Python numpy.ndarrary() Examples

The following are 3 code examples for showing how to use numpy.ndarrary(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: Deep-Metric-Learning-Baselines   Author: Confusezius   File: auxiliaries.py    License: Apache License 2.0 6 votes vote down vote up 
def run_kmeans(features, n_cluster):
    """
    Run kmeans on a set of features to find <n_cluster> cluster.

    Args:
        features:  np.ndarrary [n_samples x embed_dim], embedding training/testing samples for which kmeans should be performed.
        n_cluster: int, number of cluster.
    Returns:
        cluster_assignments: np.ndarray [n_samples x 1], per sample provide the respective cluster label it belongs to.
    """
    n_samples, dim = features.shape
    kmeans = faiss.Kmeans(dim, n_cluster)
    kmeans.n_iter, kmeans.min_points_per_centroid, kmeans.max_points_per_centroid = 20,5,1000000000
    kmeans.train(features)
    _, cluster_assignments = kmeans.index.search(features,1)
    return cluster_assignments
Example 2
Project: Deep-Metric-Learning-Baselines   Author: Confusezius   File: auxiliaries_nofaiss.py    License: Apache License 2.0 6 votes vote down vote up 
def run_kmeans(features, n_cluster):
    """
    Run kmeans on a set of features to find <n_cluster> cluster.

    Args:
        features:  np.ndarrary [n_samples x embed_dim], embedding training/testing samples for which kmeans should be performed.
        n_cluster: int, number of cluster.
    Returns:
        cluster_assignments: np.ndarray [n_samples x 1], per sample provide the respective cluster label it belongs to.
    """
    n_samples, dim = features.shape
    kmeans = faiss.Kmeans(dim, n_cluster)
    kmeans.n_iter, kmeans.min_points_per_centroid, kmeans.max_points_per_centroid = 20,5,1000000000
    kmeans.train(features)
    _, cluster_assignments = kmeans.index.search(features,1)
    return cluster_assignments
Example 3
Project: garage   Author: rlworkgroup   File: tensor_utils.py    License: MIT License 6 votes vote down vote up 
def discount_cumsum(x, discount):
    """Discounted cumulative sum.

    See https://docs.scipy.org/doc/scipy/reference/tutorial/signal.html#difference-equation-filtering  # noqa: E501
    Here, we have y[t] - discount*y[t+1] = x[t]
    or rev(y)[t] - discount*rev(y)[t-1] = rev(x)[t]

    Args:
        x (np.ndarrary): Input.
        discount (float): Discount factor.

    Returns:
        np.ndarrary: Discounted cumulative sum.

    """
    return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1],
                                axis=0)[::-1]