def train(self, global_t, sess, train_feed):
losses = []
local_t = 0
total_word_num = 0
start_time = time.time()
while True:
batch = train_feed.next_batch()
if batch is None:
break
inputs, input_lens, outputs = batch
total_word_num += np.sum(input_lens)
feed_dict = {
self.inputs: inputs,
self.input_lens: input_lens,
self.labels: outputs,
self.keep_prob: 0.5
}
_, loss, summary = sess.run(
[self.train_ops, self.loss, self.summary_op], feed_dict)
self.train_summary_writer.add_summary(summary, global_t)
losses.append(loss)
global_t += 1
local_t += 1
if local_t % 200 == 0:
utils.progress(local_t / float(train_feed.num_batch))
# finish epoch!
utils.progress(1.0)
epoch_time = time.time() - start_time
train_loss = np.sum(losses) / total_word_num * train_feed.batch_size
print("Train loss for %f and perplexity %f step time %.4f" %
(train_loss, np.exp(train_loss),
epoch_time / train_feed.num_batch))
return global_t, train_loss
def grade(q, task, verbose, callback):
'''Sets up a file-based submission for grading, then calls a callback for grading.
Sets up a sandbox directory, compiles the program, and calls the callback function for grading.'''
correct = False
metadata = {}
try:
sandbox_dir = tempfile.mkdtemp(prefix='proco')
os.chdir(sandbox_dir)
if verbose:
utils.progress('Using temporary directory: %s' % sandbox_dir)
payload = task['payload']
team_filebase = task['alias']
team_extension = task['run_metadata']['extension']
team_filename = team_filebase + '.' + team_extension
utils.compile(payload, team_filebase, team_extension, team_filename)
correct = callback(task, team_filebase, team_extension, team_filename, metadata, verbose)
except GradingException, e:
utils.progress(e.message)
metadata['error'] = e.message
def knn(trainData, trainLabel, testData, testLabel, k):
predictions = []
counter = 1
t1 = time()
log = []
for x in range(len(testData)):
temp = []
t0 = time()
neighbors = getNeighbors(trainData, trainLabel, testData[x], k)
result = getResponse(neighbors)
predictions.append(result)
progress(x + 1, len(testData))
timepredict = ("%0.3fs" % (time() - t0))
temp.append(testLabel[x])
temp.append(result)
if testLabel[x] == result:
temp.append("1")
else:
temp.append("0")
log.append(temp)
counter += 1
timeextract_feature = ("%0.5fs" % ((time() - t1) / len(testData)))
accuracy = getAccuracy(testLabel, predictions)
return accuracy, round(((time() - t1) / len(testData)), 5)
def plotOutErrors(samplesH, samplesSBQ, samplesIID, gm, kernel):
"""
Compute functions outside the RKHS (Gaussian densities with random covariances)
and their integrals with respect to the distribution
"""
N = 20
nfuncs = 250
D = 2 # dim
nSBQ = len(samplesSBQ) # plot for as many samples as SBQ
gramH = np.zeros((nSBQ,nSBQ))
zH = np.zeros((nSBQ))
fillGram(gramH,zH,kernel,gm,samplesH) # BQ weights for kernel herding
gramSBQ = np.zeros((nSBQ,nSBQ))
zSBQ = np.zeros((nSBQ))
fillGram(gramSBQ,zSBQ,kernel,gm,samplesSBQ) # BQ weights for Bayesian quadrature
weightsH = [email protected](gramH)
weightsSBQ = [email protected](gramSBQ)
errorsIID = 0; errorsH = 0; errorsH_BQ = 0; errorsSBQ = 0
stdsig = 2 # parameter for covariance matrix
ratio = [.1,2] # parameter for covariance matrix
for k in range(nfuncs):
progress(k,nfuncs,'Plot Out Errors')
beta = 2 * (np.random.rand(N) + 1)
c = 4 * 2 * (np.random.rand(N, 2) - 1/2) # mean for kernel
covs = np.zeros((N, D, D))
for k in range(N):
sig = stdsig * (np.random.rand(D)*(ratio[1] - ratio[0]) + ratio[0])
covs[k] = np.diag(sig**(-2))
U, _ = np.linalg.qr(np.random.randn(D,D))
covs[k] = U @ covs[k] @ U.T
f = lambda x : np.sum([beta[i] * multivariate_normal.pdf(x, c[i], covs[i]) for i in range(len(beta))], axis=0)
target = targetOutRKHS(beta, c, gm, covs)
errorsIID += np.abs(np.cumsum(f(samplesIID)) / np.arange(1, len(samplesIID)+1) - target[None])
errorsH += np.abs(np.cumsum(f(samplesH)) / np.arange(1, len(samplesH)+1) - target[None])
errorsH_BQ += np.abs(np.cumsum(f(samplesH) * weightsH) - target[None])
errorsSBQ += np.abs(np.cumsum(f(samplesSBQ) * weightsSBQ) - target[None])
errorsIID /= nfuncs; errorsH /= nfuncs; errorsH_BQ /= nfuncs; errorsSBQ /= nfuncs
plt.figure(figsize=(10, 7))
plt.plot(errorsSBQ,label="SBQ with BQ weights")
# plt.plot(errorsH_BQ,label="Herding with BQ weights")
plt.plot(errorsH,label="Herding with 1/N weights")
plt.plot(errorsIID,label="iid sampling")
plt.legend()
plt.xscale("log")
plt.yscale("log")
plt.title("Mean Absolute Error averaged over %s functions outside the RKHS" % nfuncs)
plt.savefig('figures/Mean Out Error')
plt.show()
def train(self, epoch):
cost = 0
target = np.zeros([self.batch_size, self.seq_length])
N = self.loader.sizes[0]
for idx in xrange(N):
target.fill(0)
x, y, x_char = self.loader.next_batch(0)
for b in xrange(self.batch_size):
for t, w in enumerate(y[b]):
target[b][t] = w
feed_dict = {
self.word_inputs: x,
self.char_inputs: x_char,
self.true_outputs: target,
}
_, loss, step, summary_str = self.sess.run(
[self.optim, self.loss, self.global_step, self.merged_summary],
feed_dict=feed_dict)
self.writer.add_summary(summary_str, step)
if idx % 50 == 0:
progress(
idx / N,
"epoch: [%2d] [%4d/%4d] loss: %2.6f" % (epoch, idx, N, loss))
cost += loss
return cost / N
def image_resized(path, path_save=None, size=None, test=None):
dirs = os.listdir(path)
clearing_files(path_save)
imgCount = int(len(dirs))
count = 1
if test is None:
for item in dirs:
if os.path.isfile(path+item):
im = Image.open(path+item)
f, e = os.path.splitext(path+item)
# print (f)
imResize = im.resize((size,size), Image.ANTIALIAS)
progress(count, imgCount, "Resizing Image")
imResize.save(path_save + item, 'JPEG', quality=100)
count+=1
else:
# for item in dirs:
# if os.path.isfile(path+item):
im = Image.open(path)
# f, e = os.path.splitext(path+item)
imResize = im.resize((size,size), Image.ANTIALIAS)
# print (f)
# progress(count, imgCount)
# imResize.save(path_save + item, 'JPEG', quality=100)
# count+=1
return imResize
def get_missing_package( packagesNeed, packagesOrigin, repodata_list, verbose = False ):
msg = "Resolving dependencies"
packagesToDownload = packagesNeed.difference( packagesOrigin )
index = 0
packagesNotFound = set()
for package in packagesToDownload:
repodata = find_package( package, repodata_list )
if repodata is not None:
packagesNeed |= repodata.get_package_dependencies( package )
else:
packagesNotFound.add( package )
if verbose: print(progress( index, len(packagesToDownload),msg )),
index += 1
max_value = len(packagesToDownload)
if max_value == 0:
if index != 0:
max_value = index
else:
max_value = 1
index = 1
if verbose: print(progress( index, max_value, msg ))
packagesToDownload = packagesNeed.difference( packagesOrigin )
packagesToDownload = packagesToDownload.difference( packagesNotFound )
return packagesToDownload
def SBQ(num_samples, kernel, gm, area, samples=[]):
"""
Sequential Bayesian quadrature: select points that
maximizes the score
Inputs:
- kernel: a Gaussian kernel
- gm: a Gaussian mixture
- area: the bounds where to herding
- samples: the existing samples in the herd
- num_queries: the number of random points to draw for a new sample
"""
n = len(samples)
gram = np.eye(n + num_samples) * kernel.pdf([0, 0], [0, 0])
z = np.zeros(n + num_samples)
fillGram(gram, z, kernel, gm, samples)
for k in range(n, num_samples + len(samples)):
progress(k - n, num_samples, status='Bayesian Quadrature')
samples.append(generate_SBQ(kernel, gm, area, samples, gram, z, k))
update(k, gram, samples[-1], kernel, gm, samples)
z[k] = E_Gaussian(np.array([samples[-1]]), gm, kernel)
return np.array(samples), gram, z
def test_folder_wav(self, folder_name):
"""
Function to extract multi pitch from file. Currently supports only HDF5 files.
"""
sess = tf.Session()
self.load_model(sess, log_dir=config.log_dir)
file_list = [x for x in os.listdir(folder_name) if x.endswith('.wav') and not x.startswith('.')]
count = 0
unprocessable = []
for file_name in file_list:
try:
mel = self.read_wav_file(os.path.join(folder_name, file_name))
out_mel, out_f0, out_vuv = self.process_file(mel, sess)
out_featss = np.concatenate((out_mel, out_f0, out_vuv), axis = -1)
audio_out = utils.feats_to_audio(out_featss)
sf.write(os.path.join(config.output_dir,'./{}_output.wav'.format(file_name.split('/')[-1][:-4])), audio_out, config.fs)
np.save(os.path.join(config.output_dir_np,file_name[:-4]), out_featss)
except:
unprocessable.append(file_name)
count+=1
utils.progress(count, len(file_list), "Files processed")
def validate_model(self, sess):
"""
Function to train the model for each epoch
"""
# feed_dict = {self.input_placeholder: ins, self.output_placeholder: outs, self.is_train: False}
#
# step_loss= sess.run(self.loss, feed_dict=feed_dict)
# summary_str = sess.run(self.summary, feed_dict=feed_dict)
# return step_loss, summary_str
val_list = config.val_list
start_index = randint(
0,
len(val_list) - (config.batches_per_epoch_val + 1))
pre_scores = []
acc_scores = []
rec_scores = []
count = 0
for file_name in val_list[start_index:start_index +
config.batches_per_epoch_val]:
pre, acc, rec = self.validate_file(file_name, sess)
pre_scores.append(pre)
acc_scores.append(acc)
rec_scores.append(rec)
count += 1
utils.progress(count,
config.batches_per_epoch_val,
suffix='validation done')
pre_score = np.array(pre_scores).mean()
acc_score = np.array(acc_scores).mean()
rec_score = np.array(rec_scores).mean()
return pre_score, acc_score, rec_score
def generateMeta(inFolder, data, sys):
root = inFolder
print("META-INF folder not detected...")
print("Generating tree structure...")
print("Generating directories...")
root = inFolder+de()+"META-INF"
progress((lambda: os.mkdir(root)), "Creating "+root+"...") # Validate progress function, wide-use later
root += de()+"com"
print "Creating "+root+"...",
os.mkdir(root)
print " Done!"
root += de()+"google"
print "Creating "+root+"...",
os.mkdir(root)
print " Done!"
root += de()+"android"
print "Creating "+root+"...",
os.mkdir(root)
print " Done!"
print "Generating update-script..."
generateUpdateScript(root, data, sys, inFolder)
print "update-script generation complete!"
response = raw_input("Would you like to inspect the generated script? (y/n): ")
if response == 'y':
genScript = open(root+de()+"update-script", 'rb')
print("###GENERATED SCRIPT (line numbers not written to file):###\n\n")
i = 1
for line in genScript.readlines():
print str(i)+": "+line,
i += 1
print("\n\n###END GENERATED SCRIPT###\n")
def mix_model(self):
sess = tf.Session()
self.load_model(sess, log_dir=self.config.log_dir)
val_generator = data_gen(self.config)
count_batch = 0
for batch_count, [
out_audios, out_envelopes, out_features, total_count
] in enumerate(val_generator):
out_features_copy = np.copy(out_features)
for j in range(int(len(out_features) / 2) - 1):
out_features[j] = out_features_copy[-1 - j]
out_features[-1 - j] = out_features_copy[j]
feed_dict = {self.input_placeholder: out_envelopes[:,:,:self.config.rhyfeats], self.cond_placeholder: out_features,\
self.output_placeholder: out_audios, self.is_train: False}
output_full = sess.run(self.output_wav, feed_dict=feed_dict)
for count in range(self.config.batch_size):
if self.config.model == "spec":
out_audio = utils.griffinlim(
np.exp(output_full[count]) - 1, self.config)
else:
out_audio = output_full[count]
output_file = os.path.join(
self.config.output_dir,
'output_{}_{}_{}.wav'.format(batch_count, count,
self.config.model))
sf.write(output_file, np.clip(out_audio, -1, 1),
self.config.fs)
sf.write(
os.path.join(self.config.output_dir,
'gt_{}_{}.wav'.format(batch_count, count)),
out_audios[count], self.config.fs)
utils.progress(batch_count, total_count)
def inver_cqt(cqt, dcs, nys, h_size):
CQIStand = standard.NSGIConstantQ(**kwargs)
recFrame = []
for j, i in enumerate(range(0,cqt.shape[0], h_size)):
cqt_frame = cqt[i:i+h_size]
# import pdb;pdb.set_trace()
inv_cqt_frame = CQIStand(cqt_frame.T, dcs[j], nys[j])
recFrame.append(inv_cqt_frame)
utils.progress(j, int(cqt.shape[0]/h_size), "Inverse Done")
frameSize = kwargs['inputSize']
y = recFrame[0]
invWindow = Windowing(type='triangular',normalized=False, zeroPhase=False)(standard.essentia.array(np.ones(frameSize)))
for i in range(1,len(recFrame)):
y = np.hstack([y,np.zeros(int(frameSize/2))])
y[-frameSize:] = y[-frameSize:] + recFrame[i]
utils.progress(i, len(recFrame), "Overlap Done")
y = y[int(frameSize/2):]
return y
def adaknn(trainData, trainLabel, testData, testLabel, k):
predictions = []
log = []
t3 = time()
radius = calculateRadius(trainData, trainLabel)
time_radius = ("%0.3fs" % (time() - t3))
# print("All calculate radius done in %s" % time_radius)
t1 = time()
for x in range(len(testData)):
temp = []
t0 = time()
neighbors = getNeighbors(trainData, trainLabel, testData[x], k, radius)
result = getResponse(neighbors)
predictions.append(result)
progress(x + 1, len(testData))
timepredict = ("%0.3fs" % (time() - t0))
temp.append(testLabel[x])
temp.append(result)
if testLabel[x] == result:
temp.append("1")
else:
temp.append("0")
log.append(temp)
timeextract_feature = ("%0.5fs" % ((time() - t1) / len(testData)))
accuracy = getAccuracy(testLabel, predictions)
return accuracy, round(((time() - t1) / len(testData)), 5), time_radius
def _news_scrapper():
for n in config()['news_sites']:
news_site = config()['news_sites'][n]
host = news_site['url']
homepage = news.HomePage(news_site, host)
total = len(homepage.article_links)
index = 1
for link in homepage.article_links:
article = _fetch_article(news_site, host, link)
if article and article.title is not None:
if (db.news.find_one({"title": article.title}) is None):
db.news.insert_one({
"title": article.title,
"content": article.body,
"category": article.category,
"image": build_link(host, article.image),
"date": datetime.datetime.utcnow()
})
progress(
index, total, 'Num of articles: {}'.format(
db.news.count_documents({})))
else:
progress(index, total, 'Article already exists!')
index += 1
client.close()
def feature_extraction():
model = load_model(CHECKPOINT)
model = Model(inputs=model.input,
outputs=model.get_layer('avg_pool').output)
model.summary()
image_labels = []
resnet_feature_list = []
image_paths = []
filenames = glob.glob(os.path.join(TRAIN, '*.jpg'))
random.shuffle(filenames)
total = len(filenames)
i = 0
print('-----Start feature extraction----')
for fname in filenames:
img_path = fname
filename = os.path.basename(fname)
img = image.load_img(img_path, target_size=(224, 224))
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
resnet_feature = model.predict(img_data)
resnet_feature_np = np.array(resnet_feature)
resnet_feature_list.append(resnet_feature_np.flatten())
image_labels.append(filename)
image_paths.append(img_path)
utils.progress(i, total=total, status='Feature extraction')
i = i + 1
# Save the extracted feature
np.savez('/home/paul/clustering/viper/features.npz', resnet_feature_list,
image_paths)
print('----End of feature extraction----')
def _run_test(grader_executer_cmd, team_input, desired_return_code, verbose):
'''Run an individual test and return whether it was accepted by the grader.'''
time_limit = 5
stdin = tempfile.TemporaryFile(bufsize=10485760)
stdin.write(team_input)
stdin.flush()
stdin.seek(0)
if verbose:
print 'Team input'
print '----'
print team_input
print '----'
grader_executer = subprocess.Popen(grader_executer_cmd, stdin=stdin, stdout=open(os.devnull, 'w'), stderr=open(os.devnull, 'w'), preexec_fn=os.setsid, close_fds=True)
start_time = time.time()
while grader_executer.poll() is None and time.time() - start_time <= time_limit:
time.sleep(0.5)
grader_finished = grader_executer.poll() is not None
if grader_executer.poll() is None:
if verbose:
utils.progress('Grader executable did not finish; killing PID %d' % grader_executer.pid)
os.killpg(grader_executer.pid, signal.SIGKILL)
return False
elif grader_executer.returncode != desired_return_code:
if verbose:
utils.progress('Grader executable returned %d, not desired %d' % (grader_executer.returncode, desired_return_code))
return False
else:
return True
def train(self, epoch):
cost = 0
target = np.zeros([self.batch_size, self.seq_length])
N = self.loader.sizes[0]
for idx in xrange(N):
target.fill(0)
x, y, x_char = self.loader.next_batch(0)
for b in xrange(self.batch_size):
for t, w in enumerate(y[b]):
target[b][t] = w
feed_dict = {
self.word_inputs: x,
self.char_inputs: x_char,
self.true_outputs: target,
}
_, loss, step, summary_str = self.sess.run(
[self.optim, self.loss, self.global_step, self.merged_summary], feed_dict=feed_dict)
self.writer.add_summary(summary_str, step)
if idx % 50 == 0:
if self.use_progressbar:
progress(idx/N, "epoch: [%2d] [%4d/%4d] loss: %2.6f" % (epoch, idx, N, loss))
else:
print("epoch: [%2d] [%4d/%4d] loss: %2.6f" % (epoch, idx, N, loss))
cost += loss
return cost / N
def run_sentiment_alignment():
query_5_result = pd.read_csv("Results/query_5_result.csv")
user_ratings = query_5_result[['restaurantID', 'rating_y']]
avg_user_ratings = user_ratings.groupby(['restaurantID']).mean()
max_sentiment_labels = []
avg_user_ratings.to_csv("Data/averaged_ratings.csv")
avg_user_ratings = pd.read_csv("Data/averaged_ratings.csv")
end = len(avg_user_ratings.index)
for i in range(end):
temp1 = query_5_result.loc[
(query_5_result['restaurantID'] == avg_user_ratings.iloc[i, 0])
& (query_5_result['Review Sentiment'] == 'positive')]
temp2 = query_5_result.loc[
(query_5_result['restaurantID'] == avg_user_ratings.iloc[i, 0])
& (query_5_result['Review Sentiment'] == 'negative')]
if len(temp1.index) > len(temp2.index):
max_sentiment_labels.append('positive')
else:
max_sentiment_labels.append('negative')
utils.progress(i, end - 1, status='Assigning sentiment to restaurants')
avg_user_ratings['average sentiments'] = max_sentiment_labels
avg_user_ratings.to_csv("Results/query_6_result.csv", index=False)
print("The output is generated in Results/query_6_result.csv")
def it2fknn(trainData, trainLabel, testData, testLabel, k):
membership = []
referenceMembership = []
testMembership = []
m = 2
labTrainSet, labTestSet, legend = remapLabels(rlabTrainSet=trainLabel,
rlabTestSet=testLabel)
nClasses = len(legend)
t0 = time()
membership = assignMembership(trainData, labTrainSet, k, nClasses)
timeextract = ("%0.5fs" % ((time() - t0)))
# print "assign membership >" + timeextract
correct = 0
t1 = time()
for x in range(len(testData)):
testMembership = computeMembership(testData[x], trainData, membership,
k, m, nClasses)
predict = computeClass(testMembership)
progress(x + 1, len(testData))
if legend[predict] == legend[labTestSet[x] - 1]:
correct += 1
timeextract_feature = ("%0.5fs" % ((time() - t1) / len(testData)))
accouracy = round(((float(correct) / float(len(testData))) * 100.0), 2)
return accouracy, round(((time() - t1) / len(testData)), 5), timeextract
def train(self):
"""
Function to train the model, and save Tensorboard summary, for N epochs.
"""
sess = tf.Session()
self.loss_function()
self.get_optimizers()
self.load_model(sess, config.log_dir_sep)
self.get_summary(sess, config.log_dir_sep)
start_epoch = int(
sess.run(tf.train.get_global_step()) /
(config.batches_per_epoch_train))
print("Start from: %d" % start_epoch)
for epoch in range(start_epoch, config.num_epochs):
data_generator = sep_gen()
start_time = time.time()
batch_num = 0
epoch_train_loss = 0
with tf.variable_scope('Training'):
for ins, f0s, feats in data_generator:
step_loss, summary_str = self.train_model(
ins, f0s, feats, sess)
if np.isnan(step_loss):
import pdb
pdb.set_trace()
epoch_train_loss += step_loss
self.train_summary_writer.add_summary(summary_str, epoch)
self.train_summary_writer.flush()
utils.progress(batch_num,
config.batches_per_epoch_train,
suffix='training done')
batch_num += 1
# import pdb;pdb.set_trace()
epoch_train_loss = epoch_train_loss / batch_num
print_dict = {"Training Loss": epoch_train_loss}
# if (epoch + 1) % config.validate_every == 0:
# pre, acc, rec = self.validate_model(sess)
# print_dict["Validation Precision"] = pre
# print_dict["Validation Accuracy"] = acc
# print_dict["Validation Recall"] = rec
end_time = time.time()
if (epoch + 1) % config.print_every == 0:
self.print_summary(print_dict, epoch, end_time - start_time)
if (epoch + 1) % config.save_every == 0 or (
epoch + 1) == config.num_epochs:
self.save_model(sess, epoch + 1, config.log_dir_sep)
def _update_progress_bar(to_do, i, N_batches, loss_sum):
if to_do == 'train':
status_string="Training | (Batch "+str(i+1)+"/"+str(N_batches)+")"+" | L:" +str(round(loss_sum.cpu().item()/(i+1),3))
if i==N_batches-1:
status_string="Training | (Batch "+str(i+1)+"/"+str(N_batches)+")"
if to_do == 'valid':
status_string="Validating | (Batch "+str(i+1)+"/"+str(N_batches)+")"
if to_do == 'forward':
status_string="Forwarding | (Batch "+str(i+1)+"/"+str(N_batches)+")"
progress(i, N_batches, status=status_string)
def make_wallets(amount):
data = []
for i in range(amount):
progress(i,amount-1,'making wallets...')
pub, pri = ppk_keygen()
wallet = {'public':pub,\
'private':pri}
data.append(wallet)
log_info('[client.Client.make_wallets] writing ({}) wallets to pickle...'.format(amount))
utils.save_pickle(data,WALLETS_DIR)
def mine(self):
while True:
progress(self.nounce,0,'[node.block.Block.mine] Mining block with nounce...')
guess_hash = self.hash_block()
if guess_hash[:self.mining_difficulty] == '0'*self.mining_difficulty:
progress(self.nounce,self.nounce,'[node.block.Block.mine] Block mined @ {}'.format(guess_hash))
break
self.nounce += 1
self.block_hash = guess_hash
return self
def extract(filename, secret=123456, length=64, strength=100, partitions=-1):
print
print '########## Retrieval started ##########'
mesh = pymesh.load_mesh(filename)
if partitions == -1:
partitions = mesh.num_vertices / 500
# Partition the watermarked mesh into patches
patches, _ = partitioning.mesh_partitioning(filename, mesh, partitions)
print 'Step 1: Mesh patched'
data = []
processed = 0
utils.progress(processed, len(patches), 'Reading data from patches...')
# Extract the data from all the patches
for i, patch in enumerate(patches):
# Get the eigenVectors, either by computing them or by reading the from a file if they already have been computed
npy_file = 'saved_eig/' + str(partitions) + '/retrieval_' + str(
i) + '.npy'
if os.path.exists(npy_file):
B = np.load(npy_file)
else:
B = compute_eigenvectors(patch.num_vertices, patch.faces)
np.save(npy_file, B)
# Get the spectral coefficients
P = np.matmul(B, patch.vertices[:, 0])
Q = np.matmul(B, patch.vertices[:, 1])
R = np.matmul(B, patch.vertices[:, 2])
# Read the data and add it to the list of data retrieved
data.append(retrieval.read(P, Q, R, strength))
# Update the progress bar
processed += 1
utils.progress(processed, len(patches), 'Reading data from patches...')
print
print 'Step 2: Data retrieved'
# Use a majority vote to get the data then unscramble it
final_data = unscramble(data_majority(data, length), secret)
print '########## Retrieval finished ##########'
print
return final_data
def main():
# maximus=np.zeros(66)
# minimus=np.ones(66)*1000
wav_files = [
x for x in os.listdir(config.wav_dir_mus)
if x.endswith('.stem.mp4') and not x.startswith(".")
]
count = 0
for lf in wav_files:
# lf = "Actions - One Minute Smile.stem.mp4"
# print(lf)
audio, fs = stempeg.read_stems(os.path.join(config.wav_dir_mus, lf),
stem_id=[0, 1, 2, 3, 4])
mixture = audio[0]
drums = audio[1]
bass = audio[2]
acc = audio[3]
vocals = audio[4]
# out_feats = utils.stft_to_feats((vocals[:,0]+vocals[:,1])/2,fs)
# utils.feats_to_audio(out_feats,lf,fs=fs)
# import pdb;pdb.set_trace()
backing = np.clip(drums + bass + acc, 0.0, 1.0)
if len(backing.shape) == 2:
backing = (backing[:, 0] + backing[:, 1]) / 2
# import pdb;pdb.set_trace()
back_stft = abs(utils.stft(backing))
hdf5_file = h5py.File(config.backing_dir + 'mus_' + lf[:-9] + '.hdf5',
mode='w')
hdf5_file.create_dataset("back_stft", back_stft.shape, np.float32)
hdf5_file["back_stft"][:, :] = back_stft
hdf5_file.close()
count += 1
utils.progress(count, len(wav_files))
def equalize(i, img_name, path, processed_path, img_names):
# print('{} out of {}'.format(i, len(img_names)))
progress("Equalizing", i, len(img_names))
dicom = pydicom.dcmread(os.path.join(path, img_name))
img_array, img_processed = equalize_and_convert(dicom)
data = Image.fromarray(img_array)
data.convert("L").save(os.path.join(path, img_name + ".bmp"))
data = Image.fromarray(img_processed)
data.convert("L").save(os.path.join(processed_path, img_name + ".bmp"))
def _run_tests(task, team_filebase, team_extension, team_filename, metadata, verbose):
'''Execute judge test cases.'''
time_limit = utils.languages[team_extension]['executer_time_limit'] * task['problem_metadata']['time_multiplier']
num_test_cases = len(task['problem_metadata']['judge_io'])
check_changes(task['problem_metadata']['judge_bugs'][team_extension], task['payload'], team_extension)
for index, test_case in enumerate(task['problem_metadata']['judge_io']):
executer_cmd = utils.languages[team_extension]['executer'].substitute(src_filebase=team_filebase, src_filename=team_filename).split()
stdin = tempfile.TemporaryFile(bufsize=52428800)
stdin.write(test_case['input'])
stdin.flush()
stdin.seek(0)
stdout = tempfile.TemporaryFile(bufsize=52428800)
if verbose:
stderr = tempfile.TemporaryFile(bufsize=52428800)
else:
stderr = open(os.devnull, 'w')
executer = subprocess.Popen(executer_cmd, stdin=stdin, stdout=stdout, stderr=stderr, preexec_fn=os.setsid, close_fds=True)
start_time = time.time()
while executer.poll() is None and (time.time() - start_time <= time_limit):
time.sleep(0.5)
if executer.poll() is None:
if verbose:
utils.progress('Team executable did not finish; killing PID %d after %d seconds' % (executer.pid, time.time() - start_time))
os.killpg(executer.pid, signal.SIGKILL)
raise GradingException('Time limit exceeded')
if executer.returncode != 0:
if verbose:
utils.progress('Team executable gave non-zero return code: %d' % executer.returncode)
stderr.seek(0)
print stderr.read()
raise GradingException('Run time error')
stdout.seek(0)
team_output = stdout.read()
team_output_lines = map(lambda line: line.strip(), team_output.splitlines())
judge_output_lines = map(lambda line: line.strip(), test_case['output'].splitlines())
if team_output_lines != judge_output_lines:
if verbose:
utils.progress('Failed %2d / %2d' % (index + 1, num_test_cases))
diff = difflib.Differ()
sys.stdout.writelines(list(diff.compare(map(lambda line: line + '\n', team_output_lines), map(lambda line: line + '\n', judge_output_lines))))
raise GradingException('Incorrect output')
utils.progress('Passed %2d / %2d' % (index + 1, num_test_cases))
utils.progress('Correct')
return True
def plotRKHSErrors(samplesH, samplesSBQ, samplesIID, gm, kernel):
"""
Compute functions in the span of the kernel functions
and their integrals with respect to the distribution
"""
N = 20
nfuncs = 250
nSBQ = len(samplesSBQ) # plot for as many samples as SBQ
gramH = np.zeros((nSBQ,nSBQ))
zH = np.zeros((nSBQ))
fillGram(gramH,zH,kernel,gm,samplesH) # BQ weights for kernel herding
gramSBQ = np.zeros((nSBQ,nSBQ))
zSBQ = np.zeros((nSBQ))
fillGram(gramSBQ,zSBQ,kernel,gm,samplesSBQ) # BQ weights for Bayesian quadrature
weightsH = [email protected](gramH)
weightsSBQ = [email protected](gramSBQ)
errorsIID = 0; errorsH = 0; errorsH_BQ = 0; errorsSBQ = 0
for k in range(nfuncs):
progress(k,nfuncs,'Plot RKHS Errors')
beta = 10 * (np.random.rand(N) + 1)
c = 4 * 2 * (np.random.rand(N, 2) - 1/2) # mean for kernel
factor = 0
for k in range(N):
for l in range(N):
factor += beta[k] * beta[l] * kernel.pdf(c[k], c[l])
beta /= (factor)**(1/2) # in the unit ball the RKHS
f = lambda x : np.sum([beta[i] * kernel.pdf(x, c[i]) for i in range(len(beta))], axis=0)
target = targetRKHS(beta, c, kernel, gm)
errorsIID += np.abs(np.cumsum(f(samplesIID)) / np.arange(1, len(samplesIID)+1) - target[None])
errorsH += np.abs(np.cumsum(f(samplesH)) / np.arange(1, len(samplesH)+1) - target[None])
errorsH_BQ += np.abs(np.cumsum(f(samplesH) * weightsH) - target[None])
errorsSBQ += np.abs(np.cumsum(f(samplesSBQ) * weightsSBQ) - target[None])
errorsIID /= nfuncs; errorsH /= nfuncs; errorsH_BQ /= nfuncs; errorsSBQ /= nfuncs
plt.figure(figsize=(10, 7))
plt.plot(errorsSBQ,label="SBQ with BQ weights")
# plt.plot(errorsH_BQ,label="Herding with BQ weights")
plt.plot(errorsH,label="Herding with 1/N weights")
plt.plot(errorsIID,label="iid sampling")
plt.legend()
plt.xscale("log")
plt.yscale("log")
plt.title("Mean Absolute Error averaged over %s functions in the RKHS" % nfuncs)
plt.savefig('figures/Mean Absolute Error')
plt.show()
def clues():
if not g_all_clues:
for xd in corpus(): # r in parse_tsv("clues.tsv", "AnswerClue"):
progress(xd.filename)
pubid = xd.publication_id()
dt = xd.date() or ""
for pos, clue, answer in xd.iterclues():
ca = ClueAnswer(pubid, dt, answer, clue)
g_all_clues.append(ca)
progress()
return g_all_clues
def main():
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('Using PyTorch device : {}'.format(device.upper()))
model = ResNet18().to(device)
train_loader, val_loader, test_loader = utils.get_cifar10_data_loaders(n_train=N_TRAIN, \
n_val=N_VAL, n_test=N_TEST)
optimizer = torch.optim.SGD(model.parameters(), lr=1e-1, momentum=0.9)
xent = nn.CrossEntropyLoss()
step = 0
n_epochs = 50
f = open(SAVE_DIR + LOSS_NAME, 'w')
f.truncate(0)
f.write('train_step, train_loss\n')
f.close()
MODEL_SAVE_PATH = SAVE_DIR + MODEL_NAME
assert model.training is True
for i in range(n_epochs):
for j, (images, labels) in enumerate(train_loader):
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = xent(outputs, labels)
loss.backward()
optimizer.step()
if step % 16 == 0:
with open(SAVE_DIR + LOSS_NAME, 'a') as f:
f.write('{}, {}\n'.format(step, loss.item()))
step += 1
utils.progress(
j + 1, len(train_loader),
'Batch [{}/{}] Epoch [{}/{}]'.format(j + 1, len(train_loader),
i + 1, n_epochs))
torch.save(model.state_dict(), MODEL_SAVE_PATH.format(i))
print('Training Complete')
model.eval()
correct = 0
for j, (images, labels) in enumerate(test_loader):
images, labels = images.to(device), labels.to(device)
logits = model(images)
correct += (torch.max(logits, 1)[-1] == labels).sum().item()
utils.progress(j + 1, len(test_loader),
'Batch [{}/{}]'.format(j + 1, len(test_loader)))
print('Test Accuracy = {}%'.format(float(correct) * 100.0 / N_TEST))
print('Done')
def go_all_in(self):
'''
Function to allow player to go all in
'''
for index, player in enumerate(self.table.players):
if self == player:
if player.chips != 0:
all_in_value = player.chips
self.table.game.bets[index] += player.chips
player.chips = 0
self.all_in = True
self.talked = True
self.turn_bet = {'action': 'all_in', 'player_name': self.player_name, 'amount' : all_in_value}
# Attempt to progress the game
progress(self.table)
def outlierScore(k, tree, data):
"""
Calculates the outlierScore for every data, by calculating the sum of
distances between this point and the k-neighrest points to it.
"""
scores = []
for index, pair in enumerate(data):
utils.progress(index, len(data), status="Calculate the outlierScore")
neirestNeighbors = tree.nearest((pair[3], pair[4]), num_results=k + 1)
#tree returns indexes +1, + the value sof the element itself
distances = [
distance2Pairs(pair, data[x - 1]) for x in neirestNeighbors
]
scores.append([index, sum(sorted(distances[:k + 1]))])
return sorted(scores, key=lambda x: x[1], reverse=True)
def frnn_vqrs(trainData, trainLabel, testData, testLabel,k):
labTrainSet, labTestSet, legend = remapLabels(rlabTrainSet=trainLabel, rlabTestSet=testLabel)
nClasses = len(legend)
index_testLabel = 0
t1 = time()
correct =0
for testDataInstance in testData:
predict = clasifyClass(trainData,labTrainSet,testDataInstance,k,nClasses)
progress(index_testLabel+1, len(testData))
if legend[predict] == legend[labTestSet[index_testLabel]]:
correct+=1
index_testLabel+=1
timeextract_feature= ("%0.5fs"%((time() - t1)/len(testData)))
accouracy = round(((float(correct)/float(len(testData)))*100.0),2)
return accouracy, round(((time() - t1)/len(testData)),5)
def herding(num_samples, kernel, gm, area, samples=[]):
"""
Kernel herding: at each step choose points that maximizes the score
Inputs:
- num_samples: the number of herding samples
- kernel: a Gaussian kernel
- gm: a Gaussian mixture
- area: the bounds where to do herding
- samples: the existing samples in the herd
"""
for k in range(num_samples):
progress(k, num_samples, 'Kernel Herding')
samples.append(generate_herding(kernel, gm, area, samples))
return np.array(samples)
def download_packages(workdir, packagesToDownload, repodata_list ):
msg = "Downloading rpm files to resolve dependencies"
packages_dir = path.join( workdir, "Packages" )
index = 0
repodata = None
packagesNotFound = set()
for package in packagesToDownload:
if package is not None and package != "":
repodata = find_package( package, repodata_list )
if repodata is not None:
for url in repodata.get_package_url( package ):
u = repodata.repo.baseurl + url
download_file( u, packages_dir )
print(progress( index, len(packagesToDownload), msg )),
index += 1
print(progress( index, len(packagesToDownload), msg ))
def get_existing_package( workdir ):
msg = "Listing rpm files"
cmd = "rpm -qp --queryformat '%{NAME}'"
packages = set()
index = 0
rpm_list = glob( workdir + "/*.rpm" )
stdout = None
for rpm in rpm_list:
stdout = command( cmd + rpm, pass_exception = True )
packages.add( stdout )
print(progress( index, len(rpm_list), msg )),
index += 1
print(progress( index, len(rpm_list), msg ))
return packages
def download_file( url, outputdir, force = False, verbose = False ):
file_name = path.join( outputdir, url.split('/')[-1] )
allow_downloading = False
status = ""
if not path.exists( file_name ) or force == True:
allow_downloading = True
if allow_downloading:
outfile = open( file_name, 'wb')
response = urllib2.urlopen( url )
meta = response.info()
file_size = int(meta.get("Content-Length")[0])
msg = "Downloading: %s Bytes: %s" % (file_name, file_size)
file_size_dl = 0
block_sz = 8192
while True:
buffer = response.read(block_sz)
if not buffer:
break
file_size_dl += len(buffer)
outfile.write(buffer)
if verbose:
status = progress(file_size_dl, file_size, msg)
print(status),
outfile.close()
if verbose:
print("")
return file_name
def _run_tests(task, team_filebase, team_extension, team_filename, metadata, verbose):
'''Compile interactive grader, and execute judge test cases.'''
if not task['problem_metadata']['grader']['valid']:
utils.progress('No grader found.')
raise Exception('No interactive grader found!')
try:
payload = task['problem_metadata']['grader']['src']
grader_filebase = task['problem_metadata']['grader']['filebase']
grader_extension = task['problem_metadata']['grader']['extension']
grader_filename = grader_filebase + '.' + grader_extension
utils.compile(payload, grader_filebase, grader_extension, grader_filename)
except Exception, e:
utils.progress('Internal error when compiling grader')
raise
async def tsh(event):
if event.reply_to_msg_id:
start = time.time()
url = await event.get_reply_message()
ilk = await event.respond("Downloading...")
try:
file_path = await url.download_media(progress_callback=lambda d, t: asyncio.get_event_loop().create_task(
progress(d, t, ilk, start, "Downloading...")
))
except Exception as e:
traceback.print_exc()
print(e)
await event.respond(f"Downloading Failed\n\n**Error:** {e}")
await ilk.delete()
try:
orta = await event.respond("Uploading to TransferSh...")
download_link, final_date, size = await send_to_transfersh_async(file_path)
zaman = str(time.time() - start)
await orta.edit(f"File Successfully Uploaded to TransferSh.\n\nLink:{download_link}\nExpired Date:{final_date}")
except Exception as e:
traceback.print_exc()
print(e)
await event.respond(f"Uploading Failed\n\n**Error:** {e}")
raise events.StopPropagation
def install(silent=False):
if utils.ADDON.getSetting('OS') == 'Windows':
return
if utils.ADDON.getSetting('OS') == 'MacOS':
installMacOS()
return
cmdLine = utils.getSudo()
cmdLine +='apt-get update;'
cmdLine +='sudo apt-get -y install openvpn;'
cmdLine +='sudo apt-get -y install psmisc'
dp = None
if not silent:
dp = utils.progress('Installing VPN application', 'Please be patient this may take a few minutes')
p = subprocess.Popen(cmdLine, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = p.communicate()
if silent:
return
import xbmc
xbmc.sleep(100)
dp.close()
success = path.getPath(utils.ADDON.getSetting('OS'), silent=True)
if success:
utils.dialogOK('VPN application successfully installed')
else:
utils.dialogOK('VPN application installation failed', 'Please try again later')
def bet(self,amount):
'''
Function to allow player to place a bet of a given amount
'''
if self.chips > amount:
for index,player in enumerate(self.table.players):
if self == player:
self.table.game.bets[index] += amount
player.chips -= amount
self.talked = True
self.turn_bet = {'action': 'bet', 'player_name': self.player_name, 'amount' : amount}
# Attemp to progress the game
progress(self.table)
else:
print 'You don\'t have enough chips --> ALL IN !!!'
self.go_all_in()
def check(self):
'''
Function to allow player to check
'''
check_allow = True
player_index = get_player_index(self)
player_bet = self.table.game.bets[player_index]
for bet in self.table.game.bets:
if bet > player_bet:
check_allow = False
if check_allow:
self.talked = True
self.turn_bet = {'action': 'check', 'player_name': self.player_name}
# Attempt to progress the game
progress(self.table)
else:
print 'Check not allowed, replay please'
def fold(self):
'''
Function to allow player to fold
'''
for index, player in enumerate(self.table.players):
if self == player:
if self.table.game.bets[index]:
bet = self.table.game.bets[index]
else:
bet = 0
self.table.game.bets[index] = 0
self.table.game.pot += bet
self.talked = True
# Mark the player as folded
self.folded = True
self.turn_bet = {'action': 'fold', 'player_name': self.player_name}
# Attempt to progress the game
progress(self.table)
def call(self):
'''
Function to allow player to call
'''
max_bet = get_max_bet(self.table.game.bets)
if self.chips > max_bet:
# Match the highest bet
for index, player in enumerate(self.table.players):
if self == player:
if self.table.game.bets[index] >= 0:
self.chips += self.table.game.bets[index]
self.chips -= max_bet
self.table.game.bets[index] = max_bet
self.talked = True
self.turn_bet = {'action': 'call', 'player_name': self.player_name, 'amount' : max_bet}
# Attempt to progress the game
progress(self.table)
else:
print 'You don\'t have enough chips --> ALL IN !!!'
self.go_all_in()
def test(self, split_idx, max_batches=None):
if split_idx == 1:
set_name = 'Valid'
else:
set_name = 'Test'
N = self.loader.sizes[split_idx]
if max_batches != None:
N = min(max_batches, N)
self.loader.reset_batch_pointer(split_idx)
target = np.zeros([self.batch_size, self.seq_length])
cost = 0
for idx in xrange(N):
target.fill(0)
x, y, x_char = self.loader.next_batch(split_idx)
for b in xrange(self.batch_size):
for t, w in enumerate(y[b]):
target[b][t] = w
feed_dict = {
self.word_inputs: x,
self.char_inputs: x_char,
self.true_outputs: target,
}
loss = self.sess.run(self.loss, feed_dict=feed_dict)
if idx % 50 == 0:
if self.use_progressbar:
progress(idx/N, "> %s: loss: %2.6f, perplexity: %2.6f" % (set_name, loss, np.exp(loss)))
else:
print(" > %s: loss: %2.6f, perplexity: %2.6f" % (set_name, loss, np.exp(loss)))
cost += loss
cost = cost / N
return cost
def _run_tests(task, team_select, team_correct, team_wrong, verbose):
'''Compile judge and run both test cases on the judge. '''
payload = task['problem_metadata']['grader']['src']
grader_filebase = task['problem_metadata']['grader']['filebase']
grader_extension = task['problem_metadata']['grader']['extension']
grader_filename = grader_filebase + '.' + grader_extension
utils.compile(payload, grader_filebase, grader_extension, grader_filename)
grader_executer_cmd = utils.languages[grader_extension]['executer'].substitute(src_filebase=grader_filebase, src_filename=grader_filename).split()
actual_type = task['division_metadata']['type']
if actual_type == 'correct' or actual_type == 'sometimes':
utils.progress('Testing team good input')
if not _run_test(grader_executer_cmd, team_correct, 100, verbose):
return False
if actual_type == 'wrong' or actual_type == 'sometimes':
utils.progress('Testing team bad input')
if not _run_test(grader_executer_cmd, team_wrong, 200, verbose):
return False
return True
def performUpdate(response, silent):
try:
version = response['Version']
link = response['Link']
md5 = response['MD5']
except:
return
path = getDownloadPath()
if utils.generateMD5(path) != md5:
if (not silent) and (not utils.yesno(1, 10, 11)):
return
dp = None
if not silent:
dp = utils.progress(1, 14, 15)
hash = 0
count = 0
nTries = 3
if not silent:
nTries = 1
while (count < nTries) and (hash != md5):
count += 1
try:
download(link,path,version,dp)
hash = utils.generateMD5(path)
except Exception, e:
utils.deleteFile(path)
if str(e) == 'Canceled':
return
if hash != md5:
utils.unflagUpdate()
utils.deleteFile(path)
utils.setSetting('dVersion', '0.0.0')
if not silent:
utils.ok(1, 24, 13)
return
def doUpgradeFW(self, filename):
utils.log('doupgradeFW %s' % filename)
dp = utils.progress(1, 0, 11)
FUNC = ctypes.CFUNCTYPE(ctypes.c_int, ctypes.c_int, ctypes.POINTER(ctypes.c_void_p))
theFunc = FUNC(callback)
theFile = ctypes.c_char_p(filename)
theDP = ctypes.c_void_p(id(dp))
response = self.lib.fl_upgrade_fw(theFile, VID, MANUFACTURER, ctypes.cast(theDP, ctypes.POINTER(ctypes.c_void_p)), theFunc)
dp.close()
utils.log('UPGRADE_FW Response = %d' % response)
check = self.checkResponse(response)
if check != utils.FLIRC_OK:
#UPGRADE_FAILED
return check
return utils.UPGRADE_OK
def build_model(self, forward_only, is_copy=True):
print(" [*] Building a NTM model")
with tf.variable_scope(self.scope):
# present start symbol
if is_copy:
_, prev_state = self.cell(self.start_symbol, state=None)
self.save_state(prev_state, 0, self.max_length)
zeros = np.zeros(self.cell.input_dim, dtype=np.float32)
tf.get_variable_scope().reuse_variables()
for seq_length in xrange(1, self.max_length + 1):
progress(seq_length/float(self.max_length))
input_ = tf.placeholder(tf.float32, [self.cell.input_dim],
name='input_%s' % seq_length)
true_output = tf.placeholder(tf.float32, [self.cell.output_dim],
name='true_output_%s' % seq_length)
self.inputs.append(input_)
self.true_outputs.append(true_output)
# present inputs
_, prev_state = self.cell(input_, prev_state)
self.save_state(prev_state, seq_length, self.max_length)
# present end symbol
if is_copy:
_, state = self.cell(self.end_symbol, prev_state)
self.save_state(state, seq_length)
self.prev_states[seq_length] = state
if not forward_only:
# present targets
outputs = []
for _ in xrange(seq_length):
output, state = self.cell(zeros, state)
self.save_state(state, seq_length, is_output=True)
outputs.append(output)
self.outputs[seq_length] = outputs
if not forward_only:
for seq_length in xrange(self.min_length, self.max_length + 1):
print(" [*] Building a loss model for seq_length %s" % seq_length)
loss = sequence_loss(logits=self.outputs[seq_length],
targets=self.true_outputs[0:seq_length],
weights=[1] * seq_length,
average_across_timesteps=False,
average_across_batch=False,
softmax_loss_function=\
binary_cross_entropy_with_logits)
self.losses[seq_length] = loss
if not self.params:
self.params = tf.trainable_variables()
#grads, norm = tf.clip_by_global_norm(
# tf.gradients(loss, self.params), 5)
grads = []
for grad in tf.gradients(loss, self.params):
if grad is not None:
grads.append(tf.clip_by_value(grad,
self.min_grad,
self.max_grad))
else:
grads.append(grad)
self.grads[seq_length] = grads
self.optims[seq_length] = self.opt.apply_gradients(
zip(grads, self.params),
global_step=self.global_step)
self.saver = tf.train.Saver()
print(" [*] Build a NTM model finished")
def performManualUpdate(response, silent):
try:
import xbmcgui
path = getDownloadPath()
select_name=['Cancel']
select_url=['Cancel']
for i in json.loads(response):
cVersion = utils.getSetting('cVersion')
if not cVersion in i['Version']:
select_name.append(i['Version'])
select_url.append(i['Link']+'*'+i['Version']+'*'+i['MD5'])
link = select_url[xbmcgui.Dialog().select('Your Current Firmware '+ cVersion , select_name)]
if 'Cancel' in link:
return
url = link.split('*')[0]
version = link.split('*')[1]
md5 = link.split('*')[2]
if utils.generateMD5(path) != md5:
if (not silent) and (not utils.yesno(1, 11, 0)):
return
dp = None
if silent:
dp = utils.progress(1, 14, 15)
hash = 0
count = 0
nTries = 3
if not silent:
nTries = 1
while (count < nTries) and (hash != md5):
count += 1
try:
download(url,path,version,dp)
hash = utils.generateMD5(path)
except Exception, e:
utils.deleteFile(path)
if str(e) == 'Canceled':
return
if hash != md5:
utils.unflagUpdate()
utils.deleteFile(path)
utils.setSetting('dVersion', '0.0.0')
if not silent:
utils.ok(1, 24, 13)
return
utils.setSetting('dVersion', version)
if not utils.okReboot(1, 23, 16, 18, delay = 15):
return
reboot()
if verbose:
stderr = tempfile.TemporaryFile(bufsize=10485760)
else:
stderr = open(os.devnull, 'w')
team_executer = subprocess.Popen(team_executer_cmd, stderr=stderr, preexec_fn=team_init)
grader_executer = subprocess.Popen(grader_executer_cmd, stderr=open(os.devnull, 'w'), preexec_fn=grader_init)
start_time = time.time()
while grader_executer.poll() is None and time.time() - start_time <= time_limit:
time.sleep(0.5)
grader_finished = grader_executer.poll() is not None
if not grader_finished:
if verbose:
utils.progress('Grader executable did not finish; killing PID %d after %d seconds' % (grader_executer.pid, time.time() - start_time))
os.killpg(grader_executer.pid, signal.SIGKILL)
if team_executer.poll() is None:
if verbose:
utils.progress('Team executable did not finish; killing PID %d after %d seconds' % (team_executer.pid, time.time() - start_time))
os.killpg(team_executer.pid, signal.SIGKILL)
if not grader_finished:
raise GradingException('Time limit exceeded')
else:
raise GradingException('Incorrect output')
if team_executer.returncode != 0:
if verbose:
utils.progress('Team executable gave non-zero return code: %d' % executer.returncode)
stderr.seek(0)
print stderr.read()
raise GradingException('Run time error')
correct = False
metadata = {}
try:
sandbox_dir = tempfile.mkdtemp(prefix='proco')
os.chdir(sandbox_dir)
if verbose:
utils.progress('Using temporary directory: %s' % sandbox_dir)
payload = task['payload']
team_filebase = task['alias']
team_extension = task['run_metadata']['extension']
team_filename = team_filebase + '.' + team_extension
utils.compile(payload, team_filebase, team_extension, team_filename)
correct = callback(task, team_filebase, team_extension, team_filename, metadata, verbose)
except GradingException, e:
utils.progress(e.message)
metadata['error'] = e.message
except Exception, e:
utils.progress('Internal error: ' + str(e))
raise
finally:
if verbose:
utils.progress('NOT removing temporary directory %s; please clean up manually!' % sandbox_dir)
else:
shutil.rmtree(sandbox_dir)
q.put({'correct' : correct, 'metadata' : metadata, 'division_id' : int(task['division_id']), 'team_id' : int(task['team_id']), 'problem_id' : int(task['problem_id'])})
sample_name = sys.argv[3]
filename = 'samples/%s.txt' % sample_name
sample_kmers = kmer_store()
with open(filename) as f:
for read in f:
read_kmers = kmers(read.strip(), k)
for kmer in read_kmers:
sample_kmers.update(kmer)
output_filename = 'pickles/%s_kmers_%d.pickle' % (os.path.basename(os.path.normpath(filename)).replace('.txt',''), k)
with open(output_filename, 'w') as f:
cPickle.dump(sample_kmers.kmers, f)
elif version =='genomes':
full = True if (len(sys.argv) == 4 and sys.argv[3] == 'full') else False
kmer_spectra = defaultdict(lambda:[0]*20)
for index, genome_filename in enumerate(progress(filter(lambda x: x.endswith('.fna'), os.listdir('genomes')))):
kmer_spectrum = {} if full else kmer_store()
for kmer in kmers(nucleotides_fna('genomes/'+genome_filename), k):
if full:
kmer_spectrum[kmer] = kmer_spectrum[kmer]+1 if kmer in kmer_spectrum else 1
else:
kmer_spectrum.update(kmer)
for kmer in kmer_spectrum:
kmer_spectra[kmer][index] = kmer_spectrum[kmer]
full_string = 'full_' if full else ''
with open('pickles/%skmer_spectra_%d.pickle' % (full_string, k), 'w') as f:
cPickle.dump(dict(kmer_spectra), f)
def attach_experiment(self,experiment_dir,error_key='dev_mae',verbose=True):
"""
Overide this to suit your purposes!
The purpose of this function is to go over each experiment in the supplied experiment_dir and aggregate the best results.
the results into a nice dictionary object. I encourage this to be overidden in ones own implementation if their experiment structure
is different. Alternativley, if an aggregation can be supplied to attach_aggregation, your life will be easier.
:param experiment_dir: String pointing to valid experiment directory as defined by experimentGenerator
:returns: 0 on success, <0 for any error.
.. side-effects:: Upon success, the class variable results will be populated.
.. note:: A valid experiment directory (if using this as is) should have sufficient permissions set and contain a var directory. The var directory should contain sub folders for each experiment, labeled like: alpha_1__gamma_2/. Underneath each sub directory there should be the folder logs underwhich there should be a file runtime.txt like this logs/runtime.txt.
"""
dirs = []
results_dir = '%s/%s' %(experiment_dir,'results')
var_dir = '%s/%s' %(experiment_dir,'var')
if not os.path.isdir(var_dir):
print('There is no var directory. This directory should have all of the sub experiments beneath it.')
return -1
if not os.path.isdir(results_dir):
if ensure_dir(results_dir) < 0:
print('There is no results directory. Failed attempt to create directory here: %s.' % (results_dir))
return -2
print('Building list of directories...')
dirs = list()
num_sep = var_dir.count(os.path.sep)
level = 1
for root, ds, fs in os.walk(var_dir):
num_sep_this = root.count(os.path.sep)
if num_sep + level <= num_sep_this:
del ds[:]
continue
dirs = ds
num_experiments = len(dirs)
results = {}
# ---------------------------------
# Go over each experiment directory
# -----------------------
print('Aggregating results.')
num_dirs = len(dirs)
param_count = None
last_params = None
for p,d in enumerate(dirs):
runtime_fn = '%s/var/%s/logs/runtime.txt' % (experiment_dir,d)
if not os.path.isfile(runtime_fn):
continue
d_end = d.split('/')[-1]
# Hyper parameters are in the name of a directory
hyper_params = self.parse_key_values(d_end,assignment='_',delimeter='__')
runtime = open(runtime_fn,'r').read().split('\n')
min_error = float('inf')
min_error_exp= None
# -----------------------------------------
# Gather the results for a given experiment
# -------------------
for line in runtime:
if not line:
continue
rt = self.parse_key_values(line)
# ----
# See if this current line in the runtime log had the best error.
try:
error = float(rt[error_key])
if error < min_error:
min_error = error
min_error_exp = copy.deepcopy(rt)
except ValueError:
raise ValueError('Expecting error key %s to evaluate to a float.' % (error_key))
except KeyError:
print('ERROR: Parsing file:',runtime_fn)
print('- Error key %s does not apear on all lines.' % error_key)
break
# -------
# -------
if not min_error_exp:
if verbose:
print('WARNING: Unable to find best result in experiment %s.' % (runtime_fn))
continue
# Add all the hyper params to the best experiment found
for key in hyper_params:
min_error_exp[key] = float(hyper_params[key])
# Update the values pointed to by every key in min_error_exp
if not param_count:
param_count = len(min_error_exp)
last_params = min_error_exp
elif len(min_error_exp) != param_count:
print("Error in file:",runtime_fn)
print("Irregular number of outputs per line.")
continue
else:
param_count = len(min_error_exp)
last_params = min_error_exp
for key in min_error_exp:
if not key in results:
results[key] = dict()
results[key]['values'] = list()
results[key]['__longest_sequence__'] = -1
val = min_error_exp[key]
try:
val = float(val)
except:
pass
max_ = max(len(str(val)),len(key),results[key]['__longest_sequence__'])
results[key]['__longest_sequence__'] = max_
results[key]['values'].append(val)
if( p % 200 == 0):
progress(p,num_experiments)
# -------
progress(num_experiments,num_experiments)
print('(%d) results' % (len(results)))
self.results = results
return 0
def grade(q, task, verbose):
'''Grades a debug submission.'''
correct = False
metadata = {}
try:
payload = json.loads(task['payload'])
team_select = payload['type']
team_correct = payload['good']
team_wrong = payload['bad']
if task['division_metadata']['type'] == team_select:
try:
sandbox_dir = tempfile.mkdtemp(prefix='proco')
os.chdir(sandbox_dir)
if verbose:
utils.progress('Using temporary directory: %s' % sandbox_dir)
correct = _run_tests(task, team_select, team_correct, team_wrong, verbose)
if correct:
utils.progress('Correct')
else:
utils.progress('Wrong')
finally:
if verbose:
utils.progress('NOT removing temporary directory %s; please clean up manually!' % sandbox_dir)
else:
shutil.rmtree(sandbox_dir)
else:
if verbose:
utils.progress('Team selected %s, should be %s', team_select, task['division_metadata']['type'])
utils.progress('Wrong (solution type mismatch)')
except Exception, e:
utils.progress('Internal error: ' + str(e))
raise
f = open('assets/tw_ht_corpus_2.txt', 'a')
p = MDB('tweets')
cols = p.client['tweets'].collection_names()
cols.remove('SPB')
cols.remove('EKB')
cols.remove('Moscow')
print cols
i = 0
counts = []
for c in cols:
ml = p.client['tweets'][c].find()
counts.append(ml.count())
total = sum(counts)
print 'total:', total, 'documents'
for c in cols:
ml = p.client['tweets'][c].find()
for t in ml:
try:
dt = text_process(t)[0]
progress(i, total)
if dt:
f.write(dt + '\n')
except Exception as e:
print e
finally:
i += 1
def __iter__(self):
for line in open(self.filename, 'r'):
if self.logging:
progress(self.count, 0, skip=1000, mode = 2)
self.count += 1
yield unicode(line).split()
judge = AutoJudge()
print time.strftime('[%H:%M:%S]:', time.localtime()),
print 'Initialized judge to %s' % judge
while True:
print time.strftime('[%H:%M:%S]:', time.localtime()),
task = judge.fetch_task()
task_type = task['task_type']
if task_type == 'grade':
task['run_metadata'] = json.loads(task['run_metadata'])
print 'Grading run_id %s (team %s, problem %s) of type %s... ' % (task['run_id'], task['team_username'], task['alias'], task['problem_type']),
utils.reset_progress(False)
problem_metadata, division_metadata = judge.get_cached_metadata(task['problem_id'], task['division_id'], task['problem_metadata_hash'], task['division_metadata_hash'])
if problem_metadata is None or division_metadata is None:
utils.progress('Refreshing metadata')
problem_metadata, division_metadata = judge.update_cached_metadata(task['problem_id'], task['division_id'], task['problem_metadata_hash'], task['division_metadata_hash'])
task['problem_metadata'] = problem_metadata
task['division_metadata'] = division_metadata
module = utils.import_module(judge.contest_type, task['problem_type'])
q = multiprocessing.Queue()
grader = multiprocessing.Process(target=module.grade, args=(q, task, False))
grader.start()
result = q.get()
grader.join()
print
judge.submit_judgment(judgment_id=int(task['judgment_id']), **result)
elif task_type == 'reset':
judge = AutoJudge()
