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 |
|---|---|---|---|---|
main.rs | }
fn mul_sub(x: Self, y: Self, z: Self) -> Self {
Self(unsafe { _mm256_fmsub_ps(x.0, y.0, z.0) })
}
}
impl Add for WideF32 {
type Output = Self;
fn add(self, other: Self) -> Self {
Self(unsafe { _mm256_add_ps(self.0, other.0) })
}
}
impl AddAssign for WideF32 {
fn add_assign... |
}
impl Div for WideF32 {
type Output = Self;
fn div(self, other: Self) -> Self {
Self(unsafe { _mm256_div_ps(self.0, other.0) })
}
}
impl Sub for WideF32 {
type Output = Self;
fn sub(self, other: Self) -> Self {
Self(unsafe { _mm256_sub_ps(self.0, other.0) })
}
}
impl Mul f... | {
Self(unsafe { _mm256_or_ps(self.0, other.0) })
} | identifier_body |
main.rs | }
fn mul_sub(x: Self, y: Self, z: Self) -> Self {
Self(unsafe { _mm256_fmsub_ps(x.0, y.0, z.0) })
}
}
impl Add for WideF32 {
type Output = Self;
fn add(self, other: Self) -> Self {
Self(unsafe { _mm256_add_ps(self.0, other.0) })
}
}
impl AddAssign for WideF32 {
fn add_assign... | {
xs: Vec<f32>,
ys: Vec<f32>,
zs: Vec<f32>,
rsqrds: Vec<f32>,
mats: Vec<Material>,
}
impl Spheres {
fn new(spheres: Vec<Sphere>) -> Self {
let len = (spheres.len() + SIMD_WIDTH - 1) / SIMD_WIDTH * SIMD_WIDTH;
let mut me = Self {
xs: Vec::with_capacity(len),
... | Spheres | identifier_name |
main.rs | unsafe { _mm256_movemask_ps(self.0) }
}
fn hmin(&self) -> f32 {
unsafe {
/*
This can be done entirely in avx with permute2f128, but that is allegedly very
slow on AMD prior to Zen2 (and is anecdotally slower on my Intels as well)
initial m256
... | self.mask() != 0
}
fn mask(&self) -> i32 { | random_line_split | |
CLCDcurve.py | 1))
masstot = mass + passmass + fuelblock
Weight_CL = [(masstot - FU_CL[i])*g for i in range(len(FU_CL))]
CLgraph_mat = Weight_CL/(0.5 * VTAS_CL**2 * rho1_CL * S)
print(min(Mach_CL), max(Mach_CL))
#find linear relation for CL measurements
clalpha_mat,ma_mat = np.polyfit(AOA_CL[:,0],CLgraph_mat[:,0],1)
CLline_CL = cla... | #Plots CL and CD##
# plt.grid()
# plt.scatter(AOA_CL,CLgraph_mat,marker= '.', label='Measure point')
# # plt.plot(AOAstat,linecl_stat, label='Stationary Flight Measurements')
# plt.plot(AOA_CL[:,0],CLline_CL,c='darkorange', label= 'Least Squares of Flightdata')
# plt.ylabel('Lift Coefficient [-]')
# plt.xlabel('Angle o... | CDstat = CD0 + linecl_stat/(pi * A * e)
| random_line_split |
mod.rs | step).
Ok(self.iterate(heap)?.iter().collect())
}
/// Produce an iterable from a value.
pub fn iterate(self, heap: &'v Heap) -> anyhow::Result<RefIterable<'v>> {
let me: ARef<'v, dyn StarlarkValue> = self.get_aref();
me.iterate()?;
Ok(RefIterable::new(
heap,
... | {
self.get_aref().get_type_value()
} | identifier_body | |
mod.rs | fn collect_repr(self, collector: &mut String) {
self.get_aref().collect_repr(collector);
}
fn to_json(self) -> anyhow::Result<String> {
self.get_aref().to_json()
}
fn equals(self, other: Value<'v>) -> anyhow::Result<bool> {
if self.to_value().ptr_eq(other) {
Ok(... | export_as | identifier_name | |
mod.rs | for FrozenValue {
fn eq(&self, other: &FrozenValue) -> bool {
let v: Value = Value::new_frozen(*self);
let other: Value = Value::new_frozen(*other);
v.equals(other).ok() == Some(true)
}
}
impl Eq for Value<'_> {}
impl Eq for FrozenValue {}
impl Equivalent<FrozenValue> for Value<'_> {... |
}
fn compare(self, other: Value<'v>) -> anyhow::Result<Ordering> {
let _guard = stack_guard::stack_guard()?;
self.get_aref().compare(other)
}
fn downcast_ref<T: AnyLifetime<'v>>(self) -> Option<ARef<'v, T>> {
let any = ARef::map(self.get_aref(), |e| e.as_dyn_any());
if... | {
let _guard = stack_guard::stack_guard()?;
self.get_aref().equals(other)
} | conditional_block |
mod.rs | }
}
impl Debug for FrozenValue {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
debug_value("FrozenValue", Value::new_frozen(*self), f)
}
}
impl<'v> PartialEq for Value<'v> {
fn eq(&self, other: &Value<'v>) -> bool {
self.equals(*other).ok() == Some(true)
}
}
impl PartialE... | debug_value("Value", *self, f) | random_line_split | |
encode.go | ) encoderFunc {
if fi, ok := encoderCache.Load(t); ok {
return fi.(encoderFunc)
}
var (
wg sync.WaitGroup
f encoderFunc
)
wg.Add(1)
fi, loaded := encoderCache.LoadOrStore(t, encoderFunc(func(e *encodeState, v reflect.Value) {
wg.Wait()
f(e, v)
}))
if loaded {
return fi.(encoderFunc)
}
f = newTy... | random_line_split | ||
encode.go | return invalidValueEncoder
}
return typeEncoder(v.Type())
}
func typeEncoder(t reflect.Type) encoderFunc {
if fi, ok := encoderCache.Load(t); ok {
return fi.(encoderFunc)
}
var (
wg sync.WaitGroup
f encoderFunc
)
wg.Add(1)
fi, loaded := encoderCache.LoadOrStore(t, encoderFunc(func(e *encodeState, v r... | (t reflect.Type, index []int) reflect.Type {
for _, i := range index {
if t.Kind() == reflect.Ptr {
t = t.Elem()
}
t = t.Field(i).Type
}
return t
}
type field struct {
name string
tags map[string]string
index []int
typ reflect.Type
}
// byIndex sorts field by index sequence.
type byIndex []field
... | typeByIndex | identifier_name |
encode.go |
return e.Bytes(), nil
}
// Marshaler is the interface implemented by types that can marshal
// themselves into valid BNET.
type Marshaler interface {
MarshalBNet() ([]byte, error)
}
// An UnsupportedTypeError occurs when attempting to marshal an
// unsupported type.
type UnsupportedTypeError struct {
Type reflect... | {
return nil, err
} | conditional_block | |
encode.go | return invalidValueEncoder
}
return typeEncoder(v.Type())
}
func typeEncoder(t reflect.Type) encoderFunc {
if fi, ok := encoderCache.Load(t); ok {
return fi.(encoderFunc)
}
var (
wg sync.WaitGroup
f encoderFunc
)
wg.Add(1)
fi, loaded := encoderCache.LoadOrStore(t, encoderFunc(func(e *encodeState, v r... |
func encodeStringSlice(e *encodeState, v reflect.Value) {
n := v.Len()
for i := 0; i < n; i++ {
stringEncoder(e, v.Index(i))
}
e.buf.WriteByte(0x00)
}
type sliceEncoder struct {
arrayEnc encoderFunc
}
func (se *sliceEncoder) encode(e *encodeState, v reflect.Value) {
if v.IsNil() {
return
}
se.arrayEnc(e... | {
fields := cachedTypeFields(t)
se := &structEncoder{
fields: fields,
fieldEncs: make([]encoderFunc, len(fields)),
}
for i, f := range fields {
se.fieldEncs[i] = typeEncoder(typeByIndex(t, f.index))
}
return se.encode
} | identifier_body |
backend.rs | Box<dyn TargetIsa>,
symbols: HashMap<String, *const u8>,
libcall_names: Box<dyn Fn(ir::LibCall) -> String>,
code_memory: Memory,
readonly_memory: Memory,
writable_memory: Memory,
}
/// A record of a relocation to perform.
struct RelocRecord {
offset: CodeOffset,
reloc: Reloc,
name: ir:... | get_finalized_function | identifier_name | |
backend.rs | .code,
None => self.lookup_symbol(name_str),
}
} else {
let (def, name_str, _writable) = namespace.get_data_definition(&name);
match def {
Some(compiled) => compiled.storage,
... | | Reloc::X86CallPCRel4 | random_line_split | |
backend.rs | {
debug_assert!(!isa.flags().is_pic(), "SimpleJIT requires non-PIC code");
let symbols = HashMap::new();
Self {
isa,
symbols,
libcall_names,
}
}
/// Define a symbol in the internal symbol table.
///
/// The JIT will use the symbol tab... |
fn declare_function(&mut self, _name: &str, _linkage: Linkage) {
// Nothing to do.
}
fn declare_data(
&mut self,
_name: &str,
_linkage: Linkage,
_writable: bool,
_align: Option<u8>,
) {
// Nothing to do.
}
fn define_function(
&m... | {
&*self.isa
} | identifier_body |
composition_patches.go | // Default
PatchTypePatchSet PatchType = "PatchSet"
PatchTypeToCompositeFieldPath PatchType = "ToCompositeFieldPath"
PatchTypeCombineFromComposite PatchType = "CombineFromComposite"
PatchTypeCombineToComposite PatchType = "CombineToComposite"
)
// A FromFieldPathPolicy determines how to patc... |
// patchFieldValueToObject, given a path, value and "to" object, will
// apply the value to the "to" object at the given path, returning
// any errors as they occur.
func patchFieldValueToObject(fieldPath string, value interface{}, to runtime.Object, mo *xpv1.MergeOptions) error {
paved, err := fieldpath.PaveObject(... | {
var err error
for i, t := range c.Transforms {
if input, err = t.Transform(input); err != nil {
return nil, errors.Wrapf(err, errFmtTransformAtIndex, i)
}
}
return input, nil
} | identifier_body |
composition_patches.go | Type) error {
if c.filterPatch(only...) {
return nil
}
switch c.Type {
case PatchTypeFromCompositeFieldPath:
return c.applyFromFieldPathPatch(cp, cd)
case PatchTypeToCompositeFieldPath:
return c.applyFromFieldPathPatch(cd, cp)
case PatchTypeCombineFromComposite:
return c.applyCombineFromVariablesPatch(cp... | }
if p.PatchSetName == nil { | random_line_split | |
composition_patches.go | // Default
PatchTypePatchSet PatchType = "PatchSet"
PatchTypeToCompositeFieldPath PatchType = "ToCompositeFieldPath"
PatchTypeCombineFromComposite PatchType = "CombineFromComposite"
PatchTypeCombineToComposite PatchType = "CombineToComposite"
)
// A FromFieldPathPolicy determines how to patc... |
switch c.Type {
case PatchTypeFromCompositeFieldPath:
return c.applyFromFieldPathPatch(cp, cd)
case PatchTypeToCompositeFieldPath:
return c.applyFromFieldPathPatch(cd, cp)
case PatchTypeCombineFromComposite:
return c.applyCombineFromVariablesPatch(cp, cd)
case PatchTypeCombineToComposite:
return c.applyC... | {
return nil
} | conditional_block |
composition_patches.go | PatchSet;ToCompositeFieldPath;CombineFromComposite;CombineToComposite
// +kubebuilder:default=FromCompositeFieldPath
Type PatchType `json:"type,omitempty"`
// FromFieldPath is the path of the field on the resource whose value is
// to be used as input. Required when type is FromCompositeFieldPath or
// ToComposit... | Combine | identifier_name | |
Training.py | 경로
TRAIN_CSV = "train_shuffle.csv" # train data .csv
VALIDATION_CSV = "validation_shuffle.csv" # validation data .csv
TEST_CSV = "test_uniform.csv" # test data .csv
MODEL_SAVE_FOLDER_PATH = './model/' # 저장될 모델 디렉토리 설정
class DataGenerator(Sequence):
def __init__(self, csv_file):
self.paths = []
... | poch, logs):
train_pos_count = 0
train_neg_count = 0
for i in range(len(self.generator)):
batch_images, gt = self.generator[i]
pred = self.model.predict_on_batch(batch_images)
train_gt_len = len(gt)
pred = np.maximum(pred, 0)
########... | def on_epoch_end(self, e | identifier_body |
Training.py | validation 경로
TRAIN_CSV = "train_shuffle.csv" # train data .csv
VALIDATION_CSV = "validation_shuffle.csv" # validation data .csv
TEST_CSV = "test_uniform.csv" # test data .csv
MODEL_SAVE_FOLDER_PATH = './model/' # 저장될 모델 디렉토리 설정
class DataGenerator(Sequence):
def __init__(self, csv_file):
self.paths ... |
class Test_set(Callback):
def __init__(self, generator):
self.generator = generator
def on_epoch_end(self, epoch, logs):
test_pos_count = 0
test_neg_count = 0
for i in range(len(self.generator)):
batch_images, gt = self.generator[i]
pred = self.model.p... |
mse = np.round(mse, 4)
logs["val_mse"] = mse
print(" - val_iou: {} - val_mse: {} - val_acc: {}".format(iou, mse, val_acc)) | random_line_split |
Training.py | validation 경로
TRAIN_CSV = "train_shuffle.csv" # train data .csv
VALIDATION_CSV = "validation_shuffle.csv" # validation data .csv
TEST_CSV = "test_uniform.csv" # test data .csv
MODEL_SAVE_FOLDER_PATH = './model/' # 저장될 모델 디렉토리 설정
class DataGenerator(Sequence):
def __init__(self, csv_file):
self.paths ... | intersections = 0
unions = 0
val_pos_count = 0
val_neg_count = 0
for i in range(len(self.generator)):
batch_images, gt = self.generator[i]
pred = self.model.predict_on_batch(batch_images)
mse += np.linalg.norm(gt - pred, ord='fro') / pred.shape[0]
... | = 0
| identifier_name |
Training.py | 경로
TRAIN_CSV = "train_shuffle.csv" # train data .csv
VALIDATION_CSV = "validation_shuffle.csv" # validation data .csv
TEST_CSV = "test_uniform.csv" # test data .csv
MODEL_SAVE_FOLDER_PATH = './model/' # 저장될 모델 디렉토리 설정
class DataGenerator(Sequence):
def __init__(self, csv_file):
self.paths = []
... | train_총 갯수
train_acc = np.round(train_pos_count / train_data_count, 4)
logs["train_acc"] = train_acc
print(" - train_acc: {}".format(train_acc))
class Validation(Callback):
def __init__(self, generator):
self.generator = generator
def on_epoch_end(self, epoch, logs):
... | shold 0.5 지정
train_pos_count += 1
else:
train_neg_count += 1
train_data_count = 59521 # | conditional_block |
models.py | 'Spanish'),
('Swedish', 'Swedish'),
('Tamil', 'Tamil'),
('Telugu', 'Telugu'),
('Turkish', 'Turkish'),
('Ukrainian', 'Ukrainian'),
('Urdu', 'Urdu'),
('Vietnamese', 'Vietnamese')
)
CITIES = (
('Abidjan', 'Abidjan'),
('Accra', 'Accra'),
('Addis Ababa', 'Addis Ababa'),
('Ahmedabad', 'Ahmedabad'),
... | ('Incheon', 'Incheon'),
('Indianapolis', 'Indianapolis'),
('Irvine', 'Irvine'),
('Irving', 'Irving'),
('Istanbul', 'Istanbul'),
('İzmir', 'İzmir'),
('Jacksonville', 'Jacksonville'),
('Jaipur', 'Jaipur'),
('Jakarta', 'Jakarta'),
('Jeddah', 'Jeddah'),
('Jersey City', 'Jersey Ci... | ('Ibadan', 'Ibadan'), | random_line_split |
models.py | 'Hong Kong'),
('Honolulu', 'Honolulu'),
('Houston', 'Houston'),
('Hyderabad', 'Hyderabad'),
('Ibadan', 'Ibadan'),
('Incheon', 'Incheon'),
('Indianapolis', 'Indianapolis'),
('Irvine', 'Irvine'),
('Irving', 'Irving'),
('Istanbul', 'Istanbul'),
('İzmir', 'İzmir'),
('Jacksonvill... | :
return | identifier_name | |
models.py | '),
('Hyderabad', 'Hyderabad'),
('Ibadan', 'Ibadan'),
('Incheon', 'Incheon'),
('Indianapolis', 'Indianapolis'),
('Irvine', 'Irvine'),
('Irving', 'Irving'),
('Istanbul', 'Istanbul'),
('İzmir', 'İzmir'),
('Jacksonville', 'Jacksonville'),
('Jaipur', 'Jaipur'),
('Jakarta', 'Jakar... | tail', kwargs = { 'pk': self.id })
# ---- BOOKING ---- | identifier_body | |
github.go | ", orgThrottler))
continue
}
if burst > hourlyTokens {
errs = append(errs, fmt.Errorf("-github-throttle-org=%s: burst must not be greater than hourlyTokens", orgThrottler))
continue
}
if _, alreadyExists := o.parsedOrgThrottlers[org]; alreadyExists {
errs = append(errs, fmt.Errorf("got multiple -git... | {
if _, err := o.GitHubClient(dryRun); err != nil {
return "", nil, fmt.Errorf("error getting GitHub client: %w", err)
}
} | conditional_block | |
github.go | (hourlyTokens, allowedBursts int) FlagParameter {
return func(o *flagParams) {
o.defaults.ThrottleHourlyTokens = hourlyTokens
o.defaults.ThrottleAllowBurst = allowedBursts
}
}
// DisableThrottlerOptions suppresses the presence of throttler-related flags,
// effectively disallowing external users to parametrize d... | ThrottlerDefaults | identifier_name | |
github.go | != 3 {
errs = append(errs, fmt.Errorf("-github-throttle-org=%s is not in org:hourlyTokens:burst format", orgThrottler))
continue
}
org, hourlyTokensString, burstString := colonSplit[0], colonSplit[1], colonSplit[2]
hourlyTokens, err := strconv.ParseInt(hourlyTokensString, 10, 32)
if err != nil {
errs ... | {
var gitClientFactory gitv2.ClientFactory
if cookieFilePath != "" && o.TokenPath == "" && o.AppPrivateKeyPath == "" {
opts := gitv2.ClientFactoryOpts{
CookieFilePath: cookieFilePath,
Persist: &persistCache,
}
if cacheDir != nil && *cacheDir != "" {
opts.CacheDirBase = cacheDir
}
var err err... | identifier_body | |
github.go | options. Note that validate updates the GitHubOptions
// to add default values for TokenPath and graphqlEndpoint.
func (o *GitHubOptions) Validate(bool) error {
endpoints := o.endpoint.Strings()
for i, uri := range endpoints {
if uri == "" {
endpoints[i] = github.DefaultAPIEndpoint
} else if _, err := url.Par... | }, | random_line_split | |
token.rs | Helper counter for testing to diagnose
/// how many rollbacks have occured
pub rollbacks: u64,
}
impl Ledger {
/// Helper method to get the account details for `owner_id`.
fn get_balance(&self, owner_id: &AccountId) -> u128 {
match self.balances.get(owner_id) {
Some(x) => return ... |
//// How many rollbacks we have had
pub fn get_rollback_count(&self) -> u64 {
self.ledger.rollbacks
}
/// Returns balance of the `owner_id` account.
pub fn get_name(&self) -> &str {
return &self.metadata.name;
}
/// Send owner's tokens to another person or a smart contrac... | {
self.ledger.get_locked_balance(&owner_id).into()
} | identifier_body |
token.rs | ) -> Balance {
match self.locked_balances.get(owner_id) {
Some(x) => return x,
None => return 0,
}
}
/**
* Send tokens to a new owner.
*
* message is an optional byte data that is passed to the receiving smart contract.
* notify is a flag to tell if w... | {
// The send() was destined to a compatible receiver smart contract.
// Build another promise that notifies the smart contract
// that is has received new tokens.
env::log(b"Constructing smart contract notifier promise");
let promise0 = env::promise_create... | conditional_block | |
token.rs | &[],
0,
SINGLE_CALL_GAS/3,
);
let promise1 = env::promise_then(
promise0,
env::current_account_id(),
b"handle_receiver",
json!({
"old_owner_id": owner_id,
"new_owner_id": new_owner_id,
... | block_index: 0,
block_timestamp: 0, | random_line_split | |
token.rs | (&new_owner_id);
let new_target_balance = target_balance + amount;
self.set_balance(&new_owner_id, new_target_balance);
// This much of user balance is lockedup in promise chains
self.set_balance(&new_owner_id, new_target_balance);
let target_lock = self.get_locked_balance(&new... | bob | identifier_name | |
mp4-tools.ts | {
let matchingTraf = trafs[index];
mdatTrafPairs.push({
mdat: mdat,
traf: matchingTraf
});
});
mdatTrafPairs.forEach(function (pair) {
let mdat = pair.mdat;
let mdatBytes = mdat.data.subarray(mdat.start, mdat.end);
let traf = pair.traf;
let trafBytes = traf.data.subarray(tr... | {
logger.log('We\'ve encountered a nal unit without data. See mux.js#233.');
break;
} | conditional_block | |
mp4-tools.ts | = mdat.data.subarray(mdat.start, mdat.end);
let traf = pair.traf;
let trafBytes = traf.data.subarray(traf.start, traf.end);
let tfhd = findBox(trafBytes, ['tfhd']);
// Exactly 1 tfhd per traf
let headerInfo = parseTfhd(tfhd[0]);
let trackId = headerInfo.trackId;
let tfdt = findBox(trafBytes... | parseTrun | identifier_name | |
mp4-tools.ts | 5, Section 8.1.1
* @see Rec. ITU-T H.264, 7.3.2.3.1
**/
export function findSeiNals (avcStream, samples, trackId) {
let
avcView = new DataView(avcStream.buffer, avcStream.byteOffset, avcStream.byteLength),
result = [] as any,
seiNal,
i,
length,
lastMatchedSample;
for (i = 0; i + 4 < avc... | {
let trafs, baseTimes, result;
// we need info from two childrend of each track fragment box
trafs = findBox(fragment, ['moof', 'traf']);
// determine the start times for each track
baseTimes = [].concat.apply([], trafs.map(function (traf) {
return findBox(traf, ['tfhd']).map(function (tfhd) {
le... | identifier_body | |
mp4-tools.ts | Object[]>} A mapping of video trackId to
* a list of seiNals found in that track
**/
export function parseCaptionNals (data, videoTrackId) {
let captionNals = [] as any;
// To get the samples
let trafs = findBox(data, ['moof', 'traf']);
// To get SEI NAL units
let mdats = findBox(data, ['mdat']);
let... | escapedRBSP: discardEmulationPreventionBytes(data),
trackId: trackId
}; | random_line_split | |
processor.rs | system, such as "Windows NT", "Mac OS X", or "Linux".
///
/// If the information is present in the dump but its value is unknown, this field will contain
/// a numeric value. If the information is not present in the dump, this field will be empty.
pub fn os_name(&self) -> String {
unsafe {
... | {
Ok(ProcessState {
internal,
_ty: PhantomData,
})
} | conditional_block | |
processor.rs | /// Returns the `CodeModule` that contains this frame's instruction.
pub fn module(&self) -> Option<&CodeModule> {
unsafe { stack_frame_module(self).as_ref() }
}
/// Returns how well the instruction pointer is trusted.
pub fn trust(&self) -> FrameTrust {
unsafe { stack_frame_trust(s... | ProcessState | identifier_name | |
processor.rs | instruction of this stack frame.
#[repr(C)]
pub struct StackFrame(c_void);
impl StackFrame {
/// Returns the program counter location as an absolute virtual address.
///
/// - For the innermost called frame in a stack, this will be an exact
/// program counter or instruction pointer value.
///
... |
/// The minidump file had no header. | random_line_split | |
level_2_optionals_cdsu.py | ('Versão', 'trendl'): 'trendline', ('Versão', 'conft'): 'confortline', ('Versão', 'highlin'): 'highline',
('Versão', 'confortine'): 'confortline', ('Versão', 'cofrtl'): 'confortline', ('Versão', 'confortlline'): 'confortline', ('Versão', 'highl'): 'highline', ('Modelo', 'up!'): 'up'}
... | project_identifier, exception_desc = level_2_optionals_cdsu_options.project_id, str(sys.exc_info()[1])
log_record(exception_desc, project_identifier, flag=2)
error_upload(level_2_optionals_cdsu_options, project_identifier, format_exc(), exception_desc, error_flag=1)
log_record('Falhou - Projeto... | conditional_block | |
level_2_optionals_cdsu.py | is {}'.format(max(df['Sell_Date'])))
return
def data_processing(df):
performance_info_append(time.time(), 'Section_B_Start')
log_record('Início Secção B...', project_id)
log_record('Checkpoint não encontrado ou demasiado antigo. A processar dados...', project_id)
df = lowercase_column_conversi... | df = remove_columns(df, ['Cor', 'Interior', 'Opcional', 'Custo', 'Versão', 'Franchise_Code'], project_id) # Remove columns not needed atm;
# Will probably need to also remove: stock_days, stock_days_norm, and one of the scores | random_line_split | |
level_2_optionals_cdsu.py | fortline', ('Versão', 'conftl'): 'confortline', ('Versão', 'hightline'): 'highline', ('Versão', 'bluem'): 'bluemotion',
('Versão', 'bmt'): 'bluemotion', ('Versão', 'up!bluemotion'): 'up! bluemotion', ('Versão', 'up!bluem'): 'up! bluemotion', ('Versão', 'trendl'): 'trendline', ('Versão', '... | log_record('Início Secção E...', project_id)
if df is not None:
df['NLR_Code'] = level_2_optionals_cdsu_options.nlr_code
# df = column_rename(df, list(level_2_optionals_cdsu_options.column_sql_renaming.keys()), list(level_2_optionals_cdsu_options.column_sql_renaming.values()))
df = df.re... | identifier_body | |
level_2_optionals_cdsu.py | uery_filters):
performance_info_append(time.time(), 'Section_A_Start')
log_record('Início Secção A...', project_id)
df = sql_retrieve_df(level_2_optionals_cdsu_options.DSN_MLG_PRD, level_2_optionals_cdsu_options.sql_info['database'], level_2_optionals_cdsu_options.sql_info['initial_table'], level_2_optiona... | ta_acquisition(q | identifier_name | |
util.js | var a = "",
b = this.indexOf(e);
if (b != -1) {
b += e.length;
var d = this.indexOf(c, b);
if (d != -1) {
a = this.substr(b, d - b)
}
}
return a
};
StringUtils.capitalize = function(e, c) {
e = StringUtils.trimLeft(e);
return c === true ? e.replace(/^.|\s+(.)/, StringUtils._upperCa... | }
return this.substr(0, e)
};
String.prototype.between = function(e, c) { | random_line_split | |
util.js | if (c == null) {
c = " "
}
var a = c.substr(0, 1);
return this.length < e ? this + this.repeat(e - this.length, a) : this
};
String.prototype.rjust = function(e, c) {
if (c == null) {
c = " "
}
var a = c.substr(0, 1);
return this.length < e ? this.repeat(e - this.length, a) + this : this
};
Strin... |
for (m = 0; m <= g; m++) {
a[0][m] = m
}
for (m = 1; m <= d; m++) {
for (var q = e.charAt(m - 1), s = 1; s <= g; s++) {
b = c.charAt(s - 1);
b = q == b ? 0 : 1;
a[m][s] = Math.min(a[m - 1][s] + 1, a[m][s - 1] + 1, a[m - 1][s - 1] + b)
}
}
return a[d][g]
};
String.prototype.editD... | {
a[m][0] = m
} | conditional_block |
util.js | ] = []
}
for (m = 0; m <= d; m++) {
a[m][0] = m
}
for (m = 0; m <= g; m++) {
a[0][m] = m
}
for (m = 1; m <= d; m++) {
for (var q = e.charAt(m - 1), s = 1; s <= g; s++) {
b = c.charAt(s - 1);
b = q == b ? 0 : 1;
a[m][s] = Math.min(a[m - 1][s] + 1, a[m][s - 1] + 1, a[m - 1][s - 1... | {
return (new Date).getTime()
} | identifier_body | |
util.js | if (c == null) {
c = " "
}
var a = c.substr(0, 1);
return this.length < e ? this + this.repeat(e - this.length, a) : this
};
String.prototype.rjust = function(e, c) {
if (c == null) {
c = " "
}
var a = c.substr(0, 1);
return this.length < e ? this.repeat(e - this.length, a) + this : this
};
Stri... | () {
throw Error("Rnd is static and cannot be instantiated.");
}
Rnd.randFloat = function(e, c) {
if (isNaN(c)) {
c = e;
e = 0
}
return Math.random() * (c - e) + e
};
Rnd.randBoolean = function(e) {
if (isNaN(e)) {
e = 0.5
}
return Math.random() < e
};
Rnd.randSign = function(e) {
if (isNaN(... | Rnd | identifier_name |
img-touch-clip.js | .path;
//从canvas-zoom迁移过来，关于边框等元素的adaption的设置
this.scaleAdaption = 1;
var indoormap =options.canvas;
var pageWidth = parseInt(indoormap.getAttribute("width")); //750
var pageHeight = parseInt(indoormap.getAttribute("height"));//1180
currentWidth = document.documentEle... | _img_sx:0,
_img_sy:0,
// 图片的高宽
_imgW:0,
_imgH:0,
init_url: function(url){
this.imgTexture = new Image();
this.imgTexture.src=url;
this.init=false;
this.box_Scale=1;
this.position = {
x: 0,
... |
// _img:this.imgTexture,
//剪裁的x y坐标 | random_line_split |
IIoT End-to-End (Pt 2).py | .secrets.get("iot","adls_key"))
# Setup storage locations for all data
ROOT_PATH = f"abfss://iot@{storage_account}.dfs.core.windows.net/"
# Pyspark and ML Imports
import os, json, requests
from pyspark.sql import functions as F
from pyspark.sql.functions import pandas_udf, PandasUDFType
import numpy as np
import pan... | (readings_pd):
return train_distributed_xgb(readings_pd, 'power_prediction', label_col, prediction_col)
# Run the Pandas UDF against our feature dataset - this will train 1 model for each turbine
power_predictions = feature_df.groupBy('deviceid').apply(train_power_models)
# Save predictions to storage
power_predict... | train_power_models | identifier_name |
IIoT End-to-End (Pt 2).py | gboost
import mlflow.azureml
from azureml.core import Workspace
from azureml.core.webservice import AciWebservice, Webservice
import random, string
# Random String generator for ML models served in AzureML
random_string = lambda length: ''.join(random.SystemRandom().choice(string.ascii_lowercase) for _ in range(length... | return train_distributed_xgb(readings_pd, 'life_prediction', label_col, prediction_col) | identifier_body | |
IIoT End-to-End (Pt 2).py | # MAGIC [Pandas UDFs](https://docs.microsoft.com/en-us/azure/databricks/spark/latest/spark-sql/udf-python-pandas?toc=https%3A%2F%2Fdocs.microsoft.com%2Fen-us%2Fazure%2Fazure-databricks%2Ftoc.json&bc=https%3A%2F%2Fdocs.microsoft.com%2Fen-us%2Fazure%2Fbread%2Ftoc.json) allow us to vectorize Pandas code across multiple no... | print(f"-----Building image for {model} model-----")
model_image, azure_model = mlflow.azureml.build_image(model_uri=path,
workspace=workspace,
model_name=model,
... | conditional_block | |
IIoT End-to-End (Pt 2).py | utils.secrets.get("iot","adls_key"))
# Setup storage locations for all data
ROOT_PATH = f"abfss://iot@{storage_account}.dfs.core.windows.net/"
# Pyspark and ML Imports
import os, json, requests
from pyspark.sql import functions as F
from pyspark.sql.functions import pandas_udf, PandasUDFType
import numpy as np
impor... | # MAGIC %md #### Automated Model Tracking in Databricks
# MAGIC As you train the models, notice how Databricks-managed MLflow automatically tracks each run in the "Runs" tab of the notebook. You can open each run and view the parameters, metrics, models and model artifacts that are captured by MLflow Autologging. For X... | # MAGIC -- Plot actuals vs. predicted
# MAGIC SELECT date, deviceid, avg(power_6_hours_ahead) as actual, avg(power_6_hours_ahead_predicted) as predicted FROM turbine_power_predictions GROUP BY date, deviceid
# COMMAND ----------
| random_line_split |
builders.rs | b| b
/// .merge(serde_json::from_str(r#"{"name":"My Server"}"#)?))
/// ```
pub fn merge(mut self, other: $name) -> $name {
self.0.extend(other.0); self
}
}
)*
}
}
builder! {
/// Patch content for the `edit_server` call.
EditServer(Object);
/// Patch content for the `edit_channel` cal... | (self, bitrate: u64) -> Self {
set!(self, "bitrate", bitrate)
}
/// Edit the voice channel's user limit. Zero (`0`) means unlimited.
pub fn user_limit(self, user_limit: u64) -> Self {
set!(self, "user_limit", user_limit)
}
}
impl EditMember {
/// Edit the member's nickname. Supply the empty string to remove ... | bitrate | identifier_name |
builders.rs | #[inline(always)]
pub fn __build<F: FnOnce($name) -> $name>(f: F) -> $inner where $inner: Default {
Self::__apply(f, Default::default())
}
#[doc(hidden)]
pub fn __apply<F: FnOnce($name) -> $name>(f: F, inp: $inner) -> $inner {
f($name(inp)).0
}
/// Merge this builder's contents w... |
impl $name {
#[doc(hidden)] | random_line_split | |
builders.rs | b| b
/// .merge(serde_json::from_str(r#"{"name":"My Server"}"#)?))
/// ```
pub fn merge(mut self, other: $name) -> $name {
self.0.extend(other.0); self
}
}
)*
}
}
builder! {
/// Patch content for the `edit_server` call.
EditServer(Object);
/// Patch content for the `edit_channel` cal... |
/// Transfer ownership of the server to a new owner.
pub fn owner(self, owner: UserId) -> Self {
set!(self, "owner_id", owner.0)
}
/// Edit the verification level of the server.
pub fn verification_level(self, verification_level: VerificationLevel) -> Self {
set!(self, "verification_level", verification_lev... | {
set!(self, "afk_timeout", timeout)
} | identifier_body |
philo2.go | maxDinner int
currentlyEating int
tableCount int
chopsticksFree []bool
freeSeats []int
}
// philosopherData contains philosopher specific data. It is used within DinnerHost.
type philosopherData struct {
respChannel chan string
eating bool
dinnersSpent int
seat int
le... | (name string) string {
if host.currentlyEating >= host.maxParallel {
return "E:FULL"
}
data := host.phiData[name]
canEat := data.CanEat(host.maxDinner)
if canEat != "OK" {
return canEat
}
seatNumber := data.Seat()
leftChop := seatNumber
rightChop := (seatNumber + 1) % host.tableCount
if host.chopstick... | AllowEating | identifier_name |
philo2.go | maxDinner int
currentlyEating int
tableCount int
chopsticksFree []bool
freeSeats []int
}
// philosopherData contains philosopher specific data. It is used within DinnerHost.
type philosopherData struct {
respChannel chan string
eating bool
dinnersSpent int
seat int
le... |
host.chopsticksFree[host.phiData[name].LeftChopstick()] = true
host.chopsticksFree[host.phiData[name].RightChopstick()] = true
host.phiData[name].FinishedEating()
fmt.Println(name + " FINISHED EATING.")
fmt.Println()
}
// PrintReport shows the status of the philosophers in a verbose format.
func (host *DinnerHos... | {
host.currentlyEating--
} | conditional_block |
philo2.go | The host of the dinner.
--- */
// DinnerHost is the main data structure for the host of the dinner.
type DinnerHost struct {
phiData map[string]*philosopherData
requestChannel chan string
finishChannel chan string
maxParallel int
maxDinner int
currentlyEating int
tableCount int
chopst... |
/* --- | random_line_split | |
philo2.go | Count
}
host.maxDinner = maxDinner
host.currentlyEating = 0
host.tableCount = tableCount
host.chopsticksFree = make([]bool, 5)
for i := range host.chopsticksFree {
host.chopsticksFree[i] = true
}
rand.Seed(time.Now().Unix())
host.freeSeats = rand.Perm(tableCount)
}
// newPhilosopherDataPtr creates and initi... | {
return pd.rightChopstick
} | identifier_body | |
verify_cert.rs | cert: &Cert,
time: time::Time,
sub_ca_count: usize,
) -> Result<(), Error> {
let used_as_ca = used_as_ca(&cert.ee_or_ca);
check_issuer_independent_properties(
cert,
time,
used_as_ca,
sub_ca_count,
required_eku_if_present,
)?;
// TODO: HPKP checks.
... | (
input: Option<&mut untrusted::Reader>,
used_as_ca: UsedAsCa,
sub_ca_count: usize,
) -> Result<(), Error> {
let (is_ca, path_len_constraint) = match input {
Some(input) => {
let is_ca = der::optional_boolean(input)?;
// https://bugzilla.mozilla.org/show_bug.cgi?id=98502... | check_basic_constraints | identifier_name |
verify_cert.rs | cert: &Cert,
time: time::Time,
sub_ca_count: usize,
) -> Result<(), Error> {
let used_as_ca = used_as_ca(&cert.ee_or_ca);
check_issuer_independent_properties(
cert,
time,
used_as_ca,
sub_ca_count,
required_eku_if_present,
)?;
// TODO: HPKP checks.
... |
// https://tools.ietf.org/html/rfc5280#section-4.1.2.5
fn check_validity(input: &mut untrusted::Reader, time: time::Time) -> Result<(), Error> {
let not_before = der::time_choice(input)?;
let not_after = der::time_choice(input)?;
if not_before > not_after {
return Err(Error::InvalidCertValidity);... | {
// TODO: check_distrust(trust_anchor_subject, trust_anchor_spki)?;
// TODO: Check signature algorithm like mozilla::pkix.
// TODO: Check SPKI like mozilla::pkix.
// TODO: check for active distrust like mozilla::pkix.
// See the comment in `remember_extension` for why we don't check the
// Key... | identifier_body |
verify_cert.rs | cert: &Cert,
time: time::Time,
sub_ca_count: usize,
) -> Result<(), Error> {
let used_as_ca = used_as_ca(&cert.ee_or_ca);
check_issuer_independent_properties(
cert,
time,
used_as_ca,
sub_ca_count,
required_eku_if_present,
)?;
// TODO: HPKP checks.
... | // * We follow the convention established by Microsoft's implementation and
// mozilla::pkix | // Notable Differences from RFC 5280:
// | random_line_split |
move_vm.go | enabled clones to image service will not be deleted")
fmt.Println("--delete Deletes source VM - ARE YOU REALLY SURE?")
fmt.Println("--overwrite Overwrites target VM/Images (delete and creates new one)")
fmt.Println("--help List this help")
fmt.Println("--version Show the move_... |
}
func evaluateFlags() (ntnxAPI.NTNXConnection, ntnxAPI.VMJSONAHV, ntnxAPI.VMJSONAHV, []ntnxAPI.VMDisks) {
//help
if *help {
printHelp()
os.Exit(0)
}
//version
if *version {
fmt.Println("Version: " + appVersion)
os.Exit(0)
}
//debug
if *debug {
log.SetLevel(log.DebugLevel)
} else {
log.SetLev... | {
defer func() {
if err := recover(); err != nil {
log.Fatal("--vdisk-mapping is not correct")
os.Exit(1)
}
}()
for i, elem := range v.Config.VMDisks {
if elem.Addr.DeviceBus != VdiskMapping[i].Addr.DeviceBus || elem.Addr.DeviceIndex != VdiskMapping[i].Addr.DeviceIndex {
log.Error("--vdisk-mapping s... | identifier_body |
move_vm.go | enabled clones to image service will not be deleted")
fmt.Println("--delete Deletes source VM - ARE YOU REALLY SURE?")
fmt.Println("--overwrite Overwrites target VM/Images (delete and creates new one)")
fmt.Println("--help List this help")
fmt.Println("--version Show the move_... |
return n, vm, vNew, VdiskMapping
}
func main() {
flag.Usage = printHelp
flag.Parse()
customFormatter := new(log.TextFormatter)
customFormatter.TimestampFormat = "2006-01-02 15:04:05"
log.SetFormatter(customFormatter)
customFormatter.FullTimestamp = true
var n ntnxAPI.NTNXConnection
var v ntnxAPI.VMJSONA... | {
VdiskMapping, err = parseVdiskMapping(&n)
if err != nil {
os.Exit(1)
}
} | conditional_block |
move_vm.go | If enabled clones to image service will not be deleted")
fmt.Println("--delete Deletes source VM - ARE YOU REALLY SURE?")
fmt.Println("--overwrite Overwrites target VM/Images (delete and creates new one)")
fmt.Println("--help List this help")
fmt.Println("--version Show the mo... | (n *ntnxAPI.NTNXConnection) ([]ntnxAPI.VMDisks, error) {
defer func() {
if err := recover(); err != nil {
log.Fatal("--vdisk-mapping seems not to have right format")
os.Exit(1)
}
}()
var vdiskMappings []ntnxAPI.VMDisks
var VMDisk ntnxAPI.VMDisks
result := strings.Split(*vdiskMapping, ",")
// add Ma... | parseVdiskMapping | identifier_name |
move_vm.go | If enabled clones to image service will not be deleted")
fmt.Println("--delete Deletes source VM - ARE YOU REALLY SURE?")
fmt.Println("--overwrite Overwrites target VM/Images (delete and creates new one)")
fmt.Println("--help List this help")
fmt.Println("--version Show the mo... | log.Error("--vdisk-mapping seems not to have right format")
os.Exit(1)
}
if !(VMDisk.Addr.DeviceIndex >= 0 && VMDisk.Addr.DeviceIndex <= 255) {
log.Error("--vdisk-mapping seems not to have right format")
os.Exit(1)
}
if res[1] != "EMPTY" {
containerUUID, err := ntnxAPI.GetContainerUUIDbyName(n,... |
// check if right format is used
if !(VMDisk.Addr.DeviceBus == "scsi" || VMDisk.Addr.DeviceBus == "pci" || VMDisk.Addr.DeviceBus == "ide") { | random_line_split |
model.py | None:
# empty model
self.model = None
self.keywords = None
elif _type == "fixed":
if pre_trained_model_json is None:
raise RatingModel.RatingModel.Error("pre_trained_model_json is None")
self.loadModelFixed(pre_trained_model_jso... |
def __trainKMWM(self,seen_chunks_words: List[str],all_tokens_chunks: List[Any],
keywords: List[str]) -> Optional[Tuple[List[float], List[float]]]:
"""
Hidden function to obtain KM and WM scores from keywords
:param seen_chunks_words: n-grams of words in doc
:param ... | return [word for word in text if word in self.top_k_words] | identifier_body |
model.py | None:
# empty model
self.model = None
self.keywords = None
elif _type == "fixed":
if pre_trained_model_json is None:
raise RatingModel.RatingModel.Error("pre_trained_model_json is None")
self.loadModelFixed(pre_trained_model_jso... |
elif self._type == "lda":
if self.lda is None or self.dictionary is None or self.top_k_words is None:
raise RatingModel.RatingModelError("No LDA found")
seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)
seen_chunks_words, all_tokens_chu... | print("working on fixed model")
if self.keywords is None:
raise RatingModel.RatingModelError("Keywords not found")
seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)
# scoring
temp_out = self.__trainKMWM(list(seen_chunks_words), lis... | conditional_block |
model.py | None:
# empty model
self.model = None
self.keywords = None
elif _type == "fixed":
if pre_trained_model_json is None:
raise RatingModel.RatingModel.Error("pre_trained_model_json is None")
self.loadModelFixed(pre_trained_model_jso... | elif self._type == "lda":
if self.lda is None or self.dictionary is None or self.top_k_words is None:
raise RatingModel.RatingModelError("No LDA found")
seen_chunks_words, all_tokens_chunks = getAllTokensAndChunks(doc)
seen_chunks_words, all_tokens_chunk... | final_score = km_score * wm_score
| random_line_split |
model.py | :
# empty model
self.model = None
self.keywords = None
elif _type == "fixed":
if pre_trained_model_json is None:
raise RatingModel.RatingModel.Error("pre_trained_model_json is None")
self.loadModelFixed(pre_trained_model_json)
... | (self,seen_chunks_words: List[str],all_tokens_chunks: List[Any],
keywords: List[str]) -> Optional[Tuple[List[float], List[float]]]:
"""
Hidden function to obtain KM and WM scores from keywords
:param seen_chunks_words: n-grams of words in doc
:param all_tokens_chunks: list o... | __trainKMWM | identifier_name |
ng-typeview.ts | Handler,
defaultTagDirectiveHandlers, defaultAttrDirectiveHandlers} from "./ng-directives"
import {extractControllerScopeInfo, extractCtrlViewConnsAngularModule,
ControllerViewInfo, ControllerScopeInfo,
ControllerViewConnector, defaultCtrlViewConnectors,
CtrlViewFragmentExtractor, defaul... |
function getViewTestFilename(ctrlFname: string, viewFname: string): string {
return `${ctrlFname}_${viewFname}_viewtest.ts`;
}
async function processControllerView(prjSettings: ProjectSettings,
controllerPath: string, viewPath: string, ngFilters: NgFilter[],
tagDirectives: TagDirectiveHandler[],
attr... | {
return "module " + moduleName + " {\n" +
scopeInfo.imports.join("\n") + "\n" +
scopeInfo.typeAliases.join("\n") + "\n" +
scopeInfo.nonExportedDeclarations.join("\n") + "\n" +
contents +
"}\n";
} | identifier_body |
ng-typeview.ts | DirectiveHandler,
defaultTagDirectiveHandlers, defaultAttrDirectiveHandlers} from "./ng-directives"
import {extractControllerScopeInfo, extractCtrlViewConnsAngularModule,
ControllerViewInfo, ControllerScopeInfo,
ControllerViewConnector, defaultCtrlViewConnectors,
CtrlViewFragmentExtracto... | ngFilters: NgFilter[];
/**
* List of controller-view connectors to use.
* [[defaultCtrlViewConnectors]] contains a default list; you can use
* that, add to that list, or specify your own.
*/
ctrlViewConnectors: ControllerViewConnector[];
/**
* Hardcoded controller/view connectio... | random_line_split | |
ng-typeview.ts | Handler,
defaultTagDirectiveHandlers, defaultAttrDirectiveHandlers} from "./ng-directives"
import {extractControllerScopeInfo, extractCtrlViewConnsAngularModule,
ControllerViewInfo, ControllerScopeInfo,
ControllerViewConnector, defaultCtrlViewConnectors,
CtrlViewFragmentExtractor, defaul... |
const viewExprs = await parseView(
prjSettings.resolveImportsAsNonScope || false,
viewPath, scopeContents.viewFragments, scopeContents.importNames,
Vector.ofIterable(tagDirectives),
Vector.ofIterable(attributeDirectives),
Vector.ofIterable(ngFilters));
const pathInfo = p... | {
// no point of writing anything if there is no scope block
return;
} | conditional_block |
ng-typeview.ts | DirectiveHandler,
defaultTagDirectiveHandlers, defaultAttrDirectiveHandlers} from "./ng-directives"
import {extractControllerScopeInfo, extractCtrlViewConnsAngularModule,
ControllerViewInfo, ControllerScopeInfo,
ControllerViewConnector, defaultCtrlViewConnectors,
CtrlViewFragmentExtracto... | (ctrlFname: string, viewFname: string): string {
return `${ctrlFname}_${viewFname}_viewtest.ts`;
}
async function processControllerView(prjSettings: ProjectSettings,
controllerPath: string, viewPath: string, ngFilters: NgFilter[],
tagDirectives: TagDirectiveHandler[],
attributeDirectives: AttributeDire... | getViewTestFilename | identifier_name |
main_glcn.py | parser.add_argument('--method', default='vat')
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--in_channels', type=int, default=3)
parser.add_argument('--out_channels', type=int, default=7)
parser.add_argument('--topk', type=int, default=10)
parser.add_argument('--ngcn_layers', type=int, def... |
def eval(y_pred, y):
# print(semi_outputs.shape)
# y_pred = semi_outputs[num_labeled:(num_labeled+num_valid)]
prob, idx = torch.max(y_pred, dim=1)
return torch.eq(idx, y).float().mean()
# Several Ways to initialize the weights
# 1. initialize different weights using different initialization
def weig... | model.train()
# ce = nn.CrossEntropyLoss() # This criterion combines nn.LogSoftmax() and nn.NLLLoss() in one single class.
# semi_outputs have been log_softmax, so only NLLLoss() here
nll_loss = nn.NLLLoss()
semi_outputs, loss_GL, S = model(x)
# print("The learned S is ", torch.sum(S, dim=-1))
... | identifier_body |
main_glcn.py | parser.add_argument('--method', default='vat')
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--in_channels', type=int, default=3)
parser.add_argument('--out_channels', type=int, default=7)
parser.add_argument('--topk', type=int, default=10)
parser.add_argument('--ngcn_layers', type=int, def... | # print("First Row of X")
# print(x[0])
# print("Adj Matrix....")
# print(S[S > 0])
return semi_outputs, loss, ce_loss
def eval(y_pred, y):
# print(semi_outputs.shape)
# y_pred = semi_outputs[num_labeled:(num_labeled+num_valid)]
prob, idx = torch.max(y_pred, dim=1)
return torch.eq... | loss.backward()
optimizer.step()
| random_line_split |
main_glcn.py | parser.add_argument('--method', default='vat')
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--in_channels', type=int, default=3)
parser.add_argument('--out_channels', type=int, default=7)
parser.add_argument('--topk', type=int, default=10)
parser.add_argument('--ngcn_layers', type=int, def... | (m):
"""
Usage: model.apply(weights_init)
:param m:
:return:
"""
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
e... | weights_init | identifier_name |
main_glcn.py | parser.add_argument('--method', default='vat')
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--in_channels', type=int, default=3)
parser.add_argument('--out_channels', type=int, default=7)
parser.add_argument('--topk', type=int, default=10)
parser.add_argument('--ngcn_layers', type=int, def... |
train_data = torch.cat(select_train_data, dim=0).to(opt.device)
train_target = torch.cat(select_train_label, dim=0).to(opt.device)
valid_data = torch.cat(select_val_data, dim=0).to(opt.device)
valid_target = torch.cat(select_val_label, dim=0).to(opt.device)
test_data = torch.cat(unlabeled_train_data, dim=0).to(opt.de... | label_mask = (train_target == label)
current_label_X = train_data[label_mask]
current_label_y = train_target[label_mask]
select_train_data.append(current_label_X[:nSamples_per_class_train])
select_train_label.append(current_label_y[:nSamples_per_class_train])
select_val_data.append(current_label_X[n... | conditional_block |
plot.go | averageLatency float64
periodicLatencies []analytics.PeriodicAvgLatency
}
func (sts *stats) fromPackets(packets []packet.Packet) {
sts.averageLatency = analytics.CalcPositiveAverageLatency(packets)
sts.periodicLatencies = analytics.CalcPeriodicAverageLatency(packets)
}
func maxPacketsValue(packets []packet.Pac... | random_line_split | ||
plot.go | (packets []packet.Packet) {
sts.averageLatency = analytics.CalcPositiveAverageLatency(packets)
sts.periodicLatencies = analytics.CalcPeriodicAverageLatency(packets)
}
func maxPacketsValue(packets []packet.Packet) (xMin int64, xMax int64, yMin float64, yMax float64) {
xMin, xMax = int64(1<<63-1), -int64(1<<63-1)
yM... | fromPackets | identifier_name | |
plot.go | ackets)
sts.periodicLatencies = analytics.CalcPeriodicAverageLatency(packets)
}
func maxPacketsValue(packets []packet.Packet) (xMin int64, xMax int64, yMin float64, yMax float64) {
xMin, xMax = int64(1<<63-1), -int64(1<<63-1)
yMin, yMax = float64(xMin), float64(xMax)
for _, pkt := range packets {
x := pkt.Receiv... | return nil
}
func verticalSteps(plot *plotter) (steps []float64) {
plot.yLineStep = int64(math.Pow10(int(math.Ceil(math.Log10((plot.yMax-plot.yMin)/2))) - 1))
lo := plot.yLineStep * (int64(plot.yMin) / plot.yLineStep)
hi := plot.yLineStep * (int64(plot.yMax) / plot.yLineStep)
for y := lo; y <= hi; y += plot.yLineS... | return
}
}
| conditional_block |
plot.go | pdf.AddTTFFont("FiraSans-Book", "/usr/share/fonts/TTF/FiraSans-Book.ttf")
if err != nil {
return
}
err = pdf.AddTTFFont("FiraSans-Medium", "/usr/share/fonts/TTF/FiraSans-Medium.ttf")
if err != nil {
return
}
err = makeTitle(&pdf, plot.inputFilename)
if err != nil {
return
}
err = makeFootnote(&pdf, plo... | float64(pkt.ReceivedAt().UnixNano() - plot.xMin)
y := pkt.Value()
pdf.SetStrokeColor(pktColor(pkt))
xOnPaper := plot.xLeftMargin + x*plot.xScale
yOnPaper := plot.yPaperSize - plot.yBottomMargin - (y-plot.yMin)*plot.yScale
pdf.Line(xOnPaper, plot.yZeroAt, xOnPaper, yOnPaper)
}
func | identifier_body | |
cc.rs | it.
let fat_ptr: [*mut (); 2] = unsafe { mem::transmute(&mut dummy as &mut dyn CcDyn) };
fat_ptr[1]
}
}
impl CcDyn for CcDummy {
fn gc_ref_count(&self) -> usize {
1
}
fn gc_traverse(&self, _tracer: &mut Tracer) {}
fn gc_clone(&self) -> Box<dyn GcClone> {
panic!("bug... | ;
// safety: ccbox_ptr cannot be null from the above code.
let non_null = unsafe { NonNull::new_unchecked(ccbox_ptr) };
let result = Self(non_null);
if is_tracked {
debug::log(|| (result.debug_name(), "new (CcBoxWithGcHeader)"));
} else {
debug::log(|| (re... | {
Box::into_raw(Box::new(cc_box))
} | conditional_block |
cc.rs | #[inline]
pub fn weak_count(&self) -> usize {
self.inner().weak_count()
}
}
impl<T: ?Sized, O: AbstractObjectSpace> RawCc<T, O> {
#[inline]
pub(crate) fn inner(&self) -> &RawCcBox<T, O> {
// safety: CcBox lifetime maintained by ref count. Pointer is valid.
unsafe { self.0.as... | cast_ref | identifier_name | |
cc.rs | it.
let fat_ptr: [*mut (); 2] = unsafe { mem::transmute(&mut dummy as &mut dyn CcDyn) };
fat_ptr[1]
}
}
impl CcDyn for CcDummy {
fn gc_ref_count(&self) -> usize {
1
}
fn gc_traverse(&self, _tracer: &mut Tracer) {}
fn gc_clone(&self) -> Box<dyn GcClone> {
panic!("bug... |
}
impl<T: Trace, O: AbstractObjectSpace> RawCc<T, O> {
/// Constructs a new [`Cc<T>`](type.Cc.html) in the given
/// [`ObjectSpace`](struct.ObjectSpace.html).
///
/// To collect cycles, call `ObjectSpace::collect_cycles()`.
pub(crate) fn new_in_space(value: T, space: &O) -> Self {
let is_t... | {
collect::THREAD_OBJECT_SPACE.with(|space| Self::new_in_space(value, space))
} | identifier_body |
cc.rs | /// Force drop the value T.
fn gc_drop_t(&self);
/// Returns the reference count. This is useful for verification.
fn gc_ref_count(&self) -> usize;
}
/// A dummy implementation without drop side-effects.
pub(crate) struct CcDummy;
impl CcDummy {
pub(crate) fn ccdyn_vptr() -> *mut () {
let... | random_line_split | ||
agent_test.py | agent did not explore enough nodes during the search; it " + \
"did not finish the first layer of available moves."
TIMER_MARGIN = 15 # time (in ms) to leave on the timer to avoid timeout
def curr_time_millis():
return 1000 * timeit.default_timer()
def timeout(time_limit):
"""
Function deco... |
@timeout(1)
# @unittest.skip("Skip alpha-beta test.") # Uncomment this line to skip test
def test_alphabeta(self):
""" Test CustomPlayer.alphabeta """
h, w = 7, 7
method = "alphabeta"
value_table = [[0] * w for _ in range(h)]
value_table[2][5] = 1
value_ta... | agentUT, board = self.initAUT(depth, eval_fn, False, method, loc1=(2, 3), loc2=(0, 0))
move = agentUT.get_move(board, board.get_legal_moves(), lambda: 1e3)
num_explored_valid = board.counts[0] == counts[idx][0]
num_unique_valid = board.counts[1] == counts[idx][1]
self.a... | conditional_block |
agent_test.py | Your agent did not explore enough nodes during the search; it " + \
"did not finish the first layer of available moves."
TIMER_MARGIN = 15 # time (in ms) to leave on the timer to avoid timeout
def curr_time_millis():
return 1000 * timeit.default_timer()
def timeout(time_limit):
"""
Function ... | set([(1, 4)]),
set([(1, 4), (2, 5)])]
counts = [(2, 2), (26, 13), (552, 36), (10564, 47)]
for idx, depth in enumerate([1, 3, 5, 7]):
agentUT, board = self.initAUT(depth, eval_fn, False, method, loc1=(0, 6), loc2=(0, 0))
move =... | random_line_split | |
agent_test.py | Your agent did not explore enough nodes during the search; it " + \
"did not finish the first layer of available moves."
TIMER_MARGIN = 15 # time (in ms) to leave on the timer to avoid timeout
def curr_time_millis():
return 1000 * timeit.default_timer()
def timeout(time_limit):
"""
Function ... | move = agentUT.get_move(board, board.get_legal_moves(), lambda: 1e4)
num_explored_valid = board.counts[0] <= counts[idx][0]
num_unique_valid = board.counts[1] <= counts[idx][1]
self.assertTrue(num_explored_valid,
WRONG_NUM_EXPLORED.format(method, depth, ... | """ Test CustomPlayer.alphabeta """
h, w = 7, 7
method = "alphabeta"
value_table = [[0] * w for _ in range(h)]
value_table[2][5] = 1
value_table[0][4] = 2
value_table[1][0] = 3
value_table[5][5] = 4
eval_fn = EvalTable(value_table)
expected_moves... | identifier_body |
agent_test.py | agent did not explore enough nodes during the search; it " + \
"did not finish the first layer of available moves."
TIMER_MARGIN = 15 # time (in ms) to leave on the timer to avoid timeout
def curr_time_millis():
return 1000 * timeit.default_timer()
def timeout(time_limit):
"""
Function deco... | (self):
new_board = CounterBoard(self.__player_1__, self.__player_2__,
width=self.width, height=self.height)
new_board.move_count = self.move_count
new_board.__active_player__ = self.__active_player__
new_board.__inactive_player__ = self.__inactive_player... | copy | identifier_name |
zoekmachine.py | "14": "Kunst & Cultuur",
"15": "Overig",
'16': "Biologie",
"17": "Wiskunde",
"18": "Natuur- en scheikunde",
"19": "Psychologie",
"20": "Sociale wetenschap",
"21": "Overig",
"22": "Auto's",
"23" : "Vliegtuigen",
"24" : "Boten",
"25" : "Openbaar vervoer",
"26" : "Motorfi... | ttps://www.startpagina.nl/v/vraag/" + doc['_source']['Number'] + "/"
title = doc['_source']['Question']
text = text + title
if int(doc['_source']['Category']) == catNr:
results.append(wrapinresults(url, title))
ma | conditional_block | |
zoekmachine.py | : "Persoon & Gezondheid",
"3" : "Maatschappij",
"4" : "Financiën & Werk",
"5" : "Vervoer",
"6" : "Computers & Internet",
"7" : "Elektronica",
"8" : "Entertainment & Muziek",
"9" : "Eten & Drinken",
"10": "Sport, Spel & Recreatie",
'11': "Huis & Tuin",
"12": "Wetenschap",
"13"... | C2(term, c | identifier_name | |
zoekmachine.py | list_results[4], list_results[5], list_results[6], list_results[7], list_results[8], list_results[9])
f.write(whole)
f.close()
open_new_tab(filename)
#Wrap results into hrefs to place in result blocks
def wrapinresults(url, question):
wrapper = """<p><a href=%s>%s</a></p>"""
whole = wrapper % (ur... | fig, ax = plt.subplots()
ax.bar(timeline, y, width = 10)
fig.autofmt_xdate()
plt.show()
#Performs actual search
def search(term, filter):
text = ''
quest = es.get(index="zoekmachine", doc_type="question", id=5)['_source']
#Connect to cloud and find results
results = []
res = es.se... | y = range(len(timeline))
| random_line_split |
zoekmachine.py | _results[4], list_results[5], list_results[6], list_results[7], list_results[8], list_results[9])
f.write(whole)
f.close()
open_new_tab(filename)
#Wrap results into hrefs to place in result blocks
def wrapinresults(url, question):
wrapper = """<p><a href=%s>%s</a></p>"""
whole = wrapper % (url, qu... |
#Ask user for query in which time setting
def advanced():
print("What are you looking for? (ADV)")
term = sys.stdin.readline()
print("From what year on?")
year = sys.stdin.readline()
search(term, year)
#Return that the choice was not available
def invalid():
print("Not a valid choice")
#Lis... | print("What are you looking for?")
term = sys.stdin.readline()
search(term, 0000) | identifier_body |
bayes.py | 所有词的出现数初始化为1， 并将分母初始化为2
"""
p0Num = ones(numWords); p1Num = ones(numWords) # change to ones()
p0Denom = 2.0; p1Denom = 2.0 # change to 2.0
for i in range(numTrainDocs):
if trainCategory[i] == 1:
# 2. （以下两行） 向量相加
p1Num += trainMatrix[i]
... | trainingSet = range(50); testSet=[] # create test set
# （以下四行） 随机构建训练集 | random_line_split | |
bayes.py | （侮辱性文档类）的单词。
"""
"""
代码有4个输入： 要分类的向量vec2Classify以及使用函数trainNB0()计算得到的三个概率。 使用NumPy的数组来计算两个
向量相乘的结果❶。 这里的相乘是指对应元素相乘， 即先将两个向量中的第1个元素相乘， 然后将第2个元素相乘， 以此类推。 接下来将词汇表
中所有词的对应值相加， 然后将该值加到类别的对数概率上。 最后， 比较类别的概率返回大概率对应的类别标签。 这一切不是很难， 对吧？
"""
def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
# 1. 元素相乘 分类计算的核心
p1 = sum... | end(0)
| identifier_name | |
bayes.py | testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry = ['stupid', 'garbage']
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
# testingNB()
# -----------------------------------使用朴素贝叶斯过滤垃圾... | conditional_block | ||
bayes.py | testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
testEntry = ['stupid', 'garbage']
thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
print (testEntry,'classified as: ',classifyNB(thisDoc,p0V,p1V,pAb))
# testingNB()
# -----------------------------------使用朴素贝叶斯过滤垃圾... | identifier_body |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.