def peakInfluenceSurface(peaksType): from tools.Tools import getDictArray import featuresMain as fm data = fm.main() surface = data.Surface.values def surfaceToInt(surface): if 'Woodchip' in surface: return 0 if 'Asphalt' in surface: return 1 if 'Track' in surface: return 2 surface = map(surfaceToInt, surface) features = getDictArray(data.Features) def plot(name): y = [v[peaksType + '.' + name] for v in features] fig, ax = plt.subplots() ax.scatter(surface, y) fig.tight_layout() ax.set_title("surface: " + name) plot('varDist') plot('varPeak') plot('maxPeak') plot('maxDist') plot('minPeak') plot('minDist') plot('avPeak') plot('avDist')
def peakInfluenceTrained(peaksType): from tools.Tools import getDictArray import app.featuresMain as fm data = fm.main() trained = data.Trained.values features = getDictArray(data.Features) def plot(name): y = [v[peaksType + '.' + name] for v in features] fig, ax = plt.subplots() ax.scatter(trained, y) fig.tight_layout() ax.set_title("trained: " + name) plot('varDist') plot('varPeak') plot('maxPeak') plot('maxDist') plot('minPeak') plot('minDist') plot('avPeak') plot('avDist') plt.show()
def main(data=None, selectFeatures='all'): if (data is None): data = fm.main() if (showSVM): print("\nSVM: trained/not trained") evalSVM(data, True, False, selectFeatures) print("\nSVM: surface") evalSVM(data, False, True, selectFeatures) print("\nSVM: trained-surface") evalSVM(data, True, True, selectFeatures) pylab.show() if (showDT): print("\nDT: trained/not trained") evalDT(data, True, False, selectFeatures) print("\nDT: surface") evalDT(data, False, True, selectFeatures) print("\nDT: trained-surface") evalDT(data, True, True, selectFeatures) pylab.show() if (showKNN): print("\nKNN: trained/not trained") evalKNN(data, True, False, selectFeatures) print("\nKNN: surface") evalKNN(data, False, True, selectFeatures) print("\nKNN: trained-surface") evalKNN(data, True, True, selectFeatures) pylab.show() if (showLR): print("\nLR: trained/not trained") evalLR(data, True, False, selectFeatures) print("\nLR: surface") evalLR(data, False, True, selectFeatures) print("\nLR: trained-surface") evalLR(data, True, True, selectFeatures) pylab.show()