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def quicksort(A, lo, hi): | |
if lo < hi: | |
p = partition(A, lo, hi) | |
quicksort(A, lo, p-1) | |
quicksort(A, p+1, hi) | |
def partition(A, lo, hi): | |
pivot = A[hi] | |
i = lo - 1 | |
for j in range(lo, hi): |
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import random | |
import time | |
from search import * | |
from utils import * | |
from string import ascii_lowercase | |
def vowel_count(word): | |
return sum(word.count(i) for i in 'aeiou') | |
def mate(p1, p2): |
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class Node(object): | |
def __init__(self, value, connected=None): | |
self.value = value | |
if connected: | |
self.connected = [node for node in connected] | |
else: | |
self.connected = [] | |
def add_connection(self, node): | |
if isinstance(node, Node): |
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class Queue: | |
"""Queue is an abstract class/interface. There are three types: | |
Stack(): A Last In First Out Queue. | |
FIFOQueue(): A First In First Out Queue. | |
PriorityQueue(order, f): Queue in sorted order (default min-first). | |
Each type supports the following methods and functions: | |
q.append(item) -- add an item to the queue | |
q.extend(items) -- equivalent to: for item in items: q.append(item) | |
q.pop() -- return the top item from the queue |
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class FIFOQueue(Queue): | |
"""A First-In-First-Out Queue.""" | |
def __init__(self): | |
self.A = [] | |
self.start = 0 | |
def append(self, item): | |
self.A.append(item) |
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import random | |
from search import * | |
class Node(object): | |
def __init__(self, label, state): | |
self.label = label | |
self.state = state | |
class Game(object): | |
def __init__(self, start, goal): |
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import time | |
import random | |
from itertools import permutations | |
class ModelBasedAgent(): | |
def __init__(self, shape, dirt_patches): | |
self.rows = shape[0] | |
self.cols = shape[1] | |
self.size = self.rows * self.cols | |
self.environment = ['' for i in range(self.size)] |
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class Node(): | |
def __init__(self, label, value, parent): | |
self.parent = parent | |
self.child = [] | |
self.label = label | |
self.value = value | |
def get_child(self): | |
return self.child | |
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import numpy as np | |
import pandas as pd | |
import time | |
from tree import Node | |
test_df = pd.read_csv('cars.txt', names=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'result']) | |
test_df.drop(['doors', 'persons', 'safety'], axis=1, inplace=True) | |
test_df['result'] = test_df['result'].map({'unacc':'notbuy', 'acc':'buy', 'vgood':'buy', 'good':'notbuy'}) | |
df = test_df | |
backup_df = df |
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class Vector(): | |
def __init__(self, vector): | |
self.vector = vector | |
def __add__(self, vector2): | |
result = Vector([]) | |
if isinstance(vector2, int): | |
for i in range(len(self.vector)): | |
result.append(self.vector[i] + vector2) | |
return result |