Auto Topic: copy
auto_copy | topic
Coverage Score
1
Mentioned Chunks
37
Mentioned Docs
14
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
copy
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: copy | CO_OCCURS | Auto Topic: self | 19 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: def | 17 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: row | 11 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: rows | 11 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: perform_move | 11 |
| Auto Topic: cols | CO_OCCURS | Auto Topic: copy | 9 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: get_board | 7 |
| Auto Topic: copy | CO_OCCURS | Propositional Logic | 6 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: int | 6 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: is_solved | 5 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: seq | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: len | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: raise | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: valueerror | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: dominoesgame | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: vertical | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: import | 4 |
| Auto Topic: col | CO_OCCURS | Auto Topic: copy | 4 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: imports | 3 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: legal_moves | 3 |
| Auto Topic: copy | CO_OCCURS | Auto Topic: new_game | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
assignments CIS5210-Assignments/M3/homework3.pdf | 0.65 | 6 | ... guration. >>> p = TilePuzzle([[1, 2], [3, 0]]) >>> p.is_solved() True >>> p = TilePuzzle([[0, 1], [3, 2]]) >>> p.is_solved() False Create a copy: In the TilePuzzle class, write a method copy(self) that returns a new TilePuzzle object initialized with adeep copy of the current boa ... |
assignments CIS5210-Assignments/M2/homework2.pdf | 0.63 | 5 | ... alse, False], [False, False]] >>> p = LightsOutPuzzle(b) >>> p.is_solved() True 6. [3 points] In the LightsOutPuzzle class, write a method copy(self) that returns a new LightsOutPuzzle object initialized with a deep copy of the current board. Changes made to the original puzzle s ... |
assignments CIS5210-Assignments/M4/homework4.pdf | 0.63 | 5 | ... True, False]] >>> g = DominoesGame(b) >>> g.game_over(True) False >>> g.game_over(False) True 9. In the DominoesGame class, write a method copy(self) that returns a new DominoesGame object initialized with a deep copy of the current board. Changes made to the original puzzle shou ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.61 | 4 | ... fault values when omitted. In some cases, it may be necessary to use the optional third parameter to specify a step size. Write a function copy(seq) that returns a new sequence containing the same elements as the input sequence. >>> copy("abc") 'abc' >>> copy((1, 2, 3)) (1, 2, 3) ... |
assignments CIS5210-Assignments/M2/homework2.py | 0.57 | 2 | ... self.perform_move(r, c) def is_solved(self): return all(not cell for row in self._board for cell in row) def copy(self): new_obj = LightsOutPuzzle.__new__(LightsOutPuzzle) new_obj._rows = self._rows new_obj._cols = self._cols new_obj._board = [row[:] for row in self._board] retu ... |
assignments CIS5210-Assignments/M4/homework4.py | 0.57 | 2 | ... def game_over(self, vertical): for _ in self.legal_moves(vertical): return False return True def copy(self): return DominoesGame([row[:] for row in self.board]) def successors(self, vertical): for move in self.legal_moves(vertical): new_game = self.copy() new_game.perform_move(m ... |
assignments CIS5210-Assignments/M4/hw4-optimized.py | 0.57 | 2 | ... s - 1 for r in range(rows): row = b[r] for c in range(col_limit): if not row[c] and not row[c + 1]: return False return True def copy(self): return DominoesGame([row[:] for row in self.board]) def successors(self, vertical): for move in self.legal_moves(vertical): new_game = self ... |
assignments CIS5210-Assignments/M5/homework5.py | 0.57 | 2 | ... f not remaining: return True cell = min(remaining, key=lambda c: len(board[c])) saved = {c: s.copy() for c, s in board.items()} for v in sorted(board[cell]): board[cell] = {v} if self.infer_with_guessing(): return True self.board = {c: s.copy() for c, s in saved.items()} board = ... |
assignments CIS5210-Assignments/M5/homework5_sudoku_gui.py | 0.57 | 2 | ### Author: Yue Yang ### # import argparse import copy # import numpy as np from tkinter import Tk, Canvas, Frame, Button, BOTH, TOP, BOTTOM, LEFT, \ RIGHT, X, OUTSIDE, Label, StringVar, Entry # import tkinter as tk from homework5 import Sudoku # define canvas size canvas_margin ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e chunks of DNA; some viruses borrow DNA from one organism and insert it into another; and there are transposable genes that do nothing but copy themselves many thousands of times within the genome. There are even genes that poison cells from potential mates that do not carry the ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... on both P and Q at once to infer R. There is one more technical aspect of the resolution rule: the resulting clause should contain only one copy of each literal.10 The removal of multiple copies of literals is called factoring. For example, if we resolve (A ∨ B)Factoring with (A ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... larly, in first-order logic we can quantify over time, so we need just one successor-state axiom for each predicate, rather than a different copy for each time step. For example, the axiom for the arrow (Equation (7.2) on page 258) becomes ∀t HaveArrow (t + 1) ⇔ (HaveArrow(t) ∧ ¬A ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ALIZE (f ×B1) is justαf ×b, by Equation (14.14). b and f together, using them to compute the smoothed estimate at each step. Since only one copy of each message is needed, the storage requirements are constant (i.e., independent of t, the length of the sequence). There are two si ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... erpart plays refuse, it will transition back to the refuse state. In sum, T IT-FOR -TAT will start by choosing refuse, and will then simply copy whatever its counterpart did on the previous round. |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... erpart plays refuse, it will transition back to the refuse state. In sum, T IT-FOR -TAT will start by choosing refuse, and will then simply copy whatever its counterpart did on the previous round. Section 17.2 Non-Cooperative Game Theory 605 testify testify testify testify testif ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ribus was able to defeat human champions at six-player poker in two formats: five copies of the program at the table with one human, and one copy of the program with five humans. There is a huge leap in complexity here. With one opponent, there are ( 50 2=1225 ) possibilities for t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... sentation from the previous layer rather than replace it entirely. If the learned perturbation is small, the next layer is close to being a copy of the previous layer. This is achieved by the following equation for layer i in terms of layer i − 1: z(i) = g(i) r (z(i−1) + f (z(i−1 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... transfer learning. For neural networks, learning consists of adjusting weights, so the most plausible approach for transfer learning is to copy over the weights learned for task A to a network that will be trained for |