How to use the nmslib.DataType function in nmslib

method_name = 'small_world_rand'
    index_name  = method_name + '.index'
    if os.path.isfile(index_name):
        os.remove(index_name)
    f = '/tmp/foo.txt'
    if not os.path.isfile(f):
        print('creating %s' % f)
        np.savetxt(f, np.random.rand(100000,1000), delimiter="\t")
        print('done')

    if fast:
        index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.DENSE_VECTOR,
                             nmslib.DistType.FLOAT)
        with TimeIt('fast add data point'):
            data = read_data_fast(f)
            nmslib.addDataPointBatch(index, np.arange(len(data), dtype=np.int32), data)
        nmslib.freeIndex(index)

    if fast_batch:
        index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.DENSE_VECTOR,
                             nmslib.DistType.FLOAT)
        with TimeIt('fast_batch add data point'):
            offset = 0
            for data in read_data_fast_batch(f, 10000):

np.save(args.out_npy, features)

        print(
            "[ Explained variance ratio: {ratio:.4} ]".format(
                ratio=pipeline.named_steps["pca"].explained_variance_ratio_.
                sum()
            )
        )

        print("[==        Saving pipeline       ==]")
        pickle.dump(pipeline, open(args.out_pipeline, "wb"))

    index = nmslib.init(
        method="hnsw",
        space=args.knn_metric,
        data_type=nmslib.DataType.DENSE_VECTOR
    )
    print("[==  Adding features to indexer  ==]")
    index.addDataPointBatch(features)

    print("[==        Creating index        ==]")
    index.createIndex({"post": 1}, print_progress=True)
    print("")
    print("[==         Saving index         ==]")
    index.saveIndex(args.out_knn)

    if args.in_npy_test is not None:
        test_features = np.load(args.in_npy_test, mmap_mode="r")
        test_features = pipeline.transform(test_features)
        np.save(args.out_npy_test, test_features)