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polynomial_r2_vali_sampling.py
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polynomial_r2_vali_sampling.py
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import numpy as np
from mylibs.polynomial import Polynomial
from myutils.plot_helper import PlotHelper
from myutils.samples import *
from mylibs.validation import Validation
from mylibs.validation import ValidationResults
from mylibs.latin_hyper_cube import LatinHyperCube
from mylibs.halton import Halton
from mylibs.structured_sample import StructuredSample
if __name__ == '__main__':
polyPlot = PlotHelper(['Eingang', 'Ausgang'], fancy=True, pgf=False)
# the smooth whole function
fx = np.linspace(0, 10, 1001)
fy = list(map(f_2d, fx))
polyPlot.ax.plot(fx, fy, 'r-', label=r'$f_{original}$')
# validate points
valiParams = np.array([1., 5. , 9.])
valiValues = np.array(list(map(f_2d, valiParams)))
valiParams = valiParams.reshape((len(valiParams), 1))
fx = fx.reshape((len(fx), 1))
vali_res = []
poly_plot_points = [2, 3, 4, 5, 6, 7]
sampling_points = [2, 3, 4, 5, 6, 7, 8, 9, 10] # [2, 3, 4, 5, 6, 7]
plot_styles = ['k', 'k', 'k', 'silver', 'm', 'darksalmon', 'limegreen', 'cornflowerblue', 'b', 'm', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b']
for n in sampling_points:
# now we pretend we only know a view points
sample = StructuredSample()
knownParams = sample.generate_sample_plan(n, 1, [(0., 10.)])
knownParams = np.array(knownParams).flatten()
#knownParams = np.array([0., 2., 4., 6., 8., 10.])
knownValues = np.array(list(map(f_2d, knownParams)))
# Polynomials
poly = Polynomial(knownParams, knownValues)
print('##############')
print('Order: {:d}'.format(n))
o = 6
poly.update_param(o)
rbfY1 = list(map(poly.predict, fx))
if n in poly_plot_points:
polyPlot.ax.scatter(knownParams, knownValues, facecolors='none', edgecolors=plot_styles[n], s=100)
polyPlot.ax.plot(fx, rbfY1, '--', color=plot_styles[n], label=r'$\widehat{f}_{polyn}$, $n = ' + str(n) + '$')
vali = Validation()
vali = Validation()
vali_r = vali.run_full_analysis(fx, fy,
knownParams.reshape((len(knownParams), 1)), knownValues,
valiParams, valiValues,
poly.predict, Polynomial,
update_params=[o])
vali_res.append(vali_r)
#deviation = vali.calc_deviation(fx, fy, poly.predict)
#rmse = vali.calc_rmse(valiParams, valiValues, poly.predict)
#mae = vali.calc_mae(valiParams, valiValues, poly.predict)
#rae = vali.calc_rae(valiParams, valiValues, poly.predict)
#press = vali.calc_press(knownParams.reshape((len(knownParams), 1)), knownValues, poly.predict, Polynomial, update_params=[i])
print('avg deviation: {:.3e} (-> {:.3f}%)'.format(vali_r.deviation, vali_r.deviation * 100.))
print('rmse: {:f}'.format(vali_r.rmse))
print('mae: {:f}'.format(vali_r.mae))
print('rae: {:s}'.format(str(vali_r.rae)))
print('press: {:f}'.format(vali_r.press))
#polyPlot.ax.plot(knownParams, knownValues, 'ro', label=r'St\"utzstellen', markersize=10)
polyPlot.ax.scatter(valiParams, valiValues, facecolors='none', edgecolors='r', label=r'Vali.-Punkte', s=100)
polyPlot.finalize(width=3.9, height=4, legendLoc='upper center', legendNcol=2, bbox_to_anchor=(0.5, -0.35))
polyPlot.ax.set_ylim([2, 10])
import matplotlib.pyplot as plt
plt.subplots_adjust(bottom=0.494)
polyPlot.save('../data_out/plot/polynomialR2sampling.pdf')
# plot validation
valiPlot = PlotHelper(['Stützstellen', 'Wert'], fancy=True, pgf=True)
valiPlot.ax.plot(sampling_points, [r.deviation for r in vali_res], '--', color='silver', label=r'$\O$ -Abweichung in $\%$')
valiPlot.ax.plot(sampling_points, [r.rmse for r in vali_res], '-', color='orange', label=r'$RMSE$')
valiPlot.ax.plot(sampling_points, [r.mae for r in vali_res], '-', color='green', label=r'$MAE$')
valiPlot.ax.plot(sampling_points, np.array([r.press for r in vali_res]) * 0.1, 'k-', label=r'$PRESS \cdot 0.1$')
valiPlot.finalize(width=2.8, height=4, legendLoc='upper center', bbox_to_anchor=(0.5, -0.25))
valiPlot.ax.set_xlim([2, 10])
valiPlot.ax.set_ylim([0, 2.5])
plt.locator_params(nbins=4)
plt.subplots_adjust(bottom=0.42)
plt.subplots_adjust(left=0.22)
polyPlot.save('../data_out/plot/polynomialR2valiSampling.pdf')
valiPlot.show()