438b2a63d195c8f5ccecae35d577d8bfde354406,framework/Optimizers/GeneticAlgorithm.py,GeneticAlgorithm,_useRealization,#GeneticAlgorithm#Any#Any#,352

Before Change

    //// IN THIS CASE we will have 3 children
    //// TODO: This is to be removed
    size = self._nChildren if self.counter > 1 else self._populationSize
    populationRlz =  xr.concat((rlz for _ in range(size)))//((rlz, rlz, rlz))
    population = xr.DataArray(populationRlz[list(self.toBeSampled)].to_array().transpose(),
                              dims=["chromosome","Gene"],
                              coords={"chromosome": np.arange(size),
                                      "Gene":list(self.toBeSampled)})//np.arange(len(self.toBeSampled))
    // TODO: This is to be removed once the rlz is consistent with the expected batch
    for i in range(1,size):
      population[i,:] = randomUtils.randomPermutation(list(population[0,:].data), self)//np.random.choice(population[0,:],len(self.toBeSampled), replace=False)
    //// TODO the whole skeleton should be here, this should be calling all classes and _private methods.
    traj = info["traj"]
    // info["optVal"] = rlz[self._objectiveVar]
    self.incrementIteration(traj)
    info["step"] = self.counter

After Change

    //   self._submitRun(newRlz, traj, self.getIteration(traj))
    // childrenDict = children.to_dict()
    // newRlz = children.to_dataset(name="data")
    d = {"dims":["chromosome","Gene"],
         "coords":{"chromosome": np.arange(size),
                   "Gene":np.arange(len(self.toBeSampled))},
         "data":children.data}
    // newRlz = xr.Dataset.from_dict(d)
    // children.to_dataset(name = "children")
    newRlz = xr.Dataset({("chromosome","Gene"):children.data})
    // newRlz = xr.Dataset(({"chromosome":[1,2,3,4,5,6,7,8,9,10],"Gene":[0,1,2,3,4,5]},children.data))

Instances