file_name large_stringlengths 4 140 | prefix large_stringlengths 0 12.1k | suffix large_stringlengths 0 12k | middle large_stringlengths 0 7.51k | fim_type large_stringclasses 4
values |
|---|---|---|---|---|
utpgo.go |
return c.baseConn.Close()
}()
// wait for socket to enter StateDestroying
<-c.baseConnDestroyed
c.setEncounteredError(net.ErrClosed)
socketCloseErr := c.utpSocket.Close()
// even if err was already set, this one is likely to be more helpful/interesting.
if socketCloseErr != nil {
err = socketCloseErr
}
... | close(c.connectChan)
}
}
| random_line_split | |
utpgo.go | error) {
s := utpDialState{
logger: &noopLogger,
}
for _, opt := range options {
opt.apply(&s)
}
switch network {
case "utp", "utp4", "utp6":
default:
return nil, fmt.Errorf("network %s not supported", network)
}
udpAddr, err := ResolveUTPAddr(network, addr)
if err != nil {
return nil, err
}
listen... | func (c *Conn) WriteContext(ctx context.Context, buf []byte) (n int, err error) {
c.stateLock.Lock()
if c.writePending {
c.stateLock.Unlock()
return 0, buffers.WriterAlreadyWaitingErr
}
c.writePending = true
deadline := c.writeDeadline
c.stateLock.Unlock()
if err != nil {
if err == io.EOF {
// remote s... | return c.WriteContext(context.Background(), buf)
}
| identifier_body |
chain_spec.rs | NDb1JRwaHHVWyP9
hex!["f26cdb14b5aec7b2789fd5ca80f979cef3761897ae1f37ffb3e154cbcc1c2663"].into(),
// 5EPQdAQ39WQNLCRjWsCk5jErsCitHiY5ZmjfWzzbXDoAoYbn
hex!["66bc1e5d275da50b72b15de072a2468a5ad414919ca9054d2695767cf650012f"].into(),
// 5DMa31Hd5u1dwoRKgC4uvqyrdK45RHv3CpwvpUC1EzuwDit4
hex!["3919132b851e... | {
fn icefrog_config_genesis() -> GenesisConfig {
darwinia_genesis(
vec![
get_authority_keys_from_seed("Alice"),
get_authority_keys_from_seed("Bob"),
],
hex!["a60837b2782f7ffd23e95cd26d1aa8d493b8badc6636234ccd44db03c41fcc6c"].into(), // 5FpQFHfKd1xQ9HLZLQoG1JAQSCJoUEVBELnKsKNcuRLZejJR
vec![
he... | identifier_body | |
chain_spec.rs | ;
const RING_ENDOWMENT: Balance = 20_000_000 * COIN;
const KTON_ENDOWMENT: Balance = 10 * COIN;
const STASH: Balance = 1000 * COIN;
GenesisConfig {
frame_system: Some(SystemConfig {
code: WASM_BINARY.to_vec(),
changes_trie_config: Default::default(),
}),
pallet_indices: Some(IndicesConfig {
ids: en... | {
vec![initial_authorities[0].clone().1, initial_authorities[1].clone().1]
} | conditional_block | |
chain_spec.rs | (Default::default()),
// pallet_treasury: Some(Default::default()),
pallet_ring: Some(BalancesConfig {
balances: endowed_accounts
.iter()
.cloned()
.map(|k| (k, RING_ENDOWMENT))
.chain(initial_authorities.iter().map(|x| (x.0.clone(), STASH)))
.collect(),
vesting: vec![],
}),
pallet_kt... | stakers: initial_authorities
.iter()
.map(|x| (x.0.clone(), x.1.clone(), STASH, StakerStatus::Validator))
.collect(),
invulnerables: initial_authorities.iter().map(|x| x.0.clone()).collect(),
slash_reward_fraction: Perbill::from_percent(10),
..Default::default()
}),
}
}
/// Staging testnet c... | pallet_staking: Some(StakingConfig {
current_era: 0,
validator_count: initial_authorities.len() as u32 * 2,
minimum_validator_count: initial_authorities.len() as u32, | random_line_split |
chain_spec.rs | 1L1LU9jaNeeu9HJkP6eyg3BwXA7iNMzKm7qqruQ
hex!["482dbd7297a39fa145c570552249c2ca9dd47e281f0c500c971b59c9dcdcd82e"].unchecked_into(),
),
(
// 5DyVtKWPidondEu8iHZgi6Ffv9yrJJ1NDNLom3X9cTDi98qp
hex!["547ff0ab649283a7ae01dbc2eb73932eba2fb09075e9485ff369082a2ff38d65"].into(),
// 5FeD54vGVNpFX3PndHPXJ2MDak... | icefrog_config_genesis | identifier_name | |
mnist_benchmark.py | us
"""
GCP_ENV = 'PATH=/tmp/pkb/google-cloud-sdk/bin:$PATH'
flags.DEFINE_string('mnist_data_dir', None, 'mnist train file for tensorflow')
flags.DEFINE_string('imagenet_data_dir',
'gs://cloud-tpu-test-datasets/fake_imagenet',
'Directory where the input data is stored')
flags.DEF... |
return samples
def MakeSamplesFromEvalOutput(metadata, output, elapsed_seconds):
"""Create a sample containing evaluation metrics.
Args:
metadata: dict contains all the metadata that reports.
output: string, command output
elapsed_seconds: float, elapsed seconds from saved checkpoint.
Example o... | global_step_sec = get_mean(regex_util.ExtractAllMatches(
r'global_step/sec: (\S+)', output))
samples.append(sample.Sample(
'Global Steps Per Second', global_step_sec,
'global_steps/sec', metadata_copy))
examples_sec = global_step_sec * metadata['train_batch_size']
if 'examples/sec: '... | conditional_block |
mnist_benchmark.py | eastus
"""
GCP_ENV = 'PATH=/tmp/pkb/google-cloud-sdk/bin:$PATH'
flags.DEFINE_string('mnist_data_dir', None, 'mnist train file for tensorflow')
flags.DEFINE_string('imagenet_data_dir',
'gs://cloud-tpu-test-datasets/fake_imagenet',
'Directory where the input data is stored')
flag... | metadata_with_index = copy.deepcopy(metadata)
metadata_with_index['index'] = index
samples.append(sample.Sample(metric, float(value), unit,
metadata_with_index))
return samples
def MakeSamplesFromTrainOutput(metadata, output, elapsed_seconds, step):
"""Create a sample ... | """
matches = regex_util.ExtractAllMatches(regex, output)
samples = []
for index, value in enumerate(matches): | random_line_split |
mnist_benchmark.py | eastus
"""
GCP_ENV = 'PATH=/tmp/pkb/google-cloud-sdk/bin:$PATH'
flags.DEFINE_string('mnist_data_dir', None, 'mnist train file for tensorflow')
flags.DEFINE_string('imagenet_data_dir',
'gs://cloud-tpu-test-datasets/fake_imagenet',
'Directory where the input data is stored')
flag... | (benchmark_spec):
"""Update the benchmark_spec with supplied command line flags.
Args:
benchmark_spec: benchmark specification to update
"""
benchmark_spec.data_dir = FLAGS.mnist_data_dir
benchmark_spec.iterations = FLAGS.tpu_iterations
benchmark_spec.gcp_service_account = FLAGS.gcp_service_account
b... | _UpdateBenchmarkSpecWithFlags | identifier_name |
mnist_benchmark.py | eastus
"""
GCP_ENV = 'PATH=/tmp/pkb/google-cloud-sdk/bin:$PATH'
flags.DEFINE_string('mnist_data_dir', None, 'mnist train file for tensorflow')
flags.DEFINE_string('imagenet_data_dir',
'gs://cloud-tpu-test-datasets/fake_imagenet',
'Directory where the input data is stored')
flag... | metadata_copy['epoch'] = step / num_examples_per_epoch
metadata_copy['elapsed_seconds'] = elapsed_seconds
return [sample.Sample('Eval Loss', float(loss), '', metadata_copy),
sample.Sample('Accuracy', float(accuracy) * 100, '%', metadata_copy)]
def Run(benchmark_spec):
"""Run MNIST on the cluster.
... | """Create a sample containing evaluation metrics.
Args:
metadata: dict contains all the metadata that reports.
output: string, command output
elapsed_seconds: float, elapsed seconds from saved checkpoint.
Example output:
perfkitbenchmarker/tests/linux_benchmarks/mnist_benchmark_test.py
Returns:... | identifier_body |
ext.rs | _len: u32,
result_ptr: *mut u8,
result_len: u32,
) -> i32;
/// Static call.
/// Corresponds to "STACICCALL" opcode in EVM
pub fn scall(
gas: i64,
address: *const u8,
input_ptr: *const u8,
input_len: u32,
... | /// Get the block's timestamp
///
/// It can be viewed as an output of Unix's `time()` function at
/// current block's inception.
pub fn timestamp() -> u64 {
unsafe { external::timestamp() as u64 }
}
/// Get the block's number
///
/// This value represents number of ancestor blocks.
/// The genesis block has a num... | unsafe { fetch_address(|x| external::coinbase(x) ) }
}
| identifier_body |
ext.rs | _len: u32,
result_ptr: *mut u8,
result_len: u32,
) -> i32;
/// Static call.
/// Corresponds to "STACICCALL" opcode in EVM
pub fn scall(
gas: i64,
address: *const u8,
input_ptr: *const u8,
input_len: u32,
... | else {
Err(Error)
}
}
}
/// Returns hash of the given block or H256::zero()
///
/// Only works for 256 most recent blocks excluding current
/// Returns H256::zero() in case of failure
pub fn block_hash(block_number: u64) -> H256 {
let mut res = H256::zero();
unsafe {
external::... | {
Ok(())
} | conditional_block |
ext.rs | _len: u32,
result_ptr: *mut u8,
result_len: u32,
) -> i32;
/// Static call.
/// Corresponds to "STACICCALL" opcode in EVM
pub fn scall(
gas: i64,
address: *const u8,
input_ptr: *const u8,
input_len: u32,
... | if external::create(
endowment_arr.as_ptr(),
code.as_ptr(),
code.len() as u32,
(&mut result).as_mut_ptr()
) == 0 {
Ok(result)
} else {
Err(Error)
}
}
}
#[cfg(feature = "kip4")]
/// Create a new account with the ... | random_line_split | |
ext.rs | _len: u32,
result_ptr: *mut u8,
result_len: u32,
) -> i32;
/// Static call.
/// Corresponds to "STACICCALL" opcode in EVM
pub fn scall(
gas: i64,
address: *const u8,
input_ptr: *const u8,
input_len: u32,
... | -> u64 {
unsafe { external::blocknumber() as u64 }
}
/// Get the block's difficulty.
pub fn difficulty() -> U256 {
unsafe { fetch_u256(|x| external::difficulty(x) ) }
}
/// Get the block's gas limit.
pub fn gas_limit() -> U256 {
unsafe { fetch_u256(|x| external::gaslimit(x) ) }
}
#[cfg(feature = "kip6")... | ock_number() | identifier_name |
networking.py | self.listen_thread.start()
def listen(self):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(("", self.port))
s.listen(1)
while True:
try:
clientsock, clientaddr = s.accept()
... | (self):
w = Writer()
w.single("H", self.dmo_id)
w.string(self.image_name)
w.single("HH", *self.position)
w.single("B", (self.hidden << 0) + (self.obstruction << 1))
w.string(self.minimapimage)
return w.data.getvalue()
@classmethod
def from_stream(cls, stre... | pack | identifier_name |
networking.py | self.listen_thread.start()
def listen(self):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(("", self.port))
s.listen(1)
while True:
try:
clientsock, clientaddr = s.accept()
... | type_id = 116
# TODO
class MResourceQuantity(Message):
type_id = 117
struct_format = "!hhh"
attrs = ("tx", "ty", "q")
# Player/connection stuff
class MNewPlayer(Message):
type_id = 120
attrs = ("player_id", "name", "color", "loading")
def pack(self):
w = Writer()
w.s... | type_id = 115
struct_format = "!HB"
attrs = ("dmo_id", "hidden")
class MDMOPosition(Message): | random_line_split |
networking.py | self.listen_thread.start()
def listen(self):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(("", self.port))
s.listen(1)
while True:
try:
clientsock, clientaddr = s.accept()
... |
self.data.write(struct.pack(format, *values))
def multi(self, format, values):
self.single("!H", len(values))
if len(format.strip("@=<>!")) > 1:
for v in values:
self.single(format, *v)
else:
for v in values:
self.single(forma... | format = "!"+format | conditional_block |
networking.py | self.listen_thread.start()
def listen(self):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
s.bind(("", self.port))
s.listen(1)
while True:
try:
clientsock, clientaddr = s.accept()
... |
def values(self):
l = []
for a in self.attrs:
l.append(getattr(self, a))
return l
@classmethod
def from_stream(cls, stream):
res = struct.unpack(cls.struct_format,
stream.read(struct.calcsize(cls.struct_format)))
return cls(*res)
de... | attrs = list(self.attrs)
for arg in pargs:
n = attrs.pop(0)
setattr(self, n, arg)
for n in attrs:
setattr(self, n, kwargs.pop(n))
if kwargs:
raise TypeError("unexpected keyword argument '%s'" %
kwargs.keys()[0]) | identifier_body |
async_await_basics.rs | });
// Drop the spawner so that our executor knows it is finished and won't
// receive more incoming tasks to run.
drop(spawner);
// Run the executor until the task queue is empty.
// This will print "howdy!", pause, and then print "done!".
executor.run();
}
struct Song {
name: String... | {
shared_state: Arc<Mutex<SharedState>>,
}
/// Shared state between the future and the waiting thread
struct SharedState {
/// Whether or not the sleep time has elapsed
completed: bool,
/// The waker for the task that `TimerFuture` is running on.
/// The thread can use this after setting `complet... | TimerFuture | identifier_name |
async_await_basics.rs | !");
});
// Drop the spawner so that our executor knows it is finished and won't
// receive more incoming tasks to run.
drop(spawner);
// Run the executor until the task queue is empty.
// This will print "howdy!", pause, and then print "done!".
executor.run();
}
struct Song {
name: S... | // function, but we omit that here to keep things simple.
shared_state.waker = Some(cx.waker().clone());
Poll::Pending
}
}
}
impl TimerFuture {
/// Create a new `TimerFuture` which will complete after the provided
/// timeout.
pub fn new(duration: Duration) -... | //
// N.B. it's possible to check for this using the `Waker::will_wake` | random_line_split |
async_await_basics.rs | });
// Drop the spawner so that our executor knows it is finished and won't
// receive more incoming tasks to run.
drop(spawner);
// Run the executor until the task queue is empty.
// This will print "howdy!", pause, and then print "done!".
executor.run();
}
struct Song {
name: String... |
async fn learn_and_sing() {
let song = learn_song().await;
sing_song(song).await;
}
async fn async_main() {
let f2 = dance();
let f1 = learn_and_sing();
futures::join!(f2, f1);
}
// Each time a future is polled, it is polled as part of a "task". Tasks are the top-level futures
// that have been... | {
println!("Dance!!")
} | identifier_body |
async_await_basics.rs | });
// Drop the spawner so that our executor knows it is finished and won't
// receive more incoming tasks to run.
drop(spawner);
// Run the executor until the task queue is empty.
// This will print "howdy!", pause, and then print "done!".
executor.run();
}
struct Song {
name: String... |
}
}
}
}
// In practice, this problem is solved through integration with an IO-aware system blocking primitive,
// such as epoll on Linux, kqueue on FreeBSD and Mac OS, IOCP on Windows, and ports on Fuchsia (all
// of which are exposed through the cross-platform Rust crate mio). These primitive... | {
// We're not done processing the future, so put it
// back in its task to be run again in the future.
*future_slot = Some(future);
} | conditional_block |
controller.py | knekkes, samt rom det skal søkes i, (muligens størrelse på hver enkelt arbeidoppgave?) og eventuell hashing-algoritme.
Fordeler arbeidoppgaver ved å motta en forespørsel, og sender ut "Kode", tegn, arbeidsnodens søkerom, samt hashing-algoritme.
Tar inn resultater (enten svar, eller beskjed om at koden ikke er i ... | int, end_point, keyword, chars, searchwidth, algorithm, id):
self.start_point = start_point # Where to start searching
self.end_point = end_point # Where to end the search
self.keyword = keyword # What you're searching for
self.chars = chars # Characters t... | start_po | identifier_name |
controller.py | ommet den ble gitt), og slutter å dele ut oppgaver basert på den koden om den er funnet (returnerer svar til den som sendte dette inn).
All kommunikasjon skal foregå via JSON api-kall
"""
#Webserver
from http.server import HTTPServer, BaseHTTPRequestHandler
from io import BytesIO
#Other needed packages
impor... | conditional_block | ||
controller.py | knekkes, samt rom det skal søkes i, (muligens størrelse på hver enkelt arbeidoppgave?) og eventuell hashing-algoritme.
Fordeler arbeidoppgaver ved å motta en forespørsel, og sender ut "Kode", tegn, arbeidsnodens søkerom, samt hashing-algoritme.
Tar inn resultater (enten svar, eller beskjed om at koden ikke er i ... | k_id(tasks):
task_id_unique = False
temp_id = floor(random()*10000)
while not task_id_unique:
task_id_unique = True
temp_id = floor(random()*10000)
for task in tasks:
if task.id == temp_id:
task_id_unique = False
return temp_id
class Task:
... | ps(get_next_job())
def gen_tas | identifier_body |
controller.py | skal knekkes, samt rom det skal søkes i, (muligens størrelse på hver enkelt arbeidoppgave?) og eventuell hashing-algoritme.
Fordeler arbeidoppgaver ved å motta en forespørsel, og sender ut "Kode", tegn, arbeidsnodens søkerom, samt hashing-algoritme.
Tar inn resultater (enten svar, eller beskjed om at koden ikke ... |
self.current_point = self.start_point # Will be increased as workers are given blocks to search through
self.finished = False
self.keyword_found = ""
def get_task(self):
return {
'id':self.id,
'finished':self.finished,
'algorithm':self.a... | random_line_split | |
main.rs | ClassStruct<Self>;
glib_object_subclass!();
fn new() -> Self {
Self {
widgets: OnceCell::new(),
counter: Cell::new(0),
}
}
}
static MUSIC_FOLDER: &str = "musics";
impl ObjectImpl for SimpleWindowPrivate {
glib_object_impl!();
// Here we are overriding the ... | {
glib::Object::new(
Self::static_type(),
&[
("application-id", &"org.gtk-rs.SimpleApplication"),
("flags", &ApplicationFlags::empty()),
],
)
.expect("Failed to create SimpleApp")
.downcast()
.expect("Created sim... | identifier_body | |
main.rs | use gio::subclass::application::ApplicationImplExt;
use gio::ApplicationFlags;
use glib::subclass;
use glib::subclass::prelude::*;
use glib::translate::*;
use gtk::subclass::prelude::*;
use once_cell::unsync::OnceCell;
use std::cell::Cell;
mod audio_handler;
#[derive(Debug)]
struct WindowWidgets {
headerbar: gtk... | audio_player_clone.pause_music();
});
// Connect our method `on_increment_clicked` to be called
// when the increment button is clicked.
increment.connect_clicked(clone!(@weak self_ => move |_| {
let priv_ = SimpleWindowPrivate::from_instance(&self_);
... | pause_button.connect_clicked(move |_| { | random_line_split |
main.rs | use gio::subclass::application::ApplicationImplExt;
use gio::ApplicationFlags;
use glib::subclass;
use glib::subclass::prelude::*;
use glib::translate::*;
use gtk::subclass::prelude::*;
use once_cell::unsync::OnceCell;
use std::cell::Cell;
mod audio_handler;
#[derive(Debug)]
struct WindowWidgets {
headerbar: gtk... | (&self) {
self.counter.set(self.counter.get() + 1);
let w = self.widgets.get().unwrap();
w.label
.set_text(&format!("Your life has {} meaning", self.counter.get()));
}
fn on_decrement_clicked(&self) {
self.counter.set(self.counter.get().wrapping_sub(1));
let w... | on_increment_clicked | identifier_name |
configfunction.js | 34\u503C\u4E0D\u80FD\u4E3A\u7A7A\u3002<br>";
}else{
var obj={attributeName:texts[0].split(":")[1],attributeCode:texts[1].split(":")[1],attributeType:texts[2].split(":")[1],lstConfigValueVO:[]};
jsonHead.push(obj);
}
}else{
msg+="\u6570\u636E\u503C\u533A\u57DF\u7B2C"+(i+1)+"\u5217\u9898\u5934\u5... | eBorder(obj){
| identifier_name | |
configfunction.js | 10)>30){
//return "值为'"+data+"'的日期类型数据"+parseInt(dateArray[1],10)+"月份天数不能超过30天。<br>";
return "\u503C\u4E3A'"+data+"'\u7684\u65E5\u671F\u7C7B\u578B\u6570\u636E"+parseInt(dateArray[1],10)+"\u6708\u4EFD\u5929\u6570\u4E0D\u80FD\u8D85\u8FC7\u0033\u0030\u5929\u3002<br>";
}
}else{
if(parseInt(dateArray[2]... | if(td){ | random_line_split | |
configfunction.js | \u6708\u4EFD\u5929\u6570\u4E0D\u80FD\u4E3A\u0030\u5929\u3002<br>";
}
}
//}else if(types=="4"||types=="布尔型"){
}else if(types=="4"||types=="\u5E03\u5C14\u578B"){
if(data!="true" && data!="false" && data!="TRUE" && data!="FALSE"){
//return "类型为布尔型的数据值必须为布尔型。如 true/false<br>";
return "\u7C7B\u578B\u4E3A\u5E0... | eDataRegexInfo;
}
flag = false;
//只有值不为空时才进行验证
//验证输入的值是否合法
var attributeType = trimSpace(jsonHead[j-1].attributeType);
var vInfo = validationData(attributeType,trimSpace(tempValues));
if(vInfo!=""){
//msg += "数据值区域第"+i+"行,第"+(j+1)+"列"+vInfo;
msg += "\u6570\u636E\u503C\u53... | conditional_block | |
configfunction.js | C\u4E0D\u80FD\u4E3A\u7A7A\u3002<br>";
}else{
var obj={attributeName:texts[0].split(":")[1],attributeCode:texts[1].split(":")[1],attributeType:texts[2].split(":")[1],lstConfigValueVO:[]};
jsonHead.push(obj);
}
}else{
msg+="\u6570\u636E\u503C\u533A\u57DF\u7B2C"+(i+1)+"\u5217\u9898\u5934\u503C\u4E... | identifier_body | ||
lstm.py | 00000)
# df.head()
# print(df.shape)
df_filtered=df[df['stars'] !=3]
# print(df_filtered.shape)
#print(df_filtered.describe().T)
text=list(df_filtered['text'])
stars=list(df_filtered['stars'])
print(type(text))
label=[]
for item in stars:
if item>= 4:
y=1
else:
y=0
label.append(y)
label... | (self, x, hidden):
"""
Perform a forward pass of our model on some input and hidden state.
"""
batch_size = x.size(0)
# embeddings and lstm_out
embeds = self.embedding(x)
lstm_out, hidden = self.lstm(embeds, hidden)
# stack up lstm outputs
... | forward | identifier_name |
lstm.py | 100000)
# df.head()
# print(df.shape)
df_filtered=df[df['stars'] !=3]
# print(df_filtered.shape)
#print(df_filtered.describe().T)
text=list(df_filtered['text'])
stars=list(df_filtered['stars'])
print(type(text))
label=[]
for item in stars:
if item>= 4:
y=1
else:
y=0
label.append(y)
labe... |
output, h = net(inputs, h)
# calculate the loss and perform backprop
loss = criterion(output.squeeze(), labels.float())
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
nn.utils.clip_grad_norm_(net.parameters(), cl... | h = net.init_hidden(batch_size, train_on_gpu)
counter = 0
# batch loop
for inputs, labels in train_loader:
counter += 1
#print('epoce: {e}, batch: {b}'.format(e=e, b=counter))
if (labels.shape[0] != batch_size):
continue
inputs = inputs.type(torch.LongTensor)
... | conditional_block |
lstm.py | 00000)
# df.head()
# print(df.shape)
df_filtered=df[df['stars'] !=3]
# print(df_filtered.shape)
#print(df_filtered.describe().T)
text=list(df_filtered['text'])
stars=list(df_filtered['stars'])
print(type(text))
label=[]
for item in stars:
if item>= 4:
y=1
else:
y=0
label.append(y)
label... |
def forward(self, x, hidden):
"""
Perform a forward pass of our model on some input and hidden state.
"""
batch_size = x.size(0)
# embeddings and lstm_out
embeds = self.embedding(x)
lstm_out, hidden = self.lstm(embeds, hidden)
# stack up lst... | """
Initialize the model by setting up the layers.
"""
super(SentimentRNN, self).__init__()
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
# embedding and LSTM layers
self.embedding = nn.Embedding(vocab_size,... | identifier_body |
lstm.py | 00000)
# df.head()
# print(df.shape)
df_filtered=df[df['stars'] !=3]
# print(df_filtered.shape)
#print(df_filtered.describe().T)
| print(type(text))
label=[]
for item in stars:
if item>= 4:
y=1
else:
y=0
label.append(y)
label=np.array(label)
#we can get punctuation from string library
from string import punctuation
print(punctuation)
all_reviews=[]
for item in text:
item = item.lower()
item = "".join([ch for ch in... | text=list(df_filtered['text'])
stars=list(df_filtered['stars'])
| random_line_split |
Master Solution.py | processing and data analysis). Please comment your code appropriately. You will be evaluated for properly structuring your code and for building checks and balances in your analysis- which should be included in your code as well.*
#
# *2. If some data visualization tool such as Tableau/PowerBI is used for presentati... | (data):
#convert text to lower-case
data['processed_text'] = data['Query Text'].apply(lambda x:' '.join(x.lower() for x in x.split()))
#remove punctuations, unwanted characters
data['processed_text_1']= data['processed_text'].apply(lambda x: "".join([char for char in x if char not in string.punctu... | text_preprocessing | identifier_name |
Master Solution.py | data processing and data analysis). Please comment your code appropriately. You will be evaluated for properly structuring your code and for building checks and balances in your analysis- which should be included in your code as well.*
#
# *2. If some data visualization tool such as Tableau/PowerBI is used for prese... |
# In[9]:
df.isnull().sum()
# In[10]:
df.drop_duplicates(subset ="Query Text",
keep = 'last', inplace = True)
# In[11]:
df.info()
# In[12]:
# check the length of documents
document_lengths = np.array(list(map(len, df['Query Text'].str.split(' '))))
print("The average number of wor... | random_line_split | |
Master Solution.py | processing and data analysis). Please comment your code appropriately. You will be evaluated for properly structuring your code and for building checks and balances in your analysis- which should be included in your code as well.*
#
# *2. If some data visualization tool such as Tableau/PowerBI is used for presentati... |
# In[15]:
#pre-processing or cleaning data
text_preprocessing(df)
df.head()
# In[16]:
#create tokenized data for LDA
df['final_tokenized'] = list(map(nltk.word_tokenize, df.final_text))
df.head()
# ## LDA training
# In[17]:
# Create Dictionary
id2word = corpora.Dictionary(df['final_tokenized'])
tex... | data['processed_text'] = data['Query Text'].apply(lambda x:' '.join(x.lower() for x in x.split()))
#remove punctuations, unwanted characters
data['processed_text_1']= data['processed_text'].apply(lambda x: "".join([char for char in x if char not in string.punctuation]))
#remove numbers
data['processed... | identifier_body |
Master Solution.py | : {}.".format(min(document_lengths)))
print("The maximum number of words in a document is: {}.".format(max(document_lengths)))
# In[13]:
print("There are {} documents with tops 5 words.".format(sum(document_lengths == 1)))
print("There are {} documents with tops 5 words.".format(sum(document_lengths == 2)))
print("... | nt.label_)
ent_common.append(ent.text)
print("U | conditional_block | |
helpers.go | updated string
// with replaced coordinates and the list of coordinates
func ExtractCoordinates(text string) (string, Coordinates) {
var (
// <a href="geo:49.976136, 36.267256">49.976136, 36.267256</a>
geoHrefRe = regexp.MustCompile("<a.+?href=\"geo:(\\d{2}[.,]\\d{3,}),?\\s*(\\d{2}[.,]\\d{3,})\">(.+?)</a>")
// ... | if mrHr := reHr.FindAllStringSubmatch(res, -1); len(mrHr) > 0 {
for _, item := range mrHr {
res = regexp.MustCompile(item[0]).ReplaceAllLiteralString(res, "\n")
}
}
if mrP := reP.FindAllStringSubmatch(res, -1); len(mrP) > 0 {
for _, item := range mrP {
res = regexp.MustCompile(regexp.QuoteMeta(item[0])).
... | for _, item := range mrBr {
res = regexp.MustCompile(item[0]).ReplaceAllLiteralString(res, "\n")
}
}
| conditional_block |
helpers.go | updated string
// with replaced coordinates and the list of coordinates
func ExtractCoordinates(text string) (string, Coordinates) {
var (
// <a href="geo:49.976136, 36.267256">49.976136, 36.267256</a>
geoHrefRe = regexp.MustCompile("<a.+?href=\"geo:(\\d{2}[.,]\\d{3,}),?\\s*(\\d{2}[.,]\\d{3,})\">(.+?)</a>")
// ... | // scripts in the task. To skip tag, it is necessary to call Next() two times:
// 1) returns TextToken with the script body
// 2) returns EndTagToken for the closed script tag
// Usually script tag doesn't have any neste tags, so this aproach should work
log.Printf("[INFO] Skipping script tag")
... | {
var (
parser = html.NewTokenizer(strings.NewReader(text))
tagStack = stack.New()
textToTag = map[int]string{}
)
for {
node := parser.Next()
switch node {
case html.ErrorToken:
result := strings.Replace(textToTag[0], " ", " ", -1)
return result
case html.TextToken:
t := string(parse... | identifier_body |
helpers.go | updated string
// with replaced coordinates and the list of coordinates
func ExtractCoordinates(text string) (string, Coordinates) {
var (
// <a href="geo:49.976136, 36.267256">49.976136, 36.267256</a>
geoHrefRe = regexp.MustCompile("<a.+?href=\"geo:(\\d{2}[.,]\\d{3,}),?\\s*(\\d{2}[.,]\\d{3,})\">(.+?)</a>")
// ... | reCenter = regexp.MustCompile("<center>((?s:.*?))</center>")
reFont = regexp.MustCompile("<font.+?color\\s*=\\\\?[\"«]?#?(\\w+)\\\\?[\"»]?.*?>((?s:.*?))</font>")
reA = regexp.MustCompile("<a.+?href=\\\\?\"(.+?)\\\\?\".*?>(.+?)</a>")
res = text
)
res = strings.Replace(text, "_", "\\_", -1)
if mrB... | reStrong = regexp.MustCompile("<strong.*?>(.*?)</strong>")
reItalic = regexp.MustCompile("<i>((?s:.+?))</i>")
reSpan = regexp.MustCompile("<span.*?>(.*?)</span>") | random_line_split |
helpers.go | (text string, re *regexp.Regexp) (string, Coordinates) {
var (
result = text
mr = re.FindAllStringSubmatch(text, -1)
coords = Coordinates{}
)
if len(mr) > 0 {
for _, item := range mr {
lon, _ := strconv.ParseFloat(item[1], 64)
lat, _ := strconv.ParseFloat(item[2], 64)
if len(item) > 3 {
coor... | extractCoordinates | identifier_name | |
trainPredictor.py |
pathjoin = os.path.join
pathexists = os.path.exists
mdy = dtime.datetime.now().strftime('%m%d%y')
product_type = 'interferogram'
cache_dir = 'cached'
train_folds = np.inf # inf = leave-one-out, otherwise k-fold cross validation
train_state = 42 # random seed
train_verbose = 0
train_jobs = -1
cv_type... | print(process,exitv,message) | identifier_body | |
trainPredictor.py | ProductMeta(prod_url,verbose=False,remove=True):
"""
curlProductMeta(prod_url,verbose=False)
Arguments:
- prod_url: product url
Keyword Arguments:
- verbose: verbose output (default=False)
Returns: metadata dict from product .met.json
"""
if prod_url.endswith... | (usertags,classmap):
'''
return dictionary of matched (tag,label) pairs in classmap for all tags
returns {} if none of the tags are present in classmap
'''
labelmap = {}
for tag in usertags:
tag = tag.strip()
for k,v in list(classmap.items()):
if tag.count(k):
... | usertags2label | identifier_name |
trainPredictor.py | search query...')
from utils.queryBuilder import postQuery, buildQuery
from utils.contextUtils import toContext
ret,status = postQuery(buildQuery(querymeta,queryoptions))
if cache and status:
# only dump the query if caching enabled and postQuery succeeds
with op... | model_clf = RandomForestClassifier(**rf_defaults)
model_tuned = [rf_tuned] | conditional_block | |
trainPredictor.py | return url.replace(product_type,'features').replace('features__','features_'+product_type+'__')
def fdict2vec(featdict,clfinputs):
'''
extract feature vector from dict given classifier parameters
specifying which features to use
'''
fvec = []
try:
featspec = clfinputs['feature... | - feature id for url
""" | random_line_split | |
photcalibration.py | ::-1]
ps1colorterms['i'] = [+0.01170, -0.00400, +0.00066, -0.00058][::-1]
ps1colorterms['z'] = [-0.01062, +0.07529, -0.03592, +0.00890][::-1]
def __init__(self, basedir):
self.basedir = basedir
self.skytable = None
def PS1toSDSS(self, table):
"""
Modify table in situ fr... | search = "%s/*-[es][19]1.fits.fz" % (directory)
inputlist = glob.glob(search)
initialsize = len (inputlist)
rejects = []
if not args.redo:
for image in inputlist:
if db.exists(image):
rejects.append (image)
for r in rejects:
inputlist.remove (r)... | identifier_body | |
photcalibration.py | , 'defaultZP': 0.0}
FILTERMAPPING['rp'] = {'refMag': 'r', 'colorTerm': 0.0, 'airmassTerm': 0.12, 'defaultZP': 0.0}
FILTERMAPPING['ip'] = {'refMag': 'i', 'colorTerm': 0.0, 'airmassTerm': 0.08, 'defaultZP': 0.0}
FILTERMAPPING['zp'] = {'refMag': 'z', 'colorTerm': 0.0, 'airmassTerm': 0.05, 'defaultZP': 0.0}
... | cat_ra_shifted[cat_ra > 180] -= 360 | conditional_block | |
photcalibration.py | '] = [-0.01062, +0.07529, -0.03592, +0.00890][::-1]
def __init__(self, basedir):
self.basedir = basedir
self.skytable = None
def PS1toSDSS(self, table):
"""
Modify table in situ from PS1 to SDSS, requires column names compatible with ps1colorterms definition.
:param ta... |
:param site:
:param camera: | random_line_split | |
photcalibration.py | only from the input catalog.
## Why reverse the order of the color term entries? Data are entered in the order as they are
## shown in paper. Reverse after the fact to avoid confusion when looking at paper
ps1colorterms = {}
ps1colorterms['g'] = [-0.01808, -0.13595, +0.01941, -0.00183][::-1]
ps1c... | crawlDirectory | identifier_name | |
genetic.py | states
if DEBUG: print(gene_pool)
counter = 0 | flips = 0
while counter < GIVE_UP:
if rand_restarts and counter>0 and not (counter % restart): # random restarts
if not tb: print("restarting: (" + str(new_vals[0]) + "/" + str(NUM_CLAUSES) + ")")
initialize_states(gene_pool, gene_pool[0])
if not tb: print("iteration", ... | restart = int(tries/5) | random_line_split |
genetic.py | states
if DEBUG: print(gene_pool)
counter = 0
restart = int(tries/5)
flips = 0
while counter < GIVE_UP:
if rand_restarts and counter>0 and not (counter % restart): # random restarts
if not tb: print("restarting: (" + str(new_vals[0]) + "/" + str(NUM_CLAUSES) + ")")
... |
# not currently using
def flip_heuristic(safe, new_pool, evaluations):
for i in range(safe, POOL_SIZE):
flipped = flip_bits(new_pool[i])
value = evaluate(flipped)
if value >= evaluations[i]:
evaluations[i] = value
new_pool[i] = flipped
def flip_bits(string):
n... | new_str = string[:index] + ("1" if string[index]=="0" else "0") + string[(index+1):]
new_eval = evaluate(new_str)
if new_eval > evaluation:
return (new_str, new_eval)
return (None, None) | identifier_body |
genetic.py | (NUM_CLAUSES) +") clauses.")
return (0, flips)
counter += 1
if not tb: # test bench
print(">> GAVE UP after " + str(GIVE_UP) + " tries.")
print(">> Current Best:", readable(gene_pool[0]))
print(">> Satisfied (" + str(new_vals[0]) + "/" + str(NUM_CLAUSES) +") clauses.")... | for j in range(num):
res[i][j] = calc_dist(cities[i][1], cities[j][1])
res[j][i] = res[i][j] | conditional_block | |
genetic.py |
if DEBUG: print(gene_pool)
counter = 0
restart = int(tries/5)
flips = 0
while counter < GIVE_UP:
if rand_restarts and counter>0 and not (counter % restart): # random restarts
if not tb: print("restarting: (" + str(new_vals[0]) + "/" + str(NUM_CLAUSES) + ")")
in... | (safe, new_pool):
for i in range(safe, POOL_SIZE):
if flip_coin(.9):
mutant = ""
for j in range(len(new_pool[i])):
if flip_coin():
mutant += str(1 - int(new_pool[i][j]))
else:
mutant += new_pool[i][j]
... | mutate | identifier_name |
Data_Exploration.py |
l.append(d)
#
theresult_json = json.dumps(l, default=json_serial)
conn.close()
return theresult_json
@app.route("/api/correlation/<col1>/<col2>")
def Correlation(col1, col2):
engine = create_engine('postgresql+psycopg2://student:123456@132.249.238.27:5432/bookstore_dp')
conn = engi... | c = request.args[item]
print (c)
d[c] = result[c] | conditional_block | |
Data_Exploration.py |
@app.route("/api/web_method/<format>")
def api_web_method(format):
engine = create_engine('postgresql+psycopg2://student:123456@132.249.238.27:5432/bookstore_dp')
conn = engine.connect()
sql = """
select *
from orderlines o, products p
where o.productid = p.productid
LIMIT 10
"""
... | """JSON serializer for objects not serializable by default json code"""
if isinstance(obj, (datetime, date)):
return obj.isoformat()
raise TypeError ("Type %s not serializable" % type(obj)) | identifier_body | |
Data_Exploration.py | (obj):
"""JSON serializer for objects not serializable by default json code"""
if isinstance(obj, (datetime, date)):
return obj.isoformat()
raise TypeError ("Type %s not serializable" % type(obj))
@app.route("/api/web_method/<format>")
def api_web_method(format):
engine = create_engine('postg... | json_serial | identifier_name | |
Data_Exploration.py |
app = Flask(__name__)
@app.route("/")
def Hello():
return "Hello World!"
@app.route('/api/service', methods=['POST'])
def api_service():
query = request.get_json(silent=True)
# needs to change to reading from xml file
xml = VirtualIntegrationSchema()
web_session = WebSession(xml)
return j... | from json import loads
import psycopg2
from sqlalchemy import create_engine, text
import pysolr
from textblob import TextBlob as tb | random_line_split | |
ConfiguredResourceUploader.js | = "";
if (parsedResponse.ResultSet.Result.length !== 0) {
script = parsedResponse.ResultSet.Result[0].script;
//sort of a hack to get this code to work with generic nrg_config return values
if(script == undefined ){
script = parsedResponse.ResultSet.Result[0].contents;
}
this.con... | if(tempConfigs.length>0){
if(value.dontHide){
$(value).color(value.defaultColor);
$(value).css('cursor:pointer');
}
$(this).click(function(){
XNAT.app.crUploader.show(this);
return false;
});
$(this).show();
}else{
if(!value.dontHide){
$(this).hid... |
var tempConfigs=XNAT.app.crConfigs.getAllConfigsByType(type,props)
| random_line_split |
ConfiguredResourceUploader.js | script = "";
if (parsedResponse.ResultSet.Result.length !== 0) {
script = parsedResponse.ResultSet.Result[0].script;
//sort of a hack to get this code to work with generic nrg_config return values
if(script == undefined ){
script = parsedResponse.ResultSet.Result[0].contents;
}
t... |
this.dialog.hide();
},
confirm : function (header, msg, handleYes, handleNo) {
var dialog = new YAHOO.widget.SimpleDialog('widget_confirm', {
visible:false,
width: '20em',
zIndex: 9998,
close: false,
fixedcenter: true,
modal: true,
draggable: true,
constraintoviewport: tru... | {
showMessage("page_body","Failed upload.",response.responseText);
} | conditional_block |
extractdicom.go | (dicomReader, nReaderBytes, nil)
if err != nil {
return nil, err
}
parsedData, err := SafelyDicomParse(p, dicom.ParseOptions{
DropPixelData: false,
})
if parsedData == nil || err != nil {
return nil, fmt.Errorf("Error reading zip: %v", err)
}
var rescaleSlope, rescaleIntercept, windowWidth, windowCenter... | random_line_split | ||
extractdicom.go | {
imgRows = int(elem.Value[0].(uint16))
} else if elem.Tag == dicomtag.Columns {
imgCols = int(elem.Value[0].(uint16))
}
// If imgPixels is still uninitialized and we're on a rows or columns
// tag, and both rows and columns are populated, initialize imgPixels'
// backing array's capacity to the numbe... | {
// 1: StoredValue to ModalityValue
var modalityValue float64
if rescaleSlope == 0 {
// Via https://dgobbi.github.io/vtk-dicom/doc/api/image_display.html :
// For modalities such as ultrasound and MRI that do not have any units,
// the RescaleSlope and RescaleIntercept are absent and the Modality
// Values ... | identifier_body | |
extractdicom.go | (zipPath, dicomName string, includeOverlay bool) (image.Image, error) {
return ExtractDicomFromGoogleStorage(zipPath, dicomName, includeOverlay, nil)
}
// ExtractDicomFromZipReader consumes a zip reader of the UK Biobank format,
// finds the dicom of the desired name, and returns that image, with or without
// the ov... | ExtractDicomFromLocalFile | identifier_name | |
extractdicom.go | Bit uint16
_, _, _ = bitsAllocated, bitsStored, highBit
var nOverlayRows, nOverlayCols int
var img *image.Gray16
var imgRows, imgCols int
var imgPixels []int
var overlayPixels []int
for _, elem := range parsedData.Elements {
// The typical approach is to extract bitsAllocated, bitsStored, and the highBit
... | {
row := i / nOverlayCols
col := i % nOverlayCols
if overlayValue != 0 {
img.SetGray16(col, row, color.White)
}
} | conditional_block | |
lib.rs | when it goes out of scope
//! ```
//! Pull from pool and `detach()`
//! ```
//! let pool: MemoryPool<Vec<u8>> = MemoryPool::new(32, || Vec::with_capacity(4096));
//! let mut reusable_buff = pool.pull().unwrap(); // returns None when the pool is saturated
//! reusable_buff.clear(); // clear the buff before using
//! le... | elf.run_block.lock();
log::trace!("attach started<<<<<<<<<<<<<<<<");
log::trace!("recyled an item ");
let mut wait_list = { self.waiting.lock() };
log::trace!("check waiting list ok :{}", wait_list.len());
if wait_list.len() > 0 && self.len() >= wait_list[0].min_request {
... | _x = s | identifier_name |
lib.rs | when it goes out of scope
//! ```
//! Pull from pool and `detach()`
//! ```
//! let pool: MemoryPool<Vec<u8>> = MemoryPool::new(32, || Vec::with_capacity(4096));
//! let mut reusable_buff = pool.pull().unwrap(); // returns None when the pool is saturated
//! reusable_buff.clear(); // clear the buff before using
//! le... | waiting.lock().len() * 60 + 2 };
log::trace!("try again :{} with retries backoff:{}", str, to_retry);
for i in 0..to_retry {
sleep(std::time::Duration::from_secs(1));
if let Ok(item) = self.objects.1.try_recv() {
log::trace!("get ok:{}", str);
... | );
(Some(Reusable::new(&self, item)), false)
/* } else if (self.pending.lock().len() == 0) {
log::trace!("get should pend:{}", str);
self.pending.lock().push(PendingInfo {
id: String::from(str),
notif... | conditional_block |
lib.rs | when it goes out of scope
//! ```
//! Pull from pool and `detach()`
//! ```
//! let pool: MemoryPool<Vec<u8>> = MemoryPool::new(32, || Vec::with_capacity(4096));
//! let mut reusable_buff = pool.pull().unwrap(); // returns None when the pool is saturated
//! reusable_buff.clear(); // clear the buff before using
//! le... | usize {
self.objects.1.len()
}
#[inline]
pub fn is_empty(&self) -> bool {
self.objects.1.is_empty()
}
#[inline]
pub fn pending(&'static self, str: &str, sender: channel::Sender<Reusable<T>>, releasable: usize) -> (Option<Reusable<T>>, bool) {
log::trace!("pending item:{... | {}", cap);
log::trace!("mempool remains:{}", cap);
let mut objects = channel::unbounded();
for _ in 0..cap {
&objects.0.send(init());
}
MemoryPool {
objects,
pending: Arc::new(Mutex::new(Vec::new())),
waiting: Arc::new(Mutex::new(Ve... | identifier_body |
lib.rs | //! some_file.read_to_end(reusable_buff);
//! // reusable_buff is automatically returned to the pool when it goes out of scope
//! ```
//! Pull from pool and `detach()`
//! ```
//! let pool: MemoryPool<Vec<u8>> = MemoryPool::new(32, || Vec::with_capacity(4096));
//! let mut reusable_buff = pool.pull().unwrap(); // retu... | //! let pool: MemoryPool<Vec<u8>> = MemoryPool::new(32, || Vec::with_capacity(4096));
//! let mut reusable_buff = pool.pull().unwrap(); // returns None when the pool is saturated
//! reusable_buff.clear(); // clear the buff before using | random_line_split | |
counter.rs | AT_LOCK: Mutex<u32> = Mutex::new(42);
}
/// Configure event counter parameters.
///
/// Unless specified, a counter is allocated in counting mode with a system-wide
/// scope, recording events across all CPUs.
///
/// ```no_run
/// let config = CounterConfig::default().attach_to(vec![0]);
///
/// let instr = config.al... | (&mut self) {
unsafe { pmc_stop(self.counter.id) };
}
}
/// An allocated PMC counter.
///
/// Counters are initialised using the [`CounterBuilder`] type.
///
/// ```no_run
/// use std::{thread, time::Duration};
///
/// let instr = CounterConfig::default()
/// .attach_to(vec![0])
/// .allocate("inst... | drop | identifier_name |
counter.rs | AT_LOCK: Mutex<u32> = Mutex::new(42);
}
/// Configure event counter parameters.
///
/// Unless specified, a counter is allocated in counting mode with a system-wide
/// scope, recording events across all CPUs.
///
/// ```no_run
/// let config = CounterConfig::default().attach_to(vec![0]);
///
/// let instr = config.al... |
handles.push(AttachHandle { id, pid })
}
c.attached = Some(handles)
}
Ok(c)
}
/// Start this counter.
///
/// The counter stops when the returned [`Running`] handle is dropped.
#[must_use = "counter only runs until handle is dropped"]
... | {
return match io::Error::raw_os_error(&io::Error::last_os_error()) {
Some(libc::EBUSY) => unreachable!(),
Some(libc::EEXIST) => Err(new_os_error(ErrorKind::AlreadyAttached)),
Some(libc::EPERM) => Err(new_os_error(ErrorKind::For... | conditional_block |
counter.rs | _FAT_LOCK: Mutex<u32> = Mutex::new(42);
}
/// Configure event counter parameters.
///
/// Unless specified, a counter is allocated in counting mode with a system-wide
/// scope, recording events across all CPUs.
///
/// ```no_run
/// let config = CounterConfig::default().attach_to(vec![0]);
///
/// let instr = config.... | // Allocate the PMC
let mut id = 0;
if unsafe {
pmc_allocate(
c_spec.as_ptr(),
pmc_mode,
0,
cpu.unwrap_or(CPU_ANY),
&mut id,
0,
)
} != 0
{
return ma... | let c_spec =
CString::new(event_spec.into()).map_err(|_| new_error(ErrorKind::InvalidEventSpec))?;
| random_line_split |
sync.js |
// for each op (colormap, pan, etc.)
for(i=0; i<xops.length; i++){
// current op
xop = xops[i];
this.syncs[xop] = this.syncs[xop] || [];
ims = this.syncs[xop];
// add images not already in the list
for(j=0; j<xlen; j++){
xim = xims[j];
if( $.inArray(xim, ims) < 0 ){
// add to list
ims.push(... | {
delete opts.reverse;
for(i=0; i<xlen; i++){
JS9.Sync.sync.call(xims[i], xops, [this]);
}
return;
} | conditional_block | |
sync.js | if( xim &&
(xim.id !== this.id || (xim.display.id !== this.display.id)) ){
xims[j++] = xim;
}
}
return xims;
};
// sync image(s) when operations are performed on an originating image
// called in the image context
JS9.Sync.sync = function(...args){
let i, j, xop, xim, xops, xims, xlen;
let ... | } else {
xim = ims[i];
}
// exclude the originating image | random_line_split | |
model_sql.go | += fmt.Sprintf(" AND %s = ?", m.FieldAddAlias(field))
}
whereValue = append(whereValue, data[field])
fileTitles = append(fileTitles, m.attr.Fields[m.attr.fieldIndexMap[field]].Title)
}
//非自增PK表，检查PK字段
if !m.attr.AutoInc {
if where == "" {
where = fmt.Sprintf("%s = ?", pk)
} else {
where = fmt.Sprin... | fields = append(fields, keyField, valueField)
// 树型必备字段
if m.attr.IsTree { | random_line_split | |
model_sql.go | //查询
data := make([]map[string]interface{}, 0)
if err = theDB.Select(fields).Find(&data).Error; err != nil {
return
}
//处理结果
desc = make(Kvs)
for i, v := range data {
key := cast.ToString(v["__mc_key"])
//树形
if m.attr.IsTree && qo.ReturnPath {
key = cast.ToString(v[m.attr.Tree.PathField])
}
inden... | 填字段
func (m *Model) CheckRequiredValues(data map[string]interface{}) (err error) {
fieldTitles := make([]string, 0)
//非自增PK表，检查PK字段
if !m.attr.AutoInc {
if cast.ToString(data[m.attr.Pk]) == "" {
fieldTitles = append(fieldTitles, m.attr.Fields[m.attr.fieldIndexMap[m.attr.Pk]].Title)
}
}
//检查配置中的必填字段
for _, ... | r
} else if total > 0 {
return &Result{Message:fmt.Sprintf("记录已存在：【%s】存在重复", strings.Join(fileTitles, "、"))}
}
return nil
}
// 检查必 | conditional_block |
model_sql.go | .attr.Pk, symbol), delIds).Delete(nil)
return db.RowsAffected, db.Error
}
// 分析查询条件 (此批条件只作用于返回的db对象上，不会作用于模型的db上)
// @param extraWhere 额外的查询条件
// @param searchValues 查询字段值
// @param notSearch 是否使用查询字段条件
func (m *Model) ParseWhere(db *gorm.DB, extraWhere []interface{}, searchValues map[string]interface{}, notSearch b... | t.ToString(data[i][m.attr.Tree.NameField])
}
}
return
}
// 分析树形结构查询必须的扩展字段
func (m *Model) ParseTreeExtraField() (field []string) {
pathField := m.FieldAddAlias(m.attr.Tree.PathField)
__mc_pathField := fmt.Sprintf("`__mc_%s`.`%s`", m.attr.Table, m.attr.Tree.PathField)
__mc_pkField := fmt.Sprintf("`__mc_%s`.`%s... | identifier_body | |
model_sql.go | != nil {
return
}
//创建数据
db := m.BaseDB(false).Create(data)
return db.RowsAffected, db.Error
}
//保存记录（根据pk自动分析是update 或 create）
func (m *Model) Save(data map[string]interface{}, oldPkValue interface{})(rowsAffected int64, err error) {
if oldPkValue == nil { //创建
return m.Create(data)
} else { //更新
return ... | ing]["__mc_va | identifier_name | |
script.padrao.js | ext = URLHOST.host.indexOf('intru') > -1;
if (ext != null && ext != undefined) {
if (ext != true) {
return;
}
}
var cod = recuperaUserCodCookie();
if (cod == '' || cod == null || cod == undefined) {
if (window.location.href == `${urlHost}/Security/Login/`)... | alert(error);
}
}
const API = {
/**
*Requisições do tipo GET
* @param {Opções para a definição da requisição} options
*/
GET: (options) => {
try {
if (options == undefined || options == null) {
return false;
}
Ajax(options);
... | }
} catch (error) {
| conditional_block |
script.padrao.js | var ext = URLHOST.host.indexOf('intru') > -1;
if (ext != null && ext != undefined) {
if (ext != true) {
return;
}
}
var cod = recuperaUserCodCookie();
if (cod == '' || cod == null || cod == undefined) {
if (window.location.href == `${urlHost}/Security/Logi... | var divSpinner = Elements.Create('div', 'divGrowing', null, null, `z-index: 150 !important; color: ${spinnerColor} !important;`, ["spinner-grow", "text-primary"]);
span = Elements.Create('span', 'loadGrowing', "visually-hidden", null);
divSpinner.... | div = Elements.Create('div', 'loadMestre', null, null, style);
| random_line_split |
mod.rs |
use rstd::prelude::*;
use codec::{Encode, Decode};
use support::{
StorageValue, StorageMap, decl_event, decl_storage, decl_module, ensure,
traits::{
Currency, ReservableCurrency,
OnFreeBalanceZero, OnUnbalanced,
WithdrawReason, ExistenceRequirement,
Imbalance, Get,
},
dispatch::Result,
};
use sr_primitives... | //! # Activity Module
//!
#![cfg_attr(not(feature = "std"), no_std)] | random_line_split | |
mod.rs | this module
type ReputationCurrency: Currency<Self::AccountId>;
/// Handler for the unbalanced reduction when taking transaction fees.
type TransactionPayment: OnUnbalanced<NegativeImbalanceOf<Self>>;
/// The overarching event type.
type Event: From<Event<Self>> + Into<<Self as system::Trait>::Event>;
/// The... | <T: Trait>(#[codec(compact)] BalanceOf<T>);
impl<T: Trait> TakeFees<T> {
/// utility constructor. Used only in client/factory code.
pub fn from(fee: BalanceOf<T>) -> Self {
Self(fee)
}
/// Compute the final fee value for a particular transaction.
///
/// The final fee is composed of:
/// - _length-fee_: Th... | TakeFees | identifier_name |
mod.rs | this module
type ReputationCurrency: Currency<Self::AccountId>;
/// Handler for the unbalanced reduction when taking transaction fees.
type TransactionPayment: OnUnbalanced<NegativeImbalanceOf<Self>>;
/// The overarching event type.
type Event: From<Event<Self>> + Into<<Self as system::Trait>::Event>;
/// The... |
// PRIVATE MUTABLES
fn charge_for_energy(who: &T::AccountId, value: BalanceOf<T>) -> Result {
// ensure reserve
if !T::Currency::can_reserve(who, value) {
return Err("not enough free funds");
}
// check current_charged
let current_charged = <Charged<T>>::get(who);
let new_charged = current_charged.ch... | {
T::EnergyCurrency::available_free_balance(who)
} | identifier_body |
compile.ts | _INSIDE_PATH_BINARY
} from "./sandbox";
import config, { serverSideConfig } from "./config";
import { runTaskQueued } from "./taskQueue";
import { getFile, getFileHash } from "./file";
import * as fsNative from "./fsNative";
export interface CompilationConfig extends SandboxConfigWithoutMountInfo {
messageFile?: str... |
public async dereference() {
if (--this.referenceCount === 0) {
await fsNative.remove(this.binaryDirectory);
}
}
async copyTo(newBinaryDirectory: string) {
this.reference();
await fsNative.copy(this.binaryDirectory, newBinaryDirectory);
await this.dereference();
return new Compile... | {
this.referenceCount++;
return this;
} | identifier_body |
compile.ts | OX_INSIDE_PATH_BINARY
} from "./sandbox";
import config, { serverSideConfig } from "./config";
import { runTaskQueued } from "./taskQueue";
import { getFile, getFileHash } from "./file";
import * as fsNative from "./fsNative";
export interface CompilationConfig extends SandboxConfigWithoutMountInfo {
messageFile?: s... | () {
if (--this.referenceCount === 0) {
await fsNative.remove(this.binaryDirectory);
}
}
async copyTo(newBinaryDirectory: string) {
this.reference();
await fsNative.copy(this.binaryDirectory, newBinaryDirectory);
await this.dereference();
return new CompileResultSuccess(
this.co... | dereference | identifier_name |
compile.ts | OX_INSIDE_PATH_BINARY
} from "./sandbox";
import config, { serverSideConfig } from "./config";
import { runTaskQueued } from "./taskQueue";
import { getFile, getFileHash } from "./file";
import * as fsNative from "./fsNative";
export interface CompilationConfig extends SandboxConfigWithoutMountInfo {
messageFile?: s... | await fsNative.copy(this.binaryDirectory, newBinaryDirectory);
await this.dereference();
return new CompileResultSuccess(
this.compileTaskHash,
this.message,
newBinaryDirectory,
this.binaryDirectorySize,
this.extraInfo
);
}
}
// Why NOT using the task hash as the directo... |
async copyTo(newBinaryDirectory: string) {
this.reference(); | random_line_split |
supervised_ml_(classification)_assignment_(final).py | how our data is distributes per each column. Though, this doesnt represent much use since some of the attributes are numerical encodings and aren’t represented well like this.
In order to see correlations, lets visually interpret this data through a heatmap of correlated values, and a pair plot to draw visual inferen... | ams(n_estimators=n_trees)
# Fit the model
RF.fit(x_train, y_train)
# Get the oob error
oob_error = 1 - RF.oob_score_
# Store it
oob_list.append(pd.Series({'n_trees': n_trees, 'oob': oob_error}))
rf_oob_d | conditional_block | |
supervised_ml_(classification)_assignment_(final).py | * Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
* thalach : maximum heart rate achieved
* target : 0= less chance of heart attack 1= more chance of heart attack
Let's begin by importing our data into pandas.
"""
#path must point to the heart.csv file.
data = pd.read_csv... | plt.show()
"""---
# Data Engineering/Modelling
Because the data is already in a numerical form (int-type), it will not be required to engineer the data or reencode values. Though, given the tasks ahead, we may require data scaling for input into specific classifier models.
We shall address this problem as we arr... | random_line_split | |
supervised_ml_(classification)_assignment_(final).py | Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
* thalach : maximum heart rate achieved
* target : 0= less chance of heart attack 1= more chance of heart attack
Let's begin by importing our data into pandas.
"""
#path must point to the heart.csv file.
data = pd.read_csv('/... | e, y_pred, label):
return pd.Series({'accuracy':accuracy_score(y_true, y_pred),
'precision': precision_score(y_true, y_pred),
'recall': recall_score(y_true, y_pred),
'f1': f1_score(y_true, y_pred)},
name=label)
train_test_full_... | e_error(y_tru | identifier_name |
supervised_ml_(classification)_assignment_(final).py | Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
* thalach : maximum heart rate achieved
* target : 0= less chance of heart attack 1= more chance of heart attack
Let's begin by importing our data into pandas.
"""
#path must point to the heart.csv file.
data = pd.read_csv(... | n_test_full_error = pd.concat([measure_error(y_train, y_train_pred, 'train'),
measure_error(y_test, y_test_pred, 'test')],
axis=1)
train_test_full_error
"""The above output shows out accuracy prediction. This is quite low, could it be improved with Grid Sear... | pd.Series({'accuracy':accuracy_score(y_true, y_pred),
'precision': precision_score(y_true, y_pred),
'recall': recall_score(y_true, y_pred),
'f1': f1_score(y_true, y_pred)},
name=label)
trai | identifier_body |
tls.go | scores = conf.categoryScores(tally)
reqACLs = conf.ACLs.requestACLs(cr, authUser)
if invalidSSL {
reqACLs["invalid-ssl"] = true
}
if r == nil {
// It's a transparently-intercepted request instead of a real
// CONNECT request.
reqACLs["transparent"] = true
}
}
session.ACLs.data = reqACLs
sessio... |
callStarlarkFunctions("ssl_bump", session)
dialer := &net.Dialer{
Timeout: 30 * time.Second,
KeepAlive: 30 * time.Second,
DualStack: true,
}
if session.SourceIP != nil {
dialer.LocalAddr = &net.TCPAddr{
IP: session.SourceIP,
}
}
session.chooseAction()
logAccess(cr, nil, 0, false, user, tally,... | {
session.PossibleActions = append(session.PossibleActions, "ssl-bump")
} | conditional_block |
tls.go | 0, 0, time.Local),
KeyUsage: x509.KeyUsageDigitalSignature | x509.KeyUsageKeyEncipherment,
DNSNames: []string{sni},
SignatureAlgorithm: x509.UnknownSignatureAlgorithm,
}
newCertBytes, err := x509.CreateCertificate(rand.Reader, template, conf.ParsedTLSCert, conf.ParsedTLSCert.PublicKey, con... | addTrustedRoots | identifier_name | |
tls.go | session.ClientIP = client
}
obsoleteVersion := false
invalidSSL := false
// Read the client hello so that we can find out the name of the server (not
// just the address).
clientHello, err := readClientHello(conn)
if err != nil {
logTLS(user, serverAddr, "", fmt.Errorf("error reading client hello: %v", err)... | client := conn.RemoteAddr().String()
if host, _, err := net.SplitHostPort(client); err == nil {
session.ClientIP = host
} else { | random_line_split | |
tls.go | s = conf.ACLs.requestACLs(cr, authUser)
if invalidSSL {
reqACLs["invalid-ssl"] = true
}
if r == nil {
// It's a transparently-intercepted request instead of a real
// CONNECT request.
reqACLs["transparent"] = true
}
}
session.ACLs.data = reqACLs
session.Scores.data = scores
session.PossibleActio... | {
return 0, errors.New("unhashable type: TLSSession")
} | identifier_body | |
kek.py | s = pd.read_csv('spam.csv', encoding = 'latin-1')
oldmails.head()
mailz = pd.read_csv('messages.csv', encoding = 'latin-1')
mailz.head()
#Преобразовани таблицы с данными, удаление лишних столбцов
oldmails.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis = 1, inplace = True)
oldmails.head()
mailz.drop(['subject'... | ие словрей спам и не спам слов
spam_words = ' '.join(list(mails[mails['label'] == 1]['message']))
ham_words = ' '.join(list(mails[mails['label'] == 0]['message']))
trainData.head()
trainData['label'].value_counts()
testData.head()
testData['label'].value_counts()
#Обработка текста сообщений
def process_message(mes... | ts()
#Формирован | conditional_block |
kek.py | ate(spam_words)
plt.figure(figsize = (10, 8), facecolor = 'k')
plt.imshow(spam_wc)
plt.axis('off')
plt.tight_layout(pad = 0)
plt.show()
#Функция визуализации словаря легетимных слов
def show_ham(ham_words):
ham_wc = WordCloud(width = 512,height = 512).generate(ham_words)
plt.figure(figsize =... | слов
def show_spam(spam_words):
spam_wc = WordCloud(width = 512,height = 512).gener | identifier_body | |
kek.py | mails = pd.read_csv('spam.csv', encoding = 'latin-1')
oldmails.head()
mailz = pd.read_csv('messages.csv', encoding = 'latin-1')
mailz.head()
#Преобразовани таблицы с данными, удаление лишних столбцов
oldmails.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis = 1, inplace = True)
oldmails.head()
mailz.drop(['subj... | = self.spam_mails / self.total_mails, self.ham_mails / self.total_mails
#Вычисление вероятностей
def calc_TF_and_IDF(self):
noOfMessages = self.mails.shape[0]
self.spam_mails, self.ham_mails = self.labels.value_counts()[1], self.labels.value_counts()[0]
self.total_mails = self.spam_mail... | _mail | identifier_name |
kek.py | from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import confusion_matrix
import pickle
#Функция сохранения состояния обученности классификатора
def save(obj):
with open('sis.pickle', 'wb') as f:
pickle.dump(obj, f)
#Функция загру... | import numpy as np
import pandas as pd
import nltk
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer | random_line_split |
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