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WordMorph.py
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WordMorph.py
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#
# CS 380 Homework 2
# April 27, 2015
# Kyle Weisel (weisel@drexel.edu)
#
# Execution settings
DEBUG = False
FINAL_STATE = "shore"
INITIAL_STATE = "beach"
from random import randint
class Rule:
index = 0
char = ""
def __init__(self, index, char):
self.index = index
self.char = char
def __str__(self):
return "(" + str(self.index) + " , " + str(self.char) + ")"
class State:
value = ""
def is_finished(self):
return self.value == FINAL_STATE
def length(self):
return len(self.value)
def __init__(self, value):
self.value = value
def __str__(self):
return str(self.value)
def __eq__(self, other):
return self.value == other.value
class Dictionary:
words = []
def list(self):
return self.words
def length(self):
return len(self.words)
def __init__(self):
with open('dict1000.txt') as f:
self.words = [line.rstrip('\n') for line in f]
def __str__(self):
return "The dictionary has " + str(len(self.words)) + " words."
#
# Applies a rule to a current state and then returns the new state.
#
# @type rule: Rule object
# @type state: State object
# @param rule: The rule to be applied to a given state.
# @param state: The "original" state to apply the rule to.
#
# @rtype: State object
# @return: A new state that is the state of the rule applied to the original state.
#
def apply_rule(rule, state):
current_state_as_list = list(state.value)
current_state_as_list[rule.index] = rule.char
new_state = State(''.join(i for i in current_state_as_list))
return new_state
#
# Implements the backtrack algorithm to find a set of rules which transform a state into the final state.
#
# @type state_list: A list of State objects
# @type dictionary: Dictionary object
# @param state_list: A list of state objects that represent the previous state "path".
# @param dictionary: An initialized dictionary object which contains words in the knowledge base.
#
# @rtype: List of Rule objects
# @return: A list of rule objects that need to be applied to a state in order to transform the state into the final
# state.
#
def back_track(state_list, dictionary):
if DEBUG:
print "back_track() -> len(stateList) = {} ".format(len(state_list))
path_to_here = ""
for state in state_list:
path_to_here += state.value + " --> "
if DEBUG:
print "\tback_track() -> current state list is: {}".format(path_to_here)
print "\tback_track() -> current state value is: {}".format(state_list[-1].value)
DEPTH_BOUND = 1000
current_state = state_list[-1]
if state_list.count(current_state) > 1:
if DEBUG:
print '\tback_track() -> State exists more than once! Returning FAILED-1'
return "FAILED-1"
if is_dead_end(current_state):
if DEBUG:
print '\tback_track() -> State is a dead end! Returning FAILED-2'
return "FAILED-2"
if goal(current_state):
if DEBUG:
print '\tback_track() -> Reached goal! Returning SUCCESS'
return []
if len(state_list) > DEPTH_BOUND:
if DEBUG:
print '\tback_track() -> State list exceeds depth bound! Returning FAILED-3'
return "FAILED-3"
rule_set = generate_rules(current_state, dictionary)
if not rule_set:
if DEBUG:
print "\tback_track() -> Returned rule set was empty! Returning FAILED-4"
return "FAILED-4"
for rule in rule_set:
new_state = apply_rule(rule, current_state)
if DEBUG:
print "\tback_track() -> Generated new state value {}!".format(new_state.value)
new_state_list = state_list[:]
new_state_list.append(new_state)
path = back_track(new_state_list, dictionary)
if type(path) == list:
path.append(rule)
return path
if DEBUG:
print "Returning from back_track() with FAILED-5"
return "FAILED-5"
#
# Describes a path of rules.
#
# @type path: List of Rule objects
# @param path: The list of rule objects to describe.
#
# @rtype: string
# @return: A pretty string that shows rule progression in a human-readable format.
#
def describe_path(path):
rules_string = ""
for rule in path:
rules_string += describe_rule(rule) + " --> "
rules_string += "END"
return rules_string
#
# Describes a rule.
#
# @type rule: Rule object
# @param rule: The rule to describe.
#
# @rtype: string
# @return: A pretty string that describes the rule in a human-readable format.
#
def describe_rule(rule):
if DEBUG:
print rule
return str(rule)
#
# Describes a state.
#
# @type state: State object
# @param state: The state to describe.
#
# @rtype: string
# @return: A pretty string that describes the state in a human-readable format.
#
def describe_state(state):
if DEBUG:
print state
return str(state)
#
# "Flails wildly" by randomly applying rules to a state until the goal state is reached.
#
# @type state: State object
# @param state: The initial state.
#
# @rtype: None
# @return: None
#
def flail_wildly(state):
loops = 0
while not (goal(state)):
if DEBUG:
print "Executing loop " + str(loops)
describe_state(state)
rules = generate_rules(state)
state = apply_rule(rules[randint(0, len(rules)-1)], state)
if DEBUG:
print "The new state is " + state.currentState
loops += 1
#
# Checks if the passed state has reached the goal.
#
# @type state: State object
# @param state: The state to check.
#
# @rtype: bool
# @return: True if the state is at the goal, False otherwise.
#
def goal(state):
return state.value == FINAL_STATE
#
# Generates a list of possible rules that can be applied to the state legally.
#
# @type state: State object
# @type dictionary: Dictionary object
# @param state: State for which we are to analyze possible rules.
# @param dictionary: An initialized dictionary object which contains words in the knowledge base.
#
# @rtype: List of Rule objects
# @return: A list of rule objects that can be applied to a state in order to form a new, valid state.
#
def generate_rules(state, dictionary):
rules = []
for i in range(0, dictionary.length()):
if num_matched_chars(state.value, dictionary.words[i]) == 4:
if DEBUG:
print "\tgenerate_rules() -> Identified possible next state: {}".format(dictionary.words[i])
# Find the changed character
state_as_list = list(state.value)
match_as_list = list(dictionary.words[i])
change_index = -1
change_char = ""
for j in range(0, len(state_as_list)):
if state_as_list[j] != match_as_list[j]:
change_index = j
change_char = match_as_list[j]
rules.append(Rule(change_index, change_char))
if DEBUG:
print "\tgenerate_rules() -> Generated {} rules this pass".format(len(rules))
return rules
#
# Determines if we're at a dead end from the passed state.
#
# @type state: State object
# @param state: State for which we are to analyze if we are at a dead end.
#
# @rtype: bool
# @return: True if there are no more possible valid states, False otherwise.
#
def is_dead_end(state):
return False
#
# Determines the number of mached characters in string a and b.
#
# @type a: string
# @type b: string
# @param a: String to compare.
# @param b: String to compare.
#
# @rtype: number
# @return: The number of matching characters between strings a and b.
#
def num_matched_chars(a, b):
if len(a) == len(b):
a_list = list(a)
b_list = list(b)
num_matches = 0
for i in range(0, len(a_list)):
if a_list[i] == b_list[i]:
num_matches += 1
return num_matches
else:
return -1
#
# Determines the legality of a rule as applied to a state.
#
# @type rule: Rule object
# @type state: State object
# @type dictionary: Dictionary object
# @param rule: The rule to be applied to a given state.
# @param state: The "original" state to apply the rule to.
# @param dictionary: An initialized dictionary object which contains words in the knowledge base.
#
# @rtype: bool
# @return: True if the new state is legal, False otherwise.
#
def precondition(rule, state, dictionary):
return True if (apply_rule(rule, state).value in dictionary.list) else False
#
# !!! This is the entry point to the application !!!
#
def main():
state = State(INITIAL_STATE)
dictionary = Dictionary()
state_list = [state]
path = back_track(state_list, dictionary)
print describe_path(path)
main()