def one_point_hybridization_knn_result(forest_old, feature_list, trainx, trainy, predictx, predicty,loop,neighbor):
res = {}
for i in range(0, loop):
forest, forest_new = one_point_hybridization_knn(forest_old, feature_list, trainx, trainy, predictx, predicty,neighbor)
forest_list = sorted(forest.items(), key=lambda item:(item[1],item[0]), reverse=True)
forest_next = []
for j in range(0, 10):
forest_next.append(forest_old[j])
for j in range(0, 30):
forest_next.append(num_to_list(forest_list[j][0]))
gene_mutation_list = []
for j in range(30, 40):
gene_mutation_list.append(num_to_list(forest_list[j][0]))
positive_table, nagative_table = calculate_table_knn(gene_mutation_list, feature_list, trainx, trainy, predictx,
predicty,neighbor)
for j in range(0, 10):
forest_next.append(gene_mutation(positive_table, nagative_table))
forest_old = forest_next
# res.append(forest_list[0][1])
res[forest_list[0][0]]=forest_list[0][1]
first=sorted(res.items(), key=lambda item:(item[1],item[0]),reverse=True)[0]
return first
def one_point_hybridization_train_tree_high_result(forest_old, feature_list,
trainx, trainy, predictx,
predicty, loop):
res = {}
for i in range(0, loop):
forest, forest_new = one_point_hybridization_train_tree(
forest_old, feature_list, trainx, trainy, predictx, predicty)
forest_list = sorted(forest.items(),
key=lambda item: (item[1], item[0]),
reverse=True)
forest_next = []
for j in range(0, 10):
forest_next.append(forest_old[j])
for j in range(0, 40):
forest_next.append(num_to_list(forest_list[j][0]))
forest_old = forest_next
# res.append(forest_list[0][1])
res[forest_list[0][0]] = forest_list[0][1]
first = sorted(res.items(),
key=lambda item: (item[1], item[0]),
reverse=True)[0]
return first
# print 'predict_sample',predict_sample
# print 'predict_y',predicty
acc = train_knn(train_sample, trainy, predict_sample, predicty, 1)
num_string = num_to_string(num)
forest[num_string] = acc
# forest_area = []
forest_old = init_forest
res = {}
for i in range(0, 5):
forest, forest_new = one_point_hybridization_knn(
forest_old, feature_list, trainx, trainy, predictx, predicty, 1)
forest_list = sorted(forest.items(),
key=lambda item: item[1],
reverse=True)
forest_next = []
for j in range(0, 10):
forest_next.append(forest_old[j])
for j in range(0, 40):
forest_next.append(num_to_list(forest_list[j][0]))
forest_old = forest_next
res[forest_list[0][0]] = forest_list[0][1]
# for j in forest_list:
# print j
# forest_area.append(string_to_numlist(forest_list[0][0]))
ans = sorted(res.items(), key=lambda item: item[1], reverse=True)
for i in ans:
print 'accuracy=', i[1], 'DR=', calculate_DR(i[0])