def helper_clarans_plugin():
# n, k, plug_in_oracle, oracle, num_local=None, max_neighbour=None)
global g, g_mat, order_val, full_mat, count
k = 5
pr = clarans_vanila(oracle, order_val, k)
print("ACTUAL: ", pr.centroids)
count = 0
start = time.time()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = clarans_plugin(order_val, k, oracle_plugin, oracle)
end = time.time()
print("SW", p.centroids)
print("COUNT Sasha Wang", count, end - start)
start = time.time()
count = 0
obj = unified_graph_lb_ub()
obj.store(g, order_val)
oracle_plugin = obj
p = clarans_plugin(order_val, k, oracle_plugin, oracle)
end = time.time()
print("LBT", p.centroids)
print("COUNT LBTree enabled", count, end - start, "\n\n")
start = time.time()
count = 0
obj = ParamTriSearch(2, obj_sw.ub_matrix)
obj.store(g, order_val)
oracle_plugin = obj
p = clarans_plugin(order_val, k, oracle_plugin, oracle)
end = time.time()
print("PARA", p.centroids)
print("PARA", count, end - start, "\n\n")
def helper_kruskals_plugin():
global g, g_mat, order_val, full_mat, count
count = 0
start = time.time()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
end = time.time()
print('COUNT Sasha Wang', count, end - start, "\n\n")
start = time.time()
count = 0
obj = unified_graph_lb_ub()
obj.store(g, order_val)
oracle_plugin = obj
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
end = time.time()
print("COUNT LBTree enabled", count, end - start, "\n\n")
start = time.time()
count = 0
obj = ParamTriSearch(2, obj_sw.ub_matrix)
obj.store(g, order_val)
oracle_plugin = obj
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
end = time.time()
print("PARA", count, end - start, "\n\n")
def prims_SW(g, pr, measure, kind, order_val, timer):
# Sasha Wang algorithm
print("Experiment Starting Sasha Wang (Prims)\n")
global full_mat, count
# g = {}
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = prims_plugin(order_val, oracle, oracle_plugin)
p.mst(0)
timer.end()
assert abs(p.mst_path_length - pr.mst_path_length) < 0.000001
print(
"Plugin with Sasha Wang Experiments\nActual(SW) Prims Path Length: {}\nSasha Wang Prims Path Length: {}\n"
"measure: {}, kind: {}, order_val: {}".format(p.mst_path_length,
pr.mst_path_length,
measure, kind,
order_val))
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang: {}, Time(total): {}, Time(SP): {}\n\n".format(
count, timer.time_elapsed, obj_sw.update_time))
def helper_sasha_wang_saver(id_graph_to_run, type_of_graph_id):
global g, g_mat, order_val, full_mat, count
graph = dict()
graph[0] = [
'normal_distances_Geometric_512.pkl',
'normal_distances_Renyi Erdos_512.pkl',
'normal_distances_ForrestFire_512.pkl',
'normal_distances_Barabasi_512.pkl'
]
graph[1] = [
'uniform_distances_Geometric_512.pkl',
'uniform_distances_Renyi Erdos_512.pkl',
'uniform_distances_ForrestFire_512.pkl',
'uniform_distances_Barabasi_512.pkl'
]
graph[2] = [
'zipf_distances_Geometric_512.pkl',
'zipf_distances_Renyi Erdos_512.pkl',
'zipf_distances_ForrestFire_512.pkl', 'zipf_distances_Barabasi_512.pkl'
]
outs = ['normal512.pkl', 'uniform512.pkl', 'zipf512.pkl']
full_mat = pickle.load(
open(os.path.join('igraph', outs[id_graph_to_run]), 'rb'))
order_val = full_mat.shape[0]
graph = graph[id_graph_to_run][type_of_graph_id]
print(graph)
ub_out_name = os.path.join(
"LB_UB", '_'.join([
'ub_sw',
str(graph.split('_')[0]),
graph.split('_')[2],
graph.split('_')[3]
]))
lb_out_name = os.path.join(
"LB_UB", '_'.join([
'lb_sw',
str(graph.split('_')[0]),
graph.split('_')[2],
graph.split('_')[3]
]))
g = pickle.load(open(os.path.join('igraph', graph), 'rb'))
count = 0
print("Graph Chosen; SW Starting.")
start = time.time()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
end = time.time()
print(
"Time for SW for algorithm(", '_'.join([
str(graph.split('_')[0]),
graph.split('_')[2],
graph.split('_')[3]
]), "): ", (end - start))
pickle.dump(np.array(obj_sw.ub_matrix), open(ub_out_name, 'wb'))
pickle.dump(np.array(obj_sw.lb_matrix), open(lb_out_name, 'wb'))
def helper_tester(order_val, measure, kind):
global full_mat, count
distance_measure = ['normal', 'uniform', 'zipf']
generation_algorithms = [
'Geometric', 'Renyi Erdos', 'ForrestFire', 'Barabasi'
]
full_mat_name = distance_measure[measure] + str(order_val) + '.pkl'
g_name = distance_measure[measure] + '_distances_' + generation_algorithms[
kind] + '_' + str(order_val) + '.pkl'
g = pickle.load(open(os.path.join("igraph", g_name), 'rb'))
full_mat = pickle.load(open(os.path.join("igraph", full_mat_name), 'rb'))
g_mat = _get_matrix_from_adj_dict(g, order_val)
count = 0
obj_sw = SashaWang()
start = time.time()
obj_sw.store(g, order_val)
sw_time = time.time() - start
obj = unified_graph_lb_ub()
start = time.time()
obj.store(g, order_val)
our_time = time.time() - start
start = time.time()
obj_tri = ParamTriSearch(2, None)
obj_tri.store(g, order_val)
tri_time = time.time() - start
average_lbt = np.average(
np.array(obj_sw.lb_matrix) - np.array(obj.lb_matrix))
mean_sw = np.average(np.array(obj_sw.lb_matrix))
mean_ours = np.average(np.array(obj.lb_matrix))
mean_tri = np.average(np.array(obj_tri.lb_matrix))
average_original = np.average(full_mat - np.array(obj_sw.lb_matrix))
results_file_name = "Error_" + str(order_val) + "_" \
+ generation_algorithms[kind] + "_" \
+ distance_measure[measure] + "_" + ".txt"
lbt_results = " Time SW: " \
+ str(sw_time) + "\n"\
+ " Time lbub: " + str(our_time) + "\n"\
+ " Average Error with LBT " + str(average_lbt) + "\n" \
+ " Average Error with Original " + str(average_original) \
+ " Mean SW " + str(mean_sw) \
+ " Mean SW " + str(mean_ours) \
+ " Mean TriSearch " + str(mean_tri) \
+ " Time Tri " + str(tri_time) \
+ "\n"
print(lbt_results)
f = open(os.path.join("quality", results_file_name), "w+")
f.write(lbt_results)
f.write("\n")
f.close()
def prims_SW(g, pr, order_val, timer, oracle, bounder=False):
# Sasha Wang algorithm
print("Experiment Starting Sasha Wang (Prims)\n")
global full_mat, count
# g = {}
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
# oracle = flicker_oracle()
p = prims_plugin(order_val, oracle, oracle_plugin)
p.mst(0)
timer.end()
print("value from SW: {}".format(p.mst_path_length))
assert abs(p.mst_path_length - pr.mst_path_length) < 0.000001
print(
"Plugin with Sasha Wang Experiments\nActual(SW) Prims Path Length: {}\nSasha Wang Prims Path Length: {}\n"
"order_val: {}".format(p.mst_path_length, pr.mst_path_length,
order_val))
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang: {}, Time(total): {}, Time(SP): {}\n\n".format(
count, timer.time_elapsed, obj_sw.update_time))
if bounder:
lb = []
ub = []
lb_name = 'lower_bounds_{}_sw.lb'.format(order_val)
ub_name = 'upper_bounds_{}_sw.ub'.format(order_val)
for i in range(order_val):
for j in range(i + 1, order_val):
a, b = p.plug_in_oracle.lookup(i, j)
lb.append(a)
ub.append(b)
path_out = os.path.join(os.getcwd(), "bounds_compare_results",
"bounds_{}_sw".format(order_val))
if not os.path.exists(path_out):
os.makedirs(path_out)
with open(os.path.join(path_out, lb_name), 'w') as f:
f.write('\n'.join([str(b) for b in lb]))
with open(os.path.join(path_out, ub_name), 'w') as f:
f.write('\n'.join([str(b) for b in ub]))
with open("dft_scale.res", 'a+') as f:
f.write("Order Val: {}\n".format(order_val))
f.write(
"COUNT Sasha Wang: {}, Time(total): {}, Time(SP): {}\n\n".format(
count, timer.time_elapsed, obj_sw.update_time))
def clarans_SW(pr, measure, kind, order_val, timer, algo, k):
print("Experiment Starting SW (CLARANS)\n")
global full_mat, count
g = {}
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = clarans_plugin(order_val, k, oracle_plugin, oracle)
print("Plug-in(CLA): ", p.centroids)
timer.end()
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang: {}, Time(total): {}, Time(SP): {}\n\n".format(count, timer.time_elapsed, obj_sw.update_time))
def navarro_SW(pr, measure, kind, order_val, timer, algo, k):
print("Experiment Starting SW (NAVARRO)\n")
global full_mat, count
g = {}
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = knnrp(order_val, k, oracle, oracle_plugin)
p.knn_queries()
# print("Plug-in(Nav): ", p.NHA)
timer.end()
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang: {}, Time(total): {}, Time(SP): {}\n\n".format(count, timer.time_elapsed, obj_sw.update_time))
def kruskals_SW(pr, measure, kind, order_val, timer, algo):
print("Experiment Starting SW (Kruskals)\n")
global full_mat, count
g = {}
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
timer.end()
print("(KRUSKAL)Sasha Wang - Original Length: {}, our lenght: {}, measure: {}, kind: {}, order_val: {}".
format(pr.mst_path_length, p.mst_path_length, measure, kind, order_val))
assert abs(p.mst_path_length - pr.mst_path_length) < 0.000001
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang: {}, Time(total): {}, Time(SP): {}\n\n".format(count, timer.time_elapsed, obj_sw.update_time))
def helper_pam_plugin(k=5):
global g, g_mat, order_val, full_mat, count
centroids = sample(list(range(order_val)), k)
copy_centroids = copy.copy(centroids)
count = 0
centroids = copy.copy(copy_centroids)
start = time.time()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = pam_plugin(oracle, oracle_plugin, order_val, k, centroids)
end = time.time()
print("SW", p.centroids)
print("COUNT Sasha Wang", count, end - start)
centroids = copy.copy(copy_centroids)
start = time.time()
count = 0
obj = unified_graph_lb_ub()
obj.store(g, order_val)
oracle_plugin = obj
p = pam_plugin(oracle, oracle_plugin, order_val, k, centroids)
end = time.time()
print("LBT", p.centroids)
print("COUNT LBTree enabled", count, end - start, "\n\n")
centroids = copy.copy(copy_centroids)
start = time.time()
count = 0
obj = ParamTriSearch(2, obj_sw.ub_matrix)
obj.store(g, order_val)
oracle_plugin = obj
p = pam_plugin(oracle, oracle_plugin, order_val, k, centroids)
end = time.time()
print("PARA", p.centroids)
print("PARA", count, end - start, "\n\n")
centroids = copy.copy(copy_centroids)
p = pam_vanila(oracle, order_val, k, centroids)
print("Original: ", p.centroids)
def helper_prims_plugin():
global g, g_mat, order_val, full_mat, count
count = 0
obj_sw = SashaWang()
obj_sw.store(g, order_val)
obj = unified_graph_lb_ub()
obj.store(g, order_val)
assert not np.any(
np.abs(np.array(obj_sw.lb_matrix) -
np.array(obj.lb_matrix)) < -0.0000001)
start = time.time()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = prims_plugin(order_val, oracle, oracle_plugin)
p.mst(0)
end = time.time()
print("COUNT Sasha Wang", count, end - start, "\n\n")
count = 0
start = time.time()
obj = unified_graph_lb_ub()
obj.store(g, order_val)
oracle_plugin = obj
p = prims_plugin(order_val, oracle, oracle_plugin)
p.mst(0)
end = time.time()
print("COUNT LBTree enabled", count, end - start, "\n\n")
start = time.time()
count = 0
obj = ParamTriSearch(2, obj_sw.ub_matrix)
obj.store(g, order_val)
oracle_plugin = obj
p = prims_plugin(order_val, oracle, oracle_plugin)
p.mst(0)
end = time.time()
print("PARA", count, end - start, "\n\n")
class SW_and_LBUB_Oracle:
def __init__(self):
self.obj_sw = SashaWang()
self.obj_lbub = unified_graph_lb_ub()
self.is_uncalculated = self.obj_sw.is_uncalculated
def store(self, graph, order_val):
self.G = copy.copy(graph)
self.obj_sw.store(graph, order_val)
self.obj_lbub.store(self.G, order_val)
def update(self, edge, val):
self.obj_sw.update(edge, val)
self.obj_lbub.update(edge, val)
def lookup(self, x, y):
sw_lb_ub = self.obj_sw.lookup(x, y)
lbub_lb_ub = self.obj_lbub.lookup(x, y)
if (lbub_lb_ub[0] - sw_lb_ub[0]) > 0.000000001:
print("Found a better lower bound for {}, {}".format(x, y))
if (-lbub_lb_ub[1] + sw_lb_ub[1]) > 0.000000001:
print("Found a better upper bound for {}, {}".format(x, y))
return lbub_lb_ub
def helper_lm_sampling(measure,
kind,
order_val,
n,
landmarks,
verbose=True,
sampling=True,
sw=False):
print("Values for the run - order_val:{}, Samples:{}, landmarks:{}".format(
order_val, n, landmarks))
distance_measure = [
'normal', 'uniform', 'zipf', 'data_flicker', 'data_sf', 'data_20'
]
generation_algorithms = [
'Geometric', 'Renyi Erdos', 'ForrestFire', 'Barabasi'
]
"""g_name usually looks like this -> "normal_distances_Barabasi_64.pkl"
<distribution>_distances_<type-of-graph>_<#-of-nodes>.pkl """
g_name = distance_measure[measure] + '_distances_' + generation_algorithms[
kind] + '_' + str(order_val) + '.pkl'
print('Path to the Input Graph File: {}'.format(
os.path.join("/Users/jeesaugustine/git_it/distance_opti_plug_in/",
"igraph", g_name)))
graph = pickle.load(open(os.path.join("..", "igraph", g_name), 'rb'))
# graph = {(0, 1): 0.3, (1, 2): 0.5, (2, 3): 0.5, (2, 4): 0.4, (4, 5): 0.6, (5, 6): 0.3}
elm = EdgeLandMark(graph, n, order_val, Sampling=sampling)
start = time.time()
elm.find_paths()
new_total_1 = elm.greedy_sampling(landmarks)
print_string = "Sampling: True\nNo of Nodes in Graph: {} \nTotal Known Edges in Graph: {} \nTotal Right Samples(" \
"n): {} \nTotal Left Edges(k): {}\n Sum Lower Bounds: {}\n Time Taken:{}\n".format(
order_val, len(graph), n, landmarks, new_total_1, (time.time()-start)/60)
file_writer(verbose,
g_name,
n=n,
landmarks=landmarks,
pretext="ELM_",
print_statement=print_string)
# print(elm.greedyK)
# elm = EdgeLandMark(graph, n, order_val, Sampling=False)
# elm.find_paths()
# elm.greedy_sampling(k)
# if verbose:
# print("Sampling: False\nNo of Nodes in Graph: {} \nTotal Known Edges in Graph: "
# "{} \n ".format(order_val, len(graph)))
# print(elm.greedyK)
if sw:
obj_sw = SashaWang()
start_sw = time.time()
obj_sw.store(graph, order_val)
print_statement_sw = "Time taken for SW on the graph is {}\n".format(
(time.time() - start_sw) / 60)
file_writer(verbose,
g_name,
n=n,
landmarks=landmarks,
pretext="SW_",
print_statement=print_statement_sw)
new_total_1 = 0
lb_total = 0
for i in range(order_val):
for j in range(i + 1, order_val):
if obj_sw.matrix[i][j] != -1:
new_total_1 += obj_sw.matrix[i][j]
else:
lb_total += obj_sw.lb_matrix[i][j]
print("\nThe SW LB Sum(Given): {}, The LB Total: {}".format(
new_total_1, lb_total))
def helper_navarro_plugin(g,
full_mat1,
g_mat,
measure,
kind,
order_val,
k,
algo,
three=False):
global full_mat, count
g, full_mat, g_mat = g, full_mat1, g_mat
timer = Timer()
timer.start()
pr = vanila_knnrp(order_val, k, oracle)
pr.knn_queries()
# print("Plug-in(Nav): ", pr.NHA)
timer.end()
base_algo_results = "COUNT Without Plugin " + str(count) + " Time " + str(
timer.time_elapsed) + "\n"
print("COUNT Without Plugin ", count, timer.time_elapsed, "\n\n")
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = knnrp(order_val, k, oracle, oracle_plugin)
p.knn_queries()
# print("Plug-in(Nav): ", p.NHA)
timer.end()
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang ", count, timer.time_elapsed - obj_sw.update_time,
"\n\n")
"""
DSS Solution Scheme
"""
g = {}
timer.start()
obj_dss = DSS(g, order_val)
oracle_plugin = obj_dss
p = knnrp(order_val, k, oracle, oracle_plugin)
p.knn_queries()
timer.end()
print("DSS Experiments - {}\nmeasure: {}, kind: {}, order_val: {}".format(
algo, measure, kind, order_val))
print("{} DSS COUNT {}\nTime: {}\n\n".format(algo, count,
timer.time_elapsed))
"""
Jees added this part to test the naive code for intersection-TriSearch(This)
This accepts a graph(graph with edge and distance format)
and converts it into adjacency list representation and stores it
This updates a new edge the moment its gets a new resolution
This computes the value of lower-bound only when needed by a query Q(a, b)
This find the Triangles through intersection of adjacency list of both end points of the query edge (a & b)
The class IntersectTriSearch: is initialized with the graph and it takes care of everything else
including conversion to adjacency list.
"""
timer.start()
oracle_plugin = IntersectTriSearch({}, order_val)
p = knnrp(order_val, k, oracle, oracle_plugin)
p.knn_queries()
# print("Plug-in(Nav): ", p.NHA)
timer.end()
print(
"IntersctionTriSearch Experiments - {}\nmeasure: {}, kind: {}, order_val: {}"
.format(algo, measure, kind, order_val))
print(
"{} COUNT intersct Trisearch: {}\nLB Time(Tri): {}\nUB Time(Tri): {}\n\n"
.format(algo, count, timer.time_elapsed - oracle_plugin.sp_time,
oracle_plugin.sp_time))
print("*" * 40)
"""
code to test the landmark based methods
"""
timer.start()
oracle_plugin = LSS({}, order_val, 6, oracle)
p = knnrp(order_val, k, oracle, oracle_plugin)
p.knn_queries()
timer.end()
print("COUNT LSS: {}\nTime(LSS): {}\n\n".format(count, timer.time_elapsed))
"""
def helper_kruskals_plugin(g,
full_mat1,
g_mat,
measure,
kind,
order_val,
algo,
three=False):
global full_mat, count
g, full_mat, g_mat = g, full_mat1, g_mat
timer = Timer()
pr = vanila_kruskals(order_val, time_waste_oracle)
timer.start()
pr.mst()
timer.end()
base_algo_results = "COUNT Without Plugin " + str(count) + " Time " + str(
timer.time_elapsed) + "\n"
print("COUNT Without Plugin ", count, timer.time_elapsed, "\n\n")
timer.start()
obj_sw = SashaWang()
obj_sw.store(g, order_val)
oracle_plugin = obj_sw
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
timer.end()
print(
"(KRUSKAL)Sasha Wang - Original Length: {}, our lenght: {}, measure: {}, kind: {}, order_val: {}"
.format(pr.mst_path_length, p.mst_path_length, measure, kind,
order_val))
assert abs(p.mst_path_length - pr.mst_path_length) < 0.000001
sasha_wang_results = "COUNT Sasha Wang " + str(count) + " Time " + str(
timer.time_elapsed - obj_sw.update_time) + "\n"
print("COUNT Sasha Wang ", count, timer.time_elapsed - obj_sw.update_time,
"\n\n")
"""
DSS Solution Scheme
"""
g = {}
timer.start()
obj_dss = DSS(g, order_val)
oracle_plugin = obj_dss
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
timer.end()
assert abs(p.mst_path_length - pr.mst_path_length) < 0.000001
print(
"Plugin with DSS\nActual(SW) {} Path Length: {}\nDSS Path Length: {}\n"
"measure: {}, kind: {}, order_val: {}".format(algo, p.mst_path_length,
pr.mst_path_length,
measure, kind,
order_val))
print("DSS COUNT {}\nTime: {}\n\n".format(
count,
timer.time_elapsed,
))
"""
Jees added this part to test the naive code for intersection-TriSearch(This)
This accepts a graph(graph with edge and distance format)
and converts it into adjacency list representation and stores it
This updates a new edge the moment its gets a new resolution
This computes the value of lower-bound only when needed by a query Q(a, b)
This find the Triangles through intersection of adjacency list of both end points of the query edge (a & b)
The class IntersectTriSearch: is initialized with the graph and it takes care of everything else
including conversion to adjacency list.
"""
timer.start()
oracle_plugin = IntersectTriSearch({}, order_val)
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
timer.end()
print(
"KRUSKAL - IntersctionTriSearch Experiments:\nActual(Vanila) KRUSKAL Path Length: {}\nIntersctionTriSearch(our plugin) Prims Path Length: {}\nmeasure: {}, kind: {}, order_val: {}"
.format(pr.mst_path_length, p.mst_path_length, measure, kind,
order_val))
print(
"COUNT intersct Trisearch: {}\nLB Time(Tri): {}\nUB Time(Tri): {}\n\n".
format(count, timer.time_elapsed - oracle_plugin.sp_time,
oracle_plugin.sp_time))
print("My Lookup count(Tri): {}".format(oracle_plugin.lookup_count))
"""
code to test the landmarkbased methods
"""
timer.start()
oracle_plugin = LSS({}, order_val, 6, oracle)
p = kruskals_plugin(order_val, oracle, oracle_plugin)
p.mst()
timer.end()
print("{} COUNT LSS: {}\nTime(LSS): {}\n\n".format(algo, count,
timer.time_elapsed))
"""
def helper_prims_plugin(num_nodes):
print(num_nodes)
graph_maker = NlogNGraphMaker(num_nodes)
graph = graph_maker.get_nlogn_edges()
obj_sw = SashaWang()
obj_sw.store(graph, num_nodes)