Initial commit

AI_A_Modern_Approach_Russell_Norvig/LICENSE.txt

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+Copyright (c) 2012 Juan Pedro Fisanotti <[email protected]>, Rafael Carrascosa <[email protected]>, Santiago Romero <[email protected]>
+
+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.

AI_A_Modern_Approach_Russell_Norvig/PKG-INFO

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+Metadata-Version: 1.1
+Name: simpleai
+Version: 0.5.3
+Summary: An implementation of AI algorithms based on aima-python
+Home-page: http://github.com/simpleai-team/simpleai
+Author: Juan Pedro Fisanotti
+Author-email: [email protected]
+License: LICENSE.txt
+Description: Simple AI
+        =========
+
+        Project home: http://github.com/simpleai-team/simpleai
+
+        This lib implements many of the artificial intelligence algorithms described on the book "Artificial Ingelligence, a Modern Approach", from Stuart Russel and Peter Norvig. We strongly recommend you to read the book, or at least the introductory chapters and the ones related to the components you want to use, because we won't explain the algorithms here.
+
+        This implementation takes some of the ideas from the Norvig's implementation (the `aima-python <https://code.google.com/p/aima-python/>`_ lib), but it's made with a more "pythonic" approach, and more emphasis on creating a stable, modern, and mantenible version. We are testing the majority of the lib, it's available via pip install, has a standar repo and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.
+
+        At this moment, the implementation includes:
+
+        * Search
+            * Traditional search algorithms (not informed and informed)
+            * Local Search algorithms
+            * Constraint Satisfaction Problems algorithms
+        * Machine Learning
+            * Statistical Classification 
+
+        And we are working on an interactive execution viewer for search algorithms (display the search tree on each iteration).
+
+
+        Installation
+        ============
+
+        Just get it:
+
+        .. code-block:: none
+
+            pip install simpleai
+
+
+        Examples
+        ========
+
+        Simple AI allows you to define problems and look for the solution with
+        different strategies. Another samples are in the ``samples`` directory, but
+        here is an easy one.
+
+        This problem tries to create the string "HELLO WORLD" using the A* algorithm:
+
+        .. code-block:: python
+
+
+            from simpleai.search import SearchProblem, astar
+
+            GOAL = 'HELLO WORLD'
+
+            class HelloProblem(SearchProblem):
+                def actions(self, state):
+                    if len(state) < len(GOAL):
+                        return [c for c in ' ABCDEFGHIJKLMNOPQRSTUVWXYZ']
+                    else:
+                        return []
+
+                def result(self, state, action):
+                    return state + action
+
+                def is_goal(self, state):
+                    return state == GOAL
+
+                def heuristic(self, state):
+                    # how far are we from the goal?
+                    wrong = sum([1 if state[i] != GOAL[i] else 0
+                                for i in range(len(state))])
+                    missing = len(GOAL) - len(state)
+                    return wrong + missing
+
+
+            problem = HelloProblem(initial_state='')
+            result = astar(problem)
+
+            print result.state
+            print result.path()
+
+
+        More detailed documentation
+        ===========================
+
+        You can read the docs online `here <http://simpleai.readthedocs.org/en/latest/>`_. Or for offline access, you can clone the project code repository and read them from the ``docs`` folder.
+
+
+        Authors
+        =======
+
+        * Juan Pedro Fisanotti <[email protected]>
+        * Rafael Carrascosa <[email protected]>
+        * Santiago Romero <[email protected]>
+        * Gonzalo García Berrotarán <[email protected]>
+
+        Special acknowledgements to `Machinalis <http://www.machinalis.com/>`_ for the
+        time provided to work on this project.
+
+Platform: UNKNOWN
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Natural Language :: English
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence

AI_A_Modern_Approach_Russell_Norvig/README.rst

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+Simple AI
+=========
+
+Project home: http://github.com/simpleai-team/simpleai
+
+This lib implements many of the artificial intelligence algorithms described on the book "Artificial Ingelligence, a Modern Approach", from Stuart Russel and Peter Norvig. We strongly recommend you to read the book, or at least the introductory chapters and the ones related to the components you want to use, because we won't explain the algorithms here.
+
+This implementation takes some of the ideas from the Norvig's implementation (the `aima-python <https://code.google.com/p/aima-python/>`_ lib), but it's made with a more "pythonic" approach, and more emphasis on creating a stable, modern, and mantenible version. We are testing the majority of the lib, it's available via pip install, has a standar repo and lib architecture, well documented, respects the python pep8 guidelines, provides only working code (no placeholders for future things), etc. Even the internal code is written with readability in mind, not only the external API.
+
+At this moment, the implementation includes:
+
+* Search
+    * Traditional search algorithms (not informed and informed)
+    * Local Search algorithms
+    * Constraint Satisfaction Problems algorithms
+* Machine Learning
+    * Statistical Classification 
+
+And we are working on an interactive execution viewer for search algorithms (display the search tree on each iteration).
+
+
+Installation
+============
+
+Just get it:
+
+.. code-block:: none
+
+    pip install simpleai
+
+
+Examples
+========
+
+Simple AI allows you to define problems and look for the solution with
+different strategies. Another samples are in the ``samples`` directory, but
+here is an easy one.
+
+This problem tries to create the string "HELLO WORLD" using the A* algorithm:
+
+.. code-block:: python
+
+
+    from simpleai.search import SearchProblem, astar
+
+    GOAL = 'HELLO WORLD'
+
+    class HelloProblem(SearchProblem):
+        def actions(self, state):
+            if len(state) < len(GOAL):
+                return [c for c in ' ABCDEFGHIJKLMNOPQRSTUVWXYZ']
+            else:
+                return []
+
+        def result(self, state, action):
+            return state + action
+
+        def is_goal(self, state):
+            return state == GOAL
+
+        def heuristic(self, state):
+            # how far are we from the goal?
+            wrong = sum([1 if state[i] != GOAL[i] else 0
+                        for i in range(len(state))])
+            missing = len(GOAL) - len(state)
+            return wrong + missing
+
+
+    problem = HelloProblem(initial_state='')
+    result = astar(problem)
+
+    print result.state
+    print result.path()
+
+
+More detailed documentation
+===========================
+
+You can read the docs online `here <http://simpleai.readthedocs.org/en/latest/>`_. Or for offline access, you can clone the project code repository and read them from the ``docs`` folder.
+
+
+Authors
+=======
+
+* Juan Pedro Fisanotti <[email protected]>
+* Rafael Carrascosa <[email protected]>
+* Santiago Romero <[email protected]>
+* Gonzalo García Berrotarán <[email protected]>
+
+Special acknowledgements to `Machinalis <http://www.machinalis.com/>`_ for the
+time provided to work on this project.

AI_A_Modern_Approach_Russell_Norvig/setup.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from distutils.core import setup
+
+setup(
+    name='simpleai',
+    version='0.5.3',
+    description=u'An implementation of AI algorithms based on aima-python',
+    long_description=open('README.rst').read(),
+    author = u'Juan Pedro Fisanotti',
+    author_email = '[email protected]',
+    url='http://github.com/simpleai-team/simpleai',
+    packages=['simpleai', 'simpleai.search', 'simpleai.machine_learning'],
+    license='LICENSE.txt',
+    classifiers = [
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: MIT License',
+        'Natural Language :: English',
+        'Operating System :: OS Independent',
+        'Programming Language :: Python',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    ],
+)

AI_A_Modern_Approach_Russell_Norvig/simpleai/machine_learning/__init__.py

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+# -*- coding: utf-8 -*-
+
+from simpleai.machine_learning.models import ClassificationProblem, \
+                                             VectorDataClassificationProblem, \
+                                             Attribute, VectorIndexAttribute, \
+                                             is_attribute, \
+                                             Classifier
+from simpleai.machine_learning.classifiers import DecisionTreeLearner, \
+                                                  DecisionTreeLearner_Queued, \
+                                               DecisionTreeLearner_LargeData, \
+                                                  NaiveBayes, \
+                                                  KNearestNeighbors
+from simpleai.machine_learning.evaluation import precision, kfold