Auto Topic: polynomial
auto_polynomial | topic
Coverage Score
1
Mentioned Chunks
70
Mentioned Docs
5
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
polynomial
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: polynomial | CO_OCCURS | Propositional Logic | 14 |
| Auto Topic: polynomial | CO_OCCURS | Inference | 12 |
| Auto Topic: polynomial | CO_OCCURS | Constraint Satisfaction Problem | 11 |
| Auto Topic: polynomial | CO_OCCURS | Auto Topic: self | 8 |
| Auto Topic: polynomial | CO_OCCURS | Problem Formulation | 7 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: polynomial | 7 |
| Auto Topic: polynomial | CO_OCCURS | Logical Agents | 6 |
| Auto Topic: get_polynomial | CO_OCCURS | Auto Topic: polynomial | 6 |
| Auto Topic: polynomial | CO_OCCURS | State-Space Search | 5 |
| Auto Topic: int | CO_OCCURS | Auto Topic: polynomial | 4 |
| Auto Topic: polynomial | CO_OCCURS | Informed Search | 3 |
| Auto Topic: polynomial | CO_OCCURS | Resolution | 3 |
| Auto Topic: polynomial | CO_OCCURS | Utility Theory | 3 |
| Auto Topic: polynomial | CO_OCCURS | Auto Topic: print | 3 |
| Auto Topic: conference | CO_OCCURS | Auto Topic: polynomial | 3 |
| Auto Topic: near | CO_OCCURS | Auto Topic: polynomial | 3 |
| Auto Topic: international | CO_OCCURS | Auto Topic: polynomial | 3 |
| Auto Topic: def | CO_OCCURS | Auto Topic: polynomial | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
assignments CIS5210-Assignments/M1/homework1.py | 0.67 | 14 | ... ######################################## # Section 6: Polynomials ############################################################ class Polynomial(object): def __init__(self, polynomial): self.polynomial = tuple(polynomial) def get_polynomial(self): return self.polynomial def __neg_ ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.67 | 11 | . Write an initialization method __init__(self, polynomial) that converts the input sequence polynomial of coefficient- power pairs into a tuple and saves it for future use. Also write a corresponding method get_polynomial(self) that returns this internal representation. >>> p = ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.67 | 9 | . >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> q = p + p; q.get_polynomial() ((2, 1), ( 1, 0), ( 2, 1), ( 1, 0)) >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> q = Polynomial([(4, 3), ( 3, 2)]) >>> r = p + q; r.get_polynomial() ((2, 1), ( 1, 0), ( 4, 3), ( 3, 2)) 4. [3 points] Write a __ ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.67 | 7 | ... 'toMixedCase' >>> to_mixed_case("__EXAMPLE__NAME__") 'exampleName' 6. Polynomials [37 points] In this section, you will implement a simple Polynomial class supporting basic arithmetic, simplification, evaluation, and pretty-printing. An example demonstrating these capabilities i ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.67 | 7 | This method can be written in one line using Python ’s exponentiation operator, **, and the built-in sum function. >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> [p(x) for x in range(5)] [1, 3, 5, 7, 9] 6 >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> q = -(p * p) + p >>> [q(x) for x in ra ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.65 | 6 | t need to match the examples below exactly, as long as the same terms are present in some order. >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> q = p * p; q.get_polynomial() ((4, 2), ( 2, 1), ( 2, 1), ( 1, 0)) >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> q = Polynomial([(4, 3), ( 3, 2)]) ... |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.65 | 6 | fter the first step, the polynomial should be simplified to the single term 0 ⋅ 𝑥0, i.e. (0, 0). >>> p = Polynomial([(2, 1), ( 1, 0)]) >>> q = -p + (p * p); q.get_polynomial() ((-2, 1), ( -1, 0), ( 4, 2), ( 2, 1), (2, 1), ( 1, 0)) >>> q.simplify(); q.get_polynomial() ((4, 2), ( 2 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | thms. The first gross division is between problems that can be solved in polynomial time and problems that cannot be solved in polynomial time, no matter what algorithm is used. The class of polynomial problems—those which can be solved in time O(nk) for some k—is called P. These ... |
assignments CIS5210-Assignments/M1/homework1.py | 0.61 | 4 | : term_dict = {} for c, p in self.polynomial: term_dict[p] = term_dict.get(p, 0) + c result = [(c, p) for p, c in term_dict.items() if c != 0] if not result: self.polynomial = ((0, 0),) return result.sort(key=lambda term: term[1], reverse=True) self.polynomial = tuple(result) def ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... which algorithms and complexity issues have been studied in great depth. For example, from the fact that linear programming is solvable in polynomial time, one can show that MDPs can be solved in time polynomial in the number of states and actions and the number of bits required ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... ty analysis analyzes problems rather Complexity analysis than algorithms. The first gross division is between problems that can be solved in polynomial time and problems that cannot be solved in polynomial time, no matter what algorithm is used. The class of polynomial |
assignments CIS5210-Assignments/M1/homework1.pdf | 0.59 | 3 | integer coefficients and non-negative integer powers. >>> p = Polynomial([(1, 1), ( 1, 0)]) >>> qs = (p, p + p, -p, -p - p, p * p) >>> for q in qs: q.simplify() ; print(str(q)) ... 'x + 1' '2x + 2' 7 '-x - 1' '-2x - 2' 'x^2 + 2x + 1' >>> p = Polynomial([(0, 1), ( 2, 3)]) >>> str( ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... S(z) = l ∏ i =1 P(zi | parents(Zi)) (13.8) 6 If it was easy, then we could approximate the desired probability to arbitrary accuracy with a polynomial number of samples. It can be shown that no such polynomial-time approximation scheme can exist. 458 Chapter 13 Probabilistic Reas ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... e have chance nodes, then compute expected utility.) The backward induction algorithm is guaranteed to terminate, and moreover runs in time polynomial in the size of the game tree. As the algorithm does its work, it traces out strategies for each player. As it turns out, these st ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... elled as “Occam.” 674 Chapter 19 Learning from Examples Then we can say that the prior probabilityP(h) is high for a smooth degree-1 or -2 polynomial and lower for a degree-12 polynomial with large, sharp spikes. We allow unusual-looking functions when the data say we really need ... |
assignments CIS5210-Assignments/M1/homework1.py | 0.57 | 2 | ... ###################################### # Just an approximation is fine. feedback_question_1 = """ 4 """ feedback_question_2 = """ Polynomial and nltk. I have never used nltk before. """ feedback_question_3 = """ I love the polynomial section. Because it allows me to learn about o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ractable if the time required to solve instances of the problem grows exponentially with the size of the instances. The distinction between polynomial and exponential growth in complexity was first emphasized in the mid-1960s (Cobham, 1964; Edmonds, 1965). It is important because ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... roblem to be NP-hard, but effective heuristic approximation methods were developed (Lin and Kernighan, 1973). Arora (1998) devised a fully polynomial approximation scheme for Euclidean TSPs. VLSI layout methods are surveyed by LaPaugh (2010), and many layout optimization papers a ... |