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()
