Source code for sqaod.cpu.bipartite_graph_annealer

from __future__ import print_function
import numpy as np
from sqaod.common.bipartite_graph_annealer_base import BipartiteGraphAnnealerBase
from . import cpu_bg_annealer as cext
import sqaod


class BipartiteGraphAnnealer(BipartiteGraphAnnealerBase) :
    
    def __init__(self, b0, b1, W, optimize, dtype, prefdict) :
        self._cobj = cext.new(dtype)
        BipartiteGraphAnnealerBase.__init__(self, cext, dtype, b0, b1, W, optimize, prefdict)

        
def bipartite_graph_annealer(b0 = None, b1 = None, W = None, \
                             optimize = sqaod.minimize, \
                             dtype = np.float64, **prefs) :
    """ factory function for sqaod.cpu.BipartiteGraphAnnealer.

    Args:
      numpy.ndarray b0, b1, W : QUBO
      optimize : specify optimize direction, `sqaod.maximize or sqaod.minimize <preference.html#sqaod-maximize-sqaod-minimize>`_.
      prefs : `preference <preference.html>`_ as \*\*kwargs
    Returns:
      sqaod.cpu.BipartiteGraphAnnealer: annealer instance
    """
    return BipartiteGraphAnnealer(b0, b1, W, optimize, dtype, prefs)


if __name__ == '__main__' :
    N0 = 40
    N1 = 40
    m = 20
    
    np.random.seed(0)
            
    W = np.random.random((N1, N0)) - 0.5
    b0 = np.random.random((N0)) - 0.5
    b1 = np.random.random((N1)) - 0.5

    an = bipartite_graph_annealer(b0, b1, W, sqaod.minimize, n_trotters=m)
    
    Ginit = 5
    Gfin = 0.01
    beta = 1. / 0.02
    tau = 0.99
    n_repeat = 10

    for loop in range(0, n_repeat) :
        an.prepare()
        an.randomize_spin()
        G = Ginit
        while Gfin < G :
            an.anneal_one_step(G, beta)
            G = G * tau
        an.make_solution()

        E = an.get_E()
        x = an.get_x()
        print(E.shape, E)
        print(x)