updated to recents

school/cpsc481/project1/src/search.py

@@ -0,0 +1,164 @@
+# search.py
+# ---------
+# Licensing Information:  You are free to use or extend these projects for
+# educational purposes provided that (1) you do not distribute or publish
+# solutions, (2) you retain this notice, and (3) you provide clear
+# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
+# 
+# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
+# The core projects and autograders were primarily created by John DeNero
+# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
+# Student side autograding was added by Brad Miller, Nick Hay, and
+# Pieter Abbeel (pabbeel@cs.berkeley.edu).
+
+
+"""
+In search.py, you will implement generic search algorithms which are called by
+Pacman agents (in searchAgents.py).
+"""
+
+import util
+# user made functions
+
+
+class SearchProblem:
+    """
+    This class outlines the structure of a search problem, but doesn't implement
+    any of the methods (in object-oriented terminology: an abstract class).
+
+    You do not need to change anything in this class, ever.
+    """
+
+    def getStartState(self):
+        """
+        Returns the start state for the search problem.
+        """
+        util.raiseNotDefined()
+
+    def isGoalState(self, state):
+        """
+          state: Search state
+
+        Returns True if and only if the state is a valid goal state.
+        """
+        util.raiseNotDefined()
+
+    def getSuccessors(self, state):
+        """
+          state: Search state
+
+        For a given state, this should return a list of triples, (successor,
+        action, stepCost), where 'successor' is a successor to the current
+        state, 'action' is the action required to get there, and 'stepCost' is
+        the incremental cost of expanding to that successor.
+        """
+        util.raiseNotDefined()
+
+    def getCostOfActions(self, actions):
+        """
+         actions: A list of actions to take
+
+        This method returns the total cost of a particular sequence of actions.
+        The sequence must be composed of legal moves.
+        """
+        util.raiseNotDefined()
+
+
+def tinyMazeSearch(problem):
+    """
+    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
+    sequence of moves will be incorrect, so only use this for tinyMaze.
+    """
+    from game import Directions
+    s = Directions.SOUTH
+    w = Directions.WEST
+    return  [s, s, w, s, w, w, s, w]
+
+def baseSearchFunction(problem, data_structure):
+    """
+    Since the implementation of a Depth First Search and Breadth First Search
+    are nearly identical, we can simply switch the date structure based on the
+    order we recieve our nodes; i.e. change the direction we pop, push nodes.
+    """
+    # The path that takes us from the start node to the destination
+    answer_path = [] 
+
+    # Set the open_nodes data structure as either a Stack (DFS) or Queue (BFS)
+    open_nodes = data_structure()
+
+    # Add the starting state and the empty list to our open nodes
+    open_nodes.push((problem.getStartState(), answer_path))
+
+    # Initialize closed nodes to contain nothing
+    closed_nodes = []
+
+    # While there is at least one item in our open_nodes
+    while(not open_nodes.isEmpty()):
+        
+        # Assign current_node and answer_path to an element from one side of the list
+        current_node, answer_path = open_nodes.pop()
+
+        # If our current node isn't closed
+        if(current_node not in closed_nodes):
+            
+            # Add the current node to the set of closed nodes
+            closed_nodes.append(current_node)
+
+            # If we're at the destination
+            if(problem.isGoalState(current_node)):
+                # Return the path we used to get here
+                return answer_path
+
+            # Set successors equal to the children of the current node
+            successors = problem.getSuccessors(current_node)
+
+            # For each successor
+            for state in successors:
+                
+                #Set the node, path equal to the node and path of the state (We're not concerned about the cost)
+                node, path, _ = state
+
+                #If the current node isn't in the set of closed nodes
+                if(node not in closed_nodes):
+                    
+                   # Add the node and path to the set of open nodes
+                   open_nodes.push((node, answer_path + [path]))
+
+    #Return the 'best' answer_path we could find; If we're returning here, we didn't find the destination
+    return answer_path
+
+def depthFirstSearch(problem):
+    """Search the deepest nodes in the search tree first."""
+
+    #Use a stack in order to perform a Depth First Search
+    return baseSearchFunction(problem, util.Stack)
+
+def breadthFirstSearch(problem):
+    """Search the shallowest nodes in the search tree first."""
+
+    #Use a Queue in order to perform a Breadth First Search
+    return baseSearchFunction(problem, util.Queue)
+
+def uniformCostSearch(problem):
+    """Search the node of least total cost first."""
+    "*** YOUR CODE HERE ***"
+    util.raiseNotDefined()
+
+def nullHeuristic(state, problem=None):
+    """
+    A heuristic function estimates the cost from the current state to the nearest
+    goal in the provided SearchProblem.  This heuristic is trivial.
+    """
+    return 0
+
+def aStarSearch(problem, heuristic=nullHeuristic):
+    """Search the node that has the lowest combined cost and heuristic first."""
+    "*** YOUR CODE HERE ***"
+    util.raiseNotDefined()
+
+
+# Abbreviations
+bfs = breadthFirstSearch
+dfs = depthFirstSearch
+astar = aStarSearch
+ucs = uniformCostSearch