def optimization(trainingParameters, trainingReflectances, testParameters,
testReflectances):
BVFs = np.unique(trainingParameters[:, 0])
Vss = np.unique(trainingParameters[:, 1])
#%% build optimization function from rectangular reflectance grid
reflectanceGrid3D = np.reshape(
trainingReflectances,
(len(BVFs), len(Vss), trainingReflectances.shape[1]))
functionToMinimize = ReflectanceError(BVFs, Vss, reflectanceGrid3D)
#%% do optimization
estimatedParameters = np.zeros_like(testParameters)
absErrors = np.zeros_like(testParameters)
for idx, (testParameter, testReflectance) in enumerate(
zip(testParameters, testReflectances)):
functionToMinimize.setReflectanceToMatch(testReflectance)
minimization = minimize(
functionToMinimize.f,
[np.median(BVFs), np.median(Vss)],
method="Nelder-Mead")
# interpolation extrapolates with constant values. We just crop it to the bounds
clippedX = np.clip(minimization.x, [min(BVFs), min(Vss)],
[max(BVFs), max(Vss)])
estimatedParameters[idx, :] = clippedX
absErrors[idx, :] = np.abs(clippedX - testParameter)
r2Score = r2_score(testParameters.T, estimatedParameters.T)
return absErrors, r2Score
#%% load data
dataFolder = "data/output/"
trainingParameters, trainingReflectances, testParameters, testReflectances = \
data.perfect(dataFolder)
BVFs = np.unique(trainingParameters[:,0])
Vss = np.unique(trainingParameters[:,1])
#%% build optimization function from rectangular reflectance grid
reflectanceGrid3D = np.reshape(trainingReflectances, (len(BVFs), len(Vss), trainingReflectances.shape[1]))
functionToMinimize = ReflectanceError(BVFs, Vss, reflectanceGrid3D)
#%% do optimization
absErrors = np.zeros_like(testParameters)
for idx, (testParameter, testReflectance) in enumerate(zip(testParameters, testReflectances)):
functionToMinimize.setReflectanceToMatch(testReflectance)
minimization = minimize(functionToMinimize.f, [np.median(BVFs), np.median(Vss)], method="Nelder-Mead")
# interpolation extrapolates with constant values. We just crop it to the bounds
clippedX= np.clip(minimization.x, [min(BVFs), min(Vss)], [max(BVFs), max(Vss)])
absErrors[idx,:] = np.abs(clippedX - testParameter)
