def validation_exp_cross3(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp1 = ExpData("../data/Al6061.csv", yname)
dataexp2 = ExpData("../data/Al7075.csv", yname)
ape = []
y = []
for _ in range(10):
print("\nIteration: {}".format(len(ape)))
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp1.X)),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp1.y)),
X_hi_test=dataexp2.X,
y_hi_test=dataexp2.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt("y.dat", np.hstack(y))
def validation_exp_cross2(yname, train_size):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp1 = ExpData("../data/B3067.csv", yname)
dataexp2 = ExpData("../data/B3090.csv", yname)
ape = []
y = []
kf = ShuffleSplit(n_splits=10, train_size=train_size, random_state=0)
for train_index, _ in kf.split(dataexp1.X):
print("\nIteration: {}".format(len(ape)))
print(train_index)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp1.X[train_index])),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp1.y[train_index])),
X_hi_test=dataexp2.X,
y_hi_test=dataexp2.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname, train_size)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
def validation_exp_cross(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp = ExpData("../data/B3067.csv", yname)
train_size = 10
ape = []
y = []
# cases = range(6)
# for train_index in itertools.combinations(cases, 3):
# train_index = list(train_index)
# test_index = list(set(cases) - set(train_index))
kf = ShuffleSplit(n_splits=10,
test_size=len(dataexp.X) - train_size,
random_state=0)
for train_index, test_index in kf.split(dataexp.X):
print("\nIteration: {}".format(len(ape)))
print(train_index, "==>", test_index)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp.X[train_index])),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp.y[train_index])),
X_hi_test=dataexp.X[test_index],
y_hi_test=dataexp.y[test_index],
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
def validation_exp(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp = ExpData("../data/B3067.csv", yname)
ape = []
y = []
for iter in range(10):
print("\nIteration: {}".format(iter))
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=dataBerkovich.X,
y_lo_train=datalow.y,
y_hi_train=dataBerkovich.y,
X_hi_test=dataexp.X,
y_hi_test=dataexp.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)