 105 insertions(+), 1 deletion(-)
 create mode 100644 examples/figure2-5_thesis.py

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 85f15b7..629a0d1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,7 @@ This project adheres to [Semantic Versioning](http://semver.org/).
 
 ### Changed
 - License
+- Name: new name is Alnos
 
 ## [v0.2.0] - 2015-10-27
 ### Added
diff --git a/README.md b/README.md
index 0e4a73c..b63cd3c 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 ## Synopsis
 
-The active-learning-nondeterministic-systems is an implementation of an
+**Alnos** is an implementation of an
 adaptation of
 [L*](http://www.cs.berkeley.edu/~dawnsong/teaching/s10/papers/angluin87.pdf) to
 nondeterministic systems. The code is based on these scientific papers:
diff --git a/examples/figure2-5_thesis.py b/examples/figure2-5_thesis.py
new file mode 100644
index 0000000..91e853d
--- /dev/null
+++ b/examples/figure2-5_thesis.py
@@ -0,0 +1,103 @@
+# Copyright (c) 2015 Michele Volpato
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+
+# This file is used to learn the IOTS depicted in Figure 2.5 of my PhD thesis.
+
+import random
+
+seed = output = random.sample(range(99999999), 1)[0]
+print(seed)
+random.seed(81077353)   # 81077353
+
+import os, inspect, sys
+# Include project dir in path
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.append(parentdir)
+from learning.learning import LearningAlgorithm
+from teachers.ltsteachers import InputOutputTeacher
+from systems.implementations import InputOutputLTS
+from teachers.ltsoracles import InputOutputPowerOracle
+import logging
+import helpers.bisimulation as bi
+from testing.randomtesting import RandomTester
+from systems.iopurpose import InputPurpose, OutputPurpose
+import helpers.graphhelper as gh
+
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+inputs = set(['b'])
+outputs = set(['t','c'])
+quiescence = 'd'
+
+I1=InputOutputLTS(8, inputs, outputs, quiescence)
+I1.addTransition(0,'b',1)
+I1.addTransition(0,'b',2)
+I1.addTransition(1,'t',3)
+I1.addTransition(1,'c',3)
+I1.addTransition(1,'b',6)
+I1.addTransition(2,'b',4)
+I1.addTransition(3,'t',0)
+I1.addTransition(3,'c',0)
+I1.addTransition(3,'b',6)
+I1.addTransition(4,'c',5)
+I1.addTransition(4,'b',6)
+I1.addTransition(5,'b',6)
+I1.addTransition(5,'t',0)
+I1.addTransition(5,'c',0)
+
+# Chaos
+I1.addTransition(6,'b',6)
+I1.addTransition(6,'t',6)
+I1.addTransition(6,'c',6)
+I1.addTransition(6,'d',7)
+I1.addTransition(7,'b',6)
+
+
+I1.makeInputEnabled()
+
+T1 = InputOutputTeacher(I1)
+O1 = InputOutputPowerOracle(I1)
+
+outputExpert = OutputPurpose(set(['t','c', quiescence]))
+inputExpert = InputPurpose(set(['b']))
+
+tester = RandomTester(T1, 10000, 50)
+
+currentdir = os.path.dirname(os.path.abspath(
+                inspect.getfile(inspect.currentframe())))
+
+path = os.path.join(currentdir, "dotFiles")
+gh.createDOTFile(I1, path + "figure2-5", "pdf")
+
+print("Starting learning...")
+
+# change printPath=None to printPath=path for dot files
+L2 = LearningAlgorithm(T1, O1, printPath=path, maxLoops=4,
+    tablePreciseness=10000, logger=logger, tester=tester, outputPurpose=outputExpert,
+    inputPurpose=inputExpert)
+minus, plus = L2.run()
+
+print("Models learned. Check language equivalence...")
+
+print("hMinus bisimilar to target: " + str(bi.bisimilar(I1,minus)))
+print("hPlus bisimilar to target: " + str(bi.bisimilar(I1,plus)))
-- 
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