data201610b.py

import pickle
import numpy as np
# D:\PYALL\RML2016

# def load_data(filename=r'D:/research_review/RML201610a/LSTM2/data/RML2016.10b.dat'):
def load_data(
        filename=r'D:\PYALL\RML2016\RML2016.10b.dat'):
    Xd = pickle.load(open(filename, 'rb'), encoding='iso-8859-1')  # Xd2(22W,2,128)
    mods, snrs = [sorted(list(set([k[j] for k in Xd.keys()]))) for j in [0, 1]]
    X = []
    # X2=[]
    lbl = []
    # lbl2=[]
    train_idx = []
    # val_idx = []
    np.random.seed(2016)
    a = 0

    for mod in mods:
        for snr in snrs:
            X.append(Xd[(mod, snr)])  # ndarray(6000,2,128)
            for i in range(Xd[(mod, snr)].shape[0]):
                lbl.append((mod, snr))
            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)  # (220000,2,128)  mods * snr * 6000,total 220000 samples
    print(len(lbl))
    n_examples = X.shape[0]
    # n_test=X2.shape[0]
    test_idx = list(set(range(0, n_examples)) - set(train_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]
    print(len(train_idx))
    # print(len(val_idx))
    print(len(test_idx))

    print(X_train.shape)
    # print(X_val.shape)
    print(X_test.shape)

    def to_onehot(yy):
        # yy1 = np.zeros([len(yy), max(yy)+1])
        yy1 = np.zeros([len(yy), len(mods)])
        yy1[np.arange(len(yy)), yy] = 1
        return yy1

    # yy = list(map(lambda x: mods.index(lbl[x][0]), train_idx))

    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)))

    print(Y_train.shape)
    # print(Y_val.shape)
    print(Y_test.shape)
    return (mods, snrs, lbl), (X_train, Y_train), (X_test, Y_test), (train_idx, test_idx)


if __name__ == '__main__':
    (mods, snrs, lbl), (X_train, Y_train), (X_test, Y_test), (
    train_idx, test_idx) = load_data()
    aa =1