def predict(master, input, center, centers):
"""
Predict the closest clusters for the datapoints in input.
"""
from kclustering_pipeline import Estimate
job = Estimate()
job.pipeline = [("split",
Stage("k_cluster_predict", input_chain =
[task_input_stream, reader], init = simple_init,
process = predict_map))]
job.params = center
job.params['centers'] = centers
job.run(input = input)
return job.wait()
def estimate(master, input, center, k, iterations):
"""
Optimize k-clustering for `iterations` iterations with cluster
center definitions as given in `center`.
"""
from kclustering_pipeline import Estimate
job = Estimate()
job.pipeline = [("split",
Stage("k_cluster_init_map", input_chain =
[task_input_stream, reader], init = map_init,
process = random_init_map)),
('group_label',
Stage("k_cluster_init_reduce", process = estimate_reduce, init = simple_init))]
job.params = center
job.params['seed'] = 0
job.params['k'] = k
job.run(input = input)
centers = [(i,c) for i,c in result_iterator(job.wait())]
job.purge()
for j in range(iterations):
job = Estimate()
job.params = center
job.params['k'] = k
job.params['centers'] = centers
job.pipeline = [('split', Stage("kcluster_map_iter_%s" %(j,),
input_chain = [task_input_stream, reader],
process=estimate_map, init = simple_init)),
('group_label', Stage("kcluster_reduce_iter_%s" %(j,),
process=estimate_reduce, init = simple_init))]
job.run(input = input)
centers = [(i,c) for i,c in result_iterator(job.wait())]
job.purge()
return centers
