The functionegcdis a pure-Python implementation of theextended Euclidean algorithmthat can be viewed as an expansion of the functionality and interface of the built-inmath.gcdfunction. When it is supplied two i
phi_n)# 选择 ee=0# 初始化 eforiinrange(2,phi_n):ifgcd(i,phi_n)==1:e=ibreakprint("选择的 e =",e)# 计算 dgcd_value,x,y=extended_gcd(e,phi_n)d=x%phi_nprint("d =",d)
return pair def main(): if len(sys.argv) < 3: print('2 integer arguments required') exit(1) m = int(sys.argv[1]) n = int(sys.argv[2]) print(extended_euclidean_algorithm(m, n)) if __name__ == '__main__': main() Copy lines Copy permalink View git blame Go ©...
gcd,x,y=extended_euclidean_gcd(30,21)print(f"GCD:{gcd}, x:{x}, y:{y}")# 输出: GCD: 3, x: 1, y: -1 1. 2. 关系图 在这里,我们使用一个关系图来描述算法中涉及的主要概念与关系。可以使用以下Mermaid语法来展示: intaintbintresultEuclideanAlgorithmGCDgcdintxintycalculates 结论 欧几里得算...
# Extended Euclidean algorithm, ab=1 (mod m), yields the multiplicative inverse b of A under module m def Extended_Eulid(a: int, m: int) -> int: def extended_eulid(a: int, m: int): if a == 0: # boundary condition return 1, 0, m else: x, y, gcd = extended_eulid(m ...
扩展欧几里得算法(英语:Extended Euclidean algorithm)是欧几里得算法(又叫辗转相除法)的扩展。已知整数a、b,扩展欧几里得算法可以在求得a、b的最大公约数的同时,能找到整数x、y(其中一个很可能是负数),使它们满足贝祖等式ax+by=gcd(a,b)。 算法实现: ...
Extended Euclidean Algorithm Recall that including any third-party codes without claiming is considered as lack of academic integrity, and results in failing this course. Example Input & Output Input: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 348628441088154302789358861148142046612421058061961344512624211...
The above formula is the basic formula for Extended Euclidean Algorithm, which takes p and q as the input parameters.Encryption FormulaConsider a sender who sends the plain text message to someone whose public key is (n,e). To encrypt the plain text message in the given scenario, use the ...
algorithm: {'auto', 'ball_tree', 'kd_tree', 'brute'},默认值为'auto',用于计算最近邻的算法。 'ball_tree':使用BallTree算法。 'kd_tree':使用KDTree算法。 'brute':使用暴力搜索。 'auto':根据传递给fit方法的值尝试确定最合适的算法。 注意:在稀疏输入上进行拟合将覆盖该参数的设置,使用暴力搜索。
from sklearn.grid_search import GridSearchCV params = {"n_neighbors": np.arange(1,3), "metric": ["euclidean", "cityblock"]} grid = GridSearchCV(estimator=knn, param_grid=params) grid.fit(X_train, y_train) print(grid.best_score_) print(grid.best_estimator_.n_neighbors) Randomized ...