Auto Topic: char
auto_char | topic
Coverage Score
1
Mentioned Chunks
6
Mentioned Docs
3
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
char
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: char | CO_OCCURS | Auto Topic: row | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
assignments CIS5210-Assignments/M5/homework5_sudoku_gui.py | 0.65 | 6 | ... d_string.split("\n")) > 1: board_list = board_string.split("\n") board_string_new = "".join(board_list) board_string = "" for char in board_string_new: if char == "*": board_string += "0" else: board_string += char if len(board_string) != 81: print("Invalid puzzle") else: self.ga ... |
assignments CIS5210-Assignments/M3/homework3_grid_navigation_gui.py | 0.61 | 4 | ... cene_path): scene = [] with open(scene_path) as infile: for row, line in enumerate(infile, start=1): scene.append([]) for col, char in enumerate(line.strip(), start=1): if char == ".": scene[-1].append(False) elif char == "X": scene[-1].append(True) else: print ("Unrecognized cha ... |
assignments CIS5210-Assignments/M5/homework5_sudoku_gui.py | 0.59 | 3 | read_board(path): board_string = "" with open(path, 'r') as file: for line in file: row = "".join("0" if char == "*" else char for char in line.strip()) board_string += row return board_string if __name__ == "__main__": game = SudokuGame("00430020900500900107006004300600208719000 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... put a good deal of effort into data structures that would allow efficient retrieval of facts; this work is covered in AI programming texts (Char- niak et al., 1987; Norvig, 1992; Forbus and de Kleer, 1993). By the early 1970s, forward chaining was well established in AI as an easi ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... s (θ, θF1, θW1, θF2, θW2) but only three (2 2 − 1) observed counts. In such a case it is not possible to recover the mixture weightθ or the char- acteristics of the two bags that were mixed together. We say that the two-attribute model is not identifiable.Identifiability Identifiabi ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | s not possible to recover the mixture weightθ or the char- acteristics of the two bags that were mixed together. We say that the two-attribute model is not identifiable.Identifiability Identifiability in Bayesian networks is a tricky issue. Note that even with three attributes and s ... |