def CreateData(self, n_samples, seed_in=5,
train_prop=0.9, bound_limit=6., n_std_devs=1.96,**kwargs):
np.random.seed(seed_in)
scale_c=1.0 # default
shift_c=1.0
# for ideal boundary
X_ideal = np.linspace(start=-bound_limit,stop=bound_limit, num=50000)
y_ideal_U = np.ones_like(X_ideal)+1. # default
y_ideal_L = np.ones_like(X_ideal)-1.
y_ideal_mean = np.ones_like(X_ideal)+0.5
if self.type_in[:1] == '~':
if self.type_in=="~boston":
path = 'boston_housing_data.csv'
data = np.loadtxt(path,skiprows=0)
elif self.type_in=="~concrete":
path = 'Concrete_Data.csv'
data = np.loadtxt(path, delimiter=',',skiprows=1)
elif self.type_in=="~wind":
path = '/content/Deep_Learning_Prediction_Intervals/code/canada_CSV.csv'
data = np.loadtxt(path,delimiter=',',skiprows=1,usecols = (1,2)) ## CHECK WHTHER TO HAVE LOADTXT OR ANYTHING ELSE PARUL
# work out normalisation constants (need when unnormalising later)
scale_c = np.std(data[:,-1])
shift_c = np.mean(data[:,-1])
# normalise data for ALL COLUMNS
for i in range(0,data.shape[1]): ## i varies from 0 to number of columns ,means it reads one by one the columns
# avoid zero variance features (exist one or two)
# nonlocal X_train, y_train, X_val, y_val ## ADDED BY PARUL
sdev_norm = np.std(data[:,i])
sdev_norm = 0.001 if sdev_norm == 0 else sdev_norm
data[:,i] = (data[:,i] - np.mean(data[:,i]) )/sdev_norm
# split into train/test
perm = np.random.permutation(data.shape[0]) ## DO THE DATA PERMUTATION OF ALL THE ROWS (shuffle)
train_size = int(round(train_prop*data.shape[0]))
train = data[perm[:train_size],:]
test = data[perm[train_size:],:]
y_train = train[:,-1].reshape(-1,1) ## LAST COLUMN IS CONSIDERED AS THE TARGET AND RESHAPED IN BETWEEN -1,1
X_train = train[:,:-1] ## INPUTS ARE ALL EXCEPT LAST COLUMN
y_val = test[:,-1].reshape(-1,1)
X_val = test[:,:-1]
# save important stuff
self.X_train = X_train
self.y_train = y_train
self.X_val = X_val
self.y_val = y_val
self.X_ideal = X_ideal
self.y_ideal_U = y_ideal_U
self.y_ideal_L = y_ideal_L
self.y_ideal_mean = y_ideal_mean
self.scale_c = scale_c
self.shift_c = shift_c
return X_train, y_train, X_val, y_val
It gives me an error 'UnboundLocalError: local variable 'X_train' referenced before assignment'
Any help will be appreciated. I am stuck at this. I have tried initialising X_train with X_train=[] and also tried making them global, but didn't get any result. Please help me so that I could move forward.
