How to use the nmslib.addDataPoint function in nmslib

os.remove(index_name)
    index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.DENSE_VECTOR,
                             nmslib.DistType.FLOAT)

    start = time.time()
    if fast:
        data = read_data_fast('sample_dataset.txt')
        print('data.shape', data.shape)
        positions = nmslib.addDataPointBatch(index, np.arange(len(data), dtype=np.int32), data)
    else:
        for id, data in enumerate(read_data('sample_dataset.txt')):
            pos = nmslib.addDataPoint(index, id, data)
            if id != pos:
                print('id %s != pos %s' % (id, pos))
                sys.exit(1)
    end = time.time()
    print('added data in %s secs' % (end - start))

    print('Let\'s print a few data entries')
    print('We have added %d data points' % nmslib.getDataPointQty(index))

    print("Distance between points (0,0) " + str(nmslib.getDistance(index, 0, 0)));
    print("Distance between points (1,1) " + str(nmslib.getDistance(index, 1, 1)));
    print("Distance between points (0,1) " + str(nmslib.getDistance(index, 0, 1)));
    print("Distance between points (1,0) " + str(nmslib.getDistance(index, 1, 0)));

    for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
       print(nmslib.getDataPoint(index, i))

]
    QUERY_STRS = ["abc", "def", "ghik"]
    space_type = 'leven'
    space_param = []
    method_name = 'small_world_rand'
    index_name  = method_name + '.index'

    index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.OBJECT_AS_STRING,
                             nmslib.DistType.INT)

    for id, data in enumerate(DATA_STRS):
        nmslib.addDataPoint(index, id, data)

    print('Let\'s print a few data entries')
    print('We have added %d data points' % nmslib.getDataPointQty(index))

    for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
        print(nmslib.getDataPoint(index,i))

    print('Let\'s invoke the index-build process')

    index_param = ['NN=17', 'efConstruction=50', 'indexThreadQty=4']
    query_time_param = ['efSearch=50']


    nmslib.loadIndex(index, index_name)

    print("The index %s is loaded" % index_name)

method_name = 'small_world_rand'
    index_name  = method_name + '.index'

    index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.OBJECT_AS_STRING,
                             nmslib.DistType.INT)

    if batch:
        print('DATA_STRS', DATA_STRS)
        positions = nmslib.addDataPointBatch(index, np.arange(len(DATA_STRS), dtype=np.int32), DATA_STRS)
    else:
        for id, data in enumerate(DATA_STRS):
            nmslib.addDataPoint(index, id, data)

    print('Let\'s print a few data entries')
    print('We have added %d data points' % nmslib.getDataPointQty(index))

    print("Distance between points (0,0) " + str(nmslib.getDistance(index, 0, 0)));
    print("Distance between points (1,1) " + str(nmslib.getDistance(index, 1, 1)));
    print("Distance between points (0,1) " + str(nmslib.getDistance(index, 0, 1)));
    print("Distance between points (1,0) " + str(nmslib.getDistance(index, 1, 0)));

    for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
        print(nmslib.getDataPoint(index,i))

    print('Let\'s invoke the index-build process')

    index_param = ['NN=17', 'efConstruction=50', 'indexThreadQty=4']
    query_time_param = ['efSearch=50']

def test_sparse_vector_fresh():
    space_type = 'cosinesimil_sparse_fast'
    space_param = []
    method_name = 'small_world_rand'
    index_name  = method_name + '_sparse.index'
    if os.path.isfile(index_name):
        os.remove(index_name)
    index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.SPARSE_VECTOR,
                             nmslib.DistType.FLOAT)

    for id, data in enumerate(read_sparse_data('sample_sparse_dataset.txt')):
        nmslib.addDataPoint(index, id, data)

    print('We have added %d data points' % nmslib.getDataPointQty(index))

    for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
       print(nmslib.getDataPoint(index,i))

    print('Let\'s invoke the index-build process')

    index_param = ['NN=17', 'efConstruction=50', 'indexThreadQty=4']
    query_time_param = ['efSearch=50']

    nmslib.createIndex(index, index_param)

    print('The index is created')

    nmslib.setQueryTimeParams(index,query_time_param)

def test_vector_loaded():
    space_type = 'cosinesimil'
    space_param = []
    method_name = 'small_world_rand'
    index_name  = method_name + '.index'
    index = nmslib.init(
                             space_type,
                             space_param,
                             method_name,
                             nmslib.DataType.DENSE_VECTOR,
                             nmslib.DistType.FLOAT)

    for id, data in enumerate(read_data('sample_dataset.txt')):
        pos = nmslib.addDataPoint(index, id, data)
        if id != pos:
            print('id %s != pos %s' % (id, pos))
            sys.exit(1)

    print('Let\'s print a few data entries')
    print('We have added %d data points' % nmslib.getDataPointQty(index))

    for i in range(0,min(MAX_PRINT_QTY,nmslib.getDataPointQty(index))):
       print(nmslib.getDataPoint(index,i))

    print('Let\'s invoke the index-build process')


    query_time_param = ['efSearch=50']

    nmslib.loadIndex(index, index_name)

if batch:
        with TimeIt('batch add'):
            positions = nmslib.addDataPointBatch(index, np.arange(len(dataset), dtype=np.int32), data_matrix)
        print('positions', positions)
    else:
        d = []
        q = []
        with TimeIt('preparing'):
            for data in dataset:
                d.append([[i, v] for i, v in enumerate(data) if v > 0])
            for data in queryset:
                q.append([[i, v] for i, v in enumerate(data) if v > 0])
        with TimeIt('adding points'):
            for id, data in enumerate(d):
                nmslib.addDataPoint(index, id, data)

    print('Let\'s invoke the index-build process')

    index_param = ['NN=17', 'efConstruction=50', 'indexThreadQty=4']
    query_time_param = ['efSearch=50']

    with TimeIt('building index'):
        nmslib.createIndex(index, index_param)

    print('The index is created')

    nmslib.setQueryTimeParams(index,query_time_param)

    print('Query time parameters are set')

    print("Results for the freshly created index:")

def test_add_points(self):
        self.assertEqual(0, nmslib.addDataPoint(self.index, 1000, "string1"))
        self.assertEqual(1, nmslib.addDataPoint(self.index, 1001, "string2"))