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Python programming was developed in the 1980s by Guido van Rossum. Python can be defined as a high-level object-oriented programming language that offers dynamic typing and dynamic binding options. Python helps programmers to work collaboratively and reduces the cost of program development and maintenance. The modules and packages feature of python makes it easy for the same program to be reused in multiple projects, thus making python one of the most widely used software globally. Python finds its applications in data science, data analysis, software development, web development, system scripting, big data, machine learning, artificial intelligence etc.
The latest Python 3.7, released in 2018 has many new features compared to Python 3.6. It has new syntax features like PEP 563, and new library modules: PEP 567 (context vars) and PEP 557 (data classes). The Python data model improvements which help customization of access to module attributes (PEP562) and core support for typing module (PEP 560) are the best thing any programmer would have asked for. There have been significant improvements in CPython implementation and standard library. This makes the latest python version, one of the most popular programming languages.
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Standard Library: Python programming language has a standard library with several components, similar to those of C++ and other advanced programming languages that developers can use while programming. This was achieved while using a language syntax that is approachable and simple. Python can easily be used for general purpose and web applications due to the many data types that are built-in, advanced handling of exceptions, interactivity of database and a file-based input/output that is comprehensive. A lot of application programmers have been aided by Python when they wanted to switch to developing web applications. Our online Python experts will help you to master Python programming.
Object-Oriented Programming: Python is an object-oriented programming language, making it a great programming language for learners. Beginners can start to learn Python programming and then easily switch to other programming languages that are also object-oriented. Python comes with object-oriented methodologies, reinforces structure for good programming and is intuitive. This is a very good language to code since it is object-oriented.
Portability: There are several features in Python programming language which make it an option that is very attractive for the development of web applications. The availability of Python interpreters in every recent operating system as well as some computing systems that are embedded makes the Python programming language portable.
Stability: Since the Python programming language was developed in the 1980s, a lot of improvement has been carried out on it. Several regression and extensive functionality testing have been carried out on the programming language so that they are stable and remain free of bugs.
Readability: The syntax for programming in Python is simple and thus the coding language can be understood easily by PYTHON writers. Based on this, it is possible to use Python as a prototype, which after the code has been tested, can be run with the aid of other languages for programming. If you need PYTHON homework help we are there.
Integration Capabilities: Python comes with a lot of integration capabilities. Some of the most important among them include:
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| PYTHON TOPICS | |
|---|---|
| Lexical Conventions and Syntax | EPM Package Manager |
| SNMP Device Control | Console Scripts |
| Object Oriented Programming | DNS Management using Python |
| Web Programming | String Pattern Matching |
| Threading | File Handling |
| Solaris Systems Administration | Queues |
| Zenoss Related Enterprise SNMP Integration | Errors And Exception Handling |
| Cross-Platform Unix Programming | Browser And Session |
| Python Integration Primer | Data Compression |
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Problem
(define (problem blackbox planner)
(:domain blackbox planner)
(:objects
G H X Grid table(5x5))
(:init
Location: ((5,1)(5,2)(4,1)(4,2)(4,3)(3,4)(1,2)(1,5)) Inaccesable
(G in Grid table, Location(4,4))
(H in Grid table Location(2,1))
(X in Grid table Location(3,3))
(clear G)
(clear H)
(clear X)
)
(:goal (and (at X G ) (at G H)))
)
Node
import heapq
class Node(object):
"""
Represent state of board in 8 puzzle problem.
"""
n = 0
def __init__(self, board, prev_state = None):
assert len(board) == 9
self.board = board[:];
self.prev = prev_state
self.step = 0
Node.n += 1
if self.prev:
self.step = self.prev.step + 1
def __eq__(self, other):
"""Check wether two state is equal."""
return self.board == other.board
def __hash__(self):
"""Return hash code of object.
Used for comparing elements in set
"""
h = [0, 0, 0]
h[0] = self.board[0] << 6 | self.board[1] << 3 | self.board[2]
h[1] = self.board[3] << 6 | self.board[4] << 3 | self.board[5]
h[2] = self.board[6] << 6 | self.board[7] << 3 | self.board[8]
h_val = 0
for h_i in h:
h_val = h_val * 31 + h_i
return h_val
def __str__(self):
string_list = [str(i) for i in self.board]
sub_list = (string_list[:3], string_list[3:6], string_list[6:])
return "\n".join([" ".join(l) for l in sub_list])
def manhattan_distance(self):
"""Return Manhattan distance of state."""
#TODO: return Manhattan distance
distance = 0
goal = [1,2,3,4,5,6,7,8,0]
for i in range(1,9):
xs, ys = self.__i2pos(self.board.index(i))
xg, yg = self.__i2pos(goal.index(i))
distance += abs(xs-xg) + abs(ys-yg)
return distance
def manhattan_score(self):
"""Return Manhattan score of state."""
#TODO: return Manhattan score of state
return 0
def hamming_distance(self):
"""Return Hamming distance of state."""
#TODO: return Hamming distance
distance = 0
goal = [1,2,3,4,5,6,7,8,0]
for i in range(9):
if goal[i] != self.board[i]: distance += 1
return distance
def hamming_score(self):
"""Return Hamming distance score of state."""
#TODO return Hamming score of state
return 0
def next(self):
"""Return next states from this state."""
next_moves = []
i = self.board.index(0)
next_moves = (self.move_up(i), self.move_down(i), self.move_left(i), self.move_right(i))
return [s for s in next_moves if s]
def move_right(self, i):
x, y = self.__i2pos(i)
if y < 2:
right_state = Node(self.board, self)
right = self.__pos2i(x, y+1)
right_state.__swap(i, right)
return right_state
def move_left(self, i):
x, y = self.__i2pos(i)
if y > 0:
left_state = Node(self.board, self)
left = self.__pos2i(x, y - 1)
left_state.__swap(i, left)
return left_state
def move_up(self, i):
x, y = self.__i2pos(i)
if x > 0:
up_state = Node(self.board, self)
up = self.__pos2i(x - 1, y)
up_state.__swap(i, up)
return up_state
def move_down(self, i):
x, y = self.__i2pos(i)
if x < 2:
down_state = Node(self.board, self)
down = self.__pos2i(x + 1, y)
down_state.__swap(i, down)
return down_state
def __swap(self, i, j):
self.board[j], self.board[i] = self.board[i], self.board[j]
def __i2pos(self, index):
return (int(index / 3), index % 3)
def __pos2i(self, x, y):
return x * 3 + y
class PriorityQueue:
def __init__(self):
self.heap = []
self.count = 0
def push(self, item, priority):
# FIXME: restored old behaviour to check against old results better
# FIXED: restored to stable behaviour
entry = (priority, self.count, item)
# entry = (priority, item)
heapq.heappush(self.heap, entry)
self.count += 1
def pop(self):
(_, _, item) = heapq.heappop(self.heap)
# (_, item) = heapq.heappop(self.heap)
return item
def isEmpty(self):
return len(self.heap) == 0
Searcher
from sys import argv
from time import time
from node import *
class Searcher(object):
def __init__(self, start, goal):
self.start = start
self.goal = goal
def print_path(self, state):
path = []
while state:
path.append(state)
state = state.prev
path.reverse()
print("\n-->\n".join([str(state) for state in path]))
def steepest_ascent_hill_climbing(self):
"""Run steepest ascent hill climbing search."""
#TODO Implement hill climbing.
stack = [self.start]
while stack:
state = stack.pop()
if state == self.goal:
self.print_path(state)
print "Find solution"
break
h_val = state.manhattan_distance() + state.hamming_distance()
next_state = False
for s in state.next():
h_val_next = s.manhattan_distance() + s.hamming_distance()
if h_val_next < h_val:
next_state = s
h_val = h_val_next
if next_state:
stack.append(next_state)
else:
self.print_path(state)
print "Cannot find solution"
# I don't know this function
def hill_climbing(self):
"""Run hill climbing search."""
#TODO Implement hill climbing.
stack = [self.start]
while stack:
state = stack.pop()
if state == self.goal:
self.print_path(state)
print "Find solution"
break
h_val = state.manhattan_distance() + state.hamming_distance()
next_state = False
for s in state.next():
h_val_next = s.manhattan_distance() + s.hamming_distance()
if h_val_next < h_val:
next_state = s
h_val = h_val_next
stack.append(next_state)
break
if not next_state:
self.print_path(state)
print "Cannot find solution"
if __name__ == "__main__":
script, strategy = argv
print("Search for solution\n")
start = Node([2,0,1,4,5,3,8,7,6])
goal = Node([1,2,3,4,5,6,7,8,0])
#print start.hamming_distance()
#print start.manhattan_distance()
search = Searcher(start, goal)
start_time = time()
if strategy == "hc":
search.hill_climbing()
elif strategy == "sahc":
search.steepest_ascent_hill_climbing()
else:
print "Wrong strategy"
end_time = time()
elapsed = end_time - start_time
print "Search time: %s" % elapsed
print "Number of initialized node: %d" % Node.n
To summarise, Python is a versatile language that is widely used in industry, making it an excellent choice for students learning to code. However, as Python's popularity grows, so does the demand for Python assignment and homework help. Python assignment help and Python homework help are services that assist students in completing their assignments and homework, ensuring that they understand and can apply the concepts correctly. With the right assistance, you can not only successfully complete your Python assignments and homework, but also gain a deeper understanding of the language and its applications.
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