pivot = L[left] l, r = left+1, right while l <= r: if pivot <= L[l]: L[l], L[r] = L[r], L[l] r = r - 1 else: l = l + 1 L[left], L[l-1] = L[l-1], L[left] return l-1 def SORT2(L, left, right): if left < right: i = Split(L, left, right) SORT2(L, left, i-1) SORT2(L, i+1, right) import util L = util.random_list(20,0,50) print(L) SORT2(L, 0, len(L)-1) print(L) # (average case, assuming a good split) Time complexity: # T(n) = 2n + 2T(n/2) is in Theta(n log n) # Space complexity # S(n) = n + 2S(n/2) is in Theta(n log n) # S(n) = c + 2S(n/2) is in Theta(n)
def training_process(self,
x,
labels,
batch_size,
node_batch,
node_epoch,
eval_interval,
out_c,
USE_GPU,
LR,
save_path,
logfile=None,
dropout_r=0.1,
svm_flag=False,
use_pairwise=True,
use_momentum=False,
criterion='iforest'):
if svm_flag:
x_ori = x.toarray()
else:
x_ori = x
labels_ori = labels
x_level = np.zeros(x_ori.shape[0])
for level in range(1, self.tree_depth + 1):
# form x and labels
keep_pos = np.where(x_level == 0)
x = x_ori[keep_pos]
labels = labels_ori[keep_pos]
group_num = int(x.shape[0] / batch_size) + 1
batch_x = np.array_split(x, group_num)
model = RDP_Model(in_c=x.shape[1],
out_c=out_c,
USE_GPU=USE_GPU,
LR=LR,
logfile=logfile,
dropout_r=dropout_r)
best_auc = best_epoch = 0
for epoch in range(0, node_epoch):
if not is_batch_replace:
random.shuffle(batch_x)
batch_cnt = 0
for batch_i in batch_x:
gap_loss = model.train_model(batch_i,
epoch,
use_pairwise=use_pairwise,
use_momentum=use_momentum)
# print("epoch ", epoch, "loss: ", loss)
batch_cnt += 1
if batch_cnt >= node_batch:
break
else:
# random sampling with replacement
for batch_i in range(node_batch):
random_pos = random_list(0, x.shape[0] - 1, batch_size)
batch_data = x[random_pos]
gap_loss = model.train_model(batch_data,
epoch,
use_pairwise=use_pairwise,
use_momentum=use_momentum)
if epoch % eval_interval == 0:
# print("epoch ", epoch, "gap_loss:", gap_loss, " recon_loss:", recon_loss)
# if logfile:
# logfile.write("epoch " + str(epoch) + " gap_loss: " + str(gap_loss) +
# " recon_loss: " + str(recon_loss) + '\n')
print("tree_id:", self.t_id, "level:", level)
print("keep_pos.size ==", keep_pos[0].size)
if logfile:
logfile.write("tree_id: " + str(self.t_id) +
" level: " + str(level) +
"keep_pos.size == " +
str(keep_pos[0].size) + '\n')
print("epoch ", epoch, "gap_loss:", gap_loss)
if logfile:
logfile.write("epoch " + str(epoch) + " gap_loss: " +
str(gap_loss) + '\n')
model.save_model(save_path + 't' + str(self.t_id) + '_l' +
str(level) + '_latest.h5')
scores = model.eval_model(x, criterion=criterion)
# eval
if is_eval:
try:
roc_auc, ap = aucPerformance(
scores, labels, logfile)
if roc_auc > best_auc:
best_auc = roc_auc
best_epoch = epoch
print("Best AUC-ROC: %.4f" % best_auc)
if logfile:
logfile.write("Best AUC-ROC: %.4f\n" %
best_auc)
print("Best Epoch %d\n" % best_epoch)
if logfile:
logfile.write("Best Epoch %d\n\n" % best_epoch)
except ValueError:
print(
"Only one class present in y_true. ROC AUC score is not defined in that case."
)
if logfile:
logfile.flush()
# filter anomaly elements. the higher the scores are, the more abnormal
ranking_scores = scores
score_ranking_idx = np.argsort(ranking_scores)
filter_num = int(self.filter_ratio * score_ranking_idx.size)
filter_idx = score_ranking_idx[score_ranking_idx.size -
filter_num:]
x_level[keep_pos[0][filter_idx]] = self.tree_depth + 1 - level
self.thresh.append(
ranking_scores[score_ranking_idx[score_ranking_idx.size -
filter_num]])
# epoch for
# level for
# save self.thresh
filename = save_path + 'threshList_t' + str(self.t_id) + '.txt'
list_save(self.thresh, filename, 'w')
"""快速排序 nlogn nlogn 不稳定"""
from util import random_list
def quick(array):
length = len(array)
if length <= 1:
return array
left = cursor = 0
right = length - 1
while cursor <= right:
if array[cursor] == array[left]:
cursor += 1
elif array[cursor] > array[left]:
array[cursor], array[right] = array[right], array[cursor]
right -= 1
else:
array[cursor], array[left] = array[left], array[cursor]
left += 1
cursor += 1
return quick(array[:left]) + array[left:cursor] + quick(array[cursor:])
if __name__ == '__main__':
l = random_list()
print(quick(l))
def main():
shutil.rmtree(save_path)
os.mkdir(save_path)
if data_path == '20newsgroups':
newsgroups_data = fetch_20newsgroups_vectorized(subset='all')
x = newsgroups_data.data.toarray()
labels = newsgroups_data.target
n_clusters = 20
elif data_path == 'r8':
df = pd.read_csv('data/r8-all-stemmed.txt')
labels_idx = [
'acq', 'crude', 'earn', 'grain', 'interest', 'money-fx', 'ship',
'trade'
]
labels = df['class'].values
labels = [labels_idx.index(ele) for ele in labels]
labels = np.asarray(labels, dtype=np.int64)
x_df = df.drop(['class'], axis=1)
corpus = np.squeeze(x_df.values)
is_TfidfVectorizer = True
if is_TfidfVectorizer:
vectorizer = TfidfVectorizer()
x = vectorizer.fit_transform(corpus).toarray()
else:
vectorizer = CountVectorizer()
x = vectorizer.fit_transform(corpus).toarray()
n_clusters = 8
elif data_path == 'olivetti_faces':
data = fetch_olivetti_faces()
x = data.data
labels = data.target
n_clusters = 40
elif data_path == 'rcv1':
# data = fetch_rcv1()
# x = data.data.toarray()
# labels = data.target.toarray()
# n_clusters = 103
x, labels = get_data_from_svmlight_file('data/rcv1_train.binary')
x = x.toarray()
n_clusters = 2
elif data_path == 'sector':
x, labels = get_data_from_svmlight_file('data/sector.scale.all')
x = x.toarray()
n_clusters = 105
else:
raise Exception("Invalid data path!")
print("Data shape: (%d, %d)" % x.shape)
data_size = labels.size
# build model
model = RDP_Model(in_c=x.shape[1],
out_c=out_c,
USE_GPU=USE_GPU,
LR=LR,
logfile=logfile,
dropout_r=dropout_r)
best_nmi = best_epoch = 0
loss = 0
for epoch in range(0, total_epoch):
# random sampling with replacement
for batch_i in range(epoch_batch):
random_pos = random_list(0, data_size - 1, batch_size)
batch_data = x[random_pos]
loss = model.train_model(batch_data, epoch)
if epoch % eval_interval == 0:
print("epoch ", epoch, "loss:", loss)
if logfile:
logfile.write("epoch " + str(epoch) + " loss: " + str(loss) +
'\n')
model.save_model(save_path + 'model_latest.h5')
# eval
if is_eval:
gap_dims = model.eval_model(x)
kmeans_results = KMeans(n_clusters=n_clusters,
random_state=0).fit(gap_dims)
# Match each learned cluster label with the true labels found in them
y_pred = kmeans_results.labels_
labels_pred = np.zeros_like(y_pred)
for i in range(n_clusters):
mask = (y_pred == i)
labels_pred[mask] = mode(labels[mask])[0]
# evaluations
nmi_scores = normalized_mutual_info_score(labels, labels_pred)
print("nmi_scores:", nmi_scores)
if logfile:
logfile.write("nmi_scores: %.4f\n" % nmi_scores)
fscores = f1_score(labels, labels_pred, average='macro')
print("fscores_macro:", fscores)
if logfile:
logfile.write("fscores_macro: %.4f\n" % fscores)
fscores = f1_score(labels, labels_pred, average='micro')
print("fscores_micro:", fscores)
if logfile:
logfile.write("fscores_micro: %.4f\n" % fscores)
fscores = f1_score(labels, labels_pred, average='weighted')
print("fscores_weighted:", fscores)
if logfile:
logfile.write("fscores_weighted: %.4f\n" % fscores)
RI_scores = adjusted_rand_score(labels, labels_pred)
print("RI_scores:", RI_scores)
if logfile:
logfile.write("RI_scores: %.4f\n" % RI_scores)
if best_nmi < nmi_scores:
best_nmi = nmi_scores
best_epoch = epoch
print("Best NMI: %.4f" % best_nmi)
print("Best Epoch %d\n" % best_epoch)
if logfile:
logfile.write("Best NMI: %.4f\n" % best_nmi)
logfile.write("Best Epoch %d\n\n" % best_epoch)
logfile.flush()
return pair_up(L, new_L)
def majority_pair_up(L):
return pair_up(L, L)
# print(votes)
# a = majority_pair_up(votes)
# b = brute_force(votes)
# print(a,b)
import util
import time
for m in range(100):
votes = util.random_list(100, 0, 2)
start1 = time.time()
a = brute_force(votes)
end1 = time.time()
start2 = time.time()
b = majority_pair_up(votes)
end2 = time.time()
#print(votes)
print("brute-force:ans=%s,time=%s pair-up:ans=%s,time=%s" %
(a, end1 - start1, b, end2 - start2))
if a != b:
print('Answers dont match!')
))
print("Init tic time.")
tic_time()
# training process
for i in range(args.forest_Tnum):
# random sampling with replacement
if random_size < 0:
warnings.warn(f'Using full size {args.data_size} by default...')
if random_size > data_size:
raise ValueError(
f'`random_size` {args.random_size} exceeds data size {args.data_size}!'
)
random_pos = random_list(0, data_size - 1, random_size)
# random sampling without replacement
# random_pos = random.sample(range(0, data_size), random_size)
# to form x and labels
x = x_ori[random_pos]
if svm_flag:
labels = labels_ori[random_pos]
else:
labels = labels_ori[random_pos].values
print("tree id:", i, "tic time.")
tic_time()
forest[i].training_process(x=x,
labels=labels,
def main():
global random_size
shutil.rmtree(save_path)
os.mkdir(save_path)
svm_flag = False
if 'svm' in data_path:
svm_flag = True
from util import get_data_from_svmlight_file
x_ori, labels_ori = get_data_from_svmlight_file(data_path)
random_size = 1024
else:
x_ori, labels_ori = dataLoading(data_path, logfile)
data_size = labels_ori.size
# build forest
forest = []
for i in range(forest_Tnum):
forest.append(
RDPTree(
t_id=i + 1,
tree_depth=tree_depth,
filter_ratio=filter_ratio,
))
print("Init tic time.")
tic_time()
# training process
for i in range(forest_Tnum):
# random sampling with replacement
random_pos = random_list(0, data_size - 1, random_size)
# random sampling without replacement
# random_pos = random.sample(range(0, data_size), random_size)
# to form x and labels
x = x_ori[random_pos]
if svm_flag:
labels = labels_ori[random_pos]
else:
labels = labels_ori[random_pos].values
print("tree id:", i, "tic time.")
tic_time()
forest[i].training_process(
x=x,
labels=labels,
batch_size=batch_size,
node_batch=node_batch,
node_epoch=node_epoch,
eval_interval=eval_interval,
out_c=out_c,
USE_GPU=USE_GPU,
LR=LR,
save_path=save_path,
logfile=logfile,
dropout_r=dropout_r,
svm_flag=svm_flag,
)
print("tree id:", i, "tic time end.")
tic_time()
ms_left = maxsum(Left)
# compute max_sum on Right half
# T(n/2)
ms_right = maxsum(Right)
# third case: ms overlaping Left and Right
#c*n
i = Len(L) // 2
ms_overlap_ending_at_im1 = L[i - 1]
cur_sum = L[i - 1]
for j in range(i - 2, -1, -1):
cur_sum = cur_sum + L[j]
if cur_sum > ms_overlap_ending_at_im1:
ms_overlap_ending_at_im1 = cur_sum
ms_overlap_ending_at_i = L[i]
cur_sum = l[i]
for j in range(i + 1, len(L)):
cur_sum = cur_sum + L[j]
if cur_sum > ms_overlap_ending_at_i:
ms_overlap_ending_at_i = cur_sum
ms_overlap = ms_overlap_ending_at_im1 + ms_overlap_ending_at_i
m = max(ms_left, ms_right, ms_overlap)
return m
profits = util.random_list(8, -5, 6)
print(profits, maxsum(profits))
if j < N and theoretical_w < cap:
theoretical_best += ((cap - theoretical_w) / weight[j]) * calories[j]
if theoretical_best < best:
return False
return True
# W = [6,3,4,2]
# C = [3000,1400,1600,900]
import util
import time
best, best_weight = None, 0
for N in range(15, 25):
W = util.random_list(N, 10, 20)
C = util.random_list(N, 2000, 3000)
Cap = W[0] * len(W) * 0.4
start_time = time.time()
Config = [None] * N
best, best_weight = None, 0
Knapsack(Cap, W, C, -1, 0)
end_time = time.time()
print(N, end_time - start_time, best, Cap, total(best, W), total(best, C))
start_time = time.time()
Config = [None] * N
best, best_weight = None, 0
Knapsack2(Cap, W, C, -1, 0, 0)
end_time = time.time()
if i not in Table:
if i == 0:
Table[i] = 1
m = 1
for j in range(i):
if (L[j] < L[i]) and (m < LIS(L, j) + 1):
m = LIS(L, j) + 1
Table[i] = m
return Table[i]
def longest_increasing_subseq(L):
return max([LIS(L, i) for i in range(len(L))])
# T[i] == Table{i} == LIS(L,i)
def iLIS(L):
T = [1] * len(L)
for i in range(len(L)):
for j in range(i):
if (L[j] < L[i]) and (T[i] < T[j] + 1):
T[i] = T[j] + 1
return max(T)
import util
for n in range(20, 1000, 10):
items = util.random_list(n, 0, 100)
Table = {}
print(len(items), longest_increasing_subseq(items), iLIS(items))
'''
This program creates a real network wherein every node is
'''
table = {}
import util
for n in range(0, 100, 1):
A = util.random_list(n, 0, 99)
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt2
import random
def real_network(A):
G = nx.Graph()
list_of_nodes = []
coordinates = []
for i in range(100):
x = random.choice(range(100))
y = random.choice(range(100))
while (x, y) in coordinates:
x = random.choice(range(100))
y = random.choice(range(100))
coordinates = coordinates + [(x, y)]
G.add_node(i, posxy=(coordinates[i][0], coordinates[i][1]))
list_of_nodes = list_of_nodes + [i]
G.add_cycle(list_of_nodes)
#B = assign_neighbors()
def majority(V, L, R):
if L == R:
return V[L]
mid = (L + R) // 2
m1 = majority(V, L, mid)
m2 = majority(V, mid + 1, R)
if m1 == m2:
return m1
count1, count2 = 0, 0
for i in range(L, R + 1):
if V[i] == m1:
count1 += 1
if V[i] == m2:
count2 += 1
if count1 > (R - L + 1) / 2:
return m1
if count2 > (R - L + 1) / 2:
return m2
return None
import util
import time
for n in range(5000, 100000, 1000):
votes = util.random_list(n, 0, 2)
start = time.time()
# brute_force(votes)
majority(votes, 0, len(votes) - 1)
end = time.time()
print(n, end - start)