Python numpy.ndarrary() Examples

The following are 3 code examples of numpy.ndarrary(). 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. You may also want to check out all available functions/classes of the module numpy , or try the search function .
Example #1
Source File: auxiliaries.py    From Deep-Metric-Learning-Baselines with 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
Source File: auxiliaries_nofaiss.py    From Deep-Metric-Learning-Baselines with 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
Source File: tensor_utils.py    From garage with 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]