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Find Shortest Word Edit Path in python
Shortest path via two intermediate pointsGoogle FooBar Level 3 - Shortest path to 14 sum challenge (part 2)Shortest path around a set of pointsShortest path challengeShortest path to transform one word into anotherDijkstra finding shortest path satisfying given constraintsFind The Duplicates using binarySearch pythonFind the shortest path between two points in a 2D matrix with obstaclesPython A Star with fewest turns and shortest path variations
$begingroup$
Find the Shortest Word Edit Path
I was given this problem from an interview.
#Given two words source and target, and a list of words words,
#find the length of the shortest series of edits that transforms source to target.
#Each edit must change exactly one letter at a time, and each
#intermediate word (and the final target word) must exist in words.
If the task is impossible, return -1.
Examples:
source = "bit", target = "dog" words = ["but", "put", "big", "pot",
"pog", "dog", "lot"]
output: 5 explanation: bit -> but -> put -> pot -> pog -> dog has 5
transitions. source = "no", target = "go" words = ["to"]
output: -1
def one_letter_difference(current_word, next_word):
count = 0
for i in range(len(current_word) -1):
if current_word[i] != next_word[i]:
count += 1;
if count == 1:
return True
else:
return False
def shortestWordEditPath(source, target, words):
"""
@param source: str
@param target: str
@param words: str[]
@return: int
Input: source = "bit", target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
Output: 5
explanation:
1 -> 1 -> 1 -> 1 -> 1
(source) bit -> but -> put -> pot -> pog -> dog (target)
has minimum of 5 transitions
"""
graph =
for i in range(-1, len(words)):
if i == -1:
current_word = source
else:
current_word = words[i]
graph[current_word] = []
for word in range(len(words)):
next_word = words[word]
if one_letter_difference(current_word, next_word):
graph[current_word].append(next_word)
#perform BFS
queue = collections.deque()
seen = set() # visited words
queue.append([source, 1])
seen.add(source)
while len(queue) > 0:
current, distance = queue.popleft()
if current == target: return distance
for neighbor in graph[current]:
if neighbor not in seen:
queue.append([neighbor, distance +1])
seen.add(neighbor)
return -1
source = "bit"
target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
test = shortestWordEditPath(source, target,words)
print(test)
python interview-questions
$endgroup$
add a comment |
$begingroup$
Find the Shortest Word Edit Path
I was given this problem from an interview.
#Given two words source and target, and a list of words words,
#find the length of the shortest series of edits that transforms source to target.
#Each edit must change exactly one letter at a time, and each
#intermediate word (and the final target word) must exist in words.
If the task is impossible, return -1.
Examples:
source = "bit", target = "dog" words = ["but", "put", "big", "pot",
"pog", "dog", "lot"]
output: 5 explanation: bit -> but -> put -> pot -> pog -> dog has 5
transitions. source = "no", target = "go" words = ["to"]
output: -1
def one_letter_difference(current_word, next_word):
count = 0
for i in range(len(current_word) -1):
if current_word[i] != next_word[i]:
count += 1;
if count == 1:
return True
else:
return False
def shortestWordEditPath(source, target, words):
"""
@param source: str
@param target: str
@param words: str[]
@return: int
Input: source = "bit", target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
Output: 5
explanation:
1 -> 1 -> 1 -> 1 -> 1
(source) bit -> but -> put -> pot -> pog -> dog (target)
has minimum of 5 transitions
"""
graph =
for i in range(-1, len(words)):
if i == -1:
current_word = source
else:
current_word = words[i]
graph[current_word] = []
for word in range(len(words)):
next_word = words[word]
if one_letter_difference(current_word, next_word):
graph[current_word].append(next_word)
#perform BFS
queue = collections.deque()
seen = set() # visited words
queue.append([source, 1])
seen.add(source)
while len(queue) > 0:
current, distance = queue.popleft()
if current == target: return distance
for neighbor in graph[current]:
if neighbor not in seen:
queue.append([neighbor, distance +1])
seen.add(neighbor)
return -1
source = "bit"
target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
test = shortestWordEditPath(source, target,words)
print(test)
python interview-questions
$endgroup$
add a comment |
$begingroup$
Find the Shortest Word Edit Path
I was given this problem from an interview.
#Given two words source and target, and a list of words words,
#find the length of the shortest series of edits that transforms source to target.
#Each edit must change exactly one letter at a time, and each
#intermediate word (and the final target word) must exist in words.
If the task is impossible, return -1.
Examples:
source = "bit", target = "dog" words = ["but", "put", "big", "pot",
"pog", "dog", "lot"]
output: 5 explanation: bit -> but -> put -> pot -> pog -> dog has 5
transitions. source = "no", target = "go" words = ["to"]
output: -1
def one_letter_difference(current_word, next_word):
count = 0
for i in range(len(current_word) -1):
if current_word[i] != next_word[i]:
count += 1;
if count == 1:
return True
else:
return False
def shortestWordEditPath(source, target, words):
"""
@param source: str
@param target: str
@param words: str[]
@return: int
Input: source = "bit", target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
Output: 5
explanation:
1 -> 1 -> 1 -> 1 -> 1
(source) bit -> but -> put -> pot -> pog -> dog (target)
has minimum of 5 transitions
"""
graph =
for i in range(-1, len(words)):
if i == -1:
current_word = source
else:
current_word = words[i]
graph[current_word] = []
for word in range(len(words)):
next_word = words[word]
if one_letter_difference(current_word, next_word):
graph[current_word].append(next_word)
#perform BFS
queue = collections.deque()
seen = set() # visited words
queue.append([source, 1])
seen.add(source)
while len(queue) > 0:
current, distance = queue.popleft()
if current == target: return distance
for neighbor in graph[current]:
if neighbor not in seen:
queue.append([neighbor, distance +1])
seen.add(neighbor)
return -1
source = "bit"
target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
test = shortestWordEditPath(source, target,words)
print(test)
python interview-questions
$endgroup$
Find the Shortest Word Edit Path
I was given this problem from an interview.
#Given two words source and target, and a list of words words,
#find the length of the shortest series of edits that transforms source to target.
#Each edit must change exactly one letter at a time, and each
#intermediate word (and the final target word) must exist in words.
If the task is impossible, return -1.
Examples:
source = "bit", target = "dog" words = ["but", "put", "big", "pot",
"pog", "dog", "lot"]
output: 5 explanation: bit -> but -> put -> pot -> pog -> dog has 5
transitions. source = "no", target = "go" words = ["to"]
output: -1
def one_letter_difference(current_word, next_word):
count = 0
for i in range(len(current_word) -1):
if current_word[i] != next_word[i]:
count += 1;
if count == 1:
return True
else:
return False
def shortestWordEditPath(source, target, words):
"""
@param source: str
@param target: str
@param words: str[]
@return: int
Input: source = "bit", target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
Output: 5
explanation:
1 -> 1 -> 1 -> 1 -> 1
(source) bit -> but -> put -> pot -> pog -> dog (target)
has minimum of 5 transitions
"""
graph =
for i in range(-1, len(words)):
if i == -1:
current_word = source
else:
current_word = words[i]
graph[current_word] = []
for word in range(len(words)):
next_word = words[word]
if one_letter_difference(current_word, next_word):
graph[current_word].append(next_word)
#perform BFS
queue = collections.deque()
seen = set() # visited words
queue.append([source, 1])
seen.add(source)
while len(queue) > 0:
current, distance = queue.popleft()
if current == target: return distance
for neighbor in graph[current]:
if neighbor not in seen:
queue.append([neighbor, distance +1])
seen.add(neighbor)
return -1
source = "bit"
target = "dog"
words = ["but", "put", "big", "pot", "pog", "dog", "lot"]
test = shortestWordEditPath(source, target,words)
print(test)
python interview-questions
python interview-questions
asked 9 mins ago
NinjaGNinjaG
825632
825632
add a comment |
add a comment |
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