# membership_vector

### cuml.cluster.hdbscan.membership_vector(clusterer, points_to_predict, int batch_size=4096)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/cluster/hdbscan/hdbscan.pyx#L1202)

Predict soft cluster membership. The result produces a vector
for each point in `points_to_predict` that gives a probability that
the given point is a member of a cluster for each of the selected clusters
of the `clusterer`.

* **Parameters:**
  **clusterer**
  : A clustering object that has been fit to the data and
    either had `prediction_data=True` set, or called the
    `generate_prediction_data` method after the fact.

  **points_to_predict**
  : The new data points to predict cluster labels for. They should
    have the same dimensionality as the original dataset over which
    clusterer was fit.

  **batch_size**
  : Lowers memory requirement by computing distance-based membership
    in smaller batches of points in the prediction data. For example, a
    batch size of 1,000 computes distance based memberships for 1,000
    points at a time. The default batch size is 4,096.
* **Returns:**
  **membership_vectors**
  : The probability that point `i` is a member of cluster `j` is
    in `membership_vectors[i, j]`.

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