du_points = DistributedInMemoryDataUnit("Points", pilot=pilot)
du_points.load(points)
f = open("centers.csv")
centers = f.readlines()
f.close()
du_centers = DistributedInMemoryDataUnit("Centers")
du_centers.load(centers)
number_of_centroids_points=len(centers)
end_data_load = time.time()
time_measures["DataLoadTime"] = end_data_load-end_start_pilot
for iteration in range(0,NUM_ITERATIONS):
iteration_start = time.time()
future = du_points.map_pilot("KMeans.closestPoint", du_centers.name, number_of_compute_units=2)
output_dus = future.result()
new_centers = []
for du in output_dus:
future=du.reduce_pilot("KMeans.averagePoints")
result_du = future.result()
new_centers.append(result_du)
du_centers = DistributedInMemoryDataUnit("Centers-%d"%(iteration+1)).merge(new_centers)
iteration_end = time.time()
time_measures["Iteration-%d"%iteration] = iteration_end - iteration_start
end = time.time()
time_measures["Runtime"] = end-start
#################################################################################################################
du_points = DistributedInMemoryDataUnit("Points", pilot=pilot)
du_points.load(points)
f = open("centers.csv")
centers = f.readlines()
f.close()
du_centers = DistributedInMemoryDataUnit("Centers")
du_centers.load(centers)
number_of_centroids_points=len(centers)
end_data_load = time.time()
time_measures["DataLoadTime"] = end_data_load-end_start_pilot
for iteration in range(0,NUM_ITERATIONS):
iteration_start = time.time()
future = du_points.map_pilot("KMeans.closestPoint", du_centers.name, number_of_compute_units=2)
output_dus = future.result()
new_centers = []
for du in output_dus:
future=du.reduce_pilot("KMeans.averagePoints")
result_du = future.result()
new_centers.append(result_du)
du_centers = DistributedInMemoryDataUnit("Centers-%d"%(iteration+1)).merge(new_centers)
iteration_end = time.time()
time_measures["Iteration-%d"%iteration] = iteration_end - iteration_start
end = time.time()
time_measures["Runtime"] = end-start
#################################################################################################################
