UnboundLocalError：赋值前引用了局部变量“X_train”

问题描述

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,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)
            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_val

它给了我一个错误 'UnboundLocalError：赋值前引用了局部变量'X_train'

任何帮助将不胜感激。我被困在这一点上。我尝试用 X_train=[] 初始化 X_train 并尝试将它们设为全局，但没有得到任何结果。请帮助我，以便我继续前进。

解决方法

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