def metrics(fit_solution):
dist, measure, simulation_adjust = fitnessCfl(fit_solution, models="true")
mse = mean_squared_error(measure.i, simulation_adjust.i)
rmse = np.sqrt(mse)
rms_v = np.sqrt(np.mean(measure.v**2))
rms_imeas = np.sqrt(np.mean(measure.i**2))
rms_isim = np.sqrt(np.mean(simulation_adjust.i**2))
norma2_diff = np.linalg.norm(measure.i - simulation_adjust.i)
square_relative_error_fromIrms = rmse / rms_imeas
relative_root_mean_square_error = rmse / sum(measure.i)
metrics = {
'fitness': dist,
'mse': mse,
'rmse': rmse,
'sqre': square_relative_error_fromIrms,
'rrmse': relative_root_mean_square_error,
'norma2Diff': norma2_diff
}
return metrics
def plotting(fit_solution):
dist, measure, simulation_adjust = fitnessCfl(fit_solution, models="true")
from pylab import cm
import matplotlib as mpl
import matplotlib.pyplot as plt
colors = cm.get_cmap('tab10', 10)
f, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(simulation_adjust.t,
simulation_adjust.i,
linewidth=3,
color=colors(0),
label=' i-simulada')
ax1.plot(measure.t,
measure.i,
linewidth=3,
color=colors(1),
label='i- medida')
ax1.set_title('Resultados')
ax1.set_xlim(0, measure.t[-1])
ax1.set_ylim(min(measure.i) - 0.1, max(measure.i) + 0.1)
# ax1.set_xlabel('Iteracion(i)')
ax1.set_ylabel(r'Corriente(A)', labelpad=10)
# ax1.spines['right'].set_visible(False)
# ax1.spines['top'].set_visible(False)
ax1.xaxis.set_tick_params(which='major', size=10, width=2, direction='in')
ax1.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in')
ax1.yaxis.set_tick_params(which='major', size=10, width=2, direction='in')
ax1.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in')
ax1.grid(True)
ax1.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1 / 120))
ax1.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(1 / 240))
ax1.yaxis.set_major_locator(mpl.ticker.MultipleLocator(max(measure.i) / 4))
ax1.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(max(measure.i) / 2))
ax1.legend(bbox_to_anchor=(0.78, 0.8), loc=10, frameon=True, fontsize=14)
ax2.plot(simulation_adjust.t,
simulation_adjust.v,
linewidth=3,
color=colors(0),
label='v-simulada')
ax2.plot(measure.t,
measure.v,
linewidth=3,
color=colors(1),
label='v-medida')
ax2.set_xlim(0, measure.t[-1])
ax2.set_ylim(min(measure.v) - 10, max(measure.v) + 10)
ax2.set_ylabel(r'Tension(V)', labelpad=10)
ax2.xaxis.set_tick_params(which='major', size=10, width=2, direction='in')
ax2.xaxis.set_tick_params(which='minor', size=7, width=2, direction='in')
ax2.yaxis.set_tick_params(which='major', size=10, width=2, direction='in')
ax2.yaxis.set_tick_params(which='minor', size=7, width=2, direction='in')
ax2.grid(True)
ax2.xaxis.set_major_locator(mpl.ticker.MultipleLocator(1 / 120))
ax2.xaxis.set_minor_locator(mpl.ticker.MultipleLocator(1 / 240))
ax2.yaxis.set_major_locator(mpl.ticker.MultipleLocator(50))
ax2.yaxis.set_minor_locator(mpl.ticker.MultipleLocator(25))
ax2.legend(bbox_to_anchor=(0.78, 0.8), loc=10, frameon=True, fontsize=14)
plt.figure()
plt.subplot(211)
plt.plot(simulation_adjust.t, simulation_adjust.i)
plt.plot(measure.t, measure.i)
plt.subplot(212)
plt.plot(simulation_adjust.t, simulation_adjust.v)
plt.plot(measure.t, measure.v)
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(measure.i, simulation_adjust.i)
rmse = np.sqrt(mse)
rms_v = np.sqrt(np.mean(measure.v**2))
rms_imeas = np.sqrt(np.mean(measure.i**2))
rms_isim = np.sqrt(np.mean(simulation_adjust.i**2))
square_relative_error_fromIrms = rmse / rms_imeas
relative_root_mean_square_error = rmse / sum(measure.i)
metrics = [
dist, mse, rmse, square_relative_error_fromIrms,
relative_root_mean_square_error
]
return metrics
if i >= 1:
if upper[i, 0] == upper[i - 1, 0]:
limit += 1
else:
limit = 0
i += 1
popu = nextPopu(popu, popu_eval, xover_rate, mut_rate)
#%% plotting
#fit_solution=np.array([upper[-1,:]])
fit_solution = np.array([[
1.946521368734263888e+03, 1.888197756923780801e+01,
1.201735957885099540e-05
]])
dist, measure, simulation_adjust = fitnessCfl(fit_solution, models="true")
from pylab import cm
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
colors = cm.get_cmap('tab10', 10)
f, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(simulation_adjust.t,
simulation_adjust.i,
linewidth=3,
color=colors(0),
label=' i-simulada')
ax1.plot(measure.t, measure.i, linewidth=3, color=colors(1), label='i- medida')
ax1.set_title('Resultados')
ax1.set_xlim(0, measure.t[-1])