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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _sample(self, maxIter=1000, c1=1.193, c2=1.193, lookback = 0.25, standard_dev = None):
 """ Launches the PSO. Yields the complete swarm per iteration :param maxIter: maximum iterations :param c1: cognitive weight :param c2: social weight :param lookback: percentange of particles to use when determining convergence :param standard_dev: standard deviation of the last lookback particles for convergence """

        self._get_fitness(self.swarm)
        i = 0
        self.i = i
        while True:

            for particle in self.swarm:
                if ((self._gbest.fitness)<particle.fitness):

                    self._gbest = particle.copy()

                if (particle.fitness > particle.pbest.fitness):
                    particle.updatePBest()

            if(i>=maxIter):
                if self._verbose:
                    print("max iteration reached! stoping")
                return

            if self._func.is_converged:
                return

            if self._converged_likelihood(maxIter*lookback, self._particleCount, standard_dev):
                return

            for particle in self.swarm:

                w = 0.5 + numpy.random.uniform(0, 1, size=self._paramCount) / 2
                #w=0.72
                part_vel = w * particle.velocity
                cog_vel = c1 * numpy.random.uniform(0, 1, size=self._paramCount) * (particle.pbest.position - particle.position)
                soc_vel = c2 * numpy.random.uniform(0, 1, size=self._paramCount) * (self._gbest.position - particle.position)
                particle.velocity = part_vel + cog_vel + soc_vel
                particle.position = particle.position + particle.velocity

            self._get_fitness(self.swarm)

            swarm = []
            for particle in self.swarm:
                swarm.append(particle.copy())
            yield swarm

            i+=1
            self.i = i