def fl_dense_py_kernel_np_numba_array64():
K_dense = helper.create_kernel_dense_np_numba(dataArray, 'euclidean')
obj = FacilityLocationFunction(n=num_samples,
mode="dense",
sijs=K_dense,
separate_rep=False,
pybind_mode="array64")
obj.maximize(budget=budget,
optimizer=optimizer,
stopIfZeroGain=False,
stopIfNegativeGain=False,
verbose=False)
#prepare data to be used in the analysis
num_samples = param[0]
num_clusters = param[1]
points, cluster_ids, centers = make_blobs(n_samples=num_samples,
centers=num_clusters,
n_features=num_features,
cluster_std=cluster_std_dev,
return_centers=True,
random_state=4)
data = list(map(tuple, points))
dataArray = np.array(data)
if first == True:
print("Pre compiling np_numba and fastdist functions")
helper.create_kernel_dense_np_numba(dataArray, metric='euclidean')
print("Threading layer chosen: %s" % threading_layer())
helper.create_kernel_dense_fastdist(dataArray, metric='euclidean')
print("Threading layer chosen: %s" % threading_layer())
first = False
for method in method_dictionary:
print("Method: ", method)
row = [num_samples, method]
func = method_dictionary[method] + "(dataArray, metric='euclidean')"
#print("Calling :", func)
setup = "from submodlib.helper import " + method_dictionary[
method] + "; from __main__ import dataArray"
#print("Setup: ", setup)
t = timeit.timeit(func, setup, number=num_executions)
t = round(t / num_executions, num_places)
