precision = 0.00001
i = 0
steps = 0
diff = 100
maxstep = 1000
while (abs(diff) > precision and steps < maxstep):
GoodDir = True
div = 2**i
step = inistep / div
while (GoodDir):
steps += 1
lambd = lambd + step * (-1)**i
if lambd <= 0: lambd = step * 1.1
KRR = KernelRidgeRegression(type="linear")
KRR.set_var(c1=-10**10, lambd=lambd)
KRR.fit(X, Y)
KRR.predict(Xv, Yv)
out2 = KRR.rmse
diff = prev - out2
if diff < 0:
GoodDir = False
prev = out2
output.append([lambd, out2])
print(str(lambd) + ", " + str(out2) + "\n", flush=True)
i += 1
with open(folder + "MINI_other_LIN_faulty", 'wb') as file:
pickle.dump([output], file)
c1_list = list(frange(-20, -5, 2))
#%% Load validation data
featureMatrixFileValidate = "validate_featureMatrix.npy"
atomicSymbolsListFileValidate = "validate_pickledAtomicSymbolsList.txt"
energiesFileValidate = "validate_pickledEnergies.txt"
largeFeatureMatrixValidate, mappedAtomicNumberValidate = simpleLargeMatrix(path,featureMatrixFileValidate, atomicSymbolsListFileValidate)
with open(path+energiesFileValidate, "rb") as pickleFile:
energiesValidate = pickle.load(pickleFile)
#Flatten description
largeFeatureMatrixValidate.shape = (largeFeatureMatrixValidate.shape[0], -1)
X_v = largeFeatureMatrixValidate
Y_v = np.array(energiesValidate)
import time
start=time.time()
KRR=KernelRidgeRegression(type="laplace")
KRR.fit(X,Y)
Y_predict_val=KRR.predict(X_v,Y_v)
print(KRR.rmse)
rmse_class=np.sqrt(np.mean(np.square(Y_predict_val-Y_v)))
print(rmse_class)
end=time.time()
print(end-start)
