# Path: brainforge/config/numeric.py

# 
# Path: brainforge/util/typing.py
# def zX(*dims, dtype=floatX):
#     return np.zeros(dims, dtype=dtype)
# 
# def zX_like(array, dtype=floatX):
#     return zX(*array.shape, dtype=dtype)


import warnings
import numpy as np
from ..config.numeric import floatX
from ..util.typing import zX, zX_like
        for ind in inds:
            gen = self.individuals[ind]
            if verbose:
                print("\rUpdating {0:>{w}}/{1}".format(int(ind)+1, lim, w=strlen), end="")
            self.fitnesses[ind] = self.fitness(gen, *fitness_args, **fitness_kw)
            self.grades[ind] = self.grade_function(self.fitnesses[ind])
        if verbose:
            print("\rUpdating {0}/{1}".format(lim, lim))

    def get_candidates(self, survivors=None):

        if isinstance(survivors, tuple):
            survivors = survivors[0]

        def indstream():
            counter = 0
            lmt = len(survivors)
            arg1, arg2 = np.copy(survivors), np.copy(survivors)
            np.random.shuffle(arg1)
            np.random.shuffle(arg2)
            while 1:
                if counter >= lmt:
                    counter = 0
                    arg1, arg2 = np.copy(survivors), np.copy(survivors)
                    np.random.shuffle(arg1)
                    np.random.shuffle(arg2)
                yield arg1[counter], arg2[counter]
                counter += 1

        prbs = rescale(self.grades)

# Path: brainforge/config/numeric.py

# 
# Path: brainforge/util/typing.py
# def zX(*dims, dtype=floatX):
#     return np.zeros(dims, dtype=dtype)
# 
# def zX_like(array, dtype=floatX):
#     return zX(*array.shape, dtype=dtype)


# Path: brainforge/evolution/_evolution.py
import warnings
import numpy as np
from ..config.numeric import floatX
from ..util.typing import zX, zX_like

        for ind in inds:
            gen = self.individuals[ind]
            if verbose:
                print("\rUpdating {0:>{w}}/{1}".format(int(ind)+1, lim, w=strlen), end="")
            self.fitnesses[ind] = self.fitness(gen, *fitness_args, **fitness_kw)
            self.grades[ind] = self.grade_function(self.fitnesses[ind])
        if verbose:
            print("\rUpdating {0}/{1}".format(lim, lim))

    def get_candidates(self, survivors=None):

        if isinstance(survivors, tuple):
            survivors = survivors[0]

        def indstream():
            counter = 0
            lmt = len(survivors)
            arg1, arg2 = np.copy(survivors), np.copy(survivors)
            np.random.shuffle(arg1)
            np.random.shuffle(arg2)
            while 1:
                if counter >= lmt:
                    counter = 0
                    arg1, arg2 = np.copy(survivors), np.copy(survivors)
                    np.random.shuffle(arg1)
                    np.random.shuffle(arg2)
                yield arg1[counter], arg2[counter]
                counter += 1

        prbs = rescale(self.grades)

        candidates = zX_like(self.individuals)