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meanshift_np.py
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meanshift_np.py
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import numpy as np
from algorithms.meanshift_base import MeanShiftBase
class MeanShiftNumpy(MeanShiftBase):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
def prepare(self, X):
return X, X.copy()
def distance(self, a, b):
# shape a: N x C
# shape b: M x C
d = np.sum(np.square(a[:, None, :] - b[None, :, :]), axis=-1)
return d
def kernel(self, distances):
return np.exp(-0.5 * ((distances / self.bandwidth ** 2)))
def _main_loop(self, X, clusters):
iteration = 0
while True:
iteration += 1
d = self.distance(clusters, X)
w = self.kernel(d)
new_centers = w[:, :, None] * X[None, :, :]
w_sum = w.sum(1)
new_centers = np.sum(new_centers, axis=1) / w_sum[:, None]
diff = np.sum(np.square(new_centers - clusters))
if self.verbose:
print('Iteration {}: {} difference'.format(iteration, diff.item()))
if diff < self.early_stop_threshold:
break
clusters = new_centers
if self.get_hook('on_iteration_end'):
_, plot_assignments = self._group_clusters(clusters)
self.call_hook('on_iteration_end', self, iteration, X, clusters, plot_assignments)
clusters, assignments = self._group_clusters(clusters)
return clusters, assignments
def _group_clusters(self, points):
from algorithms.util_np import connected_components_undirected, scatter_mean0
_, cluster_ids = connected_components_undirected(self.distance(points, points) < self.cluster_threshold)
cluster_centers = scatter_mean0(points, cluster_ids)
return cluster_centers, cluster_ids