Python numpy.ndarrary() Examples
The following are 3
code examples of numpy.ndarrary().
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Example #1
Source File: auxiliaries.py From Deep-Metric-Learning-Baselines with Apache License 2.0 | 6 votes |
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 |
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 |
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]