def forward(self, embeds, cluster_temp, num_iter=10):
mu_init, _ = cluster(embeds, self.K, 1, num_iter, cluster_temp = torch.tensor(cluster_temp), init = self.init)
#self.init = mu_init.clone().detach()
mu, r = cluster(embeds, self.K, 1, 1, cluster_temp = torch.tensor(cluster_temp), init = mu_init.clone().detach())
return mu, r
def DBSCAN(X: np.ndarray, r: float, minPts: int):
pointnum = X.shape[0]
v = visitlist(pointnum)
clustersSet = list()
noise = cluster(-1)
tree = KDTree(X)
k = 0
while v.unvisitednum > 0:
randid = random.choice(v.unvisitedlist)
v.visit(randid)
N = tree.query_ball_point(X[randid], r)
if len(N) < minPts:
noise.points.append(randid)
else:
clus = cluster(k)
clus.points.append(randid)
N.remove(randid)
while len(N) > 0:
p = N.pop()
if p in v.unvisitedlist:
v.visit(p)
clus.points.append(p)
pN = tree.query_ball_point(X[p], r)
if len(pN) >= minPts:
pN.remove(p)
N = N + pN
clustersSet.append(clus)
clustersSet.append(noise)
return clustersSet
def main():
from argparse import ArgumentParser
from time import time
parser = ArgumentParser()
parser.add_argument('--file-list', type=str, default='/fastdata/finder/streetview_train.txt', help='path to the streetview training file')
parser.add_argument('-n', '--n-clusters', type=int, default=100, help='number of cluster')
parser.add_argument('--max-files', type=int, help='maximum number of files to cluster')
parser.add_argument('output', type=str, help='output file (e.g. clusters.npy)')
args = parser.parse_args()
cluster(args.file_list, args.output, args.n_clusters, args.max_files)
def clean_data(datalist):
x = datalist.values[::, 0:14]
#y = datalist.values[::, 14:]
df = pd.DataFrame(x)
df = df.replace("?", float('nan'))
x = df.fillna(0) # замена ? на 0
replace_mas1 = ["notpresent", "yes", "good"]
replace_mas0 = ["present", "no", "poor"]
for text in replace_mas1: #замена notpresent", "yes", "good" на 1
x = x.replace(text, 1)
for text in replace_mas0:
x = x.replace(text, 0)
cluster(x)
def forward(self, x, adj, num_iter=1):
embeds = self.GCN(x, adj)
mu_init, _, dist = cluster(embeds,
self.K,
num_iter,
cluster_temp=self.cluster_temp,
init=self.init)
mu, r, dist_2 = cluster(embeds,
self.K,
1,
cluster_temp=self.cluster_temp,
init=mu_init.detach().clone())
return r, dist
def forward(self, x):
mu_init, _, _ = cluster(x,
self.K,
1,
self.num_iter,
cluster_temp=self.cluster_temp,
init=self.init)
mu, r, dist = cluster(x,
self.K,
1,
1,
cluster_temp=self.cluster_temp,
init=mu_init.detach().clone())
return r
def plot(self, data, views=2, show=False):
"""plot clustering"""
# get plotting tools
try:
from spikeplot import plt, cluster
except ImportError:
return None
# init
views = min(views, int(data.shape[1] / 2))
fig = plt.figure()
fig.suptitle('clustering [%s]' % self.clus_type)
ax = [fig.add_subplot(2, views, v + 1) for v in xrange(views)]
axg = fig.add_subplot(212)
ncmp = int(self.labels.max() + 1)
cdata = dict(zip(xrange(ncmp),
[data[self.labels == c] for c in xrange(ncmp)]))
# plot clustering
for v in xrange(views):
cluster(
cdata,
data_dim=(2 * v, 2 * v + 1),
plot_handle=ax[v],
plot_mean=sp.sqrt(self.sigma_factor),
xlabel='PC %d' % int(2 * v),
ylabel='PC %d' % int(2 * v + 1),
show=False)
# plot gof
axg.plot(self._gof, ls='steps')
for i in xrange(1, len(self.crange)):
axg.axvline(i * self.repeats - 0.5, c='y', ls='--')
axg.axvspan(self._winner - 0.5, self._winner + 0.5, fc='gray',
alpha=0.2)
labels = []
for k in self.crange:
labels += ['%d' % k]
labels += ['.'] * (self.repeats - 1)
axg.set_xticks(sp.arange(len(labels)))
axg.set_xticklabels(labels)
axg.set_xlabel('cluster count and repeats')
axg.set_ylabel(str(self.gof_type).upper())
axg.set_xlim(-1, len(labels))
# show?
if show is True:
plt.show()
return True
def __init__(self, page_tree, k_max_depth=2, k_decay=0.5,
c_eps=1.2, c_d1=1.0, c_d2=1.0, separate_descendants=True):
"""Perform all extraction operations in sequence.
Parameters
----------
k_max_depth : int
Parameter to kernel computation
k_decay : float
Parameter to kernel computation
c_eps : float
Parameter to clustering
c_d1 : float
Parameter to clustering
c_d2 : float
Parameter to clustering
separate_descendants : bool
Parameter to clustering
"""
self.page_tree = page_tree
self.kernel = _ker.kernel(page_tree, max_depth=k_max_depth, decay=k_decay)
self.labels = cluster(
page_tree, self.kernel, eps=c_eps, d1=c_d1, d2=c_d2,
separate_descendants=separate_descendants)
self.items = extract_items(page_tree, self.labels)
self.tables = [ItemTable(items, extract_item_table(page_tree, items, self.labels))
for items in self.items]
self.table_fragments = [
ItemTable([page_tree.fragment_index(np.array(root)) for root in item],
page_tree.fragment_index(fields))
for item, fields in self.tables]
def preprocess_y(self,settings):
'''
Returns preprocessed observables
'''
if self.preprocess_labels == None:
labels = self.y()[:,settings.label_index]
else:
labels = self.preprocess_labels
Y = []
# Add a bias to the data by clustering the baseline observables
# try up to 6 clusters
for k in range(1,9):
# cluster the centroids of each label
#x = self.y()[:,[settings.baseline_index,4,7,9]].astype(np.float)
x = self.y()[:,settings.baseline_index].astype(np.float)
y = self.y()[:,settings.observable_index].astype(np.float)
classification = cluster(k,x, labels)
self.classification[k-1] = classification.copy()
y = self.y()[:,settings.observable_index].astype(np.float)
# preprocess the observables
y[classification == k-1] = y[classification == k-1] \
- np.median(y[classification == k-1]) \
+ np.median(x[classification == k-1])
Y.append(y.copy())
return Y
def preprocess_y(self, settings):
'''
Returns preprocessed observables
'''
if self.preprocess_labels == None:
labels = self.y()[:, settings.label_index]
else:
labels = self.preprocess_labels
Y = []
# Add a bias to the data by clustering the baseline observables
# try up to 6 clusters
for k in range(1, 9):
# cluster the centroids of each label
#x = self.y()[:,[settings.baseline_index,4,7,9]].astype(np.float)
x = self.y()[:, settings.baseline_index].astype(np.float)
y = self.y()[:, settings.observable_index].astype(np.float)
classification = cluster(k, x, labels)
self.classification[k - 1] = classification.copy()
y = self.y()[:, settings.observable_index].astype(np.float)
# preprocess the observables
y[classification == k-1] = y[classification == k-1] \
- np.median(y[classification == k-1]) \
+ np.median(x[classification == k-1])
Y.append(y.copy())
return Y
def main():
n_clusters = int(sys.argv[1])
print suffix + ' ' + str(n_clusters)
vocabpath = basepath+'data/vocab'+suffix+'.txt'
vocab =[word.strip() for word in open(vocabpath).readlines()]
vocab = vocab[:voc_size]
matpath = basepath + 'data/F-rels'+str(voc_size)+suffix+'.mat'
datapath = basepath+'data/all-BNC-EN.txt'
print str(n_clusters)
if load<1:
fmatrix = matbuild(datapath,vocab)
scipy.io.savemat(matpath,{'fmatrix':fmatrix})
else:
a = scipy.io.loadmat(matpath)
fmatrix = a['fmatrix']
for i in range(numpy.size(fmatrix,0)):
fmatrix[i,:] = fmatrix[i,:]/sum(fmatrix[i,:]+eps)
if load<2:
simmatrix = simbuild(fmatrix)
scipy.io.savemat(basepath+'data/F-sims'+str(voc_size)+suffix+'.txt.mat', {'simmatrix': simmatrix})
else:
a = scipy.io.loadmat(basepath+'data/F-sims'+str(voc_size)+suffix+'.txt.mat')
simmatrix = a['simmatrix']
outputfile = basepath+'data/optlabels-'+str(n_clusters)+'-'+str(voc_size)+suffix+'.mat'
if load<3:
(labels,score) = cluster(simmatrix, n_clusters, outputfile)
else:
A = scipy.io.loadmat(outputfile)
labels = A['labels']
outpath = basepath + 'data/output'+str(n_clusters)+'-'+str(voc_size)+suffix+'.txt'
outwrite(vocab,labels, outpath)
def step(dataset, nn):
# 1. Do a forward pass
dataset['y'] = nn.fwd(dataset['x'])
# 2. Perform clustering
clusters = cluster(dataset)
# 3. Calculate the distances between the distributions
distances = calc_distances(clusters)
# 4. Displace the center of each cluster using force directed graph
for c in clusters:
c.calculate_displacement(clusters)
# 5. Set the target position of each item in the cluster to the mean of the cluster
x, y = extract_training_data(clusters)
# 6. Train the CNN\
for j in range(100):
nn.train(x, y)
# 7. Plot the clusters
plot_clusters(clusters)
return clusters
def cluster_all_targets(language, N=8):
logging.info('clustering {}'.format(lang))
data = load(language)
results = []
for w in tqdm.tqdm(data.words):
results.append((w, *cluster(data, w)))
return results
def do_segmentation(C, M, config, in_bound_idxs=None):
embedding = embed_beats(C, M, config)
Cnorm = np.cumsum(embedding ** 2, axis=1) ** 0.5
if config["hier"]:
est_idxs = []
est_labels = []
for k in range(1, config["num_layers"] + 1):
est_idx, est_label = cluster(embedding, Cnorm, k)
est_idxs.append(est_idx)
est_labels.append(np.asarray(est_label, dtype=np.int))
else:
est_idxs, est_labels = cluster(embedding, Cnorm, config["scluster_k"], in_bound_idxs)
est_labels = np.asarray(est_labels, dtype=np.int)
return est_idxs, est_labels, Cnorm
def do_segmentation(C, M, config, in_bound_idxs=None):
embedding = embed_beats(C, M, config)
Cnorm = np.cumsum(embedding**2, axis=1)**0.5
if config["hier"]:
est_idxs = []
est_labels = []
for k in range(1, config["num_layers"] + 1):
est_idx, est_label = cluster(embedding, Cnorm, k)
est_idxs.append(est_idx)
est_labels.append(np.asarray(est_label, dtype=np.int))
else:
est_idxs, est_labels = cluster(embedding, Cnorm, config["scluster_k"])
est_labels = np.asarray(est_labels, dtype=np.int)
return est_idxs, est_labels, Cnorm
def labeladjust(lang, n, lofeatures, feature, df):
table = cluster(lang, n, lofeatures, df)
d = [0] * n
for i in range(0, n):
d[i] = (i, min(table.loc[table['label'] == i, feature]))
order = sorted(d, key=lambda x: x[1])
dic = {}
for i in range(0, n):
dic[order[i][0]] = i
table['label'] = table['label'].replace(dic)
return (table)
def getbreaks(lang, n, lofeatures, feature, df):
table = cluster(lang, n, lofeatures, df)
minmax = [0] * n
for i in range(0, n):
minmax[i] = (min(table.loc[table['label'] == i, feature]),
max(table.loc[table['label'] == i, feature]))
breaks = [0] * (n - 1)
ordered = sorted(minmax)
for i in range(0, n - 1):
breaks[i] = numpy.mean([ordered[i][1], ordered[(i + 1)][0]])
return (breaks)
def laplacian_segmentation(matrix): #pass in an affinity matrix
matrix = np.matrix(matrix, dtype=int)
matrix = np.maximum(matrix, matrix.transpose())
embedding = decompose(matrix)
Cnorm = np.cumsum(embedding**2, axis=1)**0.5
segmentations = []
for k in range(1, MAX_TYPES):
segmentations.append(cluster(embedding, Cnorm, k))
return reindex(segmentations)
def main():
# get command line aguments
args = sys.argv
# m - # of sets of sweetwords
# n - # of sweetwords in each set
m = int(args[1])
n = int(args[2])
input_file = args[3]
# store input passwords
password_list = []
sweetwords = read_password_file(input_file)
filename = "password_choice.txt"
# Identify password for each set of sweetwords
for row in range(0,m):
sweetwords_list = []
sep = ','
sweetwords_list.append(sweetwords[row].split(sep,n))
roots = cluster(sweetwords_list, n)
root_list = []
for key,val in roots.items():
# print(key,val)
length = len(val)
root_list.append([key,length])
# root_list.append(key)
root_list=sorted(root_list,key=lambda x: x[1])
root_list_processed =[]
# print(root_list)
if len(root_list) > 5:
root_list_processed = [root_list[0][0],root_list[1][0],root_list[-1][0],root_list[-2][0]]
else:
for item in root_list:
root_list_processed.append(item[0])
print(root_list_processed)
shuffle(root_list_processed)
chosen = choose_pass(root_list_processed)
print("Final Answer", chosen)
# password_choice = "".join(str(x) for x in passSelect(roots)[0])
# print("Password Guess #",row, ": ", password_choice)
password_choice_ind = sweetwords_list[0].index(chosen)
password_list.append(str(password_choice_ind))
write_passwordchoice_file(filename, password_list)
def _run_training_epoch(self, **kwargs):
"""
"""
# predict clusters for each data point
predictions = self._pred_model.predict(self._pred_ds, steps=self._pred_steps)
# if test data was included- also predict outputs for those
if self._test:
test_preds = self._pred_model.predict(self._test_ds, steps=self._test_steps)
else:
test_preds = None
# run k-means
y_e, clusters, test_labels = cluster(predictions,
self.config["pca_dim"],
self.config["k"], init='k-means++',
kmeans_max_iter=self.config["kmeans_max_iter"],
kmeans_batch_size=self.config["kmeans_batch_size"],
testvecs=test_preds)
self._old_test_labels = self._test_labels
self._test_labels = test_labels
# record the normalized mutual information between these labels and previous
if self._old_cluster_assignments is not None:
nmi = normalized_mutual_info_score(y_e, self._old_cluster_assignments,
average_method="arithmetic")
self._record_scalars(normalized_mutual_information=nmi)
self._old_cluster_assignments = y_e
# reset the weights of the output layer
new_weights = [self._initializer(x.shape) for x in
self._output_layer.get_weights()]
self._output_layer.set_weights(new_weights)
# do some training
train_ds, num_steps = stratified_training_dataset(self.trainingdata, y_e,
imshape=self.input_config["imshape"],
num_channels=self.input_config["num_channels"],
num_parallel_calls=self.input_config["num_parallel_calls"],
batch_size=self.input_config["batch_size"],
mult=self.config["mult"],
augment=self.augment_config,
sobel=self.input_config["sobel"],
single_channel=self.input_config["single_channel"])
for x, y in train_ds:
loss = self._training_step(x, y, self._models["full"],
self._optimizer)
self._record_scalars(training_crossentropy=loss)
self.step += 1
def forward(self, x, adj, num_iter=1):
# print("INSIDE FORWARD")
embeds = self.GIN(x, adj)
# print("EMBEDS\n\n",embeds)
mu_init, _, _ = cluster(embeds,
self.K,
1,
num_iter,
cluster_temp=self.cluster_temp,
init=self.init)
mu, r, dist = cluster(embeds,
self.K,
1,
1,
cluster_temp=self.cluster_temp,
init=mu_init.detach().clone())
# print("PRINTING")
# print("mu\n\n",mu)
# print("r\n\n",r)
# print("embeds\n\n",embeds)
# print("dist\n\n",dist)
return mu, r, embeds, dist
def main():
from argparse import ArgumentParser
from time import time
parser = ArgumentParser()
parser.add_argument('--file-list',
type=str,
default='/fastdata/finder/streetview_train.txt',
help='path to the streetview training file')
parser.add_argument('-n',
'--n-clusters',
type=int,
default=100,
help='number of cluster')
parser.add_argument('--max-files',
type=int,
help='maximum number of files to cluster')
parser.add_argument('output',
type=str,
help='output file (e.g. clusters.npy)')
args = parser.parse_args()
cluster(args.file_list, args.output, args.n_clusters, args.max_files)
def recluster(df, cl, clusters, n_clusters):
lbls = cl.labels_
mask = np.array([False for i in range(len(lbls))])
for c in clusters:
mask |= lbls==c
subpipe, results = data_pipeline(df[mask])
##use cosine similarity! NLTK clustering implementation
#KMeans cluster object as carrier for consistency
subcl = cluster(results, n_clusters)
kclusterer = KMeansClusterer(n_clusters, distance=nltk.cluster.util.cosine_distance, repeats=50)
assigned_clusters = kclusterer.cluster(results, assign_clusters=True)
#assign new cluster labels and cluster centroids
subcl.labels_ = np.array(assigned_clusters)
subcl.cluster_centers_ = np.array(kclusterer.means())
return subpipe, subcl, results, df[mask]
def segment_file(filename):
print('Loading {}'.format(filename))
y, sr = librosa.load(filename)
print('Extracting features...'.format(filename))
Csync, Msync, beat_times = make_beat_sync_features(y=y, sr=sr)
print('Constructing embedding...'.format(filename))
embedding = embed_beats(Csync, Msync)
Cnorm = np.cumsum(embedding**2, axis=1)**0.5
print('Clustering...'.format(filename))
segmentations = []
for k in range(1, MAX_TYPES):
print('\tk={}'.format(k))
segmentations.append(cluster(embedding, Cnorm, k, beat_times))
print('done.')
return reindex(segmentations)
def main():
from argparse import ArgumentParser
from time import time
parser = ArgumentParser()
parser.add_argument('-bs', '--batch-size', type=int, default=32, help='batch size')
parser.add_argument('-lr', '--learning-rate', type=float, default=1e-4, help='learning rate')
parser.add_argument('-nit','--number-of-iterations', type=int, default=1000000, help='number of iterations')
parser.add_argument('--log_device_placement', action='store_true')
parser.add_argument('--file-list', type=str, default='/fastdata/finder/streetview_train.txt', help='path to the streetview training file')
parser.add_argument('--file-base-dir', type=str, default='/fastdata/finder/streetview/', help='directory of the training images')
parser.add_argument('--clusters', type=str, default=None, help='cluster file (computed with cluster.py)')
parser.add_argument('-n', '--n-clusters', type=int, default=None, help='Number of clusters to be used (if \'--clusters\' is not specified)')
parser.add_argument('--initial-weights', type=str, help='VGG weights in hdf5 format')
parser.add_argument('--num-gpus', type=int, default=1, help='How many GPUs should we use? (-1 for all available GPUs)')
parser.add_argument('train_dir', help='output directory for the model and log files')
args = parser.parse_args()
try: os.makedirs(args.train_dir)
except: pass
cluster_file = args.train_dir+'clusters.npy'
if args.clusters:
import shutil
shutil.copyfile(args.clusters, cluster_file)
if args.n_clusters is not None:
print( 'Warning clusters and n_clusters both specified! Ignoring n_clusters.' )
elif not os.path.exists(cluster_file):
print( 'No cluster file provided, clustering (this might take a while)' )
from cluster import cluster
cluster(args.file_list, cluster_file, args.n_clusters)
args.n_clusters = getNCluster(cluster_file)
files = [os.path.join(args.file_base_dir,l.strip()) for l in open(args.file_list,'r')]
# Fire up tensorflow
import tensorflow as tf
# Detect the number of GPUs
if args.num_gpus < 0:
args.num_gpus = getNumGPU()
print( 'Found %d GPUs. Using all of them'%args.num_gpus )
## Setup the graph ##
# Get the data
gpu_batch_size = (args.batch_size-1) // args.num_gpus + 1
total_batch_size = gpu_batch_size * args.num_gpus
data,gt = glocData(files, cluster_file, batch_size=total_batch_size)
# Setup the solver
solver = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
# Setup the network and loss
if args.num_gpus != 1:
# Multi gpu VGG
split_data = tf.split(0, args.num_gpus, data)
split_gt = tf.split(0, args.num_gpus, gt)
all_loss, all_grads = [], []
vars = None
for i,(d,g) in enumerate(zip(split_data,split_gt)):
with tf.device('/gpu:%d'%i) as dev:
# Define VGG
vgg = vgg16(d, n_out=args.n_clusters)
# Share the parameters
tf.get_variable_scope().reuse_variables()
# Compute the loss and gradients (per device)
avg_vgg = tf.reduce_mean(tf.reduce_mean(vgg,1),1)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(avg_vgg, g))
grads_and_vars = solver.compute_gradients(loss)
# Collect all outputs ...
all_grads.append( [g for g,v in grads_and_vars] )
all_loss.append( loss )
if vars is None:
vars = [v for g,v in grads_and_vars]
else:
assert np.all([v==vv for vv,(g,v) in zip(vars,grads_and_vars)]), "Variables differ between GPUs"
# .. and concat or sum them up
grads = [tf.add_n(g)/len(g) for g in zip(*all_grads)]
grads_and_vars = list(zip(grads, vars))
loss = tf.add_n(all_loss) / len(all_loss)
else:
vgg = vgg16(data, n_out=args.n_clusters)
avg_vgg = tf.reduce_mean(tf.reduce_mean(vgg,1),1)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(avg_vgg, gt))
grads_and_vars = solver.compute_gradients(loss)
solver_step = solver.apply_gradients(grads_and_vars)
# Create some summaries
loss_avg = tf.train.ExponentialMovingAverage(0.9, name='avg')
tf.scalar_summary('loss', loss)
loss_avg_op = loss_avg.apply([loss])
tf.scalar_summary('loss(avg)', loss_avg.average(loss))
with tf.control_dependencies([loss_avg_op]):
loss = tf.identity(loss)
for grad,var in grads_and_vars:
tf.scalar_summary(var.op.name+'/norm', tf.reduce_mean(var*var))
tf.scalar_summary(var.op.name+'/gradient_norm', tf.reduce_mean(grad*grad))
tf.scalar_summary(var.op.name+'/gradient_ratio', tf.reduce_mean(grad*grad) / tf.reduce_mean(var*var))
summary_op = tf.merge_all_summaries()
# Initialize ops
saver = tf.train.Saver(tf.all_variables())
init_op = tf.initialize_all_variables()
if args.initial_weights is not None:
from slim import load
load_op = load.loadH5(args.initial_weights)
else:
load_op = tf.no_op()
tf.get_default_graph().finalize()
with tf.Session(config=tf.ConfigProto(log_device_placement=args.log_device_placement, gpu_options=tf.GPUOptions(per_process_gpu_memory_fraction=0.6))) as sess:
# Initialize stuff
summary_writer = tf.train.SummaryWriter(args.train_dir, sess.graph)
global coord
coord = tf.train.Coordinator()
import signal
def stop(*args):
global coord
print("Training stopped")
coord.request_stop()
old_sigint = signal.signal(signal.SIGINT, stop)
threads=tf.train.start_queue_runners(sess=sess, coord=coord)
sess.run(init_op)
sess.run(load_op)
# Train
loss_values = []
for it in range(args.number_of_iterations):
t0 = time()
_, loss_value = sess.run([solver_step, loss])
t1 = time()
loss_values.append( loss_value )
if it % 10 == 0:
print('%8d, loss = %0.2f [%0.2f] (%0.1f im/sec)'%(it, loss_value, np.mean(loss_values), args.batch_size / (t1-t0)))
loss_values = loss_values[-20:]
if it % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, it)
if it % 1000 == 0:
saver.save(sess, os.path.join(args.train_dir, 'snap.ckpt'), global_step=it)
if coord.should_stop():
break
saver.save(sess, os.path.join(args.train_dir, 'final.ckpt'))
coord.join(threads)
signal.signal(signal.SIGINT, old_sigint)
def main():
from argparse import ArgumentParser
from time import time
parser = ArgumentParser()
parser.add_argument('-bs',
'--batch-size',
type=int,
default=32,
help='batch size')
parser.add_argument('-lr',
'--learning-rate',
type=float,
default=1e-4,
help='learning rate')
parser.add_argument('-nit',
'--number-of-iterations',
type=int,
default=1000000,
help='number of iterations')
parser.add_argument('--log_device_placement', action='store_true')
parser.add_argument('--file-list',
type=str,
default='/fastdata/finder/streetview_train.txt',
help='path to the streetview training file')
parser.add_argument('--file-base-dir',
type=str,
default='/fastdata/finder/streetview/',
help='directory of the training images')
parser.add_argument('--clusters',
type=str,
default=None,
help='cluster file (computed with cluster.py)')
parser.add_argument(
'-n',
'--n-clusters',
type=int,
default=None,
help=
'Number of clusters to be used (if \'--clusters\' is not specified)')
parser.add_argument('--initial-weights',
type=str,
help='VGG weights in hdf5 format')
parser.add_argument(
'--num-gpus',
type=int,
default=1,
help='How many GPUs should we use? (-1 for all available GPUs)')
parser.add_argument('train_dir',
help='output directory for the model and log files')
args = parser.parse_args()
try:
os.makedirs(args.train_dir)
except:
pass
cluster_file = args.train_dir + 'clusters.npy'
if args.clusters:
import shutil
shutil.copyfile(args.clusters, cluster_file)
if args.n_clusters is not None:
print(
'Warning clusters and n_clusters both specified! Ignoring n_clusters.'
)
elif not os.path.exists(cluster_file):
print('No cluster file provided, clustering (this might take a while)')
from cluster import cluster
cluster(args.file_list, cluster_file, args.n_clusters)
args.n_clusters = getNCluster(cluster_file)
files = [
os.path.join(args.file_base_dir, l.strip())
for l in open(args.file_list, 'r')
]
# Fire up tensorflow
import tensorflow as tf
# Detect the number of GPUs
if args.num_gpus < 0:
args.num_gpus = getNumGPU()
print('Found %d GPUs. Using all of them' % args.num_gpus)
## Setup the graph ##
# Get the data
gpu_batch_size = (args.batch_size - 1) // args.num_gpus + 1
total_batch_size = gpu_batch_size * args.num_gpus
data, gt = glocData(files, cluster_file, batch_size=total_batch_size)
# Setup the solver
solver = tf.train.AdamOptimizer(learning_rate=args.learning_rate)
# Setup the network and loss
if args.num_gpus != 1:
# Multi gpu VGG
split_data = tf.split(0, args.num_gpus, data)
split_gt = tf.split(0, args.num_gpus, gt)
all_loss, all_grads = [], []
vars = None
for i, (d, g) in enumerate(zip(split_data, split_gt)):
with tf.device('/gpu:%d' % i) as dev:
# Define VGG
vgg = vgg16(d, n_out=args.n_clusters)
# Share the parameters
tf.get_variable_scope().reuse_variables()
# Compute the loss and gradients (per device)
avg_vgg = tf.reduce_mean(tf.reduce_mean(vgg, 1), 1)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(avg_vgg, g))
grads_and_vars = solver.compute_gradients(loss)
# Collect all outputs ...
all_grads.append([g for g, v in grads_and_vars])
all_loss.append(loss)
if vars is None:
vars = [v for g, v in grads_and_vars]
else:
assert np.all([
v == vv for vv, (g, v) in zip(vars, grads_and_vars)
]), "Variables differ between GPUs"
# .. and concat or sum them up
grads = [tf.add_n(g) / len(g) for g in zip(*all_grads)]
grads_and_vars = list(zip(grads, vars))
loss = tf.add_n(all_loss) / len(all_loss)
else:
vgg = vgg16(data, n_out=args.n_clusters)
avg_vgg = tf.reduce_mean(tf.reduce_mean(vgg, 1), 1)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(avg_vgg, gt))
grads_and_vars = solver.compute_gradients(loss)
solver_step = solver.apply_gradients(grads_and_vars)
# Create some summaries
loss_avg = tf.train.ExponentialMovingAverage(0.9, name='avg')
tf.scalar_summary('loss', loss)
loss_avg_op = loss_avg.apply([loss])
tf.scalar_summary('loss(avg)', loss_avg.average(loss))
with tf.control_dependencies([loss_avg_op]):
loss = tf.identity(loss)
for grad, var in grads_and_vars:
tf.scalar_summary(var.op.name + '/norm', tf.reduce_mean(var * var))
tf.scalar_summary(var.op.name + '/gradient_norm',
tf.reduce_mean(grad * grad))
tf.scalar_summary(
var.op.name + '/gradient_ratio',
tf.reduce_mean(grad * grad) / tf.reduce_mean(var * var))
summary_op = tf.merge_all_summaries()
# Initialize ops
saver = tf.train.Saver(tf.all_variables())
init_op = tf.initialize_all_variables()
if args.initial_weights is not None:
from slim import load
load_op = load.loadH5(args.initial_weights)
else:
load_op = tf.no_op()
tf.get_default_graph().finalize()
with tf.Session(config=tf.ConfigProto(
log_device_placement=args.log_device_placement,
gpu_options=tf.GPUOptions(
per_process_gpu_memory_fraction=0.6))) as sess:
# Initialize stuff
summary_writer = tf.train.SummaryWriter(args.train_dir, sess.graph)
global coord
coord = tf.train.Coordinator()
import signal
def stop(*args):
global coord
print("Training stopped")
coord.request_stop()
old_sigint = signal.signal(signal.SIGINT, stop)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
sess.run(init_op)
sess.run(load_op)
# Train
loss_values = []
for it in range(args.number_of_iterations):
t0 = time()
_, loss_value = sess.run([solver_step, loss])
t1 = time()
loss_values.append(loss_value)
if it % 10 == 0:
print('%8d, loss = %0.2f [%0.2f] (%0.1f im/sec)' %
(it, loss_value, np.mean(loss_values), args.batch_size /
(t1 - t0)))
loss_values = loss_values[-20:]
if it % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, it)
if it % 1000 == 0:
saver.save(sess,
os.path.join(args.train_dir, 'snap.ckpt'),
global_step=it)
if coord.should_stop():
break
saver.save(sess, os.path.join(args.train_dir, 'final.ckpt'))
coord.join(threads)
signal.signal(signal.SIGINT, old_sigint)
f.write("\n")
f.close()
def setModel(raw_data):
def getModel():
data =raw_data.iloc[:,:-1]
target = raw_data.iloc[:, -1]
clf = svm.SVC(C=1, kernel='rbf', gamma=1, decision_function_shape='ovo')
# clf = svm.SVC(C=1, kernel='linear', decision_function_shape='ovr')
clf.fit(data, target)
target_hat = clf.predict(data)
acc = accuracy_score(target, target_hat)
np.set_printoptions(suppress=True)
print(u'预测正确的样本个数：%d，正确率：%.2f%%' % (round(acc * len(data)), 100 * acc))
return clf
def dump(model, filename):
with open(filename, 'wb') as fp:
pickle.dump(model, fp)
if __name__ == "__main__":
clf = getModel()
dump(clf, 'model')
data = preprocess()
data, n = cluster(data)
regression(data, n)
setModel(data)
results[i-720][0] = deltap
weights = fit_weights(vals, results)
return weights
# Train the model
if __name__=="__main__":
if (len(sys.argv)) == 1:
print "Need csv with training data"
quit()
# load dataset
data = load(sys.argv[1])
# split dataset into 2, skipping every other element
# i.e. turn 5s increment into 10s increment
cluster_data = data[:len(data)/2][::2]
train_data = data[len(data)/2:][::2]
# cluster the first part of data
clusters = cluster(cluster_data)
# fit params using second part of data
weights = train(train_data, clusters)
# save weights and clusters for later usage
pkl.dump(clusters, open("weights/clusters.pkl", "wb"))
pkl.dump(weights, open("weights/weights.pkl", "wb"))
means += [statistics.mean(segment)]
means = np.array(means)
def cluster(signal, ref):
part = mixture.BayesianGaussianMixture(n_components=2, max_iter=300)
part.fit(signal.reshape(-1, 1))
target = sorted(part.means_)
answers = []
for i in signal:
if abs(i - target[0]) < abs(i - target[1]):
answers += [0]
else:
answers += [1]
print("zero center = \t", part.means_[0])
print("one center = \t", part.means_[1])
print("reference =\t", ref)
print("answer =\t", np.array(answers))
correctness = 0
for (a, b) in zip(answers, ref):
if a == b:
correctness += 1
print("correctness =", correctness / len(answers) * 100)
n_chunks = int((len(reference) + 19) / 20)
# cluster(means, reference)
for signal, reference in zip(np.array_split(means, n_chunks),
np.array_split(np.array(reference), n_chunks)):
cluster(signal, reference)
parser.add_argument('--mov-dim', '--dm', default=200,
type=int) # fix these
args = parser.parse_args()
# Cluster
is_test = False
if os.path.exists(
_labels_fpath(args.k_usrs, args.usr_dim,
args.usr_eigenvectors_file)):
usr_km = KMeansData(
np.load(
_labels_fpath(args.k_usrs, args.usr_dim,
args.usr_eigenvectors_file)), is_test)
print('loaded user kmeans')
else:
usr_km = cluster(args.usr_eigenvectors_file, args.usr_eigenvalues_file,
args.usr_dim, args.k_usrs)
print('built user kmeans')
if os.path.exists(
_labels_fpath(args.k_movs, args.mov_dim,
args.mov_eigenvectors_file)):
mov_km = KMeansData(
np.load(
_labels_fpath(args.k_movs, args.mov_dim,
args.mov_eigenvectors_file)), is_test)
print('loaded movie kmeans')
else:
mov_km = cluster(args.mov_eigenvectors_file, args.mov_eigenvalues_file,
args.mov_dim, args.k_movs)
print('loaded movie kmeans')
# Load trainset for inference
spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack', affinity="nearest_neighbors")
# clst = kmeans, dbscan, spectral
data_titles = ['noisy_circles', 'noisy_moons', 'blobs', 'no_structure']
def cluster(clst, title):
# Create 2x2 figure for each algorithm
fig, ax = plt.subplots(2,2, figsize=(16,4) )
datasets = [noisy_circles, noisy_moons, blobs, no_structure]
plot_num = 1
for i, dataset in enumerate(datasets):
X, y = dataset
X = StandardScaler().fit_transform(X)
clst.fit(X)
y_pred = clst.labels_.astype(np.int)
plt.subplot(1, 4, plot_num)
plt.scatter(X[:, 0], X[:, 1], color=colors[y_pred].tolist(), s=10)
plt.title(data_titles[i])
plot_num +=1
cluster(spectral,'Spectral')
cluster(kmeans, 'Kmeans')
cluster(dbscan, 'DBscan')
import scipy
import scipy.io
import sklearn.metrics
import sklearn.cluster
import sys
def cluster(simmatrix,n_clusters,savefile):
print n_clusters
n_iters = 50
opt_score = -1e4
for i in range(n_iters):
print('iter '+str(i)+'\n')
labels0 = sklearn.cluster.spectral_clustering(simmatrix, n_clusters)
score0 = sklearn.metrics.silhouette_score(simmatrix,labels0,'precomputed')
if score0>opt_score:
opt_labels = labels0
opt_score = score0
scipy.io.savemat(savefile,{'labels':opt_labels, 'score':opt_score})
return(opt_labels, opt_score)
n_clusters = int(sys.argv[1])
A = scipy.io.loadmat('/u/metanet/clustering/may-2014-relations/data/F-sims.txt.mat')
a= A['simmatrix']
cluster(a, n_clusters, '/u/metanet/clustering/may-2014-relations/data/test'+str(n_clusters)+'.mat')
feature_dir = os.path.join(root_dir, "output", "features")
output_dir = os.getcwd()
# stem = '2344_00_32_40_25'
stem = "V3736-02"
# xml_file = '/Users/dcline/Downloads/TestTopDown/2344_00_32_40_25/output/2344_00_32_40_25.events.xml'
xml_files = [
"V3736-02_1_2000.events.xml",
"V3736-02_2001_4000.events.xml",
"V3736-02_4001_6000.events.xml",
"V3736-02_6001_8000.events.xml",
"V3736-02_8001_10000.events.xml",
"V3736-02_10001_12000.events.xml",
"V3736-02_12001_14000.events.xml",
"V3736-02_14001_16000.events.xml",
"V3736-02_16001_17983.events.xml",
]
width = 1920 # 706
height = 1080 # 362
frame_event_set = []
for f in xml_files:
fes = utils.parse(os.path.join(root_dir, "output", f))
frame_event_set.append(fes)
feature_types = ["PVS", "HOG_3", "JET_red", "HOG_8", "JET_blue", "JET_green"]
cluster(feature_types, 64, width, height, frame_event_set, feature_dir, stem, output_dir)
print "Done"
def forward(self, x, num_iter=1):
embeds = self.encoder(x)
embeds = embeds.view(-1, self.encoder_features_num)
mu_init, _, _ = cluster(embeds, self.K, 1, num_iter, cluster_temp = self.cluster_temp, init = self.init)
mu, r, dist = cluster(embeds, self.K, 1, 1, cluster_temp = self.cluster_temp, init = mu_init.detach().clone())
return r
# clusters.append(y)
# # X = X[X[:,0].argsort()] #SORT X BY FIRST COLUMN
# return
# df = pd.read_csv('PitchFxExample.csv')
# D = df.iloc[:,3:].values
D1 = np.random.normal(0,1,[100,50])
D2 = np.random.normal(20,1,[100,50])
D3 = np.random.normal(100,1,[100,50])
D = np.concatenate((D1,D2,D3))
r, X = cluster(D)
kmeans = cluster.KMeans(n_clusters = 3)
kmeans.fit(X)
labels = kmeans.labels_
# ind = get_clusters(X)
# X_new = get_clusters(X)
#!/usr/bin/env python
import argparse
import h5py
import numpy as np
import sklearn.cluster
def cluster(arguments):
with h5py.File(arguments.file, 'r') as handle:
data = np.array(handle['DBSCAN'])
dbscan = sklearn.cluster.DBSCAN(eps=arguments.e, min_samples=arguments.m)
dbscan.fit(data)
with h5py.File('output.h5', 'w') as handle:
handle['Clusters'] = dbscan.labels_
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-e', type=float, help='spatial search radius epsilon')
parser.add_argument('-m', type=int, help='density threshold min_points')
parser.add_argument('file', type=str, help='file to cluster')
args = parser.parse_args()
cluster(args)
similarity = -1 * np.array([[
metric(getter(str_a), getter(str_b), **kwargs) for str_a in n_samples
] for str_b in n_samples])
print 'Done calculating similarity'
affprop = sklearn.cluster.AffinityPropagation(affinity="precomputed",
verbose=True)
affprop.fit(similarity)
cluster_ids = np.unique(affprop.labels_)
centroids = [
n_samples[affprop.cluster_centers_indices_[cluster_id]]
for cluster_id in cluster_ids
]
clusters = [
n_samples[np.nonzero(affprop.labels_ == cluster_id)]
for cluster_id in cluster_ids
]
return centroids, clusters
if __name__ == '__main__':
samples = ['ala', 'aga', 'abba', 'dupa', 'kupa', 'sraka']
centroids, clusters = cluster(samples, levenshtein_distance)
for (cluster_id, cluster) in enumerate(clusters):
cluster_str = ", ".join(cluster)
print(" - *%s:* %s" % (centroids[cluster_id], cluster_str))
for i in range(len(points)):
if scrs[i] == scr:
scr_points.append(points[i])
scr_labels.append(labels[i])
pipeline.fit(scr_points, scr_labels)
pipelines[scr] = pipeline
center_labels = {}
progress = progressbar.ProgressBar()
for i in progress(range(len(scr_points))):
center = pipeline.predict([scr_points[i]])[0]
center_labels[center] = center_labels.get(center,
[]) + [scr_labels[i]]
for center in center_labels:
print("center: %r" % center, end="")
for label in sorted(set(center_labels[center])):
print(" %s: %d" % (label, center_labels[center].count(label)),
end="")
print()
if __name__ == '__main__':
if sys.argv[1:]:
files = sys.argv[1:]
else:
files = elements.collect_files(DATADIR)
cluster(files)
