diff --git "a/4856.jsonl" "b/4856.jsonl" new file mode 100644--- /dev/null +++ "b/4856.jsonl" @@ -0,0 +1,669 @@ +{"seq_id":"79375537","text":"# %load q07_get_run_counts_by_match/build.py\n# Default Imports\nimport pandas as pd\nfrom greyatomlib.pandas_project.q01_read_csv_data_to_df.build import read_csv_data_to_df\n\n# You have been give the dataset already in 'ipl_df'.\nipl_df = read_csv_data_to_df('./data/ipl_dataset.csv')\n\n# Solution\ndef get_runs_counts_by_match():\n data = ipl_df.loc[:,['match_code', 'runs']]\n runs = ipl_df.loc[:,'runs']\n pivot_data = pd.pivot_table(data, index = 'match_code',\n columns = runs, aggfunc = 'count')\n return pivot_data\n\nprint (get_runs_counts_by_match())\n\n\n","sub_path":"q07_get_run_counts_by_match/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"630494110","text":"import argparse\nimport table, projectile\n\nx_help = 'The initial x position (m)'\ny_help = 'The initial y position (m)'\nvelocity_help = 'The initial magnitude of velocity (m/s)'\npitch_help = 'The angle of the launch (degrees)'\nstep_help = 'Set step size (seconds)'\nprecision_help = 'Round to PRECISION decimal places (g = 9.80665)'\n\narg_parser = argparse.ArgumentParser()\narg_parser.add_argument('-x', '--x-position', help = x_help, type = float, default = 0)\narg_parser.add_argument('-y', '--y-position', help = y_help, type = float, default = 0)\narg_parser.add_argument('-v', '--velocity', help = velocity_help, type = float, default = 0)\narg_parser.add_argument('-a', '--angle', help = pitch_help, type = float, default = 0)\narg_parser.add_argument('-s', '--step', help = step_help, type = float, default = 1)\narg_parser.add_argument('-p', '--precision', help = precision_help, type = int, default = 2)\n\nargs = arg_parser.parse_args()\n\nx_position = args.x_position\ny_position = args.y_position\nvelocity = args.velocity\nangle = args.angle\nstep = args.step\nprecision = args.precision\n\nif __name__ == '__main__':\n projectile = projectile.projectile(\n x_position, \n y_position, \n velocity, \n angle, \n step, \n precision\n )\n \n headers = projectile.get_headers()\n display = table.init_table(headers)\n\n # Basic projectile motion objective:\n while projectile.y >= 0:\n display.add_row(projectile.get_state())\n projectile.next_step()\n\n table.print_table(display)\n\n","sub_path":"ProjectileMotion/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"141046277","text":"class Solution(object):\n def findNthDigit(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n if n <10:\n return n\n \n sum_ = 9\n cnt = 1\n while(n//sum_ !=0):\n digit_rank = (n-sum_-1)//(cnt+1)\n loc_rank = (n-sum_)%(cnt+1)\n sum_+= (cnt+1)*9*(10**cnt)\n cnt += 1\n digit = 10**(cnt-1)+digit_rank\n\n if loc_rank == 0:\n return digit%10\n else:\n return (digit//(10**(cnt-loc_rank)))%10 ","sub_path":"400. Nth Digit/400. Nth Digit.py","file_name":"400. Nth Digit.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"533703778","text":"from django.contrib import admin\n\n# Register your models here.\nfrom .models import *\n# Register your models here.\n@admin.register(copier_category)\nclass copier_categoryAdmin(admin.ModelAdmin):\n list_display = ('id', 'name')\n ordering = ('id',)\n\n@admin.register(copier)\nclass copierAdmin(admin.ModelAdmin):\n list_display = ('category','copier_name','copier_type')\n list_per_page = 50\n ordering = ('id',)\n\n","sub_path":"apps/products/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"35326765","text":"def div_2(a):\n \"\"\"\n Returns number divided by 2\n :parameter a: int or float\n :return: float\n \"\"\"\n return a / 2\n\n\ndef mult_4(a):\n \"\"\"\n Returns number multiplied by 4\n :parameter a: float or int\n :return: int\n \"\"\"\n p = a * 4\n # Turning decimal p in integer type if its decimal part looks like .0\n if p % 2 == 0:\n p = int(p)\n return p\n\nx = int(input(\"Введите число > \"))\ny = div_2(x)\nprint(str(x) + \" / 2 = \" + str(y))\nprint(str(y) + \" * 4 = \" + str(mult_4(y)))\n","sub_path":"All Projects and Solved Problems/Homework/Chapter 4. Functions/task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"199632440","text":"from itertools import combinations\nfrom copy import deepcopy\nfrom collections import deque\n\nmx = [0, 0, 1, -1]\nmy = [1, -1, 0, 0]\n\nn, m = map(int, input().split())\nboard = []\nfor i in range(n):\n board.append(list(map(int, input().split())))\n\ndef bfs(board, start):\n if board[start[0]][start[1]] == 1:\n return\n q = deque([start])\n board[start[0]][start[1]] = 1\n\n while q:\n x, y = q.popleft()\n\n for i in range(4):\n nx = x + mx[i]\n ny = y + my[i]\n\n if 0<=nx and nx 0 and com_seq <= len(com_list):\n print(good_list[com_seq - 1])\n else:\n print(\"올바른 값을 입력하여 주십시오.\")\n\n\n\n#print_co_goods1(-1)\n#print_co_goods1(1)\n\ndef print_co_goods2(*com_name):\n if com_name == ():\n for j in good_list:\n print(j)\n else:\n for i in range(len(com_list)):\n if com_list[i][\"com_name\"] == com_name:\n tmp_seq = com_list[i][\"com_seq\"]\n print(good_list[i][tmp_seq])\n\n#print_co_goods2()\nprint(good_list)\n\n","sub_path":"pkgs/07module_def_quiz_psm.py","file_name":"07module_def_quiz_psm.py","file_ext":"py","file_size_in_byte":2992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"162004791","text":"class TreeNode:\n __slots__ = [\"_key\", \"_value\", \"_parent_node\", \"_left_child\", \"_right_child\"]\n\n def __init__(self, key, value, parent_node):\n self._key = key\n self._value = value\n self._parent_node = parent_node\n self._left_child = None\n self._right_child = None\n\n def has_child(self, left=True):\n return (self._left_child is not None) if left else (self._right_child is not None)\n\n def get_child(self, left=True):\n return self._left_child if left else self._right_child\n\n def set_child(self, child, left=True):\n if left:\n self._left_child = child\n else:\n self._right_child = child\n\n def get_value(self):\n return self._value\n\n def get_key(self):\n return self._key\n\n def set_value(self, new_value):\n old_value = self._value\n self._value = new_value\n return old_value\n\n def set_key(self, key):\n old_key = self._key\n self._key = key\n return old_key\n\n def get_parent(self):\n return self._parent_node\n\n def set_parent(self, new_parent):\n old_parent = self._parent_node\n self._parent_node = new_parent\n return old_parent\n\n def __repr__(self):\n return \"[%s:%s]\" % (self._key, self._value)\n\n\nLEFT = True\nRIGHT = False\n\n\nclass BinarySearchTree:\n __slots__ = [\"_item_count\", \"_root\", \"_min_node\", \"_max_node\"]\n\n def __init__(self):\n self._item_count = 0\n self._root = None\n self._min_node = None\n self._max_node = None\n\n def _inserted_hook(self, inserted_node):\n pass\n\n def _accessed_hook(self, accessed_node):\n pass\n\n def _deleted_hook(self, parent_node):\n pass\n\n def _search(self, key):\n last = None\n walk = self._root\n\n while walk is not None and walk.get_key() != key:\n last = walk\n walk = walk.get_child(key < walk.get_key())\n\n return walk if walk is not None else last\n\n def __getitem__(self, key):\n return self._get(key)\n\n def _get(self, key):\n node = self._search(key)\n\n if node is not None and node.get_key() == key:\n return node.get_value()\n else:\n return None\n\n def __contains__(self, key):\n node = self._search(key)\n return node is not None and node.get_key() == key\n\n def __setitem__(self, key, value):\n self._insert(key, value)\n\n def _insert(self, key, value):\n \"\"\"\n Insert a node inside the tree. It will perform a search and then insert the node\n inside the tree.\n :performance: O(h), where the h is the height of the tree\n :param key: the key of the mapping\n :param value: the value of the mapping\n :return: void\n \"\"\"\n\n insertion_spot = self._search(key)\n\n if self._root is None:\n self._root = self._make_node(key, value, None)\n node = self._root\n elif insertion_spot.get_key() == key:\n insertion_spot.set_value(value)\n self._accessed_hook(insertion_spot)\n node = None\n elif insertion_spot.get_key() < key:\n right_child = self._make_node(key, value, insertion_spot)\n insertion_spot.set_child(right_child, RIGHT)\n node = right_child\n else:\n left_child = self._make_node(key, value, insertion_spot)\n insertion_spot.set_child(left_child, LEFT)\n node = left_child\n\n if node is not None:\n self._item_count += 1\n if self._min_node is None or key < self._min_node.get_key():\n self._min_node = node\n if self._max_node is None or key > self._max_node.get_key():\n self._max_node = node\n self._inserted_hook(node)\n\n def __delitem__(self, key):\n self._remove(key)\n\n def _remove(self, key):\n node = self._search(key)\n\n if node is None or node.get_key() != key:\n return\n\n one_child = node.has_child(LEFT) != node.has_child(RIGHT)\n no_child = node.has_child(LEFT) == node.has_child(RIGHT) == False\n\n if one_child or no_child:\n self._single_child_delete(node)\n else:\n # we find the predecessor and actually delete that node\n predecessor = self._inorder_predecessor(node)\n\n if predecessor == self._min_node:\n self._min_node = node\n if predecessor == self._max_node:\n self._max_node = node\n\n p_key = predecessor.get_key()\n p_value = predecessor.get_value()\n\n self._single_child_delete(predecessor)\n\n node.set_key(p_key)\n node.set_value(p_value)\n\n def _single_child_delete(self, node):\n\n ancestor = node.get_parent()\n side = LEFT if (ancestor is None or ancestor.get_child(LEFT) == node) else RIGHT\n child = node.get_child(LEFT) if node.has_child(LEFT) else node.get_child(RIGHT)\n\n if node is self._min_node:\n self._min_node = child\n\n if node is self._max_node:\n self._max_node = child\n\n if ancestor is None:\n self._root = child\n if child is not None:\n child.set_parent(None)\n elif child is not None:\n self._attach(ancestor, child, side)\n else:\n ancestor.set_child(None, side)\n\n node.set_parent(None)\n self._item_count -= 1\n\n def __iter__(self):\n for node in self._inorder_traversal(self._root):\n yield node.get_key(), node.get_value()\n\n def _inorder_traversal(self, node):\n\n if node is None:\n return\n\n yield from self._inorder_traversal(node.get_child(LEFT))\n yield node\n yield from self._inorder_traversal(node.get_child(RIGHT))\n\n def _preorder_traversal(self, node):\n\n if node is None:\n return\n\n yield node\n yield from self._preorder_traversal(node.get_child(LEFT))\n yield from self._preorder_traversal(node.get_child(RIGHT))\n\n def _postorder_traversal(self, node):\n\n if node is None:\n return\n\n yield from self._postorder_traversal(node.get_child(LEFT))\n yield from self._postorder_traversal(node.get_child(RIGHT))\n yield node\n\n # noinspection PyMethodMayBeStatic\n def _inorder_predecessor(self, node):\n \"\"\"\n Find the inorder predecessor than of the node. That is, the node with the largest smallest key than\n the node's itself\n :param node: the node from where we start\n :return: the predecessor\n \"\"\"\n if node.has_child(left=True):\n walk = node.get_child()\n while walk.has_child(RIGHT):\n walk = walk.get_child(RIGHT)\n return walk\n else:\n walk = node.get_parent()\n while walk is not None and walk.get_key() > node.get_key():\n walk = walk.get_parent()\n return walk\n\n def _inorder_successor(self, node):\n\n if node.has_child(RIGHT):\n walk = node.get_child(RIGHT)\n while walk.has_child(LEFT):\n walk = walk.get_child(LEFT)\n return walk\n else:\n walk = node.get_parent()\n while walk is not None and walk.get_key() < node.get_key():\n walk = walk.get_parent()\n return walk\n\n def __len__(self):\n return self._item_count\n\n def _make_node(self, key, value, parent):\n \"\"\"\n Make a node in the tree. Can be overriden by subclasses\n :param key: the key\n :param value: the value\n :param parent: the parent node, can be None if root\n :return:\n \"\"\"\n return TreeNode(key, value, parent)\n\n def get_max(self):\n \"\"\"\n Get the key and the value associated with the largest key\n :performance O(1)\n :return: tuple (key, value)\n \"\"\"\n if self._item_count == 0:\n raise ValueError(\"Empty Binary Search tree\")\n else:\n return self._max_node.get_key(), self._max_node.get_value()\n\n def get_min(self):\n \"\"\"\n Get the key and the value associated with the smallest key\n :performance O(1)\n :return: tuple (key, value)\n \"\"\"\n if self._item_count == 0:\n raise ValueError(\"Empty Binary Search tree\")\n else:\n return self._min_node.get_key(), self._min_node.get_value()\n\n def _attach(self, parent, new_child, side=LEFT):\n \"\"\"\n Attach and node to a parent node, and perform the two way bindings\n :param parent: the parent node\n :param new_child: the child node\n :param side: the side where the child will be\n :return: void\n \"\"\"\n parent.set_child(new_child, side)\n new_child.set_parent(parent)\n","sub_path":"pymaps/trees/BinarySearchTree.py","file_name":"BinarySearchTree.py","file_ext":"py","file_size_in_byte":8909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"631078442","text":"import requests\nfrom lxml import etree\nimport csv\n# csv后缀的格式就是excel文件打开的格式，我们等于是直接存入了excel中\nimport time\n\nheaders = {\n \"User-Agent\": \"Opera/9.80 (Windows NT 6.0) Presto/2.12.388 Version/12.14\"\n}\n\nf = open(\"软件测试工程师20200813.cvs\",\"w\",newline=\"\")\nwriter = csv.writer(f)\nwriter.writerow(['编号','职位名', '公司名', '工作地点', '薪资', '发布时间'])\n\ni = 1\nfor page in range(1,42):\n requests_get = requests.get(\n f\"https://search.51job.com/list/020000,000000,0000,00,9,99,%25E8%25BD%25AF%25E4%25BB%25B6%25E6%25B5%258B%25E8%25AF%2595%25E5%25B7%25A5%25E7%25A8%258B%25E5%25B8%2588,2,1.html?lang=c&postchannel=0000&workyear=99&cotype=99°reefrom=99&jobterm=99&companysize=99&ord_field=0&dibiaoid=0&line=&welfare=\",headers=headers\n )\n requests_get.encoding=\"GBK\"\n if requests_get.status_code == 200:\n html = etree.HTML(requests_get.text)\n els = html.xpath(\"//div[@class='el']\")[4:]\n for el in els:\n jobname = str(el.xpath(\"p[contains(@class,'t1')]/span/a/@title\")).strip(\"[']\")\n jobcom = str(el.xpath(\"span[@class='t2']/a/@title\")).strip(\"[']\")\n jobaddress = str(el.xpath(\"span[@class='t3']/text()\")).strip(\"[']\")\n jobsalary = str(el.xpath(\"span[@class='t4']/text()\")).strip(\"[']\")\n jobdate = str(el.xpath(\"span[@class='t5']/text()\")).strip(\"[']\")\n writer.writerow([i, jobname, jobcom, jobaddress, jobsalary, jobdate])\n i += 1\n print(f\"第{page}页获取完毕\")\n\n\n\n\n\n\n\n","sub_path":"python_web_learning/day17.py","file_name":"day17.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"129386507","text":"a, b = 0, 1\nsequence = [0, 1]\n\nansw = int(input('How many numbers do you need? > '))\n\nfor i in range(answ - 2):\n\ta, b = b, a + b\n\tsequence.append(b)\n\nprint(sequence)\n","sub_path":"Numbers/pybonacci.py","file_name":"pybonacci.py","file_ext":"py","file_size_in_byte":166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"33711922","text":"from cbuild.core import chroot, logger\nfrom cbuild.apk import cli\n\nimport re\n\ndef invoke(pkg):\n if not pkg.options[\"scancmd\"] or pkg.bootstrapping:\n return\n\n cmds = []\n cmdset = {}\n\n for p in pkg.provides:\n if not p.startswith(\"cmd:\"):\n continue\n cmdname = p[4:]\n cmdset[cmdname] = True\n logger.get().out_plain(f\" cmd: {cmdname} (explicit)\")\n\n for f in pkg.destdir.glob(\"usr/bin/*\"):\n if f.name in cmdset:\n continue\n logger.get().out_plain(f\" cmd: {f.name} from usr/bin\")\n cmds.append(f.name)\n\n cmds.sort()\n\n if len(cmds) == 0:\n return\n\n pkg.cmd_provides = cmds\n","sub_path":"src/cbuild/hooks/pre_pkg/06_cmd_provides.py","file_name":"06_cmd_provides.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"537739231","text":"\"\"\"\nYou are given a binary tree in which each node contains an integer value.\n\nFind the number of paths that sum to a given value.\n\nThe path does not need to start or end at the root or a leaf, but it must go downwards (traveling only from parent nodes to child nodes).\n\nThe tree has no more than 1,000 nodes and the values are in the range -1,000,000 to 1,000,000.\n\nExample:\n\nroot = [10,5,-3,3,2,null,11,3,-2,null,1], sum = 8\n\n 10\n / \\\n 5 -3\n / \\ \\\n 3 2 11\n / \\ \\\n3 -2 1\n\nReturn 3. The paths that sum to 8 are:\n\n1. 5 -> 3\n2. 5 -> 2 -> 1\n3. -3 -> 11\n\"\"\"\n\n# We simply check for every possible parent child path.\n# Truly bruteforcing.\n\ndef path_sum(root, sum):\n if not root:\n return 0\n return path_sum(root.left, sum) + path_sum(root.right) + _path_sum_helper(\n root, sum)\n\ndef _path_sum_helper(root, sum):\n if not root:\n return 0\n num_of_paths = 0\n if root.val == sum:\n num_of_paths += 1\n num_of_paths += _path_sum_helper(\n root.left, sum-root.val) + _path_sum_helper(\n root.right, sum-root.val)\n return num_of_paths\n","sub_path":"Tree/path_sum_iii.py","file_name":"path_sum_iii.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"579502977","text":"import math\n\n\"\"\"\nf = open('xprime00031.txt')\nprime = f.read()\nprime = int(prime)\n\ns = open('zz00031out.txt')\ntext2 = s.read()\n\n# for s in text2.split():\n# print(s)\n\nres = [int(i) for i in text2.split() if i.isdigit()]\n# print(res)\na = int(res[0])\nb = int(res[1])\n\n\"\"\"\n\na = 1\nb = 3\ncycles = 41\nprime = 31\nx = 1\ny = 6\nz = 1\n\n\nco = [x,y,z]\n\nList3 = []\nList3.append(co)\nprint(List3)\n\n\n# doubling\ndef doubling():\n A = y**2 % prime\n B = (4*x*A) % prime\n C = (8*(A**2)) % prime\n D = (3*(x**2) + a*(z**4)) % prime\n xjacob = (D**2 - (2 * B)) % prime\n yjacob = ((D * (B - xjacob)) - C) % prime\n zjacob = (2*y*z) % prime\n\n List = [xjacob, yjacob, zjacob]\n List3.extend(List)\n return List\n\n\n# adding\ndef adding():\n w = doubling()\n A1 = (w[2]**2) % prime\n B1 = (w[2]*A1) % prime\n C1 = (x*A1) % prime\n D1 = (y*B1) % prime\n E1 = (C1 - w[0]) % prime\n F1 = (D1 - w[1]) % prime\n G1 = (E1**2) % prime\n H1 = (G1*E1) % prime\n I1 = (w[0] * G1) % prime\n xadd = ((F1**2) - H1 - (2*I1)) % prime\n yadd = ((F1 * (I1-xadd)) - (w[1]*H1)) % prime\n zadd = (w[2]*E1) % prime\n\n List2 = [xadd, yadd, zadd]\n List3.extend(List2)\n return List2\n\n\n# kinda like main method\n\n\n\n\n\n\n","sub_path":"EllipticCurve/prog3.py","file_name":"prog3.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"482210151","text":"import pandas as pd\nimport time\nfrom multiprocessing import Pool\nimport json\nimport os\nimport time\nimport numpy as np\nimport datetime\nimport sys\n\nfrom xiaobing_chat import xiaoiceApi\n\ndef write_to_file(dict_data, file_save_dir):\n\n if not os.path.exists(file_save_dir): \n os.makedirs(file_save_dir)\n\n today = str(datetime.date.today()).replace('-', '')\n with open(file_save_dir+'/result_{0}.txt'.format(today), 'a+', encoding='utf8') as f:\n f.writelines(json.dumps(dict_data, ensure_ascii=False))\n f.writelines('\\n')\n f.close()\n\ndef xb_chat(xb_bot, xb_name, query_list):\n results = []\n for idx, query in enumerate(query_list):\n answer = xb_bot.chat(query)['text']\n dic = dicts(xb_name, query, answer)\n print(dic)\n write_to_file(dic, 'output_data/')\n\ndef dicts(xb_name, query, answer):\n return {'bot_name': xb_name, 'query': query, 'answer': answer}\n\ndef chunks(query_list, account_nums):\n return np.array_split(query_list, account_nums)\n\ndef construct_bot(query_list):\n bots = [xiaoiceApi(header_file='headers_files/{0}'.format(header_file)) \n for header_file in os.listdir('headers_files/') if header_file.endswith('.txt')]\n bots_name = ['bot_{0}'.format(i+1) for i in range(len(bots))]\n query_split = chunks(query_list, len(bots))\n return len(bots), list(zip(bots, bots_name, query_split))\n\nif __name__ == '__main__':\n\n\n query_file = pd.read_csv('input_data/'+sys.argv[1])\n # 如果是真实运行环境，就去掉[:10]\n query_test = query_file[sys.argv[2]].tolist()[:10]\n\n bots_num, parameters = construct_bot(query_test)\n with Pool(processes=bots_num) as pool:\n results = pool.starmap(xb_chat, parameters)\n time.sleep(0.2)\n print('Done')\n","sub_path":"main_run.py","file_name":"main_run.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"404798761","text":"from dataclasses import dataclass\nfrom generali.models.com.generali.enterprise_services.core.gbm.enterprise.organisation.v2.notify_organisation_gbmrequest_type import NotifyOrganisationGbmrequestType\n\n__NAMESPACE__ = \"http://generali.com/enterprise-services/core/gbm/enterprise/organisation/v2\"\n\n\n@dataclass\nclass NotifyOrganisationGbmrequest(NotifyOrganisationGbmrequestType):\n \"\"\"\n The definition of the response message that supports\n notifying on an Organisation\n \"\"\"\n class Meta:\n name = \"NotifyOrganisationGBMRequest\"\n namespace = \"http://generali.com/enterprise-services/core/gbm/enterprise/organisation/v2\"\n","sub_path":"generali/models/com/generali/enterprise_services/core/gbm/enterprise/organisation/v2/notify_organisation_gbmrequest.py","file_name":"notify_organisation_gbmrequest.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"164018594","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom django_tables2.config import RequestConfig\nfrom django.utils.html import format_html\nfrom datetime import datetime\nimport django_tables2 as tables\nfrom django.contrib.auth.decorators import login_required\n\nfrom .models import Product, ScrapingSession, Review, Category, OdSessions\n\nclass ProductsTable(tables.Table):\n\n def render_image(self, value):\n return format_html('
', value)\n\n def render_title(self, record):\n return format_html('%s' % (record.url, record.title))\n\n def render_num_of_reviews(self, record):\n return format_html('%s' % (record.product_id, record.num_of_reviews))\n\n class Meta:\n model = Product\n fields = ('image', 'title', 'price', 'favorite_by', 'num_of_reviews', 'num_in_cart', 'sold', 'date_of_latest_review')\n attrs = {'class': 'table table-striped table-bordered'}\n\n@login_required(login_url=\"/login/\")\ndef index(request):\n ondemand_sess = OdSessions.objects.values_list('session_id', flat=True)\n od_available = not OdSessions.objects.filter(session_closed=0).exists()\n od_sessions = ScrapingSession.objects.filter(pk__in=ondemand_sess)\n for sess in od_sessions:\n ods = OdSessions.objects.filter(session_id=sess.id).first()\n setattr(sess, 'keywords', ods.keywords)\n setattr(sess, 'session_closed', ods.session_closed)\n setattr(sess, 'time_left_hrs', round(sess.products_scraped/12000, 2))\n\n context = {\n 'sessions': ScrapingSession.objects.exclude(pk__in=ondemand_sess),\n 'od_sessions': od_sessions,\n 'od_available': od_available,\n 'header': 'Dashboard'\n }\n return render (request, 'index.html', context)\n\n@login_required(login_url=\"/login/\")\ndef reviews(request, productid):\n context = {\n 'reviews': Review.objects.all().filter(post_id=productid),\n 'productid': productid,\n 'header': 'Dashboard > Products > Reviews'\n }\n return render (request, 'reviews.html', context)\n\n@login_required(login_url=\"/login/\")\ndef new_session(request):\n context = {\n 'header': 'Dashboard > New On Demand Session'\n }\n return render (request, 'create_od_session.html', context)\n\n@login_required(login_url=\"/login/\")\ndef create_session(request):\n p_keywords = request.POST.get(\"keywords\", \"\")\n new_session = ScrapingSession.objects.create(\n start_time=datetime.now(),\n urls_created=0,\n article_urls_scraped=1,\n products_scraped=0,\n end_time=datetime.now(),\n inserting = 0\n )\n od_session = OdSessions.objects.create(\n session_id=new_session.id,\n keywords=p_keywords,\n session_closed=0\n )\n context = {\n 'session_created': 1,\n 'header': 'Dashboard'\n }\n return redirect('/dashboard')\n\n@login_required(login_url=\"/login/\")\ndef products(request, sessionid):\n search = request.GET.get('search')\n minprice = request.GET.get('minprice')\n maxprice = request.GET.get('maxprice')\n minfav = request.GET.get('minfav')\n maxfav = request.GET.get('maxfav')\n minrev = request.GET.get('minrev')\n maxrev = request.GET.get('maxrev')\n mincart = request.GET.get('mincart')\n maxcart = request.GET.get('maxcart')\n minsold = request.GET.get('minsold')\n maxsold = request.GET.get('maxsold')\n category = request.GET.get('category')\n title = request.GET.get('title')\n\n filter_kwargs = {'session_id': sessionid}\n if search:\n filter_kwargs['title__contains'] = search\n if title:\n filter_kwargs['title__contains'] = title\n if maxfav:\n filter_kwargs['favorite_by__lte'] = maxfav\n if minfav:\n filter_kwargs['favorite_by__gte'] = minfav\n if maxprice:\n filter_kwargs['price__lte'] = maxprice\n if minprice:\n filter_kwargs['price__gte'] = minprice\n if maxrev:\n filter_kwargs['num_of_reviews__lte'] = maxrev\n if minrev:\n filter_kwargs['num_of_reviews__gte'] = minrev\n if maxcart:\n filter_kwargs['num_in_cart__lte'] = maxcart\n if mincart:\n filter_kwargs['num_in_cart__gte'] = mincart\n if maxsold:\n filter_kwargs['sold__lte'] = maxsold\n if minsold:\n filter_kwargs['sold__gte'] = minsold\n if category:\n category = int(category)\n if category != 0:\n filter_kwargs['category_id'] = category\n\n table = ProductsTable(Product.objects.all().filter(**filter_kwargs))\n RequestConfig(request, paginate={'per_page': 25}).configure(table)\n context = {\n 'products': table,\n 'header': 'Dashboard > Products',\n 'categorylist': Category.objects.all(),\n 'minprice': minprice,\n 'maxprice': maxprice,\n 'minfav': minfav,\n 'maxfav': maxfav,\n 'minrev': minrev,\n 'maxrev': maxrev,\n 'mincart': mincart,\n 'maxcart': maxcart,\n 'minsold': minsold,\n 'maxsold': maxsold,\n 'category': category,\n 'title': title if title!=None else \"\"\n }\n if search:\n context['search'] = search\n\n return render (request, 'products.html', context)\n","sub_path":"webapp/dashboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"231150644","text":"import random\n\n\ndef maxContinuousProduct(nums):\n maxEndingHere = minEndingHere = maximum = nums[0]\n for num in nums[1:]:\n maxEndingHere = max(maxEndingHere*num, minEndingHere*num, num)\n minEndingHere = min(minEndingHere*num, maxEndingHere*num, num)\n maximum = max(maximum, maxEndingHere)\n return maximum\n\n\nn = [random.randint(-10, 20) for _ in range(10)]\nprint(n)\nprint('Max:', maxContinuousProduct(n))\n","sub_path":"array/max_continous_product.py","file_name":"max_continous_product.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"575630240","text":"import numpy as np \r\nimport normal_probability_distribution as npd \r\nimport modeling_of_uncertainty as mou\r\nimport math as mt\r\nfrom scipy.stats import chi2\r\nfrom scipy import stats as st\r\n\r\ndef chi_square_test(sample, alpha): \r\n m = mt.ceil(1 + 3.322*mt.log(len(sample),10))\r\n k = 2 \r\n f = m - 1 - k\r\n c = chi2.ppf(1-alpha, f)\r\n\r\n h = (max(sample)-min(sample))/m\r\n\r\n mean = mou.mean_sample(sample)\r\n std = mou.std_sample(sample)\r\n \r\n class_ = [min(sample)]\r\n for i in range(0, m):\r\n class_.append(class_[i] + h)\r\n\r\n e = []\r\n for i in range(1, len(class_)):\r\n if i == 1:\r\n e.append(npd.phi(class_[i], mean, std)*len(sample))\r\n elif i == (len(class_)):\r\n e.append((1 - npd.phi(class_[i-1], mean, std))*len(sample))\r\n else:\r\n e.append((npd.phi(class_[i], mean, std)-npd.phi(class_[i-1], mean, std))*len(sample))\r\n\r\n n = []\r\n t = 0\r\n i = 1\r\n sample = sorted(sample)\r\n sample.append(max(sample)+1)\r\n\r\n for j in range(0, len(sample)-1):\r\n if sample[j+1] > class_[i]:\r\n n.append((j+1)-t)\r\n t = j + 1\r\n i = i + 1\r\n\r\n test_1 = []\r\n for i in range(0, len(n)):\r\n test_1.append(((e[i]-n[i])**2)/e[i])\r\n \r\n statistics = sum(test_1)\r\n\r\n if statistics < c:\r\n print('Chi-Square Statistics = {}\\nP-Value = {}\\nConsidering that Chi-Square Statistics < P-Value, with a confidence level of {}%, the normal distribution is acceptable.'.format(statistics, c, (1-alpha)*100))\r\n \r\n elif statistics > c:\r\n print('Chi-Square Statistics = {}\\nP-Value = {}\\nConsidering that Chi-Square Statistics > P-Value, with a confidence level of {}%, the normal distribution is not acceptable.'.format(statistics, c, (1-alpha)*100))\r\n\r\ndef ks_test(sample, alpha):\r\n sort_sample = sorted(sample)\r\n n = len(sort_sample)\r\n S = np.arange(1, n + 1)/n\r\n mean = np.mean(sample)\r\n std = np.std(sample, ddof = 1)\r\n F = st.norm.cdf(sort_sample, mean, std) \r\n D = max(abs(F-S))\r\n D_ks = st.ksone.ppf(1 - alpha/2, n)\r\n\r\n if D < D_ks:\r\n print('Kolmogorov-Smirnov Statistics = {}\\nP-Value = {}\\nConsidering that K-S Test Statistics < P-Value, with a confidence level of {}%, the normal distribution is acceptable.'.format(D, D_ks, (1-alpha)*100))\r\n \r\n elif D > D_ks:\r\n print('Kolmogorov-Smirnov Statistics = {}\\nP-Value = {}\\nConsidering that K-S Test Statistics > P-Value, with a confidence level of {}%, the normal distribution is not acceptable.'.format(D, D_ks, (1-alpha)*100))\r\n","sub_path":"normal_statistical_tests.py","file_name":"normal_statistical_tests.py","file_ext":"py","file_size_in_byte":2573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"401983177","text":"def solution(id_list, report, k):\n answer = [0 for _ in range(len(id_list))]\n report = list(set(report))\n report_dict = {}\n id_dict = {}\n\n for i in range(len(id_list)):\n id_dict[id_list[i]] = i\n\n for i in range(len(report)):\n a, b = report[i].split()\n report_dict.setdefault(b, 0)\n report_dict[b] += 1\n\n for i in range(len(report)):\n a, b = report[i].split()\n if report_dict[b] >= k:\n answer[id_dict[a]] += 1\n\n return answer\n\n\"\"\"\n풀이\n처음 문제를 봤을 때는 어려워보였지만 딕셔너리를 이용했더니 금방 풀렸다.\n\"\"\"","sub_path":"season4/week1/minkyu/week1_2.py","file_name":"week1_2.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"318914751","text":"import time\nimport logging\nfrom datetime import datetime, timezone, timedelta\nfrom typing import Iterable, Optional, List, Tuple\n\nfrom .status import ModStatus\nfrom .repos import NetkanRepo\nfrom .github_pr import GitHubPR\n\n\nclass AutoFreezer:\n\n BRANCH_NAME = 'freeze/auto'\n\n def __init__(self, nk_repo: NetkanRepo, github_pr: GitHubPR, game_id: str) -> None:\n self.nk_repo = nk_repo\n self.github_pr = github_pr\n self.game_id = game_id\n\n def freeze_idle_mods(self, days_limit: int, days_till_ignore: int) -> None:\n self.nk_repo.pull_remote_primary(strategy_option='ours')\n idle_mods = self._find_idle_mods(days_limit, days_till_ignore)\n if idle_mods:\n with self.nk_repo.change_branch(self.BRANCH_NAME):\n for ident, _ in idle_mods:\n if self.nk_repo.nk_path(ident).exists():\n logging.info('Freezing %s', ident)\n self._add_freezee(ident)\n else:\n logging.info('Already froze %s', ident)\n self._submit_pr(self.BRANCH_NAME, days_limit, idle_mods)\n\n def mark_frozen_mods(self) -> None:\n with ModStatus.batch_write() as batch:\n logging.info('Marking frozen mods...')\n for mod in ModStatus.scan(rate_limit=5, filter_condition=ModStatus.game_id == self.game_id):\n if not mod.frozen and self._is_frozen(mod.ModIdentifier):\n logging.info('Marking frozen: %s', mod.ModIdentifier)\n mod.frozen = True\n # Ensure we don't exceed our table rate limit\n if len(batch.pending_operations) == 5:\n batch.commit()\n time.sleep(1)\n batch.save(mod)\n logging.info('Done!')\n\n def _is_frozen(self, ident: str) -> bool:\n return not self.nk_repo.nk_path(ident).exists()\n\n def _ids(self) -> Iterable[str]:\n return (nk.identifier for nk in self.nk_repo.netkans())\n\n def _find_idle_mods(self, days_limit: int, days_till_ignore: int) -> List[Tuple[str, datetime]]:\n update_cutoff = datetime.now(timezone.utc) - timedelta(days=days_limit)\n too_old_cutoff = update_cutoff - timedelta(days=days_till_ignore)\n # I can't get a list comprehension to do this without the datetime becoming optional\n idle_mods = []\n for ident in self._ids():\n dttm = self._last_timestamp(ident)\n if dttm and too_old_cutoff < dttm < update_cutoff:\n idle_mods.append((ident, dttm))\n idle_mods.sort(key=lambda mod: mod[1])\n return idle_mods\n\n def _last_timestamp(self, ident: str) -> Optional[datetime]:\n try:\n status = ModStatus.get(ident, range_key=self.game_id.lower())\n return getattr(status, 'release_date',\n getattr(status, 'last_indexed',\n None))\n except ModStatus.DoesNotExist:\n # No timestamp if mod isn't in the status table (very freshly merged)\n return None\n\n def _add_freezee(self, ident: str) -> None:\n self.nk_repo.git_repo.index.move([\n self.nk_repo.nk_path(ident).as_posix(),\n self.nk_repo.frozen_path(ident).as_posix()\n ])\n self.nk_repo.git_repo.index.commit(f'Freeze {ident}')\n\n def _mod_table(self, idle_mods: List[Tuple[str, datetime]]) -> str:\n return '\\n'.join([\n 'Mod | Last Update',\n ':-- | :--',\n *[f'{AutoFreezer._mod_cell(mod[0], self.game_id)} | {mod[1].astimezone(timezone.utc):%Y-%m-%d %H:%M %Z}'\n for mod in idle_mods]\n ])\n\n @staticmethod\n def _mod_cell(ident: str, game_id: str) -> str:\n status = ModStatus.get(ident, range_key=game_id.lower())\n resources = getattr(status, 'resources', None)\n if resources:\n links = r' \\| '.join(f'[{key}]({url})'\n for key, url in sorted(resources.as_dict().items()))\n return f'**{ident}**
{links}'\n return f'**{ident}**'\n\n def _submit_pr(self, branch_name: str, days: int, idle_mods: List[Tuple[str, datetime]]) -> None:\n if self.github_pr:\n logging.info('Submitting pull request for %s', branch_name)\n self.github_pr.create_pull_request(\n branch=branch_name,\n title='Freeze idle mods',\n body=(f'The attached mods have not updated in {days} or more days.'\n ' Freeze them to save the bot some CPU cycles.'\n '\\n\\n'\n f'{self._mod_table(idle_mods)}'),\n labels=['Pull request', 'Freeze', 'Needs looking into'],\n )\n","sub_path":"netkan/netkan/auto_freezer.py","file_name":"auto_freezer.py","file_ext":"py","file_size_in_byte":4801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"475811361","text":"\"\"\"Class definitions for Segmentation based on voice activity in MFA\"\"\"\nfrom __future__ import annotations\n\nimport os\nimport shutil\nfrom typing import TYPE_CHECKING, Dict, List, Optional\n\nfrom .config import TEMP_DIR\nfrom .exceptions import KaldiProcessingError\nfrom .multiprocessing.ivector import segment_vad\nfrom .utils import log_kaldi_errors, parse_logs\n\nif TYPE_CHECKING:\n from logging import Logger\n\n from .config import ConfigDict, SegmentationConfig\n from .corpus import Corpus\n\nSegmentationType = List[Dict[str, float]]\n\n__all__ = [\"Segmenter\"]\n\n\nclass Segmenter:\n \"\"\"\n Class for performing speaker classification\n\n Parameters\n ----------\n corpus : :class:`~montreal_forced_aligner.corpus.TranscribeCorpus`\n Corpus object for the dataset\n segmentation_config : :class:`~montreal_forced_aligner.config.SegmentationConfig`\n Configuration for alignment\n temp_directory : str, optional\n Specifies the temporary directory root to save files need for Kaldi.\n If not specified, it will be set to ``~/Documents/MFA``\n debug : bool\n Flag for running in debug mode, defaults to false\n verbose : bool\n Flag for running in verbose mode, defaults to false\n logger : :class:`~logging.Logger`, optional\n Logger to use\n \"\"\"\n\n def __init__(\n self,\n corpus: Corpus,\n segmentation_config: SegmentationConfig,\n temp_directory: Optional[str] = None,\n debug: Optional[bool] = False,\n verbose: Optional[bool] = False,\n logger: Optional[Logger] = None,\n ):\n self.corpus = corpus\n self.segmentation_config = segmentation_config\n\n if not temp_directory:\n temp_directory = TEMP_DIR\n self.temp_directory = temp_directory\n self.debug = debug\n self.verbose = verbose\n self.logger = logger\n self.uses_cmvn = False\n self.uses_slices = False\n self.uses_vad = False\n self.speaker_independent = True\n self.setup()\n\n @property\n def segmenter_directory(self) -> str:\n \"\"\"Temporary directory for segmentation\"\"\"\n return os.path.join(self.temp_directory, \"segmentation\")\n\n @property\n def vad_options(self) -> ConfigDict:\n \"\"\"Options for performing VAD\"\"\"\n return {\n \"energy_threshold\": self.segmentation_config.energy_threshold,\n \"energy_mean_scale\": self.segmentation_config.energy_mean_scale,\n }\n\n @property\n def use_mp(self) -> bool:\n \"\"\"Flag for whether to use multiprocessing\"\"\"\n return self.segmentation_config.use_mp\n\n def setup(self) -> None:\n \"\"\"\n Sets up the corpus and segmenter for performing VAD\n\n Raises\n ------\n :class:`~montreal_forced_aligner.exceptions.KaldiProcessingError`\n If there were any errors in running Kaldi binaries\n \"\"\"\n done_path = os.path.join(self.segmenter_directory, \"done\")\n if os.path.exists(done_path):\n self.logger.info(\"Classification already done, skipping initialization.\")\n return\n dirty_path = os.path.join(self.segmenter_directory, \"dirty\")\n if os.path.exists(dirty_path):\n shutil.rmtree(self.segmenter_directory)\n log_dir = os.path.join(self.segmenter_directory, \"log\")\n os.makedirs(log_dir, exist_ok=True)\n try:\n self.corpus.initialize_corpus(None, self.segmentation_config.feature_config)\n except Exception as e:\n with open(dirty_path, \"w\"):\n pass\n if isinstance(e, KaldiProcessingError):\n log_kaldi_errors(e.error_logs, self.logger)\n e.update_log_file(self.logger.handlers[0].baseFilename)\n raise\n\n def segment(self) -> None:\n \"\"\"\n Performs VAD and segmentation into utterances\n\n Raises\n ------\n :class:`~montreal_forced_aligner.exceptions.KaldiProcessingError`\n If there were any errors in running Kaldi binaries\n \"\"\"\n log_directory = os.path.join(self.segmenter_directory, \"log\")\n dirty_path = os.path.join(self.segmenter_directory, \"dirty\")\n done_path = os.path.join(self.segmenter_directory, \"done\")\n if os.path.exists(done_path):\n self.logger.info(\"Classification already done, skipping.\")\n return\n try:\n self.corpus.compute_vad()\n self.uses_vad = True\n segment_vad(self)\n parse_logs(log_directory)\n except Exception as e:\n with open(dirty_path, \"w\"):\n pass\n if isinstance(e, KaldiProcessingError):\n log_kaldi_errors(e.error_logs, self.logger)\n e.update_log_file(self.logger.handlers[0].baseFilename)\n raise\n with open(done_path, \"w\"):\n pass\n\n def export_segments(self, output_directory: str) -> None:\n \"\"\"\n Export the results of segmentation as TextGrids\n\n Parameters\n ----------\n output_directory: str\n Directory to save segmentation TextGrids\n \"\"\"\n backup_output_directory = None\n if not self.segmentation_config.overwrite:\n backup_output_directory = os.path.join(self.segmenter_directory, \"transcriptions\")\n os.makedirs(backup_output_directory, exist_ok=True)\n for f in self.corpus.files.values():\n f.save(output_directory, backup_output_directory)\n","sub_path":"montreal_forced_aligner/segmenter.py","file_name":"segmenter.py","file_ext":"py","file_size_in_byte":5512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"470677702","text":"weight = 0.5 #initial random weight\ngoal_pred = 0.8 #the value we are trying to predict. In a supervised example, this is the ground truth value\ninput = 0.5 #the input value that we receive - could be from a variety of sources\nlearningRate = 0.3 #how fasst/slow do we learn. Too big will lead to divergence, too small will lead to long time for convergence\nepochs = 200 #the number of epochs during which we try to learn\n\nfor iteration in range(epochs):\n\tprediction = input * weight #the prediction based on the input value\n\terror = (prediction - goal_pred) ** 2 #the wrongness of the prediction as measured by squared error\n\tderivative = input * (prediction - goal_pred) #the derivated - which shows how the prediction is impacted by the error\n\tweight = weight - (learningRate * derivative) #modify the weight by the derivative to minimize the error - i.e. learn\n\t\n\tprint(\"Error:\" + str(error) + \" Prediction: \"+ str(prediction) + \" Weight:\" + str(weight))\n\t\n\t\n\t\n#At the end of the run, we now how a single input output network which can take an input and learn the weight that will allow the input to be transformed into the output sucessfully. Remember this is a single input/output network with a single weight\n","sub_path":"simplenet.py","file_name":"simplenet.py","file_ext":"py","file_size_in_byte":1215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"340027545","text":"#!/usr/bin/python\nimport argparse\n\n'''\nSpec:\n -accepts a list of prices (integers)\n -find the max profit that can be made \n -you must \"buy\" first (cannot buy after selling, duh)\n\n\ncalculate all \"profits\" that can be made from pairs\nwill never \"buy\" the last item (nothing comes after it)\n\nnested loop\n\n'''\n\n\ndef find_max_profit(prices):\n p = prices[1] - prices[0]\n\n for i in range(len(prices)-1):\n for j in range(i+1, len(prices)-1):\n if (prices[j] - prices[i]) > p:\n p = prices[j] - prices[i]\n\n return p\n\n\nfind_max_profit([100, 90, 80, 50, 20, 10])\nprint(find_max_profit([1050, 270, 1540, 3800, 2]))\n\n# if __name__ == '__main__':\n# # This is just some code to accept inputs from the command line\n# parser = argparse.ArgumentParser(\n# description='Find max profit from prices.')\n# parser.add_argument('integers', metavar='N', type=int,\n# nargs='+', help='an integer price')\n# args = parser.parse_args()\n\n# print(\"A profit of ${profit} can be made from the stock prices {prices}.\".format(\n# profit=find_max_profit(args.integers), prices=args.integers))\n","sub_path":"stock_prices/stock_prices.py","file_name":"stock_prices.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"441907802","text":"import sys\nimport time\nimport stomp\nimport random\nimport datetime\nimport RecommenderSystem_pb2\n\n# Check the argument of the command\nif (len(sys.argv) != 3):\n\tprint (\"Need arguments: python Main.py IP PORT (10.35.23.2 61613)\")\n\tsys.exit()\n\n# Create connection to subscribe to ActiveMQ broker IP () & Port (61613) \nconnection = stomp.Connection(host_and_ports=[(sys.argv[1], sys.argv[2])])\n\n# Start connection \nconnection.start()\nconnection.connect()\n\nwhile (1):\n\t# Create protobuf\n\trequest = RecommenderSystem_pb2.Request()\n\trandomCommand = raw_input('Enter command: ')\n\n\tdateTimeString = str(datetime.datetime.now().strftime('%y%m%d%H%M%S'))\n\tif randomCommand == 'queryUser':\n\t\trequest.command = 'queryUser'\n\t\trequest.userID = raw_input('{0} Enter userID: '.format(request.command))\n\t\tprint('{0:15}{1:15}{2:15}{3}'.format('REQUEST', dateTimeString, request.command, request.userID))\n\n\telif randomCommand == 'queryDocument':\n\t\trequest.command = 'queryDocument'\n\t\trequest.documentID = raw_input('{0} Enter documentID: '.format(request.command))\n\t\tprint('{0:15}{1:15}{2:15}{3}'.format('REQUEST', dateTimeString, request.command, request.documentID))\n\n\telif randomCommand == 'querySimilarDocument':\n\t\trequest.command = 'querySimilarDocument'\n\t\trequest.documentID = raw_input('{0} Enter documentID: '.format(request.command))\n\t\tprint('{0:15}{1:15}{2:15}{3}'.format('REQUEST', dateTimeString, request.command, request.documentID))\n\n\telif randomCommand == 'updateUser':\n\t\trequest.command = 'updateUser'\n\t\trequest.userID = raw_input('{0} Enter userID: '.format(request.command))\n\t\trequest.documentID = raw_input('{0} Enter documentID: '.format(request.command))\n\t\tprint('{0:15}{1:15}{2:15}{3}'.format('UPDATE', dateTimeString, request.command, request.documentID))\n\n\telif randomCommand == 'updateDocument':\n\t\trequest.command = 'updateDocument'\n\t\trequest.documentID = raw_input('{0} Enter documentID: '.format(request.command))\n\t\trequest.userID = raw_input('{0} Enter userID: '.format(request.command))\n\t\trequest.documentTitle = raw_input('{0} Enter Title: '.format(request.command))\n\t\trequest.documentText = raw_input('{0} Enter Text: '.format(request.command))\n\t\tprint('{0:15}{1:15}{2:15}{3}'.format('UPDATE', dateTimeString, request.command, request.documentID))\n\n\telif randomCommand == 'retrain':\n\t\trequest.command = 'retrain'\n\t\tprint('{0:10}{1:15}{2:15}{3}'.format('RETRAIN', request.command, '', dateTimeString))\n\n\trequest.unique = dateTimeString\n\tconnection.send(body=request.SerializeToString(), destination='/queue/carbon')\n \n# Close connection\nconnection.disconnect()\n","sub_path":"sourceCode/Request.py","file_name":"Request.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"219348773","text":"# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\n# #\n# #\n# #\n# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ #\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# === Parameters ===\nneigs = 8 \n# === Define the srping system ===\n# uniform spacing of the nodes of the undeformed system\nnSpring = 5 \nnPoints = nSpring+1\nxx = np.arange(0.,nPoints)\nyy = np.zeros(nPoints)\nrr = np.array([xx,yy])\n# uniform mass, damping and stiffness distributions\nm1 = 1. ; c1 = .00 ; k1 = 1.\nmass = np.ones(nPoints) * m1 \ndamp = np.ones(nSpring) * c1 \nstif = np.ones(nSpring) * k1 \n\nprint('\\n === Spring system details ===')\nprint(' rr: ')\nprint(rr)\nprint(' mass: ', mass)\nprint(' damp: ', damp)\nprint(' stif: ', stif)\nprint(' -----------------------------')\n\nplt.figure(1, figsize=(7,3) )\nplt.plot(rr[0,:],rr[1,:],'-o')\nplt.grid(True) , plt.axis('equal')\n\n# === Build mass, damping and stiffness matrices ===\n# - Matrix (full format) initialisation\nK = np.zeros((nPoints,nPoints),dtype=float)\nC = np.zeros((nPoints,nPoints),dtype=float) # NumPy assigns by references\nfor ie in range(nSpring):\n K[[ie,ie,ie+1,ie+1],[ie,ie+1,ie,ie+1]] += np.array([k1,-k1,-k1,k1])\n C[[ie,ie,ie+1,ie+1],[ie,ie+1,ie,ie+1]] += np.array([c1,-c1,-c1,c1])\nM = np.diag(np.ones(nPoints))\n\n# check ----\n# print(' K: ') ; print( K )\n# print(' M: ') ; print( M )\n# check ----\n\n# Constrained system\n#\n# |o----o----o----o-- ... --o----o\n#\n# [ Kuu Kud ] d/ [ u ] + [ Cuu Cud ] d/ [ u ] + [ Kuu Kud ][ u ] = [ fu ]\n# [ Kdu Kdd ]dt^2[ u_d ] [ Cdu Cdd ] dt [ u_d ] [ Kdu Kdd ][ u_d ] [ fd ]\n# u : free dofs\n# u_d: constrained dofs (known)\n# --> \n# Kuu u'' + Cuu u' + Kuu u = fu - Kud u_d'' + Cud u_d' + Kud u_d\n\n# Constrained dofs\ni_d = [ 0 ] # indices\nu_d = [ 0.] # values\ni_n = np.setdiff1d( np.arange(0,nPoints), i_d )\nprint(' i_d: ', i_d)\nprint(' i_n: ', i_n)\nKuu = K[:,i_n] ; Kuu = Kuu[i_n,:]\nCuu = C[:,i_n] ; Cuu = Cuu[i_n,:]\nMuu = M[:,i_n] ; Muu = Muu[i_n,:]\n\n# check ----\nprint(' Kuu: ') ; print(Kuu)\nprint(' Cuu: ') ; print(Cuu)\nprint(' Muu: ') ; print(Muu)\n# check ----\n\n# === State-space representation of the dynamical system ===\n# --- A: extended matrix\nA = np.block([[np.zeros((len(i_n),len(i_n)),dtype=float) , np.eye(len(i_n))], \\\n [-np.linalg.solve(Muu,Kuu) , \\\n -np.linalg.solve(Muu,Cuu)]])\n\n# --- B: forcing at the free end\nB = np.zeros((2*(nPoints-1), 1), dtype=float)\nB[nPoints-1] = 1.\n\n# --- C: position of the nodes\nC = np.block( [ np.eye(nPoints-1) , \\\n np.zeros((nPoints-1, nPoints-1), dtype=float) ] )\n# check ----\nprint(' A: ') ; print(A)\nprint(' B: ') ; print(B)\nprint(' C: ') ; print(C)\n# check ----\n\n# === Eigenproblem of A, extended matrix ===\n# - eigensolution sorted for decreasing module of the eigenvalues\nvals, vecs = np.linalg.eig(A)\n# - eigenvectors requires normalisation (amplitude and phase) for plots TODO\n# check ----\nprint(' vals: ')\nprint(vals)\n# print(' vecs: ')\n# print(vecs)\n# check ----\n\nplt.figure(101)\nplt.plot(np.real(vals),np.imag(vals),'o')\nplt.axis([-3, 3, -3, 3])\nplt.grid(True)\n\nplt.figure(102)\nplt.plot(rr[0,1:],np.abs(vecs[0:nPoints-1,:]))\n\nm = np.zeros(nPoints-1, dtype=complex)\nfor ie in range(nPoints-1):\n m[ie] = np.conjugate(vecs[0:nPoints-1,ie].T) @ vecs[0:nPoints-1,ie]\n\nprint(' m: ', m)\n\nplt.show()\n\n","sub_path":"projects/ExperimentalModalAnalysis/springSystem_full.py","file_name":"springSystem_full.py","file_ext":"py","file_size_in_byte":3589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"25170754","text":"import numpy as np\nfrom sklearn.ensemble import ExtraTreesClassifier as ExtraTrees\nfrom hyperopt import hp\nfrom sklearn.utils import class_weight\nfrom utils import definitions\nfrom .. import model, model_classification\n\nclass ExtraTreesClassifier(model.Model, model_classification.ModelClassification):\n def __init__(self, _project_name):\n super().__init__(_project_name)\n self.model_name = 'ExtraTreesClassifier'\n self.params_list = {\n 'criterion': ['gini', 'entropy'],\n 'max_features': [None, 'auto', 'sqrt','log2'],\n 'class_weight': [None, 'balanced', 'balanced_subsample']\n }\n\n def getHyperParameterSpace(self):\n return {\n 'n_estimators': hp.quniform('n_estimators', 50, 100, 5),\n 'criterion': hp.choice('criterion', self.params_list['criterion']),\n # 'max_depth': hp.quniform('max_depth', 2, 24, 2),\n # 'min_samples_split': hp.uniform('min_samples_split', 0, 1),\n # 'min_samples_leaf': hp.uniform('min_samples_leaf', 0, 0.5),\n 'min_weight_fraction_leaf': hp.uniform('min_weight_fraction_leaf', 0, 0.5),\n 'max_features': hp.choice('max_features', self.params_list['max_features']),\n # 'max_leaf_nodes': hp.quniform('max_leaf_nodes', 2, 48, 2),\n # 'min_impurity_decrease': hp.uniform('min_impurity_decrease', 0, 1),\n # 'bootstrap': hp.choice('bootstrap', [False, True]),\n # 'oob_score': hp.choice('oob_score', [False, True]),\n 'class_weight': hp.choice('class_weight', self.params_list['class_weight']),\n # 'ccp_alpha': hp.uniform('ccp_alpha', 0, 2),\n }\n\n def getModel(self, _params): \n if _params['max_features'] == definitions.JSON_NONE:\n _params['max_features'] = None\n if _params['class_weight'] == definitions.JSON_NONE:\n _params['class_weight'] = None\n return ExtraTrees(\n n_estimators= int(_params['n_estimators']),\n criterion= _params['criterion'],\n # max_depth= int(_params['max_depth']),\n # min_samples_split= _params['min_samples_split'],\n # min_samples_leaf= _params['min_samples_leaf'],\n min_weight_fraction_leaf= _params['min_weight_fraction_leaf'],\n max_features= _params['max_features'],\n # max_leaf_nodes= int(_params['max_leaf_nodes']),\n # min_impurity_decrease= _params['min_impurity_decrease'],\n # bootstrap= _params['bootstrap'],\n # bootstrap=True,\n # oob_score= bool(_params['oob_score']),\n class_weight= _params['class_weight'],\n # ccp_alpha= _params['ccp_alpha']\n n_jobs= definitions.getNumberOfCore(),\n )\n\n def trainModel(self, x, y, _params):\n self.model = self.getModel(_params)\n self.model.fit(x, y)\n self.saveModel()\n \n def getPredictResult(self, x):\n return self.model.predict(x)\n\n def getPredictProbaResult(self, x):\n return self.model.predict_proba(x)\n \n def getMaxIterCount(self): \n return 2 ** 4","sub_path":"AutomlCore/algorithms/classification/ensemble_extra_tree_c.py","file_name":"ensemble_extra_tree_c.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"403631375","text":"import pandas as pd\r\nfrom sklearn.preprocessing import scale\r\nfrom sklearn import preprocessing,cross_validation\r\nimport numpy as np\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\ndata=pd.read_csv(\"credit_data.csv\",index_col='ID')\r\ndata.columns=data.columns.str.lower()\r\ndf=data[['limit_bal','bill_amt1','bill_amt2','bill_amt3','bill_amt4','bill_amt5','bill_amt6','pay_amt1','pay_amt1','pay_amt2','pay_amt3','pay_amt4','pay_amt5','pay_amt6']]\r\ndf=scale(df)\r\nX=np.array(data.drop(['default.payment.next.month'],1))\r\nX=preprocessing.scale(X)\r\nY=np.array(data['default.payment.next.month'])\r\nX_train,X_test,Y_train,Y_test=cross_validation.train_test_split(X,Y,test_size=0.2)\r\n\r\nclf=LogisticRegression()\r\nclf.fit(X_train,Y_train)\r\nclf.score(X_test,Y_test)\r\nY_pred=clf.predict(X_test)\r\nconfusion_matrix = confusion_matrix(Y_test, Y_pred)\r\nprint(confusion_matrix)\r\n\r\n","sub_path":"logistic regression.py","file_name":"logistic regression.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"253187062","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 19 08:31:14 2018\n\n\nGiven an integer list where each number represents the number of hops you can\nmake, determine whether you can reach to the last index starting at index 0.\n\nFor example, [2, 0, 1, 0] returns true while [1, 1, 0, 1] returns false.\n\n\n@author: carlgval\n\"\"\"\n\n\ndef hop(l, idx=0):\n i = l[idx]\n\n if idx == len(l) - 1:\n return True\n elif i == 0:\n return False\n\n l[idx] = 0\n\n if hop(l, idx + i) or hop(l, idx - i):\n return True\n\n l[idx] = i\n return False\n\n\nif __name__ == '__main__':\n l = [2, 0, 1, 0]\n print(hop(l))\n l = [1, 1, 0, 1]\n print(hop(l))\n","sub_path":"hop_in_list.py","file_name":"hop_in_list.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"528161260","text":"'''\nAuthor: David Dalisay \nGoal: Main script. Simulates a chess game.\n'''\nimport re\nimport board\nimport pieces\n\n\nclass Game:\n\tdef play(self):\n\t\t# Build chess board and pieces.\n\t\t# The board init creates pieces.\n\t\tself.board = board.ChessBoard()\n\t\tself.board.display()\n\n\t\t# Determines who goes first.\n\t\twhile True:\n\t\t\tbot_plays_first = raw_input(\"Type 1 if Voldemort plays first, otherwise type 0: \")\n\t\t\tif bot_plays_first not in (\"0\",\"1\"):\n\t\t\t\tprint(\"Invalid option: Type 1 or 0.\")\n\t\t\t\tcontinue\t\n\t\t\tself.turn = int(bot_plays_first)\n\t\t\tself.player_colors = {self.turn: \"white\", int(not self.turn): \"black\"}\n\t\t\tbreak\n\t\t\n\t\t# Game loop.\n\t\twhile not self.is_game_over():\n\t\t\t# If other player's turn, manually type in next move.\n\t\t\tif self.turn == 0:\n\t\t\t\tplayer_move_input = self.get_player_move()\n\t\t\t\treal_move = self.get_real_move(player_move_input) # , self.player_colors[self.turn])\n\t\t\t\tif not real_move:\n\t\t\t\t\tprint(\"Cannot choose an empty space.\")\n\t\t\t\t\tcontinue\n\n\t\t\t\tpiece, to_x, to_y = real_move[0], real_move[1], real_move[2]\n\t\t\t\t# If the player tries to choose opposing pieces.\n\t\t\t\tif piece.color != self.player_colors[self.turn]:\n\t\t\t\t\tprint(\"Cannot move opposing team's pieces.\")\n\t\t\t\t\tcontinue\n\n\t\t\t\t# If the move spaces are legal.\n\t\t\t\tif piece.can_move(to_x, to_y):\n\t\t\t\t\t# If the movement on the board in legal.\n\t\t\t\t\tif self.board.is_legal_move(piece, to_x, to_y):\n\t\t\t\t\t\tself.board.move_piece(piece, to_x, to_y)\n\t\t\t\telse:\n\t\t\t\t\tprint(\"Illegal move.\")\n\t\t\t\t\tcontinue\n\n\t\t\t\tself.board.display()\n\t\t\t\tself.turn = 1\n\t\t\n\t\t\t# If Voldemort's turn, AI will make it's next move by itself.\n\t\t\telif self.turn == 1:\n\t\t\t\tprint(\"Voldemort plays piece. *Snake sounds*.\")\n\t\t\t\tself.turn = 0\n\n\tdef is_game_over(self):\n\t\treturn False\n\n\tdef get_real_move(self, move_tokens):\n\t\tmove_from_x = 8-int(move_tokens[0][1])\n\t\tmove_from_y = ord(move_tokens[0][0])-65\n\t\tmove_to_x = 8-int(move_tokens[1][1])\n\t\tmove_to_y = ord(move_tokens[1][0])-65\n\n\t\tpiece_nick = self.board.board[move_from_x][move_from_y]\n\t\tif not piece_nick.strip():\n\t\t\treturn False\n\n\t\tpiece_color = piece_nick[0]\n\n\t\tif piece_color == \"w\":\n\t\t\tpiece = self.board.white_pieces[piece_nick]\n\t\tif piece_color == \"b\":\n\t\t\tpiece = self.board.black_pieces[piece_nick]\n\n\t\treturn (piece, move_to_x, move_to_y)\n\n\tdef is_legal_move_input(self, move_input):\n\t\tpattern = re.compile(\"[A-Z][1-8] [A-Z][1-8]\")\n\t\tif not pattern.match(move_input):\n\t\t\treturn False\n\t\treturn True\n\t\t\n\tdef get_player_move(self):\n\t\twhile True:\n\t\t\tmove_input = raw_input(\"Type in other player's move (i.e. A5 D3): \")\n\t\t\tif self.is_legal_move_input(move_input):\n\t\t\t\treturn move_input.split()\n\t\t\telse:\n\t\t\t\tprint(\"Invalid move format.\")\n","sub_path":"voldemort-chess-ai/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"219023888","text":"import csv\nfrom sefaria.model import *\n\nALLOWED_TAGS = (\"i\", \"b\", \"br\", \"u\", \"strong\", \"em\", \"big\", \"small\", \"img\", \"sup\", \"sub\", \"span\", \"a\")\nALLOWED_ATTRS = {\n 'sup': ['class'],\n 'span': ['class', 'dir'],\n 'i': ['data-overlay', 'data-value', 'data-commentator', 'data-order', 'class', 'data-label', 'dir'],\n 'img': lambda name, value: name == 'src' and value.startswith(\"data:image/\"),\n 'a': ['dir', 'class', 'href', 'data-ref', \"data-ven\", \"data-vhe\"],\n}\n\nnumber_map = {\n \"Chapter One\": 1,\n \"Chapter 1\": 1,\n \"Chapter Two\": 2,\n \"Chapter 2\": 2,\n \"Chapter Three\": 3,\n \"Chapter 3\": 3,\n \"Chapter Four\": 4,\n \"Chapter 4\": 4,\n \"Chapter Five\": 5,\n \"Chapter 5\": 5,\n \"Chapter Six\": 6,\n \"Chapter 6\": 6,\n \"Chapter Seven\": 7,\n \"Chapter 7\": 7,\n \"Chapter Eight\": 8,\n \"Chapter 8\": 8,\n \"Chapter Nine\": 9,\n \"Chapter 9\": 9,\n \"Chapter Ten\": 10,\n \"Chapter 10\": 10,\n \"Chapter Eleven\": 11,\n \"Chapter 11\": 11,\n \"Chapter Twelve\": 12,\n \"Chapter 12\": 12,\n \"Chapter Thirteen\": 13,\n \"Chapter 13\": 13,\n \"Chapter Fourteen\": 14,\n \"Chapter 14\": 14,\n \"Chapter Fifteen\": 15,\n \"Chapter 15\": 15,\n \"Chapter Sixteen\": 16,\n \"Chapter 16\": 16,\n \"Chapter Seventeen\": 17,\n \"Chapter 17\": 17,\n \"Chapter Eighteen\": 18,\n \"Chapter 18\": 18,\n \"Chapter Nineteen\": 19,\n \"Chapter 19\": 19,\n \"Chapter Twenty\": 20,\n \"Chapter 20\": 20,\n \"Chapter Twenty One\": 21,\n \"Chapter 21\": 21,\n \"Chapter Twenty Two\": 22,\n \"Chapter 22\": 22,\n \"Chapter Twenty Three\": 23,\n \"Chapter 23\": 23,\n \"Chapter Twenty Four\": 24,\n \"Chapter 24\": 24,\n \"Chapter Twenty Five\": 25,\n \"Chapter 25\": 25,\n \"Chapter Twenty Six\": 26,\n \"Chapter 26\": 26,\n \"Chapter Twenty Seven\": 27,\n \"Chapter 27\": 27,\n \"Chapter Twenty Eight\": 28,\n \"Chapter 28\": 28,\n \"Chapter Twenty Nine\": 29,\n \"Chapter 29\": 29,\n \"Chapter Thirty\": 30,\n \"Chapter 30\": 30,\n}\n\nchabad_book_names = ['Yesodei haTorah', \"De'ot\", 'Talmud Torah', 'Avodat Kochavim', 'Teshuvah', \"Kri'at Shema\",\n 'Tefilah and Birkat Kohanim', 'Tefillin, Mezuzah and Sefer Torah', 'Tzitzit', 'Berachot', 'Milah',\n 'Order of Prayers', 'Shabbat', 'Eruvin', 'Shevitat Asor', 'Shevitat Yom Tov', \"Chametz U'Matzah\",\n 'Shofar, Sukkah, vLulav', 'Shekalim', 'Kiddush HaChodesh', \"Ta'aniyot\", \"Megillah v'Chanukah\",\n 'Ishut', 'Gerushin', 'Yibbum vChalitzah', 'Naarah Betulah', 'Sotah', 'Issurei Biah',\n \"Ma'achalot Assurot\", 'Shechitah', 'Shvuot', 'Nedarim', 'Nezirut', 'Arachim Vacharamim',\n 'Kilaayim', 'Matnot Aniyim', 'Terumot', 'Maaser', 'Maaser Sheini', 'Bikkurim', 'Shemita',\n 'Beit Habechirah', 'Klei Hamikdash', 'Biat Hamikdash', 'Issurei Mizbeiach', 'Maaseh Hakorbanot',\n 'Temidin uMusafim', 'Pesulei Hamukdashim', 'Avodat Yom haKippurim', 'Me`ilah', 'Korban Pesach',\n 'Chagigah', 'Bechorot', 'Shegagot', 'Mechussarey Kapparah', 'Temurah', \"Tum'at Met\",\n 'Parah Adumah', \"Tum'at Tsara'at\", \"Metamme'ey Mishkav uMoshav\", \"She'ar Avot haTum'ah\",\n \"Tum'at Okhalin\", 'Kelim', 'Mikvaot', 'Hilchot Nizkei Mamon', 'Genevah', \"Gezelah va'Avedah\",\n 'Chovel uMazzik', 'Rotzeach uShmirat Nefesh', 'Mechirah', 'Zechiyah uMattanah', 'Shechenim',\n 'Sheluchin veShuttafin', 'Avadim', 'Sechirut', \"She'elah uFikkadon\", 'Malveh veLoveh',\n 'To’en veNit’an', 'Nachalot', 'Sanhedrin veha’Onashin haMesurin lahem', 'Edut', 'Mamrim', 'Avel',\n 'Melachim uMilchamot']\nsefaria_book_names = [\n 'Foundations of the Torah',\n 'Human Dispositions',\n 'Torah Study',\n 'Foreign Worship and Customs of the Nations',\n 'Repentance',\n 'Reading the Shema',\n 'Prayer and the Priestly Blessing',\n 'Tefillin, Mezuzah and the Torah Scroll',\n 'Fringes',\n 'Blessings',\n 'Circumcision',\n 'The Order of Prayer',\n 'Sabbath',\n 'Eruvin',\n 'Rest on the Tenth of Tishrei',\n 'Rest on a Holiday',\n 'Leavened and Unleavened Bread',\n 'Shofar, Sukkah and Lulav',\n 'Sheqel Dues',\n 'Sanctification of the New Month',\n 'Fasts',\n 'Scroll of Esther and Hanukkah',\n 'Marriage',\n 'Divorce',\n 'Levirate Marriage and Release',\n 'Virgin Maiden',\n 'Woman Suspected of Infidelity',\n 'Forbidden Intercourse',\n 'Forbidden Foods',\n 'Ritual Slaughter',\n 'Oaths',\n 'Vows',\n 'Nazariteship',\n 'Appraisals and Devoted Property',\n 'Diverse Species',\n 'Gifts to the Poor',\n 'Heave Offerings',\n 'Tithes',\n 'Second Tithes and Fourth Year\\'s Fruit',\n 'First Fruits and other Gifts to Priests Outside the Sanctuary',\n 'Sabbatical Year and the Jubilee',\n 'The Chosen Temple',\n 'Vessels of the Sanctuary and Those who Serve Therein',\n 'Admission into the Sanctuary',\n 'Things Forbidden on the Altar',\n 'Sacrificial Procedure',\n 'Daily Offerings and Additional Offerings',\n 'Sacrifices Rendered Unfit',\n 'Service on the Day of Atonement',\n 'Trespass',\n 'Paschal Offering',\n 'Festival Offering',\n 'Firstlings',\n 'Offerings for Unintentional Transgressions',\n 'Offerings for Those with Incomplete Atonement',\n 'Substitution',\n 'Defilement by a Corpse',\n 'Red Heifer',\n 'Defilement by Leprosy',\n 'Those Who Defile Bed or Seat',\n 'Other Sources of Defilement',\n 'Defilement of Foods',\n 'Vessels',\n 'Immersion Pools',\n 'Damages to Property',\n 'Theft',\n 'Robbery and Lost Property',\n 'One Who Injures a Person or Property',\n 'Murderer and the Preservation of Life',\n 'Sales',\n 'Ownerless Property and Gifts',\n 'Neighbors',\n 'Agents and Partners',\n 'Slaves',\n 'Hiring',\n 'Borrowing and Deposit',\n 'Creditor and Debtor',\n 'Plaintiff and Defendant',\n 'Inheritances',\n 'The Sanhedrin and the Penalties within their Jurisdiction',\n 'Testimony',\n 'Rebels',\n 'Mourning',\n 'Kings and Wars'\n]\n\n\ndef create_book_name_map(chabad_book_names, sefaria_book_names):\n \"\"\"\n This function creates a map between the Chabad Rambam names to the Sefaria Rambam names\n \"\"\"\n\n # Confirmed that book names aligned, creating map\n name_map = {}\n for i in range(len(chabad_book_names)):\n name_map[chabad_book_names[i]] = sefaria_book_names[i]\n return name_map\n\n\ndef export_data_to_csv(list, file_name, headers_list):\n \"\"\"\n This function writes the data to a new CSV\n \"\"\"\n with open(f\"{file_name}.csv\", 'w+') as csvfile:\n headers = headers_list\n writer = csv.DictWriter(csvfile, fieldnames=headers)\n writer.writerows(list)\n\n\ndef add_chabad_book_names_alt_titles():\n name_map = create_book_name_map(chabad_book_names, sefaria_book_names)\n\n for chabad_book in chabad_book_names:\n sef_book = name_map[chabad_book]\n index = library.get_index(f\"Mishneh Torah, {sef_book}\")\n new_alt_title = f\"Hilchot {chabad_book}\"\n print(f\"Adding {new_alt_title}\")\n index.nodes.add_title(new_alt_title, \"en\")\n index.save()\n","sub_path":"sources/english_mishneh_torah_touger/mt_utilities.py","file_name":"mt_utilities.py","file_ext":"py","file_size_in_byte":7191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"328156097","text":"from Class_Cheilo.Class_Cheilo_4096s import Class_Chilo_4096s\nfrom Class_Classification.NeuralNetwork_20180719.Class_C_SimpleNN_20180730 import NN_20180730\nfrom Module.DocumentationGeneration_Cheilo_Part import DocumentGeneration_Cheilo_Part\nimport tensorflow\nimport os\n\nif __name__ == '__main__':\n for scope in [['Boy'], ['Girl'], ['Boy', 'Girl']]:\n\n if len(scope) == 2:\n totalNumber = 22\n normalLine = 12\n foldName = 'Total'\n else:\n if scope[0] == 'Boy':\n totalNumber = 12\n normalLine = 7\n foldName = 'Boy'\n if scope[0] == 'Girl':\n totalNumber = 10\n normalLine = 5\n foldName = 'Girl'\n\n for network in ['alexnet', 'vgg16', 'vgg19']:\n for layer in ['fc6', 'fc7']:\n for appoint in range(totalNumber):\n if appoint < normalLine:\n normalFold = 'Innormal'\n else:\n normalFold = 'Normal'\n\n if os.path.exists('F:\\\\NeuralParameter\\\\Spectrogram\\\\' + foldName + '\\\\' + network +\n '-' + layer + '\\\\' + normalFold + '\\\\Cheilo-Part-' +\n str(appoint)):\n continue\n\n graph = tensorflow.Graph()\n with graph.as_default():\n dataClass = Class_Chilo_4096s(\n loadpath='D:\\\\ProjectData\\\\Cheilo\\\\Spectrogram-Transform\\\\Spectrogram-' + network + '-' +\n layer + '-Assembly\\\\',\n scope=scope, appoint=appoint)\n\n classifier = NN_20180730(trainData=dataClass.trainData, trainLabel=dataClass.trainLabel,\n units=32,\n learningRate=0.00001)\n\n DocumentGeneration_Cheilo_Part(dataClass=dataClass, classifier=classifier,\n documentSavePath='F:\\\\NeuralParameter\\\\Spectrogram\\\\' + foldName + '\\\\' + network +\n '-' + layer + '\\\\' + normalFold + '\\\\Cheilo-Part-' +\n str(appoint) + '\\\\',\n information=classifier.information, totalEpisode=100,\n keepProbability=0.8)\n","sub_path":"Main_Cheilo/Main_PartTrain.py","file_name":"Main_PartTrain.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"364499798","text":"import bs4 as bs\n\nimport time\n\nimport csv\n\nfrom selenium import webdriver\n\nfrom urllib2 import urlopen\n\nda = {}\n\ngenre_list = ['science_fiction', 'fantasy', 'romance' , 'textbooks' , 'business']\n\n#genre_list = ['science_fiction']\nbase_add = \"https://openlibrary.org/subjects/\"\n\np = 170\n\nbrowser = webdriver.Firefox()\n\n\n\nfor gen in genre_list:\n\n\tadr = base_add + gen\n\n\tbrowser.get(adr)\n\n\tfor i in range(p):\n\t\tbrowser.find_element_by_xpath(\"/html/body/div[3]/div[2]/div/div[1]/div[5]/div/div[1]/div/div[2]\").click()\n\t\ttime.sleep(12)\n\n\tcontent2 = browser.page_source\n\n\tsoup2 = bs.BeautifulSoup(content2,\"lxml\")\n\n\tproductDivs2 = soup2.findAll('div', attrs={'class' : 'SRPCover'})\n\tfor div in productDivs2:\n\t\tname = div.find('img')['alt']\n\t\timage = div.find('img')['src']\n\t\tif name not in da:\n\t\t\tda[name] = (image, gen)\n\nwith open(\"finaldata.csv\", \"wb\") as csv_file:\n\t\tfor i in da.keys():\n\t\t\twr = str(i.encode('utf-8') + \"||\"+ da[i][0] + \"||\"+ da[i][1])\n\t\t\tcsv_file.write(wr)\n\t\t\tcsv_file.write('\\n')\n","sub_path":"33_10/scrapping.py","file_name":"scrapping.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"571524738","text":"\n\nfrom xai.brain.wordbase.nouns._regret import _REGRET\n\n#calss header\nclass _REGRETTING(_REGRET, ):\n\tdef __init__(self,): \n\t\t_REGRET.__init__(self)\n\t\tself.name = \"REGRETTING\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"regret\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_regretting.py","file_name":"_regretting.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"464004887","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 5 20:26:55 2019\n\n@author: abdulhannanmustajab\n\"\"\"\nimport os\nimport time\nfrom datetime import timedelta, datetime\n\n# Connection\nfrom pymongo import MongoClient\n\n# import Location\n\n# import train_images as ft\n\ncluster = MongoClient(\n \"mongodb+srv://mustajabhannan:Hannan786@cluster0-n7aqf.mongodb.net/test?retryWrites=true&w=majority\")\n\ndb = cluster[\"attendance\"]\ncollection = db[\"users\"]\n\nlocation_collection = db[\"locations\"]\n\n\nclass Employee:\n \"\"\"Docstring of employee\"\"\"\n\n # Flag for trusted employee\n __flag = False # Untrusted by default\n\n def __init__(self, emp_id):\n \"\"\"Initialize class with First Name , Last Name and Employee ID\"\"\"\n self.emp_id = emp_id\n self.__flag = False\n\n def checkEmployee(self):\n \"\"\"\n Check if employee exists with the ID.\n Returns true if employee exists.\n :rtype: object\n :return:\n \"\"\"\n result = collection.find_one({\"emp_id\": self.emp_id})\n # result is none means employee does not exist.\n if result is None:\n return False\n return True\n\n def fullName(self):\n result = collection.find_one({\"emp_id\": int(self.emp_id)})\n if result is None:\n return (\"No record found\")\n else:\n firstName = result[\"first_name\"]\n lastName = result[\"last_name\"]\n return '{} {}'.format(firstName.capitalize(), lastName.capitalize())\n\n def addUser(self, first_name, last_name):\n \"\"\" Add user to the database,\n Employee is already initialized with an emp_id. Use this method to add it to the database.\n \"\"\"\n if self.checkEmployee() is True:\n return \"Employee Exists\"\n\n if collection.find_one({\"emp_id\": self.emp_id}) is None:\n\n # Userdata to be added to database.\n userData = {\n\n \"emp_id\": self.emp_id,\n \"first_name\": first_name,\n \"last_name\": last_name\n\n }\n result = collection.insert_one(userData)\n\n # If data is successfully updated, Returns True else Returns False .\n if result.acknowledged:\n return True\n else:\n return False\n else:\n print(\"Employee Already Exists with ID: \", self.emp_id)\n\n def checkAttendance(self):\n \"\"\"\n Check Attendance.\n Check if attendance is already marked for the day\n\n :return: Boolean ( True or False)\n :returns false if attendance is not marked.\n \"\"\"\n if self.checkEmployee() is True: # Check if employee exists with the ID.\n\n ts = time.time()\n date = datetime.fromtimestamp(ts).strftime('%Y-%m-%d')\n checkAttendance = collection.find({\"$and\": [{\"emp_id\": self.emp_id}, {\"attendance.date\": date}]})\n checkAttendance = list(checkAttendance)\n if checkAttendance == []:\n return False\n else:\n return True\n return \"Employee Does Not Exist\"\n\n def addAttendance(self, verified=False, method=\"manual\"):\n \"\"\"\n Add Attendance for the user each day.\n First it checks if the attendance is already marked or not.\n Takes the current timestamp and date and pushes it to the database.\n \"\"\"\n if self.checkEmployee() is True: # Check if employee exists with the ID.\n\n # TODO\n # \"Mark attendance for trusted employees and alot special locations\"\n\n # Make all checks before uploading data.\n penaltyCheck = self.checkPenalty() # Returns true if penalised.\n attendanceCheck = self.checkAttendance() # Returns True if attendance is already marked.\n dayOff = self.checkDayOff()\n\n if dayOff is True:\n return \"Not Work Day!\"\n else:\n\n ts = time.time()\n date = datetime.fromtimestamp(ts).strftime('%Y-%m-%d')\n timeStamp = datetime.fromtimestamp(ts).strftime('%H:%M:%S')\n\n # Case 1: If the user is marking attendance for the first time.\n\n firstAttendance = collection.find({\"emp_id\": self.emp_id},\n {\"attendance.date\": date})\n location = self.__getLocation()\n\n if firstAttendance is None:\n data = {\n \"attendance\":\n {\n \"date\": date,\n \"time\": timeStamp,\n \"location\": location['locationID'],\n \"location_name\": location['locationName'],\n \"verified\": verified,\n \"method\": method\n }\n }\n\n collection.update(\n {\"emp_id\": self.emp_id},\n {\"$push\": data},\n upsert=True\n )\n\n # Once attendance is added successfully, then update the location with available slots.\n\n location_collection.update({\"locationID\": location['locationID']}\n , {\"$inc\": {\"available_spots\": -1}})\n # Location.Location(int(location['locationID'])).resetSpots()\n\n self.generateAttendancePDF()\n\n return 'First Attendance Marked For {} at {}'.format(self.emp_id, location['locationName'])\n\n # Case 2 : If the user has no penalty and attendance isn't already marked.\n # elif penaltyCheck and attendanceCheck is False:\n elif attendanceCheck is False:\n\n data = {\n \"attendance\":\n {\n \"date\": date,\n \"time\": timeStamp,\n \"location\": location['locationID'],\n \"location_name\": location['locationName'],\n \"verified\": verified,\n \"method\": method\n }\n }\n\n collection.update(\n {\"emp_id\": self.emp_id},\n {\"$push\": data},\n upsert=True\n\n )\n\n # Once attendance is added successfully, then update the location with available slots.\n\n location_collection.update({\"locationID\": location['locationID']}\n , {\"$inc\": {\"available_spots\": -1}})\n # Location.Location(int(location['locationID'])).resetSpots()\n\n self.generateAttendancePDF()\n\n return 'Attendance Marked For {} at {}'.format(self.fullName(), location['locationName'])\n\n elif attendanceCheck is True:\n return \"Attendance Already Marked\"\n\n elif penaltyCheck is True:\n return \"Penalty Found\"\n\n else:\n return \"Unknown Error\"\n else:\n return \"No Employee Found\"\n\n def addPenalty(self, number_of_days):\n \"\"\"\n Penalise the employee for being absent on a particular day.\n Takes input the number of days. It will penalise the employee for that particular number of days from the current date.\n His attendance wont be marked for those days then.\n \"\"\"\n\n if self.checkEmployee() is True: # Check if employee exists with the ID.\n\n base = datetime.today()\n date_list = [base + timedelta(days=x) for x in\n range(number_of_days)] # Returns a list of dates which are to be penalised.\n # date_array = []\n\n dateListLambda = list(map(lambda date: date.strftime(\"%x\"),\n date_list)) # Doing the same thing as in the next line using Lambda Function.\n\n # for date in date_list:\n # date = date.strftime(\"%x\")\n # date_array.append(date)\n\n data = {\"penalty\": dateListLambda}\n\n collection.update(\n {\"emp_id\": self.emp_id},\n {\"$push\": data},\n upsert=True\n\n )\n return (\"Successfully Penalised For \" + str(number_of_days) + \" Days\")\n return \"No Record Found.\"\n\n def checkPenalty(self):\n \"\"\"\n This function checks that if the employee is penalised for that particular day and returns true or false.\n \"\"\"\n\n if self.checkEmployee() is True: # Check if employee exists with the ID.\n\n ts = time.time()\n date = datetime.fromtimestamp(ts).strftime('%Y-%m-%d')\n dateforpenalty = datetime.fromtimestamp(ts).strftime('%m/%d/%y')\n checkPenalty = collection.find(\n {\n \"emp_id\": self.emp_id\n },\n {\n \"penalty\": dateforpenalty\n }\n )\n\n return list(checkPenalty)\n # if dateforpenalty in list(checkPenalty):\n # return \"Yes Date Found\"\n # else:\n # return \"Date not found\"\n\n # if checkPenalty is not None or not []:\n # return checkPenalty\n #\n # else:\n # return False\n else:\n return False\n\n def knowTrusted(self):\n # type: (self) -> bool\n \"\"\"\n Get the data from database and check if the employee is trusted or not.\n \"\"\"\n\n if self.checkEmployee() is False: # Check if employee exists with the ID.\n return \"No Record Found.\"\n\n if self.__flag is True:\n return True\n else:\n return False\n\n def setOffDay(self, day):\n \"\"\" Set weekly day off for each employee.\n Enter any integer between 0 and 6.\n Enter 0 for Monday and 6 for Sunday.\n \"\"\"\n if self.checkEmployee() is False: # Check if employee exists with the ID.\n return \"No Record Found.\"\n\n if abs(int(day)) > 6:\n return \"Error, Please enter valid input\"\n else:\n offDay = day\n\n # Used upsert to create if not exists.\n collection.update(\n {\n \"emp_id\": self.emp_id\n },\n {\n \"$set\":\n {\n \"offDay\": offDay\n }\n },\n upsert=True\n )\n\n def isWorkday(self):\n \"\"\"\n Checks the database for the day off value and compares it with todays date. If it is a offday, then the employee isn't marked absent.\n Returns true if it is workday and false if it is not.\n \"\"\"\n result = collection.find_one({\"emp_id\": self.emp_id})\n\n return result\n\n def viewAttendance(self):\n data = collection.find({\"emp_id\": self.emp_id}, {\"attendance\"})\n return data\n\n def __getLocation(self):\n # data = location_collection.find({\"available_spots\": {\"$gt\": 1}})\n try:\n data = location_collection.aggregate(\n [{\"$match\": {\"available_spots\": {\"$gte\": 1}}},\n\n {\"$sample\": {\"size\": 1}}])\n data = list(data)\n data = data[0]\n self.locationData = data\n return data\n except Exception as e:\n print (e)\n\n def checkDayOff(self):\n \"\"\"\n Check if it is a dayOff for the employee.\n :return: True if it is dayoff for the employee.\n \"\"\"\n import datetime\n i = datetime.datetime.now()\n dayInteger = int(i.strftime('%w'))\n data = collection.find({\"$and\": [{\"emp_id\": self.emp_id}, {\"offDay\": dayInteger}]})\n data = list(data)\n if data == []:\n return False\n else:\n return True\n\n def viewDayOff(self):\n \"\"\"\n Returns the name of the day.\n :return:\n \"\"\"\n data = list(collection.find({\"emp_id\": self.emp_id}, {\"offDay\"}))\n try:\n return data[0]['offDay']\n except:\n return False\n\n def generateAttendancePDF(self):\n\n ts = time.time()\n date = datetime.fromtimestamp(ts).strftime('%Y-%m-%d')\n data = list(collection.find({\"$and\": [{\"emp_id\": self.emp_id}, {\"attendance.date\": date}]}))\n if len(data) is not 0:\n data = data[0]['attendance']\n\n for record in data:\n try:\n if record['date'] == date:\n locationName = record['location_name']\n locationID = record['location']\n timeStamp = record['time']\n name = self.fullName()\n\n os.chdir(\"includes\")\n txt_data = \"\"\"\n ###############\n Proctor Office\n DUTY SLIP\n ###############\n \n Name : {}\n \n Location : {}\n \n LocationID : {}\n \n Time : {}\n \n \n \n \"\"\".format((name), (str(locationName)), (str(locationID)), (str(timeStamp)))\n\n f = open(str(self.emp_id) + \".txt\", 'w')\n f.write(txt_data)\n f.close()\n path = (str(self.emp_id))\n # pdf.output(path + \".pdf\")\n os.chdir(\"../\")\n\n return \"Saving PDF As..\" + str(path) + \".pdf\"\n\n except:\n print(\"error\")\n\n\n else:\n print(\"Length Zero\")\n return \"No Record Found\"\n\n # def addFace(self):\n # result = ft.captureImage(self.emp_id)\n # return result\n\n\ndef getAll():\n \"\"\"\n Returns all list of all the employees\n :return:\n \"\"\"\n data = collection.find()\n return data\n\n\ndef getAttendanceByDate():\n ts = time.time()\n date = datetime.fromtimestamp(ts).strftime('%Y-%m-%d')\n data = list(collection.find({\"attendance.date\": date}))\n return data\n","sub_path":"GUI/Employee.py","file_name":"Employee.py","file_ext":"py","file_size_in_byte":14668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"457185090","text":"#zapis do pliku binarnego\n\nimport pickle, shelve\n\ns = shelve.open(\"pikle2.dat\")\ns[\"odmiana\"] = [\"łagodny\", \"pikanty\", \"kwaszony\"]\ns[\"kształt\"] = [\"krojony\", \"cały\", \"w plasterkach\"]\ns[\"marka\"] = [\"marka 1\", \"marka 2\", \"marka 3\"]\ns.sync()\n\nprint (\"Pobieranie informacji z półek\")\n\nprint(\"marka - \", s[\"marka\"])\nprint(\"kształt - \", s[\"kształt\"])\nprint(\"odmiana - \", s[\"odmiana\"])\ns.close()","sub_path":"MiTP 24.04/pikle/pikle.py","file_name":"pikle.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"531230121","text":"#By: Belal El-Shinnawey\r\n#If you have useful comments Email: belalshinnawey@gmail.com\r\nfrom mpl_toolkits import mplot3d\r\nfrom mlxtend.data import loadlocal_mnist\r\nimport scipy.interpolate as spin \r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport xlsxwriter\r\nimport random\r\nimport math\r\nimport csv\r\nimport cv2\r\nclass SVM_SMO():\r\n def __init__(self,maxNumberOfPasses=1000,C=1,tol=0.001,kernel='linear'):\r\n self.kernelTypes={'quadratic': self.QuadraticKernel,'linear': self.LinearKernel}\r\n self.c=C\r\n self.is2D=0\r\n self.W=None\r\n self.tol=tol\r\n self.ThetaO=0\r\n self.allError=[]\r\n self.alltheta=[]\r\n self.alltheta=[]\r\n self.isTrained=0\r\n self.allOffsets=[]\r\n self.kernelType=kernel\r\n self.maxNumberOfPasses=maxNumberOfPasses\r\n self.kernelFunction=self.kernelTypes[self.kernelType] \r\n ################################################## Training Function ################################\r\n def trainingFunction(self,Y,X):\r\n n, d = X.shape[0], X.shape[1] #n: number of data points, d: dimension of each data point\r\n if d==2: self.is2D=1\r\n Theta=np.zeros([n]) #initial value for multipliers\r\n currentNumberOfPasses=0\r\n isFirstTime=0\r\n while(currentNumberOfPasses < self.maxNumberOfPasses):\r\n numberOfOptimiztions=0 \r\n for i in range(0,n):\r\n ThetaOld=Theta\r\n j=i\r\n while j==i : j=random.randint(0,n-1) #Make sure j!=i\r\n Xi=np.asarray(X[i])\r\n Xj=np.asarray(X[j])\r\n Yi=Y[i]\r\n Yj=Y[j]\r\n Thetai=Theta[i]\r\n Thetaj=Theta[j] #Save the old values \r\n (L,H)=self.CalculateLH(self.c,Thetai,Thetaj,Yi,Yj) #Calculate SMO parameters \r\n Eta=self.CalculateEta(Xi,Xj)\r\n if Eta==0: continue\r\n self.W= self.CalculateW(X,Y,Theta) #Update the weight vector \r\n self.ThetaO=self.CalculateOffset(X,Y,self.W) #Update Offset bias \r\n Ei=self.CalculateEn(Xi,Yi,self.W,self.ThetaO) #Find the ith and jth Error\r\n Ej=self.CalculateEn(Xj,Yj,self.W,self.ThetaO)\r\n Theta[j]= self.UpdateThetaj(Eta,Ei,Ej,Yj,H,L,Thetaj) #Update the ith and jth multipliers\r\n Theta[i]= self.UpdateThetai(Thetai,Thetaj,Theta[j],Yi,Yj) \r\n if np.linalg.norm(ThetaOld-Theta)H:return H\r\n return ThetajUpdated\r\n ##################################################### L & H Function ##################################\r\n def CalculateLH(self,C,Thetai,Thetaj,Yi,Yj):\r\n if(Yi != Yj): return (max(0,Thetaj-Thetai) , min(C,C-Thetai+Thetaj))\r\n return (max(0,Thetai+Thetaj-C), min(C,Thetai+Thetaj)) \r\n ###################################################### Eta Function ##################################\r\n def CalculateEta(self,Xi,Xj):\r\n return self.kernelFunction(Xi,Xi)+self.kernelFunction(Xj,Xj)-2*self.kernelFunction(Xi,Xj)\r\n ##################################################### Output Function #################################\r\n def F(self,X,W,thetaO):\r\n return np.sign(self.kernelFunction(X,W)+thetaO)\r\n ###################################################### Error Function ################################# \r\n def CalculateEn(self,Xn,Yn,W,ThetaO):\r\n return self.F(Xn,W,ThetaO)-Yn\r\n ################################################## Types of Kernel functions ##########################\r\n def QuadraticKernel(self,Xi,Xj): #quadratic\r\n return (np.dot(Xi,Xj)+1)**2\r\n def LinearKernel(self,Xi,Xj): #linear\r\n return np.dot(Xi,Xj)\r\n ################################################## classify ###########################################\r\n def classify(self,X):\r\n return self.F(X,self.W,self.ThetaO)\r\n #################################### Probability of (Y=1|+thetaO) ################################\r\n def ProbabilityOfOne(self,X):\r\n return (1+math.exp(-1*(self.kernelFunction(self.W,X)+self.ThetaO)))**-1,self.classify(X)\r\n ##################################### A function for ploting the data in 2-d only ###########################\r\n def Decision_boundary_Plot(self,theta,thetaO,X): \r\n if self.is2D==0: return\r\n y=[] \r\n plt.figure(figsize=(10,6))\r\n plt.grid(True) \r\n for row in X:\r\n y.append(np.sign(np.dot(row,theta)+thetaO))\r\n for X,Y in zip(X,y):\r\n plt.plot(X[0],X[1],'r*' if (Y == 1.0) else 'b*',)\r\n slope=-1*(theta[1]/theta[0]) \r\n i = np.linspace(-100*np.amin(X),100*np.amax(X))\r\n z=slope*i+thetaO\r\n plt.plot(i, z) \r\n plt.show() \r\n #################################### Plot the error surface for 2-dim only ###################################\r\n def errorSurfacePlot(alltheta,allError,allOffsets):\r\n xaxis=[]\r\n yaxis=[]\r\n zaxis=np.multiply(allError,1/max(allError)) \r\n for elm in alltheta: \r\n xaxis.append(elm[0])\r\n yaxis.append(elm[1])\r\n old_indicesX = np.arange(0,len(xaxis))\r\n old_indicesy = np.arange(0,len(yaxis))\r\n old_indicesz = np.arange(0,len(zaxis))\r\n new_indicesX = np.linspace(0,len(xaxis)-1,10000)\r\n new_indicesy = np.linspace(0,len(yaxis)-1,10000)\r\n new_indicesz = np.linspace(0,len(zaxis)-1,10000)\r\n splx = spin.UnivariateSpline(old_indicesX,xaxis,k=3,s=0)\r\n sply = spin.UnivariateSpline(old_indicesy,yaxis,k=3,s=0)\r\n splz = spin.UnivariateSpline(old_indicesz,zaxis,k=3,s=0)\r\n newX = splx(new_indicesX) \r\n newy = sply(new_indicesy)\r\n newZ = splz(new_indicesz) \r\n fig = plt.figure()\r\n ax = plt.axes(projection='3d')\r\n surf=ax.plot_trisurf(np.array(newy), np.array(newX), np.array(newZ), cmap='plasma',antialiased=True,shade=True) \r\n ax.set_xlabel('Theta(i)', fontsize=10, rotation=150)\r\n ax.set_ylabel('Theta(J)', fontsize=10, rotation=130)\r\n ax.set_zlabel('||Error||', fontsize=10, rotation=-60)\r\n fig.colorbar(surf, shrink=0.5, aspect=10)\r\n plt.show() \r\n ################################################ Get Functions ########################################\r\n def getW(self):\r\n return self.W\r\n def getOffset(self):\r\n return self.ThetaO\r\n def getkernelType(self):\r\n return self.kernelType \r\n def getAllError(self):\r\n return self.allError\r\n def getAllOffsets(self):\r\n return self.allOffsets\r\n def getAllTheta(self):\r\n return self.alltheta\r\n def is2D(self):\r\n return self.is2D \r\n def isTrained(self):\r\n return self.isTrained \r\n ################################################### Set Functions #####################################\r\n def setTol(self,tol):\r\n self.tol=tol\r\n self.isTrained=0 \r\n def setC(self,C):\r\n self.c=C\r\n self.isTrained=0\r\n def setKernel(self,kernelType):\r\n self.kernelType=kernelType\r\n self.kernelFunction=self.kernelTypes[kernelType]\r\n self.isTrained=0 \r\n def setMaxNumberofPasses(self,maxNumberOfPasses):\r\n self.maxNumberOfPasses=maxNumberOfPasses\r\n self.isTrained=0\r\n###################################################### Test Function #################################### \r\n#The rest of the code is an example on how to use the function to classify\r\ndef main():\r\n ##################### Replace this part with your method of reading data into vectors ################## \r\n X, unfilteredLabels = loadlocal_mnist(\r\n images_path='MNIST/train-images.idx3-ubyte', \r\n labels_path='MNIST/train-labels.idx1-ubyte')\r\n np.multiply(X,1/255)\r\n X=X+0.0001 \r\n Y0=[]\r\n Y1=[]\r\n Y2=[]\r\n Y3=[]\r\n Y4=[]\r\n Y5=[]\r\n Y6=[]\r\n Y7=[]\r\n Y8=[]\r\n Y9=[]\r\n for item in unfilteredLabels:\r\n if item==0: \r\n Y0.append(1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==1:\r\n Y0.append(-1)\r\n Y1.append(1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==2:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==3:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==4:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==5:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1) \r\n elif item==6:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==7:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(1)\r\n Y8.append(-1)\r\n Y9.append(-1)\r\n elif item==8:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(1)\r\n Y9.append(-1)\r\n elif item==9:\r\n Y0.append(-1)\r\n Y1.append(-1)\r\n Y2.append(-1)\r\n Y3.append(-1)\r\n Y4.append(-1)\r\n Y5.append(-1)\r\n Y6.append(-1)\r\n Y7.append(-1)\r\n Y8.append(-1)\r\n Y9.append(1) \r\n ##################################### Example on Training The Classifier ############################### \r\n classifierFor0=SVM_SMO() #Example on how to use the training function \r\n classifierFor0.setC(100) #Train 10 classifiers on 10 handwritten numbers\r\n classifierFor0.setTol(0.001)\r\n classifierFor0.setKernel('linear') #A higher order kernel doesn't mean better classifier \r\n classifierFor0.setMaxNumberofPasses(100)\r\n classifierFor0.trainingFunction(np.asarray(Y0),np.asarray(X)) #Train The Classifier\r\n w0=classifierFor0.getW() #Output\r\n offsetFor0=classifierFor0.getOffset()\r\n\r\n classifierFor1=SVM_SMO() \r\n classifierFor1.setC(100)\r\n classifierFor1.setTol(0.001) \r\n classifierFor1.setKernel('linear') \r\n classifierFor1.setMaxNumberofPasses(100)\r\n classifierFor1.trainingFunction(np.asarray(Y1),np.asarray(X)) \r\n w1=classifierFor1.getW() \r\n offsetFor1=classifierFor1.getOffset()\r\n\r\n classifierFor2=SVM_SMO() \r\n classifierFor2.setC(100)\r\n classifierFor2.setTol(0.001) \r\n classifierFor2.setKernel('linear') \r\n classifierFor2.setMaxNumberofPasses(100)\r\n classifierFor2.trainingFunction(np.asarray(Y2),np.asarray(X)) \r\n w2=classifierFor2.getW() \r\n offsetFor2=classifierFor2.getOffset()\r\n \r\n classifierFor3=SVM_SMO() \r\n classifierFor3.setC(100)\r\n classifierFor3.setTol(0.001) \r\n classifierFor3.setKernel('linear') \r\n classifierFor3.setMaxNumberofPasses(100)\r\n classifierFor3.trainingFunction(np.asarray(Y3),np.asarray(X)) \r\n w3=classifierFor3.getW() \r\n offsetFor3=classifierFor3.getOffset()\r\n \r\n classifierFor4=SVM_SMO() \r\n classifierFor4.setC(100)\r\n classifierFor4.setTol(0.001) \r\n classifierFor4.setKernel('linear') \r\n classifierFor4.setMaxNumberofPasses(100)\r\n classifierFor4.trainingFunction(np.asarray(Y4),np.asarray(X)) \r\n w4=classifierFor4.getW() \r\n offsetFor4=classifierFor4.getOffset()\r\n\r\n classifierFor5=SVM_SMO() \r\n classifierFor5.setC(100)\r\n classifierFor5.setTol(0.001) \r\n classifierFor5.setKernel('linear') \r\n classifierFor5.setMaxNumberofPasses(100)\r\n classifierFor5.trainingFunction(np.asarray(Y5),np.asarray(X)) \r\n w5=classifierFor5.getW() \r\n offsetFor5=classifierFor5.getOffset()\r\n\r\n classifierFor6=SVM_SMO() \r\n classifierFor6.setC(100)\r\n classifierFor6.setTol(0.001) \r\n classifierFor6.setKernel('linear') \r\n classifierFor6.setMaxNumberofPasses(100)\r\n classifierFor6.trainingFunction(np.asarray(Y6),np.asarray(X)) \r\n w6=classifierFor6.getW() \r\n offsetFor6=classifierFor6.getOffset()\r\n\r\n classifierFor7=SVM_SMO() \r\n classifierFor7.setC(100)\r\n classifierFor7.setTol(0.001) \r\n classifierFor7.setKernel('linear') \r\n classifierFor7.setMaxNumberofPasses(100)\r\n classifierFor7.trainingFunction(np.asarray(Y7),np.asarray(X)) \r\n w7=classifierFor7.getW() \r\n offsetFor7=classifierFor7.getOffset()\r\n\r\n classifierFor8=SVM_SMO() \r\n classifierFor8.setC(100) \r\n classifierFor8.setTol(0.001) \r\n classifierFor8.setKernel('linear') \r\n classifierFor8.setMaxNumberofPasses(100)\r\n classifierFor8.trainingFunction(np.asarray(Y8),np.asarray(X)) \r\n w8=classifierFor8.getW() \r\n offsetFor8=classifierFor8.getOffset()\r\n\r\n classifierFor9=SVM_SMO() \r\n classifierFor9.setC(100)\r\n classifierFor9.setTol(0.001) \r\n classifierFor9.setKernel('linear') \r\n classifierFor9.setMaxNumberofPasses(100)\r\n classifierFor9.trainingFunction(np.asarray(Y9),np.asarray(X)) \r\n w9=classifierFor9.getW() \r\n offsetFor9=classifierFor9.getOffset() \r\n\r\n workbook = xlsxwriter.Workbook('W0.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w0 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor0) \r\n workbook.close() \r\n\r\n workbook = xlsxwriter.Workbook('W1.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w1 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor1) \r\n workbook.close()\r\n\r\n workbook = xlsxwriter.Workbook('W2.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w2 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor2) \r\n workbook.close()\r\n \r\n workbook = xlsxwriter.Workbook('W3.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w3 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor3) \r\n workbook.close()\r\n\r\n workbook = xlsxwriter.Workbook('W4.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w4 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor4) \r\n workbook.close()\r\n\r\n workbook = xlsxwriter.Workbook('W5.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w5 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor5) \r\n workbook.close()\r\n \r\n workbook = xlsxwriter.Workbook('W6.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w6 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor6) \r\n workbook.close()\r\n\r\n workbook = xlsxwriter.Workbook('W7.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w7 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor7) \r\n workbook.close() \r\n\r\n workbook = xlsxwriter.Workbook('W8.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w8 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor8) \r\n workbook.close()\r\n\r\n workbook = xlsxwriter.Workbook('W9.xlsx') \r\n worksheet = workbook.add_worksheet() \r\n row = 0\r\n column = 0 \r\n for item in w9 : \r\n worksheet.write(row, column, item) \r\n row += 1\r\n worksheet.write(0,1,offsetFor9) \r\n workbook.close() \r\nif __name__ == \"__main__\": \r\n main() ","sub_path":"SVM_SMO.py","file_name":"SVM_SMO.py","file_ext":"py","file_size_in_byte":23115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"115637416","text":"#是要用进程词 大量创建子进程\n\nfrom multiprocessing import Pool\nimport os,time,random\n\ndef long_time_task(name,i):\n print('run proccess is ',os.getpid())\n statt = time.time()\n time.sleep(random.random()*3)#sleep用来暂停线程执行\n end = time.time()\n print('task{0},run{1}秒'.format(name,(end-statt)))\nif __name__ == '__main__':\n print('parent process is{}'.format(os.getpid()))\n p = Pool(4)#pool的默认大小是cpu的核数\n for i in range(5):\n p.apply_async(long_time_task,args=(i,i+1))\n print('Waiting for all subprocess done....')\n p.close()#close之后就不能添加新的进程\n p.join()#会等待所有子程序执行完毕\n print('all subprocess done')","sub_path":"线程和进程/进程/02进程池/pool_user.py","file_name":"pool_user.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"203751569","text":"# Tu pišite svoje funkcije:\r\nfrom math import *\r\n\r\n\r\ndef koordinate(ime, kraji):\r\n for kraj, x, y in kraji:\r\n if ime == kraj:\r\n return x, y\r\n return None\r\n\r\n\r\ndef razdalja_koordinat(x1, y1, x2, y2):\r\n razdalja = sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\r\n return razdalja\r\n\r\n\r\ndef razdalja(ime1, ime2, kraji):\r\n x1, y1 = koordinate(ime1, kraji)\r\n x2, y2 = koordinate(ime2, kraji)\r\n return razdalja_koordinat(x1, y1, x2, y2)\r\n\r\n\r\ndef v_dometu(ime, domet, kraji):\r\n tarce=[]\r\n for kraj, x, y in kraji:\r\n dolzina = razdalja(ime, kraj, kraji)\r\n if domet >= dolzina and ime != kraj:\r\n tarce.append(kraj)\r\n return tarce\r\n\r\n\r\ndef najbolj_oddaljeni(ime, imena, kraji):\r\n max_dolzina = 0\r\n for kraj in imena:\r\n dolzina = razdalja(ime, kraj, kraji)\r\n if dolzina > max_dolzina:\r\n max_dolzina = dolzina\r\n max_kraj = kraj\r\n return max_kraj\r\n\r\n\r\ndef zalijemo(ime, domet, kraji):\r\n max_dolzina = 0\r\n for kraj, x, y in kraji:\r\n dolzina = razdalja(ime, kraj, kraji)\r\n if domet > dolzina > max_dolzina and ime != kraj:\r\n max_dolzina = dolzina\r\n max_kraj = kraj\r\n return max_kraj\r\n\r\n\r\ndef presek(s1, s2):\r\n return list(set(s1).intersection(set(s2)))\r\n\r\n\r\ndef skupno_zalivanje(ime1, ime2, domet, kraji):\r\n tarce1 = v_dometu(ime1, domet, kraji)\r\n tarce2 = v_dometu(ime2, domet, kraji)\r\n return presek(tarce1, tarce2)\r\n\r\n\r\n","sub_path":"code/batch-2/vse-naloge-brez-testov/DN4-M-211.py","file_name":"DN4-M-211.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"636396711","text":"#!/usr/bin/env python\n\"\"\"\n.. See the NOTICE file distributed with this work for additional information\n regarding copyright ownership.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n\nfrom __future__ import print_function\nimport copy\n\n\nclass Metadata(object): # pylint: disable=too-few-public-methods\n \"\"\"\n Object containing all information pertaining to a specific data element.\n \"\"\"\n def __init__(self, data_type=None, file_type=None, file_path=None, # pylint: disable=too-many-arguments\n sources=None, meta_data=None, taxon_id=None):\n \"\"\"\n Initialise the Metadata; for more information see the documentation for\n the MuG DMP API.\n\n\n Parameters\n ----------\n data_type : str\n The type of information in the file\n file_type : str\n File format\n file_path : str\n Relative path of the file\n sources : list\n List of paths of files that were processed to generate this file\n meta_data : dict\n Dictionary object containing the extra data related to the\n generation of the file or describing the way it was processed\n \"\"\"\n self.data_type = data_type\n self.file_type = file_type\n self.file_path = file_path\n self.taxon_id = taxon_id\n if sources is None:\n sources = []\n self.sources = sources\n if meta_data is None:\n meta_data = {}\n self.meta_data = meta_data\n\n def __repr__(self):\n return \"\"\"\"\"\".format(md=self)\n\n @classmethod\n def get_child(cls, parents, path):\n \"\"\"\n Generate a stub for the metadata of a new data element generated\n from the data element described in the specified parents.\n\n Fields \"data_type\" and \"file_type\" are taken from the first parent; the\n \"meta_data\" fields are merged from all parents, in their respective\n order (i.e. values in the last parent prevail).\n\n While making a copy, ensure the copy is deep enough that changing the\n child instance will not affect the parents.\n\n\n Parameters\n ----------\n parents : list\n List of Metadata instances\n\n\n Returns\n -------\n Metadata\n An instance of Metadata generated as described above\n\n\n Example\n -------\n >>> import Metadata\n >>> metadata1 = Metadata(...)\n >>> metadata2 = Metadata(...)\n >>> child_metadata =\n >>> \tMetadata.get_child([metadata1, metadata2], 'child_file')\n \"\"\"\n if isinstance(parents, (list, tuple)) is False:\n parents = (parents,)\n meta_data = copy.deepcopy(parents[0].meta_data)\n\n for parent in parents[1:]:\n meta_data.update(parent.meta_data)\n\n return cls(parents[0].data_type,\n parents[0].file_type,\n path,\n sources=[parent.file_path for parent in parents],\n meta_data=meta_data,\n taxon_id=parents[0].taxon_id)\n","sub_path":"basic_modules/metadata.py","file_name":"metadata.py","file_ext":"py","file_size_in_byte":3821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"165686206","text":"class Non_overlappingIntervals:\n def eraseOverlapIntervals(self, intervals: list) -> int:\n if not intervals:\n return 0\n n=len(intervals)\n intervals = sorted(intervals, key=lambda b:b[1])#按照数组中的[[a,b]]的b位从小到大排序\n total,prev=0,intervals[0][1]\n for i in range(1,n):\n if (intervals[i][0]= 1:\n print(versionstring())\n print(\"CWL document required, no input file was provided\")\n parser.print_usage()\n return 1\n\n if parsed_args.version:\n print(versionstring())\n return 0\n\n if parsed_args.tes is None:\n print(versionstring())\n parser.print_usage()\n print(\"cwl-tes: error: argument --tes is required\")\n return 1\n\n if parsed_args.quiet:\n log.setLevel(logging.WARN)\n if parsed_args.debug:\n log.setLevel(logging.DEBUG)\n\n blacklist_false = [\"no_container\", \"disable_pull\", \"disable_net\",\n \"custom_net\", \"no_match_user\"]\n for f in blacklist_false:\n if vars(parsed_args).get(f):\n log.warning(\"arg: '%s' has no effect in cwl-tes\" % (f))\n\n blacklist_true = [\"enable_pull\"]\n for f in blacklist_true:\n if not vars(parsed_args).get(f):\n log.warning(\"arg: '%s' has no effect in cwl-tes\" % (f))\n\n # custom\n if not parsed_args.rm_container:\n log.warning(\"arg: 'leave_container' has no effect in cwl-tes\")\n\n tes_workflow = TESWorkflow(parsed_args.tes, vars(parsed_args))\n\n # setup signal handler\n def signal_handler(*args):\n log.info(\n \"recieved control-c signal\"\n )\n log.info(\n \"terminating thread(s)...\"\n )\n log.warning(\n \"remote TES processes %s may keep running\" %\n ([t.id for t in tes_workflow.threads])\n )\n sys.exit(1)\n signal.signal(signal.SIGINT, signal_handler)\n\n return cwltool.main.main(\n args=parsed_args,\n executor=tes_workflow.executor,\n makeTool=tes_workflow.make_tool,\n versionfunc=versionstring,\n logger_handler=console\n )\n\n\ndef add_args(parser):\n parser.add_argument(\n \"--tes\",\n type=str,\n help=\"GA4GH TES Service URL\"\n )\n return parser\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"cwl_tes/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"344168188","text":"from objet import *\nimport copy\n\n## infos communes à toutes les unités du jeu\n\nclass Unite(Objet) :\n\tdef __init__(self,leid,coords,proprio,pv,serverKey,nom=\"\") :\n\t\tObjet.__init__(self,leid,coords,proprio,serverKey,nom)\n\t\t#pv : les points de vie de l'unité\n\t\tself.server[serverKey][\"pv\"] = pv\n\t\t#mode : le mode actuel de l'unité\n\t\t# ce mode représente ce qu'elle fait \"toute seule\", sans ordre du joueur\n\t\t# par exemple le fait de demander a un worker de récolter le fait entrer en mode \"harvest\", ce qui fera qu'il recoltera des ressources non pas qu'une seule fois\n\t\t# mais à tous les tours, jusqu'à être plein\n\t\tself.server[serverKey][\"mode\"] = []\n\n\tdef getPv(self) :\n\t\treturn copy.copy(self.server[serverKey][\"pv\"])\n\n\tdef getMode(self) :\n\t\tglobal serverKey\n\t\treturn copy.copy(self.server[serverKey][\"mode\"])\n\n\tdef setMode(self,key,mode) :\n\t\tif key == serverKey :\n\t\t\tself.server[serverKey][\"mode\"] = mode\n\n\tdef reducePv(self, key, quantite) :\n\t\tif key == serverKey :\n\t\t\tself.server[serverKey][\"pv\"] -= quantite\n\n\t#move : ordre qui peut être lancé par un joueur\n\t# ordonne à l'unité de se déplacer vers la destination indiquée en paramètre\n\tdef move(self,j,destination) :#avec destination tel que : (x,y)\n\t\tif not(self.getAAgit()) :\n\t\t\tif j.getUid() == self.getProprio() :\n\t\t\t\t#on vérifie que l'on ordonne à l'unité de se déplacer sur une case adjacente pour pas qu'elle se téléporte\n\t\t\t\tif (abs(destination[0]-self.getCoords()[0])+abs(destination[1]-self.getCoords()[1])) <= 1 :\n\t\t\t\t\t#déplacement à une case distance en 4 connexité\n\t\t\t\t\tplateau = j.carte.carte\n\t\t\t\t\t#si la destination est bien du sol\n\t\t\t\t\tif plateau[destination].type == \"sol\" :\n\t\t\t\t\t\t# et si la destination n'est pas obstruée par un autre objet\n\t\t\t\t\t\tif plateau[destination].objet == None :\n\t\t\t\t\t\t\t#on peut déplacer l'unite :)\n\t\t\t\t\t\t\tplateau[self.getCoords()].objet = None\n\t\t\t\t\t\t\tself.setCoords(serverKey,destination)\n\t\t\t\t\t\t\tplateau[destination].objet = self\n\t\t\t\t\t\t\tself.setAAgit(serverKey,True)\n\n\t\t\t\t\t\t\t#le fait de se déplacer coupe tout mode déjà en court\n\t\t\t\t\t\t\tself.setMode(serverKey,[])\n\t\t\t\t\t\t\treturn 0\n\t\t\t\t\t\telse :\n\t\t\t\t\t\t\treturn -1\n\t\t\t\t\telse :\n\t\t\t\t\t\treturn -1\n\t\t\t\telse :\n\t\t\t\t\treturn -1\n\n\tdef nextMove(self,key,j) :\n\t\tif key == serverKey :\n\t\t\tmode = self.getMode()\n\t\t\tif mode[0] == \"move\" :\n\t\t\t\tpath = mode[1]\n\t\t\t\tif len(path) > 0 :\n\t\t\t\t\tt = self.move(j,path[0])\n\t\t\t\t\tif t == 0 :\n\t\t\t\t\t\tdel path[0]\n\t\t\t\t\t\tif len(path) > 0 :\n\t\t\t\t\t\t\tself.setMode(serverKey,[\"move\",path])\n\t\t\t\t\t\telse :\n\t\t\t\t\t\t\tself.setMode(serverKey,[])\n\t\t\t\t\telse :\n\t\t\t\t\t\tself.setMode(serverKey,[])\n\t\t\t\telse :\n\t\t\t\t\tself.setMode(serverKey,[])\n\n\tdef moveVia(self,j,map,destination, eviter=[]) :\n\t\tif not(self.getAAgit()) :\n\t\t\tif j.getUid() == self.getProprio() :\n\t\t\t\t#si on veut se déplacer sur des cases non adjacentes\n\t\t\t\tif not((abs(destination[0]-self.getCoords()[0])+abs(destination[1]-self.getCoords()[1])) <= 1) :\n\t\t\t\t\tdepart = self.getCoords()\n\t\t\t\t\tpath = map.pathfinding(depart,destination,eviter)\n\t\t\t\t\tif path != None :\n\t\t\t\t\t\tself.setMode(serverKey,[\"move\",path[1::]])\n\t\t\t\t\t\tself.nextMove(serverKey,j)\n\t\t\t\t\t\t#le setAAgit sera fait par l'ordre \"move\" qui est utilisé par nextMove()\n\n\t\t\t\telse :\n\t\t\t\t\t#sinon on voulait se déplacer sur des cases adjacentes, on peut juste utiliser l'ordre move de base\n\t\t\t\t\tself.move(j,destination)\n\n","sub_path":"serveur/unite.py","file_name":"unite.py","file_ext":"py","file_size_in_byte":3306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"405820363","text":"\"\"\"\nDescription: Function implements the simulation of a single tennis match.\n- IMPORTS\n- FUNCTIONS\n\"\"\"\n\n# ------------------------------------------------------------------------\n# IMPORTS\n# ------------------------------------------------------------------------\n# Logic dependencies.\nimport numpy as np\nimport operator\n\n# ------------------------------------------------------------------------\n# FUNCTIONS\n# ------------------------------------------------------------------------\ndef match_simulation(players, set_count, updates=False):\n \"\"\"\n Description: Function simulates a single match between two player agent objects.\n Inputs: \n - players (list): A list with two player agent objects.\n - set_count (int): The number of sets to run in this simulation.\n - updates (boolean): Whether or not to update users on the match outcomes.\n Output: winner (String): The name of the winner of this match.\n \"\"\"\n # Instantiating Variables\n # --–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–\n sets_to_win = (set_count // 2) + 1\n player_name_list = [player['name'] for player in players]\n player2match_data = dict(zip(player_name_list, [{'points':0, 'games':0, 'sets':0}, {'points':0, 'games':0, 'sets':0}]))\n\n # The names of each player as a key to index match structures.\n player_A = player_name_list[0]\n player_B = player_name_list[1]\n\n # Each unique player passed into this function.\n player2info = dict(zip(player_name_list, players))\n match_over = False\n\n # The Main Match Loop\n # --–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–\n while match_over == False: # While the sets needed to win the match haven't been won.\n # Setting up a single set in this match.\n set_over = False\n server_switch = True # A switch to alternate which player is serving.\n game_number = 0 \n # Setting the number of games won by each player. \n six_more_games = False\n\n if updates == True:\n print(\"------------------------------------------------\")\n print(\" New set\" )\n print(\"------------------------------------------------\")\n \n while set_over == False:\n # Setting up a particular game in this set. \n game_number += 1 # The game number increments (starts at one as it is initialized at zero).\n game_over = False\n four_more_points = False # If a player has reached four or more points. The code is set this way to not have to check -\n # - the number of points each loop iteration.\n \n # Now to simulate a single game within this set.\n if updates == True:\n print(\" New game\" )\n print(\"------------------------------------------------\")\n \n server = player_A if server_switch == True else player_B # Assigning a server for this game. \n if updates == True:\n print(\"Serving player: \", server)\n \n # Single Match Simulation\n # --–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–\n while game_over == False:\n p = player2info[server]['deuce_serve_p']\n serve_win_P = (p**4) + 4*(p**4)*(1-p) + ((10*(p**4)*((1-p)**2)/(1-(2*p)*(1-p))))\n reciever = [player for player in player_name_list if player not in [server]]\n name_list_ordered_by_server = [server, reciever[0]]\n \n # Sampling Match Outcome Based on Servers Win Probability\n # --–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–\n try:\n winner = np.random.choice(name_list_ordered_by_server, p=[serve_win_P, 1-serve_win_P])\n\n except ValueError: # DOUBLE CHECK MONTE CARLO IMPLEMENTATION.\n winner = server\n # print(serve_win_P)\n # CAn this happend? \n return server\n # sys.exit()\n\n player2match_data[winner]['points'] += 1\n\n if updates == True:\n print(\"Points to:\", winner)\n \n # Stepping Current Game Forward\n # --–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–\n # Checking if this game is over (first to at least four points with a margin of two).\n if four_more_points == False: # First checking if four points have been gathered by either player. \n if (player2match_data[player_A][\"points\"] > 3) or (player2match_data[player_B]['points'] > 3): # If at least four points belong to a player.\n four_more_points = True\n\n if four_more_points == True:\n if abs(player2match_data[player_A][\"points\"] - player2match_data[player_B][\"points\"]) >= 2: # Checking the winning margin.\n game_over = True\n # Switching the serve switch so the other player now serves.\n server_switch = not server_switch\n # Assigning a winner for this game.\n player2match_data[winner]['games'] += 1\n \n if updates == True:\n print(\"Game! Winner: \", winner)\n\n # Resetting points for the next game. \n player2match_data[player_A][\"points\"] = 0\n player2match_data[player_B][\"points\"] = 0\n \n # Moving a Set Forward\n # --–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–--–\n # A set ends when a player has won 6 games with winning margin of at least two games.\n if six_more_games == False:\n if (player2match_data[player_A][\"games\"] > 5) or (player2match_data[player_B][\"games\"] > 5): # If at least six points belong to a team.\n six_more_games = True\n \n if six_more_games == True:\n if abs(player2match_data[player_A][\"games\"] - player2match_data[player_B][\"games\"]) >= 2: # Checking the winning margin.\n # A player has won this individual set.\n set_over = True\n \n # Assigning a winner for this set.\n # player2sets = {player_A:player2match_data[player_A][\"sets\"], player_B:player2match_data[player_B][\"sets\"]}\n # set_winner = max(player2sets.items(), key=operator.itemgetter(1))[0]\n set_winner = winner # The winner of the last game ended this set if we are in this loop. \n player2match_data[set_winner][\"sets\"] += 1\n \n # Resetting the games counter.\n player2match_data[player_A][\"games\"] = 0\n player2match_data[player_B][\"games\"] = 0\n\n if updates == True:\n print(\"Set! Winner: \", set_winner)\n print(\"------------------------------------------------\")\n \n # Checking if enough sets have been one by a player to end the match.\n if player2match_data[set_winner][\"sets\"] >= sets_to_win: # Only need to check the most recent set winner. \n match_over = True\n if updates == True:\n print(\"Match! Winner: \", set_winner) # The player to have won the last match won this game.\n \n return set_winner","sub_path":"toy_model/src/match_simulation.py","file_name":"match_simulation.py","file_ext":"py","file_size_in_byte":8107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"12"} +{"seq_id":"318811490","text":"# Grabs some sweet sweet XKCD\n\nimport os, bs4, urllib.request, sys\nfrom random import randint\n\nurl = 'https://xkcd.com/'\n\n\ndef grabComic(num):\n\tprint('Attempting to grab comic #{}'.format(num))\n\ttry:\n\t\tpage = url + num\n\t\tresponse = urllib.request.urlopen(page)\n\t\ttext = str(response.read())\n\t\t\n\t\tli = text.find('embedding')\n\t\tlj = text.find('