def _fuzzy_kmeans_mapper(array, ex, old_centers, centers, counts, labels, m):
'''
Update the new centers, new counts and labels using fuzzy kmeans method.
Args:
array(DistArray): the input data points matrix.
ex(Extent): region being processed.
old_centers(DistArray): the current centers of each cluster.
centers(DistArray): the new centers to be updated.
counts(DistArray): the new counts to be updated.
labels(DistArray): the new labels for each point to be updated.
m(float): the parameter of fuzzy kmeans.
'''
points = array.fetch(ex)
old_centers = old_centers[:]
new_centers = np.zeros_like(old_centers)
new_counts = np.zeros((old_centers.shape[0], 1))
new_labels = np.zeros(points.shape[0], dtype=np.int)
for i in range(points.shape[0]):
point = points[i]
prob = _calc_probability(point, old_centers, m)
new_labels[i] = np.argmax(prob)
for i in prob.nonzero()[0]:
new_counts[i] += prob[i]
new_centers[i] += prob[i] * point
centers.update(extent.from_shape(centers.shape), new_centers)
counts.update(extent.from_shape(counts.shape), new_counts)
labels.update(extent.create((ex.ul[0],), (ex.lr[0],), labels.shape), new_labels)
return []
def _shortest_path_mapper(ex,
kng,
directed,
dist_matrix):
"""
Mapper kernel for finding shortest path for a subset of points.
kng is supposed to be a sparse matrix which represents the distance among each pair
of points.
dist_matrix is the target matrix which we need to fill with the shortest path between
each pair of points.
Each kernel is responsible for finding the shortests path among a subset of points.
"""
row_beg = ex.ul[0]
row_end = ex.lr[0]
local_dist_matrix = graph_shortest_path(kng,
row_beg,
row_end,
directed=directed)
'''
local_dist_matrix is a NxN matrix where the M(i,j) is the shortest
path between i and j if it's positive, otherwise it's zero.
'''
dist_matrix.update(extent.from_shape(local_dist_matrix.shape),
local_dist_matrix)
result = core.LocalKernelResult()
return result
def _find_cluster_mapper(inputs, ex, d_pts, old_centers,
new_centers, new_counts, labels):
centers = old_centers
pts = d_pts.fetch(ex)
closest = _find_closest(pts, centers)
l_counts = np.zeros((centers.shape[0], 1), dtype=np.int)
l_centers = np.zeros_like(centers)
for i in range(centers.shape[0]):
matching = (closest == i)
l_counts[i] = matching.sum()
l_centers[i] = pts[matching].sum(axis=0)
# update centroid positions
new_centers.update(extent.from_shape(new_centers.shape), l_centers)
new_counts.update(extent.from_shape(new_counts.shape), l_counts)
labels.update(extent.create(ex.ul, (ex.lr[0], 1), labels.shape),
closest.reshape(pts.shape[0], 1))
return []
def _find_cluster_mapper(inputs, ex, d_pts, old_centers, new_centers,
new_counts, labels):
centers = old_centers
pts = d_pts.fetch(ex)
closest = _find_closest(pts, centers)
l_counts = np.zeros((centers.shape[0], 1), dtype=np.int)
l_centers = np.zeros_like(centers)
for i in range(centers.shape[0]):
matching = (closest == i)
l_counts[i] = matching.sum()
l_centers[i] = pts[matching].sum(axis=0)
# update centroid positions
new_centers.update(extent.from_shape(new_centers.shape), l_centers)
new_counts.update(extent.from_shape(new_counts.shape), l_counts)
labels.update(extent.create(ex.ul, (ex.lr[0], 1), labels.shape),
closest.reshape(pts.shape[0], 1))
return []
