kaiwu.sampler package#

Module contents#

模块: sampler

功能: 提供一系列数据后处理工具

class kaiwu.sampler.SimulatedAnnealingSampler(initial_temperature=100, alpha=0.99, cutoff_temperature=0.001, iterations_per_t=10, size_limit=100, flag_evolution_history=False, verbose=False, rand_seed=None, process_num=1)#

Bases: SimulatedAnnealingSamplerBase

求解CIM Ising模型的模拟退火采样器。每个解都独立求解

Args:

initial_temperature (float): 初始温度.

alpha (float): 降温系数.

cutoff_temperature (float): 截止温度.

iterations_per_t (int): 每个温度迭代深度.

size_limit (int): 输出解的个数，默认输出100个解

flag_evolution_history (bool): 是否输出哈密顿量演化历史，默认False，当值为True时，通过get_ha_history方法获取演化历史

verbose (bool): 是否在控制台输出计算进度，默认False

rand_seed (int, optional): numpy随机数生成器的随机种子

process_num (int, optional): 并行进程数 (-1为自动调用所有可用核心，1为单进程). Defaults to 1.

Examples:
>>> import numpy as np
>>> import kaiwu as kw
>>> matrix = -np.array([[ 0. ,  1. ,  0. ,  1. ,  1. ],
...                     [ 1. ,  0. ,  0. ,  1.,   1. ],
...                     [ 0. ,  0. ,  0. ,  1.,   1. ],
...                     [ 1. ,  1.,   1. ,  0. ,  1. ],
...                     [ 1. ,  1.,   1. ,  1. ,  0. ]])
>>> worker = kw.classical.SimulatedAnnealingOptimizer(initial_temperature=100,
...                                                   alpha=0.99,
...                                                   cutoff_temperature=0.001,
...                                                   iterations_per_t=10,
...                                                   size_limit=10)
>>> # This value is random and cannot be predicted
>>> worker.solve(matrix) 
array([[-1,  1, -1,  1,  1],
       [-1,  1,  1, -1,  1],
       [-1,  1,  1, -1, -1],
       [ 1, -1, -1,  1,  1],
       [ 1, -1,  1, -1,  1],
       [ 1, -1,  1, -1, -1],
       [ 1, -1, -1, -1,  1],
       [ 1, -1,  1,  1, -1],
       [ 1,  1,  1, -1, -1],
       [-1, -1, -1,  1,  1]])
on_matrix_change()#

更新矩阵相关信息

get_ha_history()#

获取哈密顿量演化历史

Returns:

dict: 哈密顿量随时间演化历史，key是时间，单位为秒，value是 hamilton

single_process_solve(ising_matrix=None, init_solution=None, rand_seed=None, size_limit=None)#

单进程求解Ising矩阵

Args:

ising_matrix (np.ndarray, optional): Ising矩阵. Defaults to None.

init_solution (np.ndarray, optional): 初始解向量. Defaults to None.

rand_seed (int, optional): numpy随机数生成器的随机种子

Returns:

np.ndarray: 解向量

set_matrix(ising_matrix)#

设置矩阵并更新相关内容

solve(ising_matrix=None, init_solution=None, size_limit=None)#

SA求解Ising矩阵solve接口

Args:

ising_matrix (np.ndarray, optional): Ising矩阵. Defaults to None.

init_solution (np.ndarray, optional): 初始解向量. Defaults to None.

Returns:

np.ndarray: 多个进程合并去重之后的解向量.

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