vanilla_cgan.py 文件源码

def load_data(data_dir, num_files=30):
    files_list = os.listdir(data_dir)
    data = None
    ac_data = None
    for fname in files_list[:num_files]:
        print fname
        f = os.path.join(data_dir, fname)
        with netcdf.netcdf_file(f, 'r') as fid:
            m = fid.variables['outputMeans'][:].copy()
            s = fid.variables['outputStdevs'][:].copy()
            feats = fid.variables['targetPatterns'][:].copy()
            ac_feats = fid.variables['inputs'][:].copy()
            scaler = preprocessing.StandardScaler()
            scaler.mean_ = m
            scaler.scale_ = s
            feats = scaler.inverse_transform(feats)
            assert feats.shape[0] == ac_feats.shape[0]
            # feats = np.concatenate((feats,ac_feats),axis=1)
        if data == None and ac_data == None:
            data = feats
            ac_data = ac_feats
        else:
            data = np.vstack((data, feats))
            ac_data = np.vstack((ac_data, ac_feats))
    return data, ac_data