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How to use swarm method in locust

Best Python code snippet using locust

multiswarm.py

Source:multiswarm.py

...104    logbook = tools.Logbook()105    logbook.header = "gen", "nswarm", "evals", "error", "offline_error", "avg", "max"106    107    # Generate the initial population108    population = [toolbox.swarm(n=NPARTICLES) for _ in range(NSWARMS)]109    110    # Evaluate each particle111    for swarm in population:112        for part in swarm:113            part.fitness.values = toolbox.evaluate(part)114            # Update swarm's attractors personal best and global best115            if not part.best or part.fitness > part.bestfit:116                part.best = toolbox.clone(part[:])          # Get the position117                part.bestfit.values = part.fitness.values   # Get the fitness118            if not swarm.best or part.fitness > swarm.bestfit:119                swarm.best = toolbox.clone(part[:])         # Get the position120                swarm.bestfit.values = part.fitness.values  # Get the fitness121    record = stats.compile(itertools.chain(*population))122    logbook.record(gen=0, evals=mpb.nevals, nswarm=len(population),123                   error=mpb.currentError(), offline_error=mpb.offlineError(), **record)124    if verbose:125        print(logbook.stream)126    127    generation = 1128    while mpb.nevals < 5e5:129        # Check for convergence130        rexcl = (BOUNDS[1] - BOUNDS[0]) / (2 * len(population)**(1.0/NDIM))131        132        not_converged = 0133        worst_swarm_idx = None134        worst_swarm = None135        for i, swarm in enumerate(population):136            # Compute the diameter of the swarm137            for p1, p2 in itertools.combinations(swarm, 2):138                d = math.sqrt(sum((x1 - x2)**2. for x1, x2 in zip(p1, p2)))139                if d > 2*rexcl:140                    not_converged += 1141                    # Search for the worst swarm according to its global best142                    if not worst_swarm or swarm.bestfit < worst_swarm.bestfit:143                        worst_swarm_idx = i144                        worst_swarm = swarm145                    break146        147        # If all swarms have converged, add a swarm148        if not_converged == 0:149            population.append(toolbox.swarm(n=NPARTICLES))150        # If too many swarms are roaming, remove the worst swarm151        elif not_converged > NEXCESS:152            population.pop(worst_swarm_idx)153            154        # Update and evaluate the swarm155        for swarm in population:156            # Check for change157            if swarm.best and toolbox.evaluate(swarm.best) != swarm.bestfit.values:158                # Convert particles to quantum particles159                swarm[:] = toolbox.convert(swarm, rcloud=RCLOUD, centre=swarm.best)160                swarm.best = None161                del swarm.bestfit.values162            163            for part in swarm:164                # Not necessary to update if it is a new swarm165                # or a swarm just converted to quantum166                if swarm.best and part.best:167                    toolbox.update(part, swarm.best)168                part.fitness.values = toolbox.evaluate(part)169                170                # Update swarm's attractors personal best and global best171                if not part.best or part.fitness > part.bestfit:172                    part.best = toolbox.clone(part[:])173                    part.bestfit.values = part.fitness.values174                if not swarm.best or part.fitness > swarm.bestfit:175                    swarm.best = toolbox.clone(part[:])176                    swarm.bestfit.values = part.fitness.values177        178        record = stats.compile(itertools.chain(*population))179        logbook.record(gen=generation, evals=mpb.nevals, nswarm=len(population),180                       error=mpb.currentError(), offline_error=mpb.offlineError(), **record)181        if verbose:182            print(logbook.stream)183        184        # Apply exclusion185        reinit_swarms = set()186        for s1, s2 in itertools.combinations(range(len(population)), 2):187            # Swarms must have a best and not already be set to reinitialize188            if population[s1].best and population[s2].best and not (s1 in reinit_swarms or s2 in reinit_swarms):189                dist = 0190                for x1, x2 in zip(population[s1].best, population[s2].best):191                    dist += (x1 - x2)**2.192                dist = math.sqrt(dist)193                if dist < rexcl:194                    if population[s1].bestfit <= population[s2].bestfit:195                        reinit_swarms.add(s1)196                    else:197                        reinit_swarms.add(s2)198        199        # Reinitialize and evaluate swarms200        for s in reinit_swarms:201            population[s] = toolbox.swarm(n=NPARTICLES)202            for part in population[s]:203                part.fitness.values = toolbox.evaluate(part)204                205                # Update swarm's attractors personal best and global best206                if not part.best or part.fitness > part.bestfit:207                    part.best = toolbox.clone(part[:])208                    part.bestfit.values = part.fitness.values209                if not population[s].best or part.fitness > population[s].bestfit:210                    population[s].best = toolbox.clone(part[:])211                    population[s].bestfit.values = part.fitness.values212        generation += 1213if __name__ == "__main__":...

operators.py

Source:operators.py

...18    You can use this method to update your personal best positions.19    .. code-block:: python20        import pyswarms.backend as P21        from pyswarms.backend.swarms import Swarm22        my_swarm = P.create_swarm(n_particles, dimensions)23        # Inside the for-loop...24        for i in range(iters):25            # It updates the swarm internally26            my_swarm.pbest_pos, my_swarm.pbest_cost = P.update_pbest(my_swarm)27    It updates your :code:`current_pbest` with the personal bests acquired by28    comparing the (1) cost of the current positions and the (2) personal29    bests your swarm has attained.30    If the cost of the current position is less than the cost of the personal31    best, then the current position replaces the previous personal best32    position.33    Parameters34    ----------35    swarm : pyswarms.backend.swarm.Swarm36        a Swarm instance37    Returns38    -------39    numpy.ndarray40        New personal best positions of shape :code:`(n_particles, n_dimensions)`41    numpy.ndarray42        New personal best costs of shape :code:`(n_particles,)`43    """44    try:45        # Infer dimensions from positions46        dimensions = swarm.dimensions47        # Create a 1-D and 2-D mask based from comparisons48        mask_cost = swarm.current_cost < swarm.pbest_cost49        mask_pos = np.repeat(mask_cost[:, np.newaxis], dimensions, axis=1)50        # Apply masks51        new_pbest_pos = np.where(~mask_pos, swarm.pbest_pos, swarm.position)52        new_pbest_cost = np.where(53            ~mask_cost, swarm.pbest_cost, swarm.current_cost54        )55    except AttributeError:56        rep.logger.exception(57            "Please pass a Swarm class. You passed {}".format(type(swarm))58        )59        raise60    else:61        return (new_pbest_pos, new_pbest_cost)62def compute_velocity(swarm, clamp, vh, bounds=None):63    """Update the velocity matrix64    This method updates the velocity matrix using the best and current65    positions of the swarm. The velocity matrix is computed using the66    cognitive and social terms of the swarm. The velocity is handled67    by a :code:`VelocityHandler`.68    A sample usage can be seen with the following:69    .. code-block :: python70        import pyswarms.backend as P71        from pyswarms.swarms.backend import Swarm, VelocityHandler72        my_swarm = P.create_swarm(n_particles, dimensions)73        my_vh = VelocityHandler(strategy="invert")74        for i in range(iters):75            # Inside the for-loop76            my_swarm.velocity = compute_velocity(my_swarm, clamp, my_vh, bounds)77    Parameters78    ----------79    swarm : pyswarms.backend.swarms.Swarm80        a Swarm instance81    clamp : tuple of floats, optional82        a tuple of size 2 where the first entry is the minimum velocity83        and the second entry is the maximum velocity. It84        sets the limits for velocity clamping.85    vh : pyswarms.backend.handlers.VelocityHandler86        a VelocityHandler object with a specified handling strategy.87        For further information see :mod:`pyswarms.backend.handlers`.88    bounds : tuple of numpy.ndarray or list, optional89        a tuple of size 2 where the first entry is the minimum bound while90        the second entry is the maximum bound. Each array must be of shape91        :code:`(dimensions,)`.92    Returns93    -------94    numpy.ndarray95        Updated velocity matrix96    """97    try:98        # Prepare parameters99        swarm_size = swarm.position.shape100        c1 = swarm.options["c1"]101        c2 = swarm.options["c2"]102        w = swarm.options["w"]103        # Compute for cognitive and social terms104        cognitive = (105            c1106            * np.random.uniform(0, 1, swarm_size)107            * (swarm.pbest_pos - swarm.position)108        )109        social = (110            c2111            * np.random.uniform(0, 1, swarm_size)112            * (swarm.best_pos - swarm.position)113        )114        # Compute temp velocity (subject to clamping if possible)115        temp_velocity = (w * swarm.velocity) + cognitive + social116        updated_velocity = vh(117            temp_velocity, clamp, position=swarm.position, bounds=bounds118        )119    except AttributeError:120        rep.logger.exception(121            "Please pass a Swarm class. You passed {}".format(type(swarm))122        )123        raise124    except KeyError:125        rep.logger.exception("Missing keyword in swarm.options")126        raise127    else:128        return updated_velocity129def compute_position(swarm, bounds, bh):130    """Update the position matrix131    This method updates the position matrix given the current position and the132    velocity. If bounded, the positions are handled by a133    :code:`BoundaryHandler` instance134    .. code-block :: python135        import pyswarms.backend as P136        from pyswarms.swarms.backend import Swarm, VelocityHandler137        my_swarm = P.create_swarm(n_particles, dimensions)138        my_bh = BoundaryHandler(strategy="intermediate")139        for i in range(iters):140            # Inside the for-loop141            my_swarm.position = compute_position(my_swarm, bounds, my_bh)142    Parameters143    ----------144    swarm : pyswarms.backend.swarms.Swarm145        a Swarm instance146    bounds : tuple of numpy.ndarray or list, optional147        a tuple of size 2 where the first entry is the minimum bound while148        the second entry is the maximum bound. Each array must be of shape149        :code:`(dimensions,)`.150    bh : pyswarms.backend.handlers.BoundaryHandler151        a BoundaryHandler object with a specified handling strategy...

task1.py

Source:task1.py

1import numpy as np 2import random3from matplotlib import pyplot as plt4def createSwarm(n):5    xpos = np.random.uniform(0,1,n)6    swarm = []7    for i in range(n):8            swarm.append([xpos[i],random.getrandbits(1)])9    return swarm10# def testSwarm():11#     return [[0.98,1],[0.99,1],[0.01,0], [0.02,0]]12def sim(n, timesteps, r):13    14    swarm = createSwarm(n)15    swarmRange=range(0,n)16    17    lefties=[]18    for i in range(0,timesteps):19        leftGoers=0 20        for j in swarmRange:21            if swarm[j][1]:22                 leftGoers+=1  23        lefties.append(leftGoers)24        25        neighbourhood = getNeighbourhood(swarm, swarmRange)26        27        switchers = getSwitchers(neighbourhood, swarm, swarmRange)28        29        30     31        for j in switchers:32            swarm[j][1] = 1-swarm[j][1]33       34        35        print(leftGoers)36        for j in swarmRange:37            38            #move swarm         39            #right40            if(swarm[j][1]):41                if(swarm[j][0] == 1):42                    swarm[j][0] == 043                swarm[j][0] += 0.00144                45            #left46            else:47                if(swarm[j][0] == 0):48                    swarm[j][0] == 149                swarm[j][0] -= 0.00150    return lefties51def getSwitchers(neighbourhood, swarm, swarmRange):52    #switch?53    switchers = []54    55    for i in swarmRange:56        p=np.random.uniform(0,100,1)[0]57        if p <= 0.15:58            switchers.append(i)59        else:60            count = 061            for n in neighbourhood[i]:62                if(swarm[n][1]!=swarm[i][1]):63                    count+=164            #half=np.floor(len(neighbourhood[i])/2)65            half=len(neighbourhood[i])/266            if (count>half):67                switchers.append(i)68    return switchers69def getNeighbourhood(swarm, swarmRange):70    neighbourhood=[]71    for i in swarmRange:72        ncount=[]73        if(swarm[i][0] > 0.045 and swarm[i][0] < 0.955):74            for j in swarmRange:75                76                if(j!=i):77                    if(abs(swarm[i][0] - swarm[j][0]) < 0.045):78                    79                        ncount.append(j)80            81        elif(swarm[i][0] <= 0.045):82            for j in swarmRange:83                if(j!=i):84                    xi = swarm[i][0]85                    xj = swarm[j][0]86                    dist1 = xi - xj87                    dist2 = 1-xi - xj88                    if(abs(dist1) < 0.045 or abs(dist2) < 0.045):89                        ncount.append(j) 90        elif(swarm[i][0] >= 0.955):91            for j in swarmRange:92                if(j!=i):93                    xi = swarm[i][0]94                    xj = swarm[j][0]95                    dist1 =abs(xi - xj)96                    dist2 = abs(xi - 1 - xj)97                    if(dist1 < 0.045): 98                    99                        ncount.append(j) 100                    elif (dist2< 0.045):101                        ncount.append(j) 102        neighbourhood.append(ncount)103    return  neighbourhood104n = 20105timesteps=500106r = 0.045107x = []108for i in range(timesteps): 109    x.append(i)110plt.xlim(0,timesteps)111plt.ylim(0,20)112plt.plot(x,sim(n, timesteps, r))113plt.show()...

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