prompt large_stringlengths 72 9.34k | completion large_stringlengths 0 7.61k |
|---|---|
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
<|fim_middle|>
def compute_interaction(data):
interaction = {}
for line in data:
(userA,userB,t... | if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | interaction = {}
for line in data:
(userA,userB,times,action) = line.split(' ')
action = action[:-1]
key = userB + " " + action
interaction.setdefault(key, 0)
interaction[key] += 1
return interaction |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | records = []
lineList = []
lineNum = 1
result = []
lineList = [line for line in file(trainFile)]
for line in lineList:
if lineNum == 1: #ignore the title in first line
lineNum += 1
continue
records.append(line)
lineNum += 1
interaction = compute_interaction(records)
out =... |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | pop = 0;
if action == "rec":
pop = 1
elif action == "click":
pop = 10
elif action == "msg":
pop = 100
return pop; |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | popDict = {}
rankedscores = []
result = []
print "-----compute_user_history_interaction ... "
interaction = compute_user_history_interaction(trainFile)
print "-----compute_user_popularity ... "
for (user, action, times) in interaction[0:len(interaction)]:
popDict.setdefault(user, 0)
po... |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
<|fim_middle|>
elif action == "click" :
return 1
else:
return... | return 0 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
<|fim_middle|>
else:
return... | return 1 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
<|fim_middle|... | return 2 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | lineNum += 1
continue |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | pop = 1 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | pop = 10 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | pop = 100 |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def <|fim_middle|>(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | getActionScore |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def <|... | compute_interaction |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | compute_user_history_interaction |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | get_action_weight |
<|file_name|>compute_user_popularity.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
# compute the times of action(rec|click|msg) for each user
from math import sqrt
def getActionScore(action):
if action == "rec":
return 0
elif action == "click" :
return 1
else:
return 2
def co... | compute_user_popularity |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)
# Optimize the model |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | train_x = torch.linspace(0, 1, 100)
train_y = torch.sin(train_x * (2 * pi))
train_y.add_(torch.randn_like(train_y), alpha=1e-2)
test_x = torch.rand(51)
test_y = torch.sin(test_x * (2 * pi))
if cuda:
train_x = train_x.cuda()
train_y = train_y.cuda()
test_x = test_x.cuda()
... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | def __init__(self, train_x, train_y, likelihood):
super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1e-5, 1e-5))
self.base_covar_module = ScaleKernel(RBFKernel(lengthscale_prior=SmoothedBoxPrior(exp(-5), exp(6), sigm... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1e-5, 1e-5))
self.base_covar_module = ScaleKernel(RBFKernel(lengthscale_prior=SmoothedBoxPrior(exp(-5), exp(6), sigma=0.1)))
self.covar_module = InducingPointKern... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | mean_x = self.mean_module(x)
covar_x = self.covar_module(x)
return MultivariateNormal(mean_x, covar_x) |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | def setUp(self):
if os.getenv("UNLOCK_SEED") is None or os.getenv("UNLOCK_SEED").lower() == "false":
self.rng_state = torch.get_rng_state()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
random.seed(0)
def... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | if os.getenv("UNLOCK_SEED") is None or os.getenv("UNLOCK_SEED").lower() == "false":
self.rng_state = torch.get_rng_state()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
random.seed(0) |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | if hasattr(self, "rng_state"):
torch.set_rng_state(self.rng_state) |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | warnings.simplefilter("ignore", NumericalWarning)
train_x, train_y, test_x, test_y = make_data()
likelihood = GaussianLikelihood()
gp_model = GPRegressionModel(train_x, train_y, likelihood)
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)
# Optimize ... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | warnings.simplefilter("ignore", NumericalWarning)
train_x, train_y, test_x, test_y = make_data()
likelihood = GaussianLikelihood()
gp_model = GPRegressionModel(train_x, train_y, likelihood)
mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)
# Optimize ... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | warnings.simplefilter("ignore", NumericalWarning)
if not torch.cuda.is_available():
return
with least_used_cuda_device():
train_x, train_y, test_x, test_y = make_data(cuda=True)
likelihood = GaussianLikelihood().cuda()
gp_model = GPRegressionModel... |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | train_x = train_x.cuda()
train_y = train_y.cuda()
test_x = test_x.cuda()
test_y = test_y.cuda() |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | self.rng_state = torch.get_rng_state()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
random.seed(0) |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | torch.cuda.manual_seed_all(0) |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | torch.set_rng_state(self.rng_state) |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | return |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | unittest.main() |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | make_data |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | __init__ |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | forward |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | setUp |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | tearDown |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | test_sgpr_mean_abs_error |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | test_sgpr_fast_pred_var |
<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3
import os
import random
import unittest
import warnings
from math import exp, pi
import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, Scal... | test_sgpr_mean_abs_error_cuda |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project<｜fim▁hole｜>
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'output'
DEPLOY_PATH = env.deploy_path
# Remote ... | import os
import shutil
import sys
import SocketServer |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Remove generated files"""
if os.path.isdir(DEPLOY_PATH):
shutil.rmtree(DEPLOY_PATH)
os.makedirs(DEPLOY_PATH) |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Build local version of site"""
local('pelican -s pelicanconf.py') |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """`build` with the delete switch"""
local('pelican -d -s pelicanconf.py') |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Automatically regenerate site upon file modification"""
local('pelican -r -s pelicanconf.py') |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Serve site at http://localhost:8000/"""
os.chdir(env.deploy_path)
class AddressReuseTCPServer(SocketServer.TCPServer):
allow_reuse_address = True
server = AddressReuseTCPServer(('', PORT), ComplexHTTPRequestHandler)
sys.stderr.write('Serving on port {0} ...\n'.format(PORT))
server.... |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | allow_reuse_address = True |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """`build`, then `serve`"""
build()
serve() |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Build production version of site"""
local('pelican -s publishconf.py') |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Publish to Rackspace Cloud Files"""
rebuild()
with lcd(DEPLOY_PATH):
local('swift -v -A https://auth.api.rackspacecloud.com/v1.0 '
'-U {cloudfiles_username} '
'-K {cloudfiles_api_key} '
'upload -c {cloudfiles_container} .'.format(**env)) |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Publish to production via rsync"""
local('pelican -s publishconf.py')
project.rsync_project(
remote_dir=dest_path,
exclude=".DS_Store",
local_dir=DEPLOY_PATH.rstrip('/') + '/',
delete=True,
extra_opts='-c',
) |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | """Publish to GitHub Pages"""
rebuild()
local("ghp-import -b {github_pages_branch} {deploy_path} -p".format(**env)) |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | shutil.rmtree(DEPLOY_PATH)
os.makedirs(DEPLOY_PATH) |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | clean |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | build |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | rebuild |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | regenerate |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | serve |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | reserve |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | preview |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | cf_upload |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | publish |
<|file_name|>fabfile.py<|end_file_name|><｜fim▁begin｜>from fabric.api import *
import fabric.contrib.project as project
import os
import shutil
import sys
import SocketServer
from pelican.server import ComplexHTTPRequestHandler
# Local path configuration (can be absolute or relative to fabfile)
env.deploy_path = 'outp... | gh_pages |
<|file_name|>parts_descriptor_test.py<|end_file_name|><｜fim▁begin｜>import amitgroup as ag
import numpy as np
<｜fim▁hole｜>
pd = ag.features.PartsDescriptor((5, 5), 20, patch_frame=1, edges_threshold=5, samples_per_image=10)
# Use only 100 of the digits
pd.train_from_images(data)
# Save the model to a file.
#pd.save('... | ag.set_verbose(True)
# This requires you to have the MNIST data set.
data, digits = ag.io.load_mnist('training', selection=slice(0, 100)) |
<|file_name|>util.py<|end_file_name|><｜fim▁begin｜># proxy module<｜fim▁hole｜><｜fim▁end｜> | from apptools.logger.util import * |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | tested with iTHX-W
"""
|
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | """
http://www.omega.com/Manuals/manualpdf/M3861.pdf
iServer MicroServer
tested with iTHX-W
"""
scan_func = 'read_temperature'
def read_temperature(self, **kw):
v = self.ask('*SRTF', timeout=1.0, **kw)
return self._parse_response(v)
def read_humidity(self, **kw):
... |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | v = self.ask('*SRTF', timeout=1.0, **kw)
return self._parse_response(v) |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | v = self.ask('*SRH', timeout=1.0, **kw)
return self._parse_response(v) |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | try:
return float(v)
except (AttributeError, ValueError, TypeError):
return self.get_random_value() |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | read_temperature |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | read_humidity |
<|file_name|>environmental_probe.py<|end_file_name|><｜fim▁begin｜># ===============================================================================
# Copyright 2014 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may o... | _parse_response |
<|file_name|>testroute.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from .base import BaseHandler<｜fim▁hole｜>class TestRoute(BaseHandler):
def get(self, file):
return self.render(str(file) + '.jade', show_h1=1)<｜fim▁end｜> | |
<|file_name|>testroute.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from .base import BaseHandler
class TestRoute(BaseHandler):
<|fim_middle|>
<｜fim▁end｜> | def get(self, file):
return self.render(str(file) + '.jade', show_h1=1) |
<|file_name|>testroute.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from .base import BaseHandler
class TestRoute(BaseHandler):
def get(self, file):
<|fim_middle|>
<｜fim▁end｜> | return self.render(str(file) + '.jade', show_h1=1) |
<|file_name|>testroute.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from .base import BaseHandler
class TestRoute(BaseHandler):
def <|fim_middle|>(self, file):
return self.render(str(file) + '.jade', show_h1=1)
<｜fim▁end｜> | get |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | print "Mem: +", process.memory_info()[0] / float(2 ** 20) - s_mem |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
<|fim_middle|>
if __name__ == "__main__":
import psutil
process = psutil.Process(os.getpid())
s_mem = process.memory_info()[... | def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log = self.site.log
self.db = ContentDb.getContentDb()
self.db_id = self.db.needSite(site)
self.num_loaded = 0
super(ContentDbDict, self).__init__(se... |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
<|fim_middle|>
def loadItem(self, key):
try:
self.num_loaded ... | s = time.time()
self.site = site
self.cached_keys = []
self.log = self.site.log
self.db = ContentDb.getContentDb()
self.db_id = self.db.needSite(site)
self.num_loaded = 0
super(ContentDbDict, self).__init__(self.db.loadDbDict(site)) # Load keys from datab... |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | try:
self.num_loaded += 1
if self.num_loaded % 100 == 0:
if config.verbose:
self.log.debug("Loaded json: %s (latest: %s) called by: %s" % (self.num_loaded, key, Debug.formatStack()))
else:
self.log.debug("Loaded json... |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | return self.site.storage.getSize(key) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | if len(self.cached_keys) > 10:
key_deleted = self.cached_keys.pop(0)
dict.__setitem__(self, key_deleted, False) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | if key not in self.cached_keys and key != "content.json" and len(key) > 40: # Always keep keys smaller than 40 char
self.cached_keys.append(key) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | val = dict.get(self, key)
if val: # Already loaded
return val
elif val is None: # Unknown key
raise KeyError(key)
elif val is False: # Loaded before, but purged from cache
return self.loadItem(key) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | self.addCachedKey(key)
self.checkLimit()
size = self.getItemSize(key)
self.db.setContent(self.site, key, val, size)
dict.__setitem__(self, key, val) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | self.db.deleteContent(self.site, key)
dict.__delitem__(self, key)
try:
self.cached_keys.remove(key)
except ValueError:
pass |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | for key in dict.keys(self):
try:
val = self[key]
except Exception as err:
self.log.warning("Error loading %s: %s" % (key, err))
continue
yield key, val |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | back = []
for key in dict.keys(self):
try:
val = self[key]
except Exception as err:
self.log.warning("Error loading %s: %s" % (key, err))
continue
back.append((key, val))
return back |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | back = []
for key, val in dict.iteritems(self):
if not val:
try:
val = self.loadItem(key)
except Exception:
continue
back.append(val)
return back |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | try:
return self.__getitem__(key)
except KeyError:
return default
except Exception as err:
self.site.bad_files[key] = self.site.bad_files.get(key, 1)
dict.__delitem__(self, key)
self.log.warning("Error loading %s: %s" % (key, err))
... |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | params["site_id"] = self.db_id
return self.db.execute(query, params) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | if config.verbose:
self.log.debug("Loaded json: %s (latest: %s) called by: %s" % (self.num_loaded, key, Debug.formatStack()))
else:
self.log.debug("Loaded json: %s (latest: %s)" % (self.num_loaded, key)) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | self.log.debug("Loaded json: %s (latest: %s) called by: %s" % (self.num_loaded, key, Debug.formatStack())) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | self.log.debug("Loaded json: %s (latest: %s)" % (self.num_loaded, key)) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | self.__delitem__(key) # File not exists anymore |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | key_deleted = self.cached_keys.pop(0)
dict.__setitem__(self, key_deleted, False) |
<|file_name|>ContentDbDict.py<|end_file_name|><｜fim▁begin｜>import time
import os
import ContentDb
from Debug import Debug
from Config import config
class ContentDbDict(dict):
def __init__(self, site, *args, **kwargs):
s = time.time()
self.site = site
self.cached_keys = []
self.log... | self.cached_keys.append(key) |
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