133 lines
4.4 KiB
Python
133 lines
4.4 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sho import *
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########################################################################
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# Interface
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########################################################################
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if __name__=="__main__":
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import argparse
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# Dimension of the search space.
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d = 2
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can = argparse.ArgumentParser()
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can.add_argument("-n", "--nb-sensors", metavar="NB", default=3, type=int,
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help="Number of sensors")
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can.add_argument("-r", "--sensor-range", metavar="RATIO", default=0.3, type=float,
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help="Sensors' range (as a fraction of domain width)")
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can.add_argument("-w", "--domain-width", metavar="NB", default=30, type=int,
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help="Domain width (a number of cells)")
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can.add_argument("-i", "--iters", metavar="NB", default=100, type=int,
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help="Maximum number of iterations")
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can.add_argument("-s", "--seed", metavar="VAL", default=None, type=int,
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help="Random pseudo-generator seed (none for current epoch)")
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solvers = ["num_greedy","bit_greedy"]
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can.add_argument("-m", "--solver", metavar="NAME", choices=solvers, default="num_greedy",
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help="Solver to use, among: "+", ".join(solvers))
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# TODO add the corresponding stopping criterion.
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can.add_argument("-t", "--target", metavar="VAL", default=1e-3, type=float,
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help="Function value target delta")
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the = can.parse_args()
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# Minimum checks.
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assert(0 < the.nb_sensors)
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assert(0 < the.sensor_range <= 1)
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assert(0 < the.domain_width)
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assert(0 < the.iters)
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# Do not forget the seed option,
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# in case you would start "runs" in parallel.
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np.random.seed(the.seed)
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# Weird numpy way to ensure single line print of array.
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np.set_printoptions(linewidth = np.inf)
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# Common termination and checkpointing.
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history = []
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iters = make.iter(
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iters.several,
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agains = [
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make.iter(iters.max,
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nb_it = the.iters),
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make.iter(iters.save,
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filename = the.solver+".csv",
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fmt = "{it} ; {val} ; {sol}\n"),
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make.iter(iters.log,
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fmt="\r{it} {val}"),
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make.iter(iters.history,
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history = history)
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]
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)
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# Erase the previous file.
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with open(the.solver+".csv", 'w') as fd:
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fd.write("# {} {}\n".format(the.solver,the.domain_width))
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val,sol,sensors = None,None,None
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if the.solver == "num_greedy":
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val,sol = algo.greedy(
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make.func(num.cover_sum,
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domain_width = the.domain_width,
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sensor_range = the.sensor_range * the.domain_width),
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make.init(num.rand,
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dim = d * the.nb_sensors,
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scale = the.domain_width),
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make.neig(num.neighb_square,
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scale = the.domain_width/10), # TODO think of an alternative.
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iters
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)
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sensors = num.to_sensors(sol)
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elif the.solver == "bit_greedy":
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val,sol = algo.greedy(
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make.func(bit.cover_sum,
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domain_width = the.domain_width,
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sensor_range = the.sensor_range),
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make.init(bit.rand,
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domain_width = the.domain_width,
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nb_sensors = the.nb_sensors),
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make.neig(bit.neighb_square,
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scale = the.domain_width/10),
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iters
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)
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sensors = bit.to_sensors(sol)
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# Fancy output.
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print("\n{} : {}".format(val,sensors))
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shape=(the.domain_width, the.domain_width)
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fig = plt.figure()
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if the.nb_sensors ==1 and the.domain_width <= 50:
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ax1 = fig.add_subplot(121, projection='3d')
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ax2 = fig.add_subplot(122)
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f = make.func(num.cover_sum,
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domain_width = the.domain_width,
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sensor_range = the.sensor_range * the.domain_width)
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plot.surface(ax1, shape, f)
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plot.path(ax1, shape, history)
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else:
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ax2=fig.add_subplot(111)
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domain = np.zeros(shape)
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domain = pb.coverage(domain, sensors,
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the.sensor_range * the.domain_width)
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domain = plot.highlight_sensors(domain, sensors)
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ax2.imshow(domain)
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plt.show()
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