added binary and multiclass SVM with tests (#1135)

* changed queue to set in AC3

Changed queue to set in AC3 (as in the pseudocode of the original algorithm) to reduce the number of consistency-check due to the redundancy of the same arcs in queue. For example, on the harder1 configuration of the Sudoku CSP the number consistency-check has been reduced from 40464 to 12562!

* re-added test commented by mistake

* added the mentioned AC4 algorithm for constraint propagation

AC3 algorithm has non-optimal worst case time-complexity O(cd^3 ), while AC4 algorithm runs in O(cd^2) worst case time

* added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference

* removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py

* added map coloring SAT problems

* fixed typo errors and removed unnecessary brackets

* reformulated the map coloring problem

* Revert "reformulated the map coloring problem"

This reverts commit 20ab0e5.

* Revert "fixed typo errors and removed unnecessary brackets"

This reverts commit f743146.

* Revert "added map coloring SAT problems"

This reverts commit 9e0fa55.

* Revert "removed useless doctest for AC4 in Sudoku because AC4's tests are already present in test_csp.py"

This reverts commit b3cd24c.

* Revert "added doctest in Sudoku for AC4 and and the possibility of choosing the constant propagation algorithm in mac inference"

This reverts commit 6986247.

* Revert "added the mentioned AC4 algorithm for constraint propagation"

This reverts commit 03551fb.

* added map coloring SAT problem

* fixed build error

* Revert "added map coloring SAT problem"

This reverts commit 93af259.

* Revert "fixed build error"

This reverts commit 6641c2c.

* added map coloring SAT problem

* removed redundant parentheses

* added Viterbi algorithm

* added monkey & bananas planning problem

* simplified condition in search.py

* added tests for monkey & bananas planning problem

* removed monkey & bananas planning problem

* Revert "removed monkey & bananas planning problem"

This reverts commit 9d37ae0.

* Revert "added tests for monkey & bananas planning problem"

This reverts commit 24041e9.

* Revert "simplified condition in search.py"

This reverts commit 6d229ce.

* Revert "added monkey & bananas planning problem"

This reverts commit c74933a.

* defined the PlanningProblem as a specialization of a search.Problem & fixed typo errors

* fixed doctest in logic.py

* fixed doctest for cascade_distribution

* added ForwardPlanner and tests

* added __lt__ implementation for Expr

* added more tests

* renamed forward planner

* Revert "renamed forward planner"

This reverts commit c4139e5.

* renamed forward planner class & added doc

* added backward planner and tests

* fixed mdp4e.py doctests

* removed ignore_delete_lists_heuristic flag

* fixed heuristic for forward and backward planners

* added SATPlan and tests

* fixed ignore delete lists heuristic in forward and backward planners

* fixed backward planner and added tests

* updated doc

* added nary csp definition and examples

* added CSPlan and tests

* fixed CSPlan

* added book's cryptarithmetic puzzle example

* fixed typo errors in test_csp

* fixed #1111

* added sortedcontainers to yml and doc to CSPlan

* added tests for n-ary csp

* fixed utils.extend

* updated test_probability.py

* converted static methods to functions

* added AC3b and AC4 with heuristic and tests

* added conflict-driven clause learning sat solver

* added tests for cdcl and heuristics

* fixed probability.py

* fixed import

* fixed kakuro

* added Martelli and Montanari rule-based unification algorithm

* removed duplicate standardize_variables

* renamed variables known as built-in functions

* fixed typos in learning.py

* renamed some files and fixed typos

* fixed typos

* fixed typos

* fixed tests

* removed unify_mm

* remove unnecessary brackets

* fixed tests

* moved utility functions to utils.py

* fixed typos

* moved utils function to utils.py, separated probability learning classes from learning.py, fixed typos and fixed imports in .ipynb files

* added missing learners

* fixed Travis build

* fixed typos

* fixed typos

* fixed typos

* fixed typos

* fixed typos in agents files

* fixed imports in agent files

* fixed deep learning .ipynb imports

* fixed typos

* added SVM

* added .ipynb and fixed typos

* adapted code for .ipynb

* fixed typos

* updated .ipynb

* updated .ipynb

* updated logic.py

* updated .ipynb

* updated .ipynb

* updated planning.py

* updated inf definition

* fixed typos

* fixed typos

* fixed typos

* fixed typos

* Revert "fixed typos"

This reverts commit 658309d.

* Revert "fixed typos"

This reverts commit 08ad660.

* fixed typos

* fixed typos

* fixed typos

* fixed typos

* fixed typos and utils imports in *4e.py files

* fixed typos

* fixed typos

* fixed typos

* fixed typos

* fixed import

* fixed typos

* fixed typos

* fixd typos

* fixed typos

* fixed typos

* updated SVM

* added svm test

* fixed SVM and tests

* fixed some definitions and typos

* fixed svm and tests

* added SVMs also in learning4e.py

* fixed inf definition

* fixed .travis.yml

* fixed .travis.yml

* fixed import

* fixed inf definition

* replaced cvxopt with qpsolvers

* replaced cvxopt with quadprog

* fixed some definitions

* fixed typos and removed unnecessary tests

* replaced quadprog with qpsolvers

* fixed extend in utils

* specified error type in try-catch block

* fixed extend in utils

* fixed typos

* fixed learning.py

* fixed doctest errors

* added comments

* removed unnecessary if condition

* updated learning.py

* fixed imports

* removed unnecessary imports

* fixed keras imports

* fixed typos

* fixed learning_curve

* added comments

Author: dmeoli

.travis.yml

@@ -1,28 +1,31 @@
-language:
-  - python
+language: python
 
 python:
-  - "3.4"
+  - 3.4
+  - 3.5
+  - 3.6
+  - 3.7
 
 before_install:
   - git submodule update --remote
 
 install:
-  - pip install six
   - pip install flake8
   - pip install ipython
-  - pip install matplotlib
-  - pip install networkx
-  - pip install ipywidgets
-  - pip install Pillow
-  - pip install pytest-cov
   - pip install ipythonblocks
+  - pip install ipywidgets
   - pip install keras
+  - pip install matplotlib
+  - pip install networkx
   - pip install numpy
-  - pip install tensorflow
   - pip install opencv-python
+  - pip install Pillow
+  - pip install pytest-cov
+  - pip install qpsolvers
+  - pip install quadprog
+  - pip install six
   - pip install sortedcontainers
-
+  - pip install tensorflow
 
 script:
   - py.test --cov=./

agents.py

@@ -1,4 +1,5 @@
-"""Implement Agents and Environments (Chapters 1-2).
+"""
+Implement Agents and Environments. (Chapters 1-2)
 
 The class hierarchies are as follows:
 
@@ -23,16 +24,14 @@
 EnvToolbar ## contains buttons for controlling EnvGUI
 
 EnvCanvas ## Canvas to display the environment of an EnvGUI
-
 """
 
-# TO DO:
+# TODO
 # Implement grabbing correctly.
 # When an object is grabbed, does it still have a location?
 # What if it is released?
 # What if the grabbed or the grabber is deleted?
 # What if the grabber moves?
-#
 # Speed control in GUI does not have any effect -- fix it.
 
 from utils import distance_squared, turn_heading
@@ -90,8 +89,7 @@ def __init__(self, program=None):
         self.holding = []
         self.performance = 0
         if program is None or not isinstance(program, collections.Callable):
-            print("Can't find a valid program for {}, falling back to default.".format(
-                self.__class__.__name__))
+            print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__))
 
             def program(percept):
                 return eval(input('Percept={}; action? '.format(percept)))
@@ -122,10 +120,13 @@ def new_program(percept):
 
 
 def TableDrivenAgentProgram(table):
-    """This agent selects an action based on the percept sequence.
+    """
+    [Figure 2.7]
+    This agent selects an action based on the percept sequence.
     It is practical only for tiny domains.
     To customize it, provide as table a dictionary of all
-    {percept_sequence:action} pairs. [Figure 2.7]"""
+    {percept_sequence:action} pairs.
+    """
     percepts = []
 
     def program(percept):
@@ -154,7 +155,10 @@ def RandomAgentProgram(actions):
 
 
 def SimpleReflexAgentProgram(rules, interpret_input):
-    """This agent takes action based solely on the percept. [Figure 2.10]"""
+    """
+    [Figure 2.10]
+    This agent takes action based solely on the percept.
+    """
 
     def program(percept):
         state = interpret_input(percept)
@@ -166,7 +170,10 @@ def program(percept):
 
 
 def ModelBasedReflexAgentProgram(rules, update_state, model):
-    """This agent takes action based on the percept and state. [Figure 2.12]"""
+    """
+    [Figure 2.12]
+    This agent takes action based on the percept and state.
+    """
 
     def program(percept):
         program.state = update_state(program.state, program.action, percept, model)
@@ -219,7 +226,9 @@ def TableDrivenVacuumAgent():
 
 
 def ReflexVacuumAgent():
-    """A reflex agent for the two-state vacuum environment. [Figure 2.8]
+    """
+    [Figure 2.8]
+    A reflex agent for the two-state vacuum environment.
     >>> agent = ReflexVacuumAgent()
     >>> environment = TrivialVacuumEnvironment()
     >>> environment.add_thing(agent)
@@ -436,13 +445,13 @@ def move_forward(self, from_location):
         """
         x, y = from_location
         if self.direction == self.R:
-            return (x + 1, y)
+            return x + 1, y
         elif self.direction == self.L:
-            return (x - 1, y)
+            return x - 1, y
         elif self.direction == self.U:
-            return (x, y - 1)
+            return x, y - 1
         elif self.direction == self.D:
-            return (x, y + 1)
+            return x, y + 1
 
 
 class XYEnvironment(Environment):
@@ -497,7 +506,7 @@ def execute_action(self, agent, action):
                 agent.holding.pop()
 
     def default_location(self, thing):
-        return (random.choice(self.width), random.choice(self.height))
+        return random.choice(self.width), random.choice(self.height)
 
     def move_to(self, thing, destination):
         """Move a thing to a new location. Returns True on success or False if there is an Obstacle.
@@ -525,7 +534,7 @@ def add_thing(self, thing, location=(1, 1), exclude_duplicate_class_items=False)
     def is_inbounds(self, location):
         """Checks to make sure that the location is inbounds (within walls if we have walls)"""
         x, y = location
-        return not (x < self.x_start or x >= self.x_end or y < self.y_start or y >= self.y_end)
+        return not (x < self.x_start or x > self.x_end or y < self.y_start or y > self.y_end)
 
     def random_location_inbounds(self, exclude=None):
         """Returns a random location that is inbounds (within walls if we have walls)"""
@@ -723,7 +732,7 @@ def percept(self, agent):
         status = ('Dirty' if self.some_things_at(
             agent.location, Dirt) else 'Clean')
         bump = ('Bump' if agent.bump else 'None')
-        return (status, bump)
+        return status, bump
 
     def execute_action(self, agent, action):
         agent.bump = False
@@ -752,12 +761,11 @@ def __init__(self):
                        loc_B: random.choice(['Clean', 'Dirty'])}
 
     def thing_classes(self):
-        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,
-                TableDrivenVacuumAgent, ModelBasedVacuumAgent]
+        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent, TableDrivenVacuumAgent, ModelBasedVacuumAgent]
 
     def percept(self, agent):
         """Returns the agent's location, and the location status (Dirty/Clean)."""
-        return (agent.location, self.status[agent.location])
+        return agent.location, self.status[agent.location]
 
     def execute_action(self, agent, action):
         """Change agent's location and/or location's status; track performance.
@@ -992,8 +1000,8 @@ def is_done(self):
             else:
                 print("Death by {} [-1000].".format(explorer[0].killed_by))
         else:
-            print("Explorer climbed out {}.".format("with Gold [+1000]!"
-                                                    if Gold() not in self.things else "without Gold [+0]"))
+            print("Explorer climbed out {}."
+                  .format("with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]"))
         return True
 
     # TODO: Arrow needs to be implemented

agents4e.py

@@ -1,4 +1,5 @@
-"""Implement Agents and Environments (Chapters 1-2).
+"""
+Implement Agents and Environments. (Chapters 1-2)
 
 The class hierarchies are as follows:
 
@@ -23,16 +24,14 @@
 EnvToolbar ## contains buttons for controlling EnvGUI
 
 EnvCanvas ## Canvas to display the environment of an EnvGUI
-
 """
 
-# TO DO:
+# TODO
 # Implement grabbing correctly.
 # When an object is grabbed, does it still have a location?
 # What if it is released?
 # What if the grabbed or the grabber is deleted?
 # What if the grabber moves?
-#
 # Speed control in GUI does not have any effect -- fix it.
 
 from utils4e import distance_squared, turn_heading
@@ -90,8 +89,7 @@ def __init__(self, program=None):
         self.holding = []
         self.performance = 0
         if program is None or not isinstance(program, collections.Callable):
-            print("Can't find a valid program for {}, falling back to default.".format(
-                self.__class__.__name__))
+            print("Can't find a valid program for {}, falling back to default.".format(self.__class__.__name__))
 
             def program(percept):
                 return eval(input('Percept={}; action? '.format(percept)))
@@ -122,10 +120,13 @@ def new_program(percept):
 
 
 def TableDrivenAgentProgram(table):
-    """This agent selects an action based on the percept sequence.
+    """
+    [Figure 2.7]
+    This agent selects an action based on the percept sequence.
     It is practical only for tiny domains.
     To customize it, provide as table a dictionary of all
-    {percept_sequence:action} pairs. [Figure 2.7]"""
+    {percept_sequence:action} pairs.
+    """
     percepts = []
 
     def program(percept):
@@ -154,7 +155,10 @@ def RandomAgentProgram(actions):
 
 
 def SimpleReflexAgentProgram(rules, interpret_input):
-    """This agent takes action based solely on the percept. [Figure 2.10]"""
+    """
+    [Figure 2.10]
+    This agent takes action based solely on the percept.
+    """
 
     def program(percept):
         state = interpret_input(percept)
@@ -166,7 +170,10 @@ def program(percept):
 
 
 def ModelBasedReflexAgentProgram(rules, update_state, trainsition_model, sensor_model):
-    """This agent takes action based on the percept and state. [Figure 2.12]"""
+    """
+    [Figure 2.12]
+    This agent takes action based on the percept and state.
+    """
 
     def program(percept):
         program.state = update_state(program.state, program.action, percept, trainsition_model, sensor_model)
@@ -219,7 +226,9 @@ def TableDrivenVacuumAgent():
 
 
 def ReflexVacuumAgent():
-    """A reflex agent for the two-state vacuum environment. [Figure 2.8]
+    """
+    [Figure 2.8]
+    A reflex agent for the two-state vacuum environment.
     >>> agent = ReflexVacuumAgent()
     >>> environment = TrivialVacuumEnvironment()
     >>> environment.add_thing(agent)
@@ -333,8 +342,7 @@ def run(self, steps=1000):
 
     def list_things_at(self, location, tclass=Thing):
         """Return all things exactly at a given location."""
-        return [thing for thing in self.things
-                if thing.location == location and isinstance(thing, tclass)]
+        return [thing for thing in self.things if thing.location == location and isinstance(thing, tclass)]
 
     def some_things_at(self, location, tclass=Thing):
         """Return true if at least one of the things at location
@@ -437,13 +445,13 @@ def move_forward(self, from_location):
         """
         x, y = from_location
         if self.direction == self.R:
-            return (x + 1, y)
+            return x + 1, y
         elif self.direction == self.L:
-            return (x - 1, y)
+            return x - 1, y
         elif self.direction == self.U:
-            return (x, y - 1)
+            return x, y - 1
         elif self.direction == self.D:
-            return (x, y + 1)
+            return x, y + 1
 
 
 class XYEnvironment(Environment):
@@ -498,7 +506,7 @@ def execute_action(self, agent, action):
                 agent.holding.pop()
 
     def default_location(self, thing):
-        return (random.choice(self.width), random.choice(self.height))
+        return random.choice(self.width), random.choice(self.height)
 
     def move_to(self, thing, destination):
         """Move a thing to a new location. Returns True on success or False if there is an Obstacle.
@@ -724,7 +732,7 @@ def percept(self, agent):
         status = ('Dirty' if self.some_things_at(
             agent.location, Dirt) else 'Clean')
         bump = ('Bump' if agent.bump else 'None')
-        return (status, bump)
+        return status, bump
 
     def execute_action(self, agent, action):
         agent.bump = False
@@ -753,12 +761,11 @@ def __init__(self):
                        loc_B: random.choice(['Clean', 'Dirty'])}
 
     def thing_classes(self):
-        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent,
-                TableDrivenVacuumAgent, ModelBasedVacuumAgent]
+        return [Wall, Dirt, ReflexVacuumAgent, RandomVacuumAgent, TableDrivenVacuumAgent, ModelBasedVacuumAgent]
 
     def percept(self, agent):
         """Returns the agent's location, and the location status (Dirty/Clean)."""
-        return (agent.location, self.status[agent.location])
+        return agent.location, self.status[agent.location]
 
     def execute_action(self, agent, action):
         """Change agent's location and/or location's status; track performance.
@@ -994,8 +1001,7 @@ def is_done(self):
                 print("Death by {} [-1000].".format(explorer[0].killed_by))
         else:
             print("Explorer climbed out {}."
-                .format(
-                "with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]"))
+                  .format("with Gold [+1000]!" if Gold() not in self.things else "without Gold [+0]"))
         return True
 
     # TODO: Arrow needs to be implemented