Ejemplo n.º 1
0
            kmc.update_V()
            kmc.simulate_discrete(prehops=prehops)
            for l in range(cf.avg):
                kmc.simulate_discrete(hops=hops)
                output[k * cf.avg + l] = kmc.current[cf.output]
        outputArray[i, j] = output
        fitness_list.append(cf.Fitness(output, cf.target))

        genePool.fitness[j] = min(fitness_list)
        fitnessArray[i, j] = genePool.fitness[j]
    # Status print
    print("Generation nr. " + str(i + 1) + " completed")
    print("Highest fitness: " + str(max(genePool.fitness)))

    # Evolve to the next generation
    genePool.NextGen()

    # Save experiment
    SaveLib.saveArrays(filepath,
                       geneArray=geneArray,
                       fitnessArray=fitnessArray,
                       outputArray=outputArray,
                       target=cf.target,
                       P=P,
                       Q=Q)

domain = kmc_dn_utils.visualize_basic(kmc)
plt.figure()
plt.plot(output)
plt.show()
Ejemplo n.º 2
0
                voltage[:, k * cf.avg + l] = kmc.electrodes[:, 3]
        voltageArray[i, j] = voltage
        currentArray[i, j] = output

        #fitness_list.append(cf.Fitness(voltage, output, cf.target))
        fitness_list.append(cf.Fitness(output[2], cf.target))

        genePool.fitness[j] = min(fitness_list)
        fitnessArray[i, j] = genePool.fitness[j]
    # Status print
    print("Generation nr. " + str(i + 1) + " completed")
    print("Highest fitness: " + str(max(genePool.fitness)))

    # Evolve to the next generation
    genePool.NextGen()

    # Save experiment
    SaveLib.saveArrays(filepath,
                       geneArray=geneArray,
                       fitnessArray=fitnessArray,
                       currentArray=currentArray,
                       voltageArray=voltageArray,
                       target=cf.target,
                       P=P,
                       Q=Q)

domain = kmc_dn_utils.visualize_basic(kmc)
plt.figure()
plt.plot(output)
plt.show()
Ejemplo n.º 3
0
            current_array = kmc_dn_utils.IV(kmc,
                                            0,
                                            voltagelist,
                                            prehops=prehops,
                                            hops=hops)
            output = current_array[1]
        outputArray[i, j] = output
        fitness_list.append(cf.Fitness(output, target))

        genePool.fitness[j] = min(fitness_list)
        fitnessArray[i, j] = genePool.fitness[j]

    # Status print
    print("Generation nr. " + str(i + 1) + " completed")
    print("Highest fitness: " + str(max(genePool.fitness)))

    # Evolve to the next generation
    genePool.NextGen()

    # Save experiment
    SaveLib.saveArrays(
        filepath,
        geneArray=geneArray,
        fitnessArray=fitnessArray,
        outputArray=outputArray,
        voltagelist=voltagelist,
        target=target,
    )

print('All done!')