def getTestData():
a = ABB()
r = ABBRandom()
seq = RandomSequence(10**4, 10**5)
k = int(0.005*seq.length)
abb_seq = seq.k_swaps(k)
for i in range(0, seq.length):
a.insert(abb_seq[i])
r.insert(seq.get_element(i))
return (a, r)
def main(elements, adjust, exp):
'''
Principal
'''
insertions = elements
searches = int(0.5*elements)
not_found_searches = int(0.25*searches)
swaps_amount = int(0.05*elements) # aumentando la cantidad de swaps de cada iteracion
init = False
analizer = ExperimentAnalyzer(exp)
print('n :' + str(elements))
print('experiments: ' + str(exp))
for e in range(exp):
sequence = RandomSequence(elements, 10**5)
while (not init) or abb_comps - abbr_comps > abbr_comps*adjust:
if not init:
mean_height = 0
iteration = 0
init = True
# Número de iteracion para acercar comportamiento de árboles
iteration += 1
# print('.'),
sys.stdout.flush()
# Se definen las estructuras
abb_tree = ABB()
abbr_tree = ABBRandom()
skip_list = SkipList()
# Contadores
abb_comps = 0
abbr_comps = 0
skip_comps = 0
abb_search_comps = 0
abbr_search_comps = 0
skip_search_comps = 0
# Lista para ABB
abb_elements = sequence.k_swaps(swaps_amount)
# Inserciones
for i in range(insertions):
abb_comps += abb_tree.insert(abb_elements[i])
abbr_comps += abbr_tree.insert(sequence.get_element(i))
skip_comps += skip_list.insert(sequence.get_element(i))
# Busquedas
for i in range(searches):
if i < not_found_searches:
elem = sequence.get_random_free_element()
else:
elem = sequence.get_random_element()
abb_search_comps += abb_tree.search(elem)[1]
abbr_search_comps += abbr_tree.search(elem)[1]
skip_search_comps += skip_list.search(elem)[1]
# Altura de skip list
mean_height += 1.0*skip_list.max_height
# Siguiente iteración
sequence.elements = abb_elements
# print(str(1.0*abb_comps/insertions) + ' ' + str(1.0*abbr_comps/insertions) + ' ' + str(1.0*skip_comps/insertions))
# Suma de resultados promedio (n operaciones)
# Resultados: Inserciones
abb_comps = 1.0*abb_comps/insertions
abbr_comps = 1.0*abbr_comps/insertions
skip_comps = 1.0*skip_comps/insertions
# Resultados: Busquedas
abb_search_comps = 1.0*abb_search_comps/searches
abbr_search_comps = 1.0*abbr_search_comps/searches
skip_search_comps = 1.0*skip_search_comps/searches
# Resultados: Altura de Skip List
mean_height = 1.0*mean_height/iteration
print('\ninsertions: ' + str(abb_comps) + ',' + str(abbr_comps) + ',' + str(skip_comps))
print('searches: ' + str(abb_search_comps) + ',' + str(abbr_search_comps) + ',' + str(skip_search_comps))
print('skiplist height: ' + str(mean_height))
print('total swaps: ' + str(swaps_amount*iteration))
analizer.add_abb_result(abb_comps, abb_search_comps, swaps_amount*iteration)
analizer.add_abbr_result(abbr_comps, abbr_search_comps)
analizer.add_skip_list_result(skip_comps, skip_search_comps, mean_height)
init = False
print('\n')
analizer.compute_errors()
analizer.show_results()
print('\n')
def main(elements, exp):
'''
Principal
'''
insertions = elements
searches = int(0.5*elements)
not_found_searches = int(0.25*searches)
analizer = ExperimentAnalyzer(exp)
print('n :' + str(elements))
print('experiments: ' + str(exp))
for e in range(exp):
sequence = RandomSequence(elements, 10**5)
swaps_amount = int(0.7*elements) # swaps
# Contadores
abb_comps = 0
abbr_comps = 0
skip_comps = 0
abb_search_comps = 0
abbr_search_comps = 0
skip_search_comps = 0
# Se definen las estructuras
abb_tree = ABB()
abbr_tree = ABBRandom()
skip_list = SkipList()
# Lista para ABB
abb_elements = sequence.k_swaps(swaps_amount)
# Inserciones
for i in range(insertions):
abb_comps += abb_tree.insert(abb_elements[i])
abbr_comps += abbr_tree.insert(abb_elements[i])
skip_comps += skip_list.insert(abb_elements[i])
# Busquedas
for i in range(searches):
if i < not_found_searches:
elem = sequence.get_random_free_element()
else:
elem = sequence.get_random_element()
abb_search_comps += abb_tree.search(elem)[1]
abbr_search_comps += abbr_tree.search(elem)[1]
skip_search_comps += skip_list.search(elem)[1]
# Altura de skip list
mean_height = 1.0*skip_list.max_height
# Suma de resultados promedio (n operaciones)
# Resultados: Inserciones
abb_comps = 1.0*abb_comps/insertions
abbr_comps = 1.0*abbr_comps/insertions
skip_comps = 1.0*skip_comps/insertions
# Resultados: Busquedas
abb_search_comps = 1.0*abb_search_comps/searches
abbr_search_comps = 1.0*abbr_search_comps/searches
skip_search_comps = 1.0*skip_search_comps/searches
print('\ninsertions: ' + str(abb_comps) + ',' + str(abbr_comps) + ',' + str(skip_comps))
print('searches: ' + str(abb_search_comps) + ',' + str(abbr_search_comps) + ',' + str(skip_search_comps))
print('skiplist height: ' + str(mean_height))
print('total swaps: ' + str(swaps_amount))
analizer.add_abb_result(abb_comps, abb_search_comps, swaps_amount)
analizer.add_abbr_result(abbr_comps, abbr_search_comps)
analizer.add_skip_list_result(skip_comps, skip_search_comps, mean_height)
analizer.compute_errors()
analizer.show_results()
print('\n')
