dataset2016.py

"Adapted from the code (https://github.com/leena201818/radioml) contributed by leena201818"
import pickle
import numpy as np
def l2_normalize(x, axis=-1):
    y = np.max(np.sum(x ** 2, axis, keepdims=True), axis, keepdims=True)
    return x / np.sqrt(y)

   
def load_data(filename,data):
    # RadioML2016.10a: (220000,2,128), mods*snr*1000, total 220000 samples; 
    # RadioML2016.10b: (1200000,2,128), mods*snr*6000, total 1200000 samples;
    Xd =pickle.load(open(filename,'rb'),encoding='iso-8859-1')
    mods,snrs = [sorted(list(set([k[j] for k in Xd.keys()]))) for j in [0,1] ] 
    X = []
    lbl = []
    train_idx=[]
    val_idx=[]
    np.random.seed(2016)
    a=0

    for mod in mods:
        for snr in snrs:
            X.append(Xd[(mod,snr)]) 
            for i in range(Xd[(mod,snr)].shape[0]):
                lbl.append((mod,snr))
            if data==0:
                train_idx+=list(np.random.choice(range(a*1000,(a+1)*1000), size=600, replace=False))
                val_idx+=list(np.random.choice(list(set(range(a*1000,(a+1)*1000))-set(train_idx)), size=200, replace=False))
            elif data==1:
                train_idx+=list(np.random.choice(range(a*6000,(a+1)*6000), size=3600, replace=False))
                val_idx+=list(np.random.choice(list(set(range(a*6000,(a+1)*6000))-set(train_idx)), size=1200, replace=False))
            a+=1
    X = np.vstack(X) 

    # Scramble the order between samples 
    # and get the serial number of training, validation, and test sets
    n_examples=X.shape[0]
    test_idx=list(set(range(0,n_examples))-set(train_idx)-set(val_idx))
    np.random.shuffle(train_idx)
    np.random.shuffle(val_idx)
    np.random.shuffle(test_idx)
    X_train =X[train_idx]
    X_val=X[val_idx]
    X_test =X[test_idx]

    # transfor the label form to one-hot
    def to_onehot(yy):
        yy1=np.zeros([len(yy), len(mods)])
        yy1[np.arange(len(yy)), yy]=1
        return yy1
    Y_train=to_onehot(list(map(lambda x: mods.index(lbl[x][0]),train_idx)))
    Y_val=to_onehot(list(map(lambda x: mods.index(lbl[x][0]),val_idx)))
    Y_test=to_onehot(list(map(lambda x: mods.index(lbl[x][0]),test_idx)))

    return (mods,snrs,lbl),(X_train,Y_train),(X_val,Y_val),(X_test,Y_test),(train_idx,val_idx,test_idx)