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Python also has wide academic applications and students across almost all universities need to solve Python assignments and homework. If you are one of the students who need Python Assignment Help then you must reach out to our programming experts as they provide clean and well-commented python codes. Before we discuss more academic services, let us first learn Python Programming.
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.
The key features of Python as a programming language are explained below.
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:
Solving the Python assignment or preparing a Python project needs the application of all such features. All of our Python tutors are well versed in Python features and provide instant Python help for graduate and postgraduate students. They follow the simplistic approach to prepare the solutions and thereby ensure excellent grades for the students. We as a leading online Python services provider, aim to enhance the overall understanding of the students rather than focusing only on providing solutions.
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|Lexical Conventions and Syntax||EPM Package Manager|
|SNMP Device Control||Console Scripts|
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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 = self.board << 6 | self.board << 3 | self.board h = self.board << 6 | self.board << 3 | self.board h = self.board << 6 | self.board << 3 | self.board 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
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