# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Functions used for the analysis of randomized benchmarking results.
"""
from scipy.optimize import curve_fit
import numpy as np
from qiskit import QiskitError
from ..tomography import marginal_counts
from ...characterization.fitters import build_counts_dict_from_list
try:
from matplotlib import pyplot as plt
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
[docs]class RBFitter:
"""
Class for fitters for randomized benchmarking
"""
def __init__(self, backend_result, cliff_lengths,
rb_pattern=None):
"""
Args:
backend_result: list of results (qiskit.Result).
cliff_lengths: the Clifford lengths, 2D list i x j where i is the
number of patterns, j is the number of cliffords lengths
rb_pattern: the pattern for the rb sequences.
"""
if rb_pattern is None:
rb_pattern = [[0]]
self._cliff_lengths = cliff_lengths
self._rb_pattern = rb_pattern
self._raw_data = []
self._ydata = []
self._fit = []
self._nseeds = 0
self._result_list = []
self.add_data(backend_result)
@property
def raw_data(self):
"""Return raw data."""
return self._raw_data
@property
def cliff_lengths(self):
"""Return clifford lengths."""
return self.cliff_lengths
@property
def ydata(self):
"""Return ydata (means and std devs)."""
return self._ydata
@property
def fit(self):
"""Return fit."""
return self._fit
@property
def seeds(self):
"""Return the number of loaded seeds."""
return self._nseeds
@property
def results(self):
"""Return all the results."""
return self._result_list
[docs] def add_data(self, new_backend_result, rerun_fit=True):
"""
Add a new result. Re calculate the raw data, means and
fit.
Args:
new_backend_result: list of rb results
rerun_fit: re caculate the means and fit the result
Additional information:
Assumes that 'result' was executed is
the output of circuits generated by randomized_becnhmarking_seq,
"""
if new_backend_result is None:
return
if not isinstance(new_backend_result, list):
new_backend_result = [new_backend_result]
for result in new_backend_result:
self._result_list.append(result)
# update the number of seeds *if* new ones
# added. Note, no checking if we've done all the
# cliffords
for rbcirc in result.results:
nseeds_circ = int(rbcirc.header.name.split('_')[-1])
if (nseeds_circ+1) > self._nseeds:
self._nseeds = nseeds_circ+1
for result in self._result_list:
if not len(result.results) == len(self._cliff_lengths[0]):
raise ValueError(
"The number of clifford lengths must match the number of "
"results")
if rerun_fit:
self.calc_data()
self.calc_statistics()
self.fit_data()
@staticmethod
def _rb_fit_fun(x, a, alpha, b):
"""Function used to fit rb."""
# pylint: disable=invalid-name
return a * alpha ** x + b
[docs] def calc_data(self):
"""
Retrieve probabilities of success from execution results. Outputs
results into an internal variable _raw_data which is a 3-dimensional
list, where item (i,j,k) is the probability to measure the ground state
for the set of qubits in pattern "i" for seed no. j and vector length
self._cliff_lengths[i][k].
Additional information:
Assumes that 'result' was executed is
the output of circuits generated by randomized_becnhmarking_seq,
"""
circ_counts = {}
circ_shots = {}
for seedidx in range(self._nseeds):
for circ, _ in enumerate(self._cliff_lengths[0]):
circ_name = 'rb_length_%d_seed_%d' % (circ, seedidx)
count_list = []
for result in self._result_list:
try:
count_list.append(result.get_counts(circ_name))
except (QiskitError, KeyError):
pass
circ_counts[circ_name] = \
build_counts_dict_from_list(count_list)
circ_shots[circ_name] = sum(circ_counts[circ_name].values())
self._raw_data = []
startind = 0
for patt_ind in range(len(self._rb_pattern)):
string_of_0s = ''
string_of_0s = string_of_0s.zfill(len(self._rb_pattern[patt_ind]))
self._raw_data.append([])
endind = startind+len(self._rb_pattern[patt_ind])
for i in range(self._nseeds):
self._raw_data[-1].append([])
for k, _ in enumerate(self._cliff_lengths[patt_ind]):
circ_name = 'rb_length_%d_seed_%d' % (k, i)
counts_subspace = marginal_counts(
circ_counts[circ_name],
np.arange(startind, endind))
self._raw_data[-1][i].append(
counts_subspace.get(string_of_0s, 0)
/ circ_shots[circ_name])
startind += (endind)
[docs] def calc_statistics(self):
"""
Extract averages and std dev from the raw data (self._raw_data).
Assumes that self._calc_data has been run. Output into internal
_ydata variable:
ydata is a list of dictionaries (length number of patterns).
Dictionary ydata[i]:
ydata[i]['mean'] is a numpy_array of length n;
entry j of this array contains the mean probability of
success over seeds, for vector length
self._cliff_lengths[i][j].
And ydata[i]['std'] is a numpy_array of length n;
entry j of this array contains the std
of the probability of success over seeds,
for vector length self._cliff_lengths[i][j].
"""
self._ydata = []
for patt_ind in range(len(self._rb_pattern)):
self._ydata.append({})
self._ydata[-1]['mean'] = np.mean(self._raw_data[patt_ind], 0)
if len(self._raw_data[patt_ind]) == 1: # 1 seed
self._ydata[-1]['std'] = None
else:
self._ydata[-1]['std'] = np.std(self._raw_data[patt_ind], 0)
[docs] def fit_data(self):
"""
Fit the RB results to an exponential curve.
Fit each of the patterns
Puts the results into a list of fit dictionaries:
where each dictionary corresponds to a pattern and has fields:
'params' - three parameters of rb_fit_fun. The middle one is the
exponent.
'err' - the error limits of the parameters.
'epc' - error per Clifford
"""
self._fit = []
for patt_ind, (lens, qubits) in enumerate(zip(self._cliff_lengths,
self._rb_pattern)):
# if at least one of the std values is zero, then sigma is replaced
# by None
if not self._ydata[patt_ind]['std'] is None:
sigma = self._ydata[patt_ind]['std'].copy()
if len(sigma) - np.count_nonzero(sigma) > 0:
sigma = None
else:
sigma = None
params, pcov = curve_fit(self._rb_fit_fun, lens,
self._ydata[patt_ind]['mean'],
sigma=sigma,
p0=(1.0, 0.95, 0.0),
bounds=([-2, 0, -2], [2, 1, 2]))
alpha = params[1] # exponent
params_err = np.sqrt(np.diag(pcov))
alpha_err = params_err[1]
nrb = 2 ** len(qubits)
epc = (nrb-1)/nrb*(1-alpha)
epc_err = epc*alpha_err/alpha
self._fit.append({'params': params, 'params_err': params_err,
'epc': epc, 'epc_err': epc_err})
[docs] def plot_rb_data(self, pattern_index=0, ax=None,
add_label=True, show_plt=True):
"""
Plot randomized benchmarking data of a single pattern.
Args:
pattern_index: which RB pattern to plot
ax (Axes or None): plot axis (if passed in).
add_label (bool): Add an EPC label
show_plt (bool): display the plot.
Raises:
ImportError: If matplotlib is not installed.
"""
fit_function = self._rb_fit_fun
if not HAS_MATPLOTLIB:
raise ImportError('The function plot_rb_data needs matplotlib. '
'Run "pip install matplotlib" before.')
if ax is None:
plt.figure()
ax = plt.gca()
xdata = self._cliff_lengths[pattern_index]
# Plot the result for each sequence
for one_seed_data in self._raw_data[pattern_index]:
ax.plot(xdata, one_seed_data, color='gray', linestyle='none',
marker='x')
# Plot the mean with error bars
ax.errorbar(xdata, self._ydata[pattern_index]['mean'],
yerr=self._ydata[pattern_index]['std'],
color='r', linestyle='--', linewidth=3)
# Plot the fit
ax.plot(xdata,
fit_function(xdata, *self._fit[pattern_index]['params']),
color='blue', linestyle='-', linewidth=2)
ax.tick_params(labelsize=14)
ax.set_xlabel('Clifford Length', fontsize=16)
ax.set_ylabel('Ground State Population', fontsize=16)
ax.grid(True)
if add_label:
bbox_props = dict(boxstyle="round,pad=0.3",
fc="white", ec="black", lw=2)
ax.text(0.6, 0.9,
"alpha: %.3f(%.1e) EPC: %.3e(%.1e)" %
(self._fit[pattern_index]['params'][1],
self._fit[pattern_index]['params_err'][1],
self._fit[pattern_index]['epc'],
self._fit[pattern_index]['epc_err']),
ha="center", va="center", size=14,
bbox=bbox_props, transform=ax.transAxes)
if show_plt:
plt.show()


| Package | Description |
|---|---|
| edu.cmu.sphinx.api | |
| edu.cmu.sphinx.decoder |
Provides a set of high level classes that can
be used to configure and initiate the speech recognition decoding
process.
|
| edu.cmu.sphinx.decoder.pruner |
Provides an interface that represents the pruning facility
|
| edu.cmu.sphinx.decoder.scorer |
Provides an interface that represents entities that can be
scored, and an interface and several implementations of a scorer that
can score these entities.
|
| edu.cmu.sphinx.decoder.search |
Provides classes and interfaces that are used to manage the search
through the search graph.
|
| edu.cmu.sphinx.frontend |
Provides a set of high level classes and interfaces that are used
to perform digital signal processing for speech recognition.
|
| edu.cmu.sphinx.frontend.databranch |
This package contains some classes to create a subbranch of a FronEnd at an arbitrary position.
|
| edu.cmu.sphinx.frontend.denoise | |
| edu.cmu.sphinx.frontend.endpoint |
Provides classes and interfaces used for speech endpointing.
|
| edu.cmu.sphinx.frontend.feature |
Provides classes that processes features.
|
| edu.cmu.sphinx.frontend.filter |
Provides classes that implement frequency filters.
|
| edu.cmu.sphinx.frontend.frequencywarp |
Provides classes that perform frequency warping.
|
| edu.cmu.sphinx.frontend.transform |
Provides classes that transform data from one domain into another.
|
| edu.cmu.sphinx.frontend.util |
Provides classes that are generally useful to the various frontend
classes.
|
| edu.cmu.sphinx.frontend.window |
Provides classes that implement windowing functions.
|
| edu.cmu.sphinx.instrumentation |
Provides a set of classes that monitor and track operational aspects of the Sphinx system.
|
| edu.cmu.sphinx.jsgf | |
| edu.cmu.sphinx.linguist |
Provides a set of interfaces and classes that are used to define the
search graph used by the decoder.
|
| edu.cmu.sphinx.linguist.acoustic |
Provides classes that represent the acoustic model.
|
| edu.cmu.sphinx.linguist.acoustic.tiedstate |
Provides classes that represent acoustic model in terms of a set of
tied states.
|
| edu.cmu.sphinx.linguist.acoustic.tiedstate.trainer |
Provides classes that represent a trainable acoustic model.
|
| edu.cmu.sphinx.linguist.acoustic.trivial |
Provides classes that represent a trivial acoustic model.
|
| edu.cmu.sphinx.linguist.aflat | |
| edu.cmu.sphinx.linguist.allphone | |
| edu.cmu.sphinx.linguist.dflat | |
| edu.cmu.sphinx.linguist.dictionary |
Provides a generic interface to a dictionary as well as several
implementations.
|
| edu.cmu.sphinx.linguist.flat |
Provides an implementation of the
Linguist that
statically represents the search space as a flat graph, where each
word in the vocabulary has its own branch. |
| edu.cmu.sphinx.linguist.language.classes | |
| edu.cmu.sphinx.linguist.language.grammar |
Provides classes and interfaces that can be used to represent a graph of words and word transitions.
|
| edu.cmu.sphinx.linguist.language.ngram |
Provides classes and interfaces that represent a stochastic language model
|
| edu.cmu.sphinx.linguist.language.ngram.large |
Provides an implementation of the LanguageModel interface.
|
| edu.cmu.sphinx.linguist.lextree |
Provides an implementation of the
Linguist that
represents the search space as a lex tree. |
| edu.cmu.sphinx.linguist.util |
Provides a set of classes that are useful by implementations of the
Linguist interface. |
| edu.cmu.sphinx.recognizer |
Provides a set of high level classes and interfaces that are used
to perform speech recognition with the Sphinx-4 speech recognition system.
|
| edu.cmu.sphinx.tools.batch |
Provides an tool that performs batch-mode speech recognition.
|
| edu.cmu.sphinx.tools.live |
Provides an tool that performs pseudo-live-mode speech recognition.
|
| edu.cmu.sphinx.trainer |
Provides a set of high level classes that can
be used to configure and initiate the speech recognition training
process.
|
| edu.cmu.sphinx.util.props |
Provides a mechanism for managing persistent configuration data.
|
| Modifier and Type | Method and Description |
|---|---|
<C extends Configurable> |
Context.getInstance(Class<C> clazz)
Returns instance of the XML configuration by its class.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ResultListener
The listener interface for being informed when new results are generated.
|
interface |
ResultProducer
Some API-elements shared by components which are able to produce
Result s. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDecoder
An abstract decoder which implements all functionality which is independent of the used decoding-paradigm (pull/push).
|
class |
Decoder
The primary decoder class
|
class |
FrameDecoder
A decoder which does not use the common pull-principle of S4 but processes only one single frame on each call of
decode() . |
| Modifier and Type | Interface and Description |
|---|---|
interface |
Pruner
Provides a mechanism for pruning a set of StateTokens
|
| Modifier and Type | Class and Description |
|---|---|
class |
NullPruner
A Null pruner.
|
class |
SimplePruner
Performs the default pruning behavior which is to invoke the purge on the active list
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
AcousticScorer
Provides a mechanism for scoring a set of HMM states
|
interface |
ScoreNormalizer
Describes all API-elements that are necessary to normalize token-scores after these have been computed by an
AcousticScorer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BackgroundModelNormalizer
Normalizes a set of Tokens against the best scoring Token of a background model.
|
class |
MaxScoreNormalizer
Performs a simple normalization of all token-scores by
|
class |
SimpleAcousticScorer
Implements some basic scorer functionality, including a simple default
acoustic scoring implementation which scores within the current thread, that
can be changed by overriding the
SimpleAcousticScorer.doScoring(java.util.List<T>, edu.cmu.sphinx.frontend.Data) method. |
class |
ThreadedAcousticScorer
An acoustic scorer that breaks the scoring up into a configurable number of separate threads.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ActiveListManager
An active list is maintained as a sorted list
|
interface |
SearchManager
Defines the interface for the SearchManager.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ActiveListFactory
Creates new active lists.
|
class |
PartitionActiveListFactory
A factory for PartitionActiveLists
|
class |
SimpleActiveListFactory
A factory for simple active lists
|
class |
SimpleActiveListManager
A list of ActiveLists.
|
class |
SimpleBreadthFirstSearchManager
Provides the breadth first search.
|
class |
SortingActiveListFactory |
class |
TokenHeapSearchManager
The token heap search manager that maintains the heap of best tokens for each
search state instead of single one best token
|
class |
TokenSearchManager |
class |
WordActiveListFactory
A factory for WordActiveList.
|
class |
WordPruningBreadthFirstLookaheadSearchManager
Provides the breadth first search with fast match heuristic included to
reduce amount of tokens created.
|
class |
WordPruningBreadthFirstSearchManager
Provides the breadth first search.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DataProcessor
A processor that performs a signal processing function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AutoCepstrum
Cepstrum is an auto-configurable DataProcessor which is used to compute a
specific cepstrum (for a target acoustic model) given the spectrum.
|
class |
BaseDataProcessor
An abstract DataProcessor implementing elements common to all concrete DataProcessors, such as name, predecessor, and
timer.
|
class |
DataBlocker
A
DataProcessor which wraps incoming DoubleData -objects into equally size blocks of defined
length. |
class |
FrontEnd
FrontEnd is a wrapper class for the chain of front end processors.
|
class |
GainControlProcessor
Allows to modify the gain of an audio-signal.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DataProducer
Some API-elements which are shared by components which can generate
Data s. |
| Modifier and Type | Class and Description |
|---|---|
class |
DataBufferProcessor
A FIFO-buffer for
Data -elements. |
class |
FrontEndSplitter
Creates push-branches out of a Frontend.
|
| Constructor and Description |
|---|
DataBufferProcessor(int maxBufferSize,
boolean waitIfEmpty,
int waitTime,
List<? extends Configurable> listeners) |
| Modifier and Type | Class and Description |
|---|---|
class |
Denoise
The noise filter, same as implemented in sphinxbase/sphinxtrain/pocketsphinx.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractVoiceActivityDetector
An abstract analyzer that signals about presense of speech in last processing frame.
|
class |
SpeechClassifier
Implements a level tracking endpointer invented by Bent Schmidt Nielsen.
|
class |
SpeechMarker
Converts a stream of SpeechClassifiedData objects, marked as speech and
non-speech, and mark out the regions that are considered speech.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractFeatureExtractor
Abstract base class for windowed feature extractors like DeltasFeatureExtractor, ConcatFeatureExtractor
or S3FeatureExtractor.
|
class |
BatchAGC
Applies automatic gain control (CMN)
|
class |
BatchCMN
Applies cepstral mean normalization (CMN), sometimes called channel mean normalization, to incoming cepstral data.
|
class |
BatchVarNorm
Applies cepstral variance normalization (CVN), so that each coefficient
will have unit variance.
|
class |
ConcatFeatureExtractor
This component concatenate the cepstrum from the sequence of frames according to the window size.
|
class |
DeltasFeatureExtractor
Computes the delta and double delta of input cepstrum (or plp or ...).
|
class |
FeatureTransform
Implements a linear feature transformation transformation.
|
class |
FrameDropper
Drops certain feature frames, usually to speed up decoding.
|
class |
KaldiDeltasFeatureExtractor |
class |
LiveCMN
Subtracts the mean of all the input so far from the Data objects.
|
class |
S3FeatureExtractor
Computes the delta and double delta of input cepstrum (or plp or ...).
|
| Modifier and Type | Class and Description |
|---|---|
class |
Dither
Implements a dither for the incoming packet.
|
class |
EnergyFilter
EnergyFilter silently drops zero energy frames from the stream.
|
class |
Preemphasizer
Implements a high-pass filter that compensates for attenuation in the audio data.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MelFrequencyFilterBank
Filters an input power spectrum through a bank of number of mel-filters.
|
class |
MelFrequencyFilterBank2
Filters an input power spectrum through a bank of number of mel-filters.
|
class |
PLPCepstrumProducer
Computes the PLP cepstrum from a given PLP Spectrum.
|
class |
PLPFrequencyFilterBank
Filters an input power spectrum through a PLP filterbank.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DiscreteCosineTransform
Applies a logarithm and then a Discrete Cosine Transform (DCT) to the input data.
|
class |
DiscreteCosineTransform2
Applies the optimized MelCosine filter used in pocketsphinx to the given melspectrum.
|
class |
DiscreteFourierTransform
Computes the Discrete Fourier Transform (FT) of an input sequence, using Fast Fourier Transform (FFT).
|
class |
KaldiDiscreteCosineTransform
DCT implementation that conforms to one used in Kaldi.
|
class |
Lifter
Applies the Lifter to the input mel-cepstrum to
smooth cepstrum values
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
AudioFileProcessListener
An interface which is describes the functionality which is required to handle new file signals fired by the
audio-data sources.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AudioContinuityTester
FrontEnd element that asserts the audio-stream to be continuous. |
class |
AudioFileDataSource
An AudioFileDataSource generates a stream of audio data from a given audio file.
|
class |
ConcatAudioFileDataSource
Concatenates a list of audio files as one continuous audio stream.
|
class |
ConcatFileDataSource
Concatenates a list raw headerless audio files as one continuous audio stream.
|
class |
DataConverter
A simple converter which converts
DoubleData to FloatData and vv (depending on its
configuration). |
class |
DataDumper
Dumps the data
|
class |
EnergyPlotter
Plots positive energy values of a cepstrum to stdout.
|
class |
Microphone
A Microphone captures audio data from the system's underlying audio input systems.
|
class |
StreamCepstrumSource
Produces Mel-cepstrum data from an InputStream.
|
class |
StreamDataSource
A StreamDataSource converts data from an InputStream into Data objects.
|
class |
StreamHTKCepstrum
Produces Mel-cepstrum data from an InputStream.
|
class |
VUMeterMonitor
A VU meter to be plugged into a front-end.
|
class |
WavWriter
Stores audio data into numbered (MS-)wav files.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RaisedCosineWindower
Slices up a Data object into a number of overlapping windows (usually referred to as "frames" in the speech world).
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Monitor
Common empty interface for Configurable monitors
|
| Modifier and Type | Class and Description |
|---|---|
class |
AccuracyTracker
Tracks and reports recognition accuracy
|
class |
BestPathAccuracyTracker
Tracks and reports recognition accuracy based upon the highest scoring path in a Result.
|
class |
ConfigMonitor
Shows the configuration currently in use.
|
class |
MemoryTracker
Monitors a recognizer for memory usage
|
class |
RecognizerMonitor
Monitor the state transitions of a given recognizer.
|
class |
RejectionTracker
Tracks and reports rejection accuracy.
|
class |
SpeedTracker
Monitors a recognizer for speed
|
| Modifier and Type | Class and Description |
|---|---|
class |
GrXMLGrammar
Grammar for GrXML W3C Standard
|
class |
JSGFGrammar
Defines a BNF-style grammar based on JSGF grammar rules in a file.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Linguist
The linguist is responsible for representing and managing the search space for the decoder.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LinguistProcessor
A standard interface for a linguist processor
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
AcousticModel
Represents the generic interface to the Acoustic Model for sphinx4
|
| Modifier and Type | Class and Description |
|---|---|
class |
UnitManager
Manages the set of units for a recognizer
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Loader
Generic interface for a loader of acoustic models
|
interface |
Saver
Generic interface for a saver of acoustic models
|
| Modifier and Type | Class and Description |
|---|---|
class |
HTKLoader
Remark1: S4 does not use HMM tying: the HTK "tiedlist" is not loaded nor user
for now
Remark2: HTK does nearly never backoff to monophones, whereas S4 might do it.
|
class |
KaldiLoader |
class |
Sphinx3Loader
Loads a tied-state acoustic model generated by the Sphinx-3 trainer.
|
class |
SphinxAsciiLoader
Sphinx loader that loads ASCII versions.
|
class |
TiedStateAcousticModel
Loads a tied-state acoustic model generated by the Sphinx-3 trainer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ModelInitializerLoader
an acoustic model loader that initializes models
|
class |
Sphinx3Saver
An acoustic model saver that saves sphinx3 ascii data.
|
class |
TrainerAcousticModel
Represents the generic interface to the Acoustic Model for sphinx4
|
| Modifier and Type | Class and Description |
|---|---|
class |
TrivialAcousticModel
Represents the generic interface to the Acoustic Model for sphinx4
|
| Modifier and Type | Class and Description |
|---|---|
class |
AFlatLinguist
A simple form of the linguist.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AllphoneLinguist |
| Modifier and Type | Class and Description |
|---|---|
class |
DynamicFlatLinguist
A simple form of the linguist.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Dictionary
Provides a generic interface to a dictionary.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MappingDictionary
Maps the phones from one phoneset to another to use dictionary from the one
acoustic mode with another one.
|
class |
TextDictionary
Creates a dictionary by quickly reading in an ASCII-based Sphinx-3 format
dictionary.
|
| Modifier and Type | Class and Description |
|---|---|
class |
FlatLinguist
A simple form of the linguist.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ClassBasedLanguageModel
An LM that computes a probability of a word sequence by converting words to
classes and asking the class-based probability from a delegate LM.
|
class |
ClassMap
A component that knows how to map words to classes and vice versa.
|
class |
ClassPresentingDictionary
The only purpose of this class is to present all classes
as words in the getWord() method.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AlignerGrammar |
class |
BatchForcedAlignerGrammar |
class |
ForcedAlignerGrammar
Creates a grammar from a reference sentence.
|
class |
FSTGrammar
Loads a grammar from a file representing a finite-state transducer (FST) in the 'ARPA' grammar format.
|
class |
Grammar
Classes that implement this interface create grammars.
|
class |
LatticeGrammar
A grammar build from a lattice.
|
class |
LMGrammar
Defines a simple grammar based upon a language model.
|
class |
SimpleWordListGrammar
Defines a grammar based upon a list of words in a file.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
LanguageModel
Represents the generic interface to an N-Gram language model.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DynamicTrigramModel
3-gram language model that can change its content at runtime.
|
class |
InterpolatedLanguageModel
Simple interpolated LM implementation.
|
class |
KeywordOptimizerModel |
class |
NetworkLanguageModel |
class |
SimpleNGramModel
An ASCII ARPA language model loader.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KeywordOptimizerLargeNGramModel
Use a largeNGramModel that also can be adjusted depending on context.
|
class |
LargeNGramModel
Language model that uses a binary NGram language model file ("DMP file")
generated by the SphinxBase sphinx_lm_convert.
|
class |
LargeTrigramModel
A wrapper for LargeNGramModel base on the old LargeTrigramModel class.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LexTreeLinguist
A linguist that can represent large vocabularies efficiently.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GDLDumper
A linguist processor that dumps out the sentence hmm in GDL format.
|
class |
LinguistDumper
A linguist processor that dumps out the search space in a simple format.
|
class |
LinguistStats
A linguist processor that dumps out statistics about the search space
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
StateListener
The listener interface for receiving recognizer status events
|
| Modifier and Type | Class and Description |
|---|---|
class |
Recognizer
The Sphinx-4 recognizer.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BatchForcedAlignerRecognizer
Copyright 1999-2002 Carnegie Mellon University.
|
class |
BatchModeRecognizer
Decodes a batch file containing a list of files to decode.
|
class |
BatchNISTRecognizer
Copyright 1999-2002 Carnegie Mellon University.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LiveModeRecognizer
Decodes a batch file containing a list of files to decode.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ControlFile
Provides mechanisms for reading a control file (or a pair control file plus transcription file).
|
interface |
Learner
Provides mechanisms for computing statistics given a set of states and input data.
|
interface |
TrainManager
Manages inputs and outputs to the other trainer classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BaumWelchLearner
Provides mechanisms for computing statistics given a set of states and input data.
|
class |
FlatInitializerLearner
Provides mechanisms for computing statistics given a set of states and input data.
|
class |
SimpleControlFile
Provides mechanisms for accessing a next utterance's file name and transcription.
|
class |
SimpleTrainManager
This is a dummy implementation of a TrainManager.
|
class |
Trainer
Trains models given a set of audio files.
|
class |
TrainerDictionary
Dummy trainer dictionary.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ConfigurableAdapter
An default (abstract) implementation of a configurable that implements a meaning
toString() and keeps a
references to the Confurable 's logger. |
| Modifier and Type | Method and Description |
|---|---|
static <C extends Configurable> |
ConfigurationManager.getInstance(Class<C> targetClass)
Creates an instance of the given
Configurable by using the default parameters as defined by the
class annotations to parameterize the component. |
static <C extends Configurable> |
ConfigurationManager.getInstance(Class<C> targetClass,
Map<String,Object> props)
Creates an instance of the given
Configurable by using the default parameters as defined by the
class annotations to parameterize the component. |
static <C extends Configurable> |
ConfigurationManager.getInstance(Class<C> targetClass,
Map<String,Object> props,
String compName)
Creates an instance of the given
Configurable by using the default parameters as defined by the
class annotations to parameterize the component. |
<C extends Configurable> |
ConfigurationManager.lookup(Class<C> confClass)
Returns a
Configurable instance of a given type C , if such a component (or a derived
one) is registered to this ConfigurationManager instance, and there is one and only match. |
<C extends Configurable> |
ConfigurationManager.lookup(String instanceName)
Looks up a configurable component by name.
|
| Modifier and Type | Method and Description |
|---|---|
Configurable |
PropertySheet.getComponent(String name)
Gets a component associated with the given parameter name.
|
Configurable |
PropertySheet.getOwner() |
| Modifier and Type | Method and Description |
|---|---|
Class<? extends Configurable> |
PropertySheet.getComponentClass(String propName)
Returns the class of of a registered component property without instantiating it.
|
Class<? extends Configurable> |
PropertySheet.getConfigurableClass() |
| Modifier and Type | Method and Description |
|---|---|
void |
ConfigurationManager.addConfigurable(Configurable configurable,
String name)
Adds an already instantiated
Configurable to this configuration manager. |
void |
PropertySheet.setComponent(String name,
String cmName,
Configurable value)
Sets the given property to the given name
|
| Modifier and Type | Method and Description |
|---|---|
void |
ConfigurationManager.addConfigurable(Class<? extends Configurable> confClass,
String name)
Registers a new configurable to this configuration manager.
|
void |
ConfigurationManager.addConfigurable(Class<? extends Configurable> confClass,
String name,
Map<String,Object> props)
Registers a new configurable to this configuration manager.
|
Collection<String> |
ConfigurationManager.getInstanceNames(Class<? extends Configurable> type)
Gets all instances that are of the given type.
|
List<PropertySheet> |
ConfigurationManager.getPropSheets(Class<? extends Configurable> confClass)
Given a
Configurable -class/interface, all property-sheets which are subclassing/implemting this
class/interface are collected and returned. |
static void |
ConfigurationManagerUtils.setClass(PropertySheet ps,
Class<? extends Configurable> confClass) |
void |
PropertySheet.setComponentList(String name,
List<String> valueNames,
List<Configurable> value)
Sets the given property to the given name
|
| Constructor and Description |
|---|
PropertySheet(Configurable configurable,
String name,
RawPropertyData rpd,
ConfigurationManager ConfigurationManager) |
| Constructor and Description |
|---|
PropertySheet(Class<? extends Configurable> confClass,
String name,
ConfigurationManager cm,
RawPropertyData rpd) |
Copyright © 2017. All rights reserved.


This vignette gives a brief overview of the main functions within the bitmexr
package. The package supports both the testnet and live API endpoints, simply use the tn_
prefix to any of the functions in the package (e.g., bitmexr::trades()
accesses trade data from the live API whereas bitmexr::tn_trades()
accesses trade data from the testnet API). This is useful is you want to test out placing/managing orders in a risk free environment.
To access trades that have been executed on the exchange you can use either:
trades()
: to get individual trades that have been executed.bucket_trades()
: to get bucketed trade data in 1-minute, 5-minute, 1-hour or 1-day bucketsbucket_trades()
is typically more useful when wanting data over an extended period of time due to the sheer volume of trades that are executed on the exchange each day. These functions also have map_* variants to help overcome the 1000 row per request limit.
Please see the Trade data vignette for more details on these functions.
bitmexr
also supports placing and managing orders on the exchange. These functions require an API key and secret to work. Please read the vignette Authentication for information on how to set this up in bitmexr
.
The following functions are available:
place_order()
for placing ordersedit_order()
for editing orderscancel_order()
or cancel_all_orders()
to cancel ordersPlease see the vignette Placing and managing order with bitmexr for more information.
It is recommended to have a thorough understanding of the BitMEX API documentation prior to using the package. Please take some time to read both https://www.bitmex.com/app/apiOverview and https://www.bitmex.com/api/explorer/.


To use this collection, you ideally need bash 2.05a or later. You can also use
bash 2.05 if you apply the group name completion patch available at
http://www.caliban.org/files/bash/bash-2.05-group_completion.patch.
Alternatively, you can just comment out the lines that contain
'comp{lete,gen} -g'.
If you're using bash 2.04, in addition to commenting out the lines discussed
in the previous paragraph, you'll also to comment out the '-o <option>'
part of each invocation of 'complete' and edit /etc/bashrc to reflect this
version in the $BASH_VERSION test.
-rw-r--r-- 1 root root 54159 Mar 5 2002 /etc/bash_completion drwxr-xr-x 2 root root 0 Mar 5 2002 /usr/doc/bash-completion-20020304 -rw-r--r-- 1 root root 42155 Mar 4 2002 /usr/doc/bash-completion-20020304/Changelog -rw-r--r-- 1 root root 4029 Mar 1 2002 /usr/doc/bash-completion-20020304/README drwxr-xr-x 2 root root 0 Mar 3 2002 /usr/doc/bash-completion-20020304/contrib -rw-r--r-- 1 root root 207 Mar 3 2002 /usr/doc/bash-completion-20020304/contrib/isql -rw-r--r-- 1 root root 1784 Mar 1 2002 /usr/doc/bash-completion-20020304/contrib/larch


|
|
Container_bptreePerformance of basic algorithms |
The performance analysis for a particular computer system can be a quite complex task even in the case of basic algorithms. The dependence of the running time on the number of elements is often a non-trivial function, which deviates from theoretical bounds. For a process, which accesses system resources through a virtual memory manager, theoretically constant parameters vary in a very wide range. The most important factors, which can make an unexpected effect on performance of algorithms, are cache misses and page faults. In addition to this there are other less obvious factors. For example, several applications can compete for system resources and interrupt each other. The memory fragmentation can also cause the performance degradation if a system has been running for an extended period of time since its last reboot. For these reasons the measured values of running times of algorithms can be used only as first order estimates.
In this project the main point of performance tests is the comparison of B+ trees with other types of data structures. The C++ standard containers are an excellent choice for this task, since their interfaces are supported by various data structures.
The values of running times of algorithms have been measured using Windows high
resolution performance counter, for details see description of method
QueryPerformanceCounter()
in MSDN Library for Visual Studio.
The file "perform_tests.hpp" provides the code developed for these
performance tests and their analysis. The test code has been compiled with
optimization option - maximize speed. To improve accuracy the tests of sequential
processing have been iterated in a for
loop. The error of the values
shown in the tables below is about 5%.
Several desktop and laptop computers running Windows XP or Windows 7 have been tested. The performance tests have produced similar results for all of these systems. The data shown below have been obtained using the desktop PC with the following system info: Intel Core 2 Duo Processor E6400, 2.13GHz, 2.00MB of L2 Cache, 2.00GB of RAM, Windows XP 32 bits.
vector_std |
std::vector |
list_std |
std::list |
mset_std |
std::multiset |
mset_mul_idx |
boost::multi_index_container as multiset |
seqce_bp_tree_idx |
container_bptree::sequence<bp_tree_idx> |
seqce_bp_tree_idx_acc |
container_bptree::sequence<bp_tree_idx_acc> |
mset_bp_tree |
container_bptree::multiset<bp_tree> |
mset_bp_tree_idx |
container_bptree::multiset<bp_tree_idx> |
mset_bp_tree_idx_acc |
container_bptree::multiset<bp_tree_idx_acc> |
accumulate()+ |
algorithm of bp_tree_idx_acc::accumulate() |
The test algorithm: push_back()
N elements.
The table shows values of running times in milliseconds against
the number of elements added to a container.
| N | 1,000 | 10,000 | 100,000 | 1,000,000 |
vector_std |
0.02 | 0.09 | 1.20 | 14.0 |
list_std |
0.20 | 2.3 | 25.0 | 260.0 |
seqce_bp_tree_idx |
0.23 | 3.0 | 32.0 | 320.0 |
seqce_bp_tree_idx_acc |
0.24 | 3.1 | 34.0 | 330.0 |
push_back()
is the fastest member function of STL sequences that
inserts elements into a container. The test shows the superiority of
std::vector
despite the theoretical fact that in the worst case
the cost of this function is linear and despite the other theoretical fact that
std::list
guarantees constant cost of this function.
These facts do not apply to these performance results. The test measures
the amortized cost of the function push_back()
.
std::vector
has the highest efficiency due to its better
locality of reference compared to any other tested data structure.
The sequences based on the augmented B+ trees in theory have logarithmic running
time of this function, but they show the performance that is not significantly worse
than that of std::list
. The difference between std::list
and
std::vector
is much more considerable.
The test algorithm: insert N elements into a sequence container before the middle position. The table shows values of running times in milliseconds against the number of elements processed by the algorithm.
| N | 1,000 | 10,000 | 100,000 | 1,000,000 |
vector_std |
0.3 | 15.0 | 2.1e+3 | 2.2e+5 |
list_std |
0.19 | 2.3 | 25.0 | 260.0 |
seqce_bp_tree_idx |
0.24 | 2.9 | 32.0 | 320.0 |
seqce_bp_tree_idx_acc |
0.25 | 3.0 | 34.0 | 330.0 |
The result of this test is interesting, since std::vector
has the best
performance in other tests: push_back()
and sequential processing
discussed below.
The total cost of the insertion of N elements into std::vector
is quadratic due to linear cost of this operation for a single element. This
computational cost makes std::vector
unacceptable for an algorithm,
which inserts or erases a large number of elements at arbitrary positions.
std::list
has the best result in this test, since it guarantees constant
cost of the test operation for a single element. The sequences based on B+ trees have
logarithmic cost of the insertion of a single element in the worst case. These containers
perform a bit worse than std::list
, but much better than
std::vector
.
The test algorithm: insert N elements into a multiset container. Three kinds of input data have been used in this test: ordered, random and specially prepared to insert elements before the middle position of a container. The tables show values of running times in milliseconds against the number of elements processed by the algorithm.
| N | 1,000 | 10,000 | 100,000 | 1,000,000 |
mset_std |
0.32 | 4.1 | 56.0 | 1500.0 |
mset_mul_idx |
0.37 | 4.2 | 46.0 | 480.0 |
mset_bp_tree |
0.26 | 3.1 | 32.0 | 320.0 |
mset_bp_tree_idx |
0.26 | 3.2 | 35.0 | 350.0 |
mset_bp_tree_idx_acc |
0.28 | 3.3 | 37.0 | 360.0 |
| N | 1,000 | 10,000 | 100,000 | 1,000,000 |
mset_std |
0.34 | 4.1 | 55.0 | 780.0 |
mset_mul_idx |
0.39 | 4.5 | 51.0 | 550.0 |
mset_bp_tree |
0.34 | 4.1 | 49.0 | 530.0 |
mset_bp_tree_idx |
0.37 | 4.5 | 55.0 | 600.0 |
mset_bp_tree_idx_acc |
0.39 | 4.6 | 59.0 | 620.0 |
| N | 1,000 | 10,000 | 100,000 | 1,000,000 |
mset_std |
0.39 | 4.9 | 72.0 | 2100.0 |
mset_mul_idx |
0.42 | 5.0 | 67.0 | 1800.0 |
mset_bp_tree |
0.43 | 5.7 | 87.0 | 2800.0 |
mset_bp_tree_idx |
0.45 | 5.9 | 90.0 | 3100.0 |
mset_bp_tree_idx_acc |
0.47 | 6.0 | 105.0 | 3300.0 |
The running times of these insertion operations into multisets can be used to compare the efficiency of update operations in B+ trees and red black trees. The Boost Multi-index library implements red black trees with the space optimization technique . These trees perform better than red black trees of the standard library. Note that, in theory, multisets are less efficient in these tests than sequences, since the insertion of an element into a multiset involves the additional cost of search for a valid position.
These tests show that B+ trees have better performance of the insertion of ordered data. Both data structures have approximately the same running time of inserting N elements before the middle position of a container. Red black trees insert data a bit more efficiently in the test with random input data.
The measurements show the performance degradation for both red black trees and B+ trees when the number of input elements becomes large (N=1,000,000). The most likely cause of this effect is the increased frequency of cache misses.
accumulate()
The standard library algorithm accumulate()
is a typical example of sequential
processing of data. This algorithm can be used to measure performance of ordered
and unordered containers based on arrays, linked lists and trees. It is also useful
and interesting to compare the results with performance of the fast algorithm
accumulate+()
implemented in the class bp_tree_idx_acc
of the namespace container_bptree
.
The table shows values of running times in microseconds against the number of elements processed by algorithms.
| N | 500 | 5,000 | 50,000 | 500,000 |
mset_std |
12.0 | 120.0 | 1200.0 | 14000.0 |
mset_mul_idx |
6.5 | 60.0 | 630.0 | 8800.0 |
mset_bp_tree_idx_acc |
2.5 | 21.0 | 270.0 | 3800.0 |
mset_bp_tree_idx |
2.5 | 21.0 | 250.0 | 3600.0 |
mset_bp_tree |
2.1 | 21.0 | 240.0 | 3700.0 |
seqce_bp_tree_idx_acc |
2.3 | 22.0 | 260.0 | 3800.0 |
seqce_bp_tree_idx |
2.4 | 21.0 | 250.0 | 3700.0 |
list_std |
2.2 | 18.0 | 190.0 | 2900.0 |
vector_std |
0.4 | 3.5 | 38.0 | 390.0 |
accumulate+() |
0.12 | 0.16 | 0.17 | 0.34 |
These tests show that std::multiset
based on a red black tree is
the least efficient container for sequential processing. The efficiency of containers
using the B+ trees is three to four times better and only slightly worse than
that of std::list
.
Note that sequence
and multiset
containers of the namespace
container_bptree
based on the same B+ tree have practically the same
efficiency of sequential processing. In theory, it should be exactly the same as
the efficiency of std::list
, since all of these B+ trees store container
elements in external nodes arranged into doubly linked lists. This connectivity gives
the advantage of more efficient sequential access and processing of data compared
to basic search trees such as red black trees, for more details, see the section
Design and implementation of B+ trees.
These performance data confirm the well known fact that std::vector
is the best choice of a container for sequential processing of data. This container
quite noticeably beats the second best container std::list
.
The comparison also shows that none of the containers based on a B+ tree can
outperform std::vector
, when the user algorithm calls
std::accumulate()
.
The situation changes dramatically if the same user algorithm calls the member function
accumulate()
of a container based on the B+ tree with the optimized data
processing implemented in the class bp_tree_idx_acc
. This algorithm
demonstrates the performance improvement by orders of magnitude. It is also important that
the running time of this efficient algorithm grows very slowly as the number of
processed elements increases. The superiority of fast algorithm accumulate()
is explained by the fact that in theory its running time is logarithmic function of
the number of elements. This testing confirms that in the asymptotical case linear algorithm
can not compete with logarithmic algorithm even if this linear algorithm takes advantage
of the most efficient sequence container.
The method of augmenting of B+ trees and other data structures adds new functionality, but involves the increased space requirement. This fact can have an effect on the locality of reference and can cause the performance degradation of basic algorithms that are supported by data structures occupying less space without augmenting. The other important factor is an additional computational cost associated with augmenting data. The tests of this section help answer the practically important question about the effect of augmenting on the performance of algorithms of modified B+ trees.
In the tests of sequential processing the differences in the values of running times are quite small or within the error of the measurements. All three variants of B+ trees have practically the same efficiency of sequential processing.
The tests of update operations of B+ trees of the classes bp_tree_idx
and bp_tree_idx_acc
produce close values of running times.
The difference in the efficiency is more noticeable in the comparison of values
for B+ trees of the classes bp_tree
and bp_tree_idx_acc
,
which provide the minimum and maximum of functionality, correspondingly.
The increase in the functionality can cost up to 15% of performance.
The tests also suggest that update operations are more sensitive to augmenting of
data structures than sequential processing.
The interpretation of these data is not straight forward. In the case of update operations both the increased space requirement and the increased computational cost are important.
The increase in the memory consumption for augmenting data can be estimated using
the simplified model from section "Space requirement" of the document
Design and implementation of B+ trees.
There is no difference in the memory consumption between B+ trees of classes
bp_tree
and bp_tree_idx
, since both of them store the same data,
but use the counter of nodes for different purposes as discussed in the section
"Data scheme" of the same document
Design and implementation of B+ trees.
The difference in the memory consumption between internal nodes of B+ trees of classes
bp_tree_idx
and bp_tree_idx_acc
is the size of a container element,
which in these test is of type int
. This gives for the tested B+ trees
the 2 per cent increase in the total space requirement.
As for the difference in the implementations of update operations, in theory, the most
efficient is the B+ tree of the class bp_tree
. It uses the counter of
nodes as the number of children nodes at every level of the tree. These data help reduce
the computational cost of update operations. The other two variants of B+ trees do not
implement this optimization. This is why, theoretically, the B+ tree of the class
bp_tree_idx_acc
provides the least efficient implementation of update
operations.
The results of these performance tests are important not only for B+ trees of the library
container_bptree
, but also for other types of trees and data structures.
They demonstrate that the method of augmenting of data structures provides the optimized
use of the memory space with the minimal effect on locality of reference and a relatively
small effect on the performance of algorithms. This method can deliver a new efficient
functionality without a significant effect on the efficiency of other functionalities
and algorithms. Thus, this method has a great potential for the development of
new efficient algorithms and programming solutions.
Revised 23 June, 2012
Copyright © 2011
Vadim Stadnik
<EMAIL_ADDRESS>Distributed under the Boost Software License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)


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--- title: Form Group section: Components ---
The helper class form-group-sm will size inputs, buttons, and spacing inside form-group or form-group-autofit to be smaller.
form-group-sm
form-group
form-group-autofit


Leaked Strictly exit enrages fans as surprise couple is booted out
Strictly Come Dancing fans have once again been left devastated as the result of Sunday's show have been leaked on Twitter
by Sam Elliott, https://www.mirror.co.uk/authors/sam-elliott/Strictly viewers have been left reeling again after after the results of Sunday's show were leaked online.
The show is now filmed over one evening - and often spoilers get out on social media.
With fans searching for gossip using the Strictly hashtag on twitter, many have stumbled across the result by accident.
Mirror Online knows who has left the show but won't spoil the result for viewers keen to watch it unfold tomorrow.
However some furious fans who have discovered the result have flocked to Twitter in fury. After a number of controversial exits in recent weeks, it doesn't seem like this Sunday's departure will please everyone either.
The competition is heating up and week even produced many big moments.
Karim Zeroual and Amy Dowden soared to the top of the leaderboard after their Quickstep performance - everyone loved it as they rose to the occasion.
At the other end of the table are Emma Barton and Anton du Beke, who finished last on the leaderboard with 22.
But who is leaving? Although of course we will keep tight-lipped, it has already been revealed online.
Those who couldn't resist looking are not happy.
" #StrictlySpoiler #Strictlyresults Why am I not surprised! #biased " wrote one fan with eye-rolling emojis.
"Absolute dog s**t" put another after discovering the dancers who get the boot.
 Absolutely livid," another posted on twitter.
" #strictlyspoiler sad to see but I think fair enough" accepted another viewer.
*Strictly results air tomorrow on BBC One


Fixing Scotland's 150,000 potholes will cost taxpayers £3billion
MSPs have been told more than a third of local streets and 13 per cent of major roads need work done.
by Tom EdenThe cost of fixing Scotland’s pothole-blighted roads has reached £3billion, according to a Holyrood committee.
More than a third of local streets and 13 per cent of major roads need work done, the rural economy and connectivity committee found.
In a letter to Transport Secretary Michael Matheson, convener Edward Mountain wrote: “The committee is concerned that there is an estimated £1.2billion backlog for maintenance of trunk roads and around £1.8billion needs to be spent to bring the local roads network up to a satisfactory standard.”
Mountain cited Audit Scotland reports that show there has been a 26 per cent decline in spending on local roads over five years.
He said the committee “is agreed that a new approach is needed to make significant inroads into addressing the accepted maintenance backlogs”.
Tory MSP Jamie Greene said: “After 12 years of Nationalist neglect, Scotland has £3billion of repair backlogs and 150,000 potholes.
A Scottish Government spokesman said: “We are making significant efforts to maximise every penny that is spent on maintenance. The budget for maintaining roads Transport Scotland looks after has increased by £33million in 2019-20.”


Based on the conceptual model of , we choose the design discussed in in the subsection "The Medical-Department-1b design model" and defined by the following information design model:
Notice that this model
Does not define an object type Doctor, since the doctors of the department are not modeled as a collection of individual persons, but as an abstract aggregate in the form of a count pool.
The random variable recurrence for modeling the random variation of the time between new cases samples from the exponential probability distribution with an event rate of 0.3, while the random variable for the duration of an examination samples from the uniform probability distribution with lower bound 5 and upper bound 10 (representing minutes).
The NewCase class can be coded with OESjs-Core2 in the following way:
class NewCase extends eVENT { constructor({ occTime, delay}) { super({occTime, delay}); } onEvent() {return [];} createNextEvent() { return new NewCase({delay: NewCase.recurrence()}); } static recurrence() {return rand.exponential( 0.3);} }
The onEvent method is empty since no event rules are needed for this simple model. Its dynamics is entirely determined by the standard logic of resource-dependent activity scheduling built into OES Core 2.
The Examination class can be coded in the following way:
class Examination extends aCTIVITY { constructor({id, startTime, duration}={}) { super({id, startTime, duration}); } static duration() {return rand.uniform( 5, 10);} } Examination.resourceRoles = { "doctor": {countPoolName:"doctors", card:1} }
The process resulting from NewCase events followed by Examination activities is modeled with a Resource-Dependent Activity Scheduling arrow:
In OESjs-Core2, this simple process model is coded with one line of code in the file NewCase.js:
NewCase.successorActivity = "Examination";
The following information design model of a medical department with two types of activities (discussed in ) is based on the conceptual information model discussed in :
The class implementing the event type NewCase is defined as above. The class implementing the activity type WalkToRoom is defined as follows:
class WalkToRoom extends aCTIVITY { constructor({id, startTime, duration}={}) { super({id, startTime, duration}); } static duration() {return rand.uniform( 0.5, 2.5);} } // A walk to a room requires a room and a nurse WalkToRoom.resourceRoles = { "nurse": {range: Nurse, card:1}, "room": {countPoolName:"rooms", card:1} }
The class implementing the activity type Examination is defined as follows:
class Examination extends aCTIVITY { constructor({id, startTime, duration}={}) { super({id, startTime, duration}); } static duration() {return rand.uniform( 5, 10);} } // An examination requires a room and a doctor Examination.resourceRoles = { "doctor": {range: Doctor, card:1}, "room": {countPoolName:"rooms", card:1} }
The following process design model (discussed in ) is based on the conceptual process model discussed in :
This process design model with its two Resource-Dependent Activity Scheduling arrows is implemented with just two statements on top of the classes NewCase and WalkToRoom:
// Enqueue a new planned walk NewCase.successorActivity = "WalkToRoom"; // Enqueue a new planned examination WalkToRoom.successorActivity = "Examination";
You can run this Medical-Department-2a model from the project's GitHub website.
When an activity type is defined without defining a class-level duration function, the exponential PDF is used as a built-in default random variable for setting the durations of activities of that type according to the following settings:
aCTIVITY.defaultMean = 1; aCTIVITY.defaultDuration = function () { return rand.exponential( 1/aCTIVITY.defaultMean) };
It is possible to overwrite these defaults, both the defaultMean and the defaultDuration function, in a simulation.js file.


If you have any questions, write to us and we will get back to you within 48 business hours.
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A multiple choice Javascript Quiz aimed at evaluating your mastery of concepts taught in Class
*Answer all Questions before submitting*
**Click on the Questions to display the multiple choices**
1. Which tag is used to insert Javascript into an HTML page?
2. What is the correct syntax for referring to an external script called example.js?
3. Which of the following is the correct syntax to display “Moringa School” in an alert box using JavaScript?
4. What is the syntax for creating a function in JavaScript named as doSomething?
5. How do we write an IF statement in Javascript?


| 003:044 Khan | : |
This is a part of the news of the Ghaib (unseen, i.e. the news of the past nations of which you have no knowledge) which We inspire you with (O Muhammad SAW). You were not with them, when they cast lots with their pens as to which of them should be charged with the care of Maryam (Mary); nor were you with them when they disputed. |
| 003:044 Maulana | : |
This is of the tidings of things unseen which We reveal to thee. And thou wast not with them when they cast their pens (to decide) which of them should have Mary in his charge, and thou wast not with them when they contended one with another. |
| 003:044 Pickthal | : |
This is of the tidings of things hidden. We reveal it unto thee (Muhammad). Thou wast not present with them when they threw their pens (to know) which of them should be the guardian of Mary, nor wast thou present with them when they quarrelled (thereupon). |
| 003:044 Rashad | : |
This is news from the past that we reveal to you. You were not there when they drew their raffles to select Mary's guardian. You were not present when they argued with one another. |
| 003:044 Sarwar | : |
(Muhammad), that was some of the news about the unseen, that We have revealed to you. You were not among those who cast lots by throwing their arrows to find out who would take custody of Mary, nor were you among those who disputed the matter. |
| 003:044 Shakir | : |
This is of the announcements relating to the unseen which We reveal to you; and you were not with them when they cast their pens (to decide) which of them should have Marium in his charge, and you were not with them when they contended one with another. |
| 003:044 Sherali | : |
This is of the tidings of things unseen which WE reveal to thee. And thou was not with them when they cast their arrows, as to which of them should be the guardian of Mary, nor was thou with them when they disputed with one another. |
| 003:044 Yusufali | : |
This is part of the tidings of the things unseen, which We reveal unto thee (O Messenger!) by inspiration: Thou wast not with them when they cast lots with arrows, as to which of them should be charged with the care of Mary: Nor wast thou with them when they disputed (the point). |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > الوحي Pillars of Islam> The Blessed Muhammad(PBUH)> The Revelation القصص والتاريخ > عُمران > مريم ابنة عمران The Stories and the History>Imran>Blessed Mary(Imran's Daughter)
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


About this page
This blog is about my learning journey through the tasks and challenges set up through Dev Academy. It may look rough and at times glitchy, but please be patience as I learn the skills in creating a great user experience for you.
Please click on the numbers 1 - 5 on the left for blogs relating to each Sprint
FYI. I will update the page regularly so if there was a cute picture of a puppy that you really liked somewhere in this site and it disappeared... I am sorry!


Copywriting. Are you kidding me?
Writing good and "juicy" messages is a necessity nowadays.
Whenever used right, it will allow you to get best candidates, work offers and deliver your message to the final audience at a minimum cost.
Especially it’s important for all of the recruiters (sales pitch to get attention of candidate), business owners (set your values, mission, ideas, etc.), people who are posting on social media (getting more likes, reactions), or literally anyone who wants to have better engaging texts.
Part one - 30% theory
We learn / refresh knowledge that we require for good writing
Why should you care about your texts?
Quick and on point
Attention span of a goldfish modern human.
Important aka “Bait” first
How to engage your audience
Writing for people
Writing != talent
Part two: 60% workshop
We do practical trainings together with instant feedback
Part three: 10% - question and answer session Your feedback and questions Our help with actual struggles that you have with copywritingPlaceholder text by Space Ipsum. Photographs by NASA on The Commons.


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8.9 Related Topics
- Direct-address tables
-
A simple type of hash table in which
there is a one-to-one mapping between all possible keys and
positions in the table. Since no two keys map to the same
position, there is no need for collision resolution.
However, if there are many possible keys, the table will be
large. Generally, direct addressing works well when the
universe of possible keys is small.
- Linear congruential generators
-
A common class of random number generators.
Understanding the principles behind random number generators
can help in devising good hash functions.
- Quadratic probing
-
An alternative to linear
probing and double hashing for probing an open-addressed
hash table. In quadratic probing,
the sequence of positions probed is determined using a
quadratic-form hash function. In general, quadratic probing
performs better than linear probing, but it does not perform
as well as double hashing. Quadratic probing results in
secondary clustering, a form of clustering that is less
severe than the primary clustering of linear probing.
- Universal hashing
-
A hashing method in which hashing functions
are generated randomly at runtime so that no particular set
of keys is likely to produce a bad distribution of elements
in the hash table. Because the hash functions are generated
randomly, even hashing the same set of keys during different
executions may result in different measures of performance.
|


Strength
Strength measures bodily power, athletic training, and the extent to which you can exert raw physical force.
Dexterity
Dexterity measures agility, reflexes, and balance.
Constitution
Constitution measures health, stamina, and vital force.
Intelligence
Intelligence measures mental acuity, accuracy of recall, and the ability to reason.
Wisdom
Wisdom reflects how attuned you are to the world around you and represents perceptiveness and intuition.
Charisma
Charisma measures your ability to interact effectively with others. It includes such factors as confidence and eloquence, and it can represent a charming or commanding personality.


Training made easy
We are all about maintaining our dedication to the highest moral principles and providing a challenging and secure environment for our people.
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Dealing with network cybersecurity risks.
We provide the best solutions to your problems following proper risk management procedures we can train your employees to handle issues in any risky enviroment.
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Endless Plains [ah69], plain, in Imperia, peasants: 110
Last updated Turn #103
Endless Plains [ah69], plain, in Imperia, peasants: 110
------------------------------------------------------------------------
25: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
25: 33% damaged, arrived from Ocean [ah68], owner:
25: Phantasm [n4b], Imperial Empire, duke, with eight sailors
26: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
26: 33% damaged, departed for Ocean [ag69].
30: The population grows by one peasant.
Routes leaving Endless Plains:
North, to Ocean [ag69], Crystal Sea, 2 days
East, plain, to Endless Plains [ah70], 7 days
South, plain, to Endless Plains [ai69], 7 days
West, to Ocean [ah68], Crystal Sea, 2 days
It is raining.
Seen here:
Stack of nazgul [er2h], nazgul, number: 4


Heroku: Ruby Platform Sees 50 Percent Increase in Apps Since November
If I was running a Rails shop, I would be here. But in the big scheme of this, it’s still easier to find Java developers.I’ll be back for the next startup.
The number of apps hosted on the Ruby platform Heroku has increased 50 percent since November, pointing out how cloud-based platforms are becoming the norm for the capabilities the services provide in terms of testing and reliability.
Heroku had more than 40,000 apps on its platform last November. Now more than 60,000 apps are using the Heroku service.
Heroku’s success is not alone. Amazon Web Services (AWS) and Rackspace are rapidly expanding. AWS announced its expansion to the Asia Pacific region this past week. In its announcement, AWS featured Kim Eng, one of Asia’a largest security brokers. The firm said the availability of the AWS service helped minimize its latency for its KE Trade iPhone application.
Rackspace developer Mike Mayo built the company’s latest iPad app for customers to monitor Rackspace cloud networks. He said the cloud provides a way for developers to test with better efficiency. For example, using cloud platforms, apps are continually tested for bugs. Those bugs can be fixed one at a time as they pop up. That’s easier than doing a build and then fixing the 20 bugs that are discovered.
Heroku
Heroku is a Ruby-on-Rails platform that according to the company web site “eliminates the need to manage servers, slices or clusters.” Developers focus on the code and that’s pretty much it.
Heroku hosts the Flightcaster application, an interesting example of a service that uses two cloud providers to serve its app. Flightcaster is a service that checks your flight to see if it is on time. It can predict up to six hours in advance if your flight will be late.
Flightcaster uses Heroku to serve the application. But the actual data analysis is done through Amazon S3 where Hadoop manages the data analytics. The data is passed from Heroku to Amazon S3 and then back to Heroku where it is served back to the customer making the request.
Heroku is an example of a service that is making it infinitely easier for developers to develop and manage applications. It’s part of a new breed of platforms emerging that are growing in popularity.
We’ll see if Heroku will grow 100 percent over the span of a year. That would mean 80,000 apps. Seems plausible, doesn’t it?
Discuss


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Accessing the molecular PAH data
The molecular PAH data is accessed through their associated unique identifier (UID). However, a search-interface is available that allows retrieval of these UIDs through simplified queries. The syntax is the same as used on the NASA Ames PAH IR Spectroscopic Database website.
uids = pahdb->Search("c<=20 neutral n=2 neutral")
The molecular PAH data contained in the database XML-files consists of four main parts:
- Molecular properties, .e.g, molecular weight, zero point energy, total energy, etc.
- Fundamental vibrational transitions
- Molecular geometric data
- Raw laboratory spectra for a subset of molecules in the experimental database
pahdb = OBJ_NEW('AmesPAHdbIDLSuite')
uids = pahdb->Search("c<=20 neutral n=2 neutral")
pahs = pahdb->getSpeciesByUID(uids)
transitions = pahdb->getTransitionsByUID(uids)
geometries = pahdb->getGeometryByUID(uids)
laboratory = pahdb->getLaboratoryByUID(uids)
... work ...
OBJ_DESTROY,[laboratory, $
geometries, $
transitions, $
pahs, $
pahdb]
Alternatively, one can access these data components through the 'AmesPAHdbIDLSuite_Species'-object.
pahdb = OBJ_NEW('AmesPAHdbIDLSuite')
pahs = pahdb->getSpeciesByUID( $
pahdb->Search("c<=20 neutral n=2 neutral"))
transitions = pahs->transitions()
geometries = pahs->geometry()
laboratory = pahs->laboratory()
... work ...
OBJ_DESTROY,[laboratory, $
geometries, $
transitions, $
pahs, $
pahdb]


What is Recaf?
Recaf is an open-source Java bytecode editor built on top of Objectweb's ASM. ASM is a bytecode manipulation library that abstracts away the constant pool and a few other class-file attributes. Since keeping track of the constant pool and managing proper stackframes are no longer necessary, complex changes can be made with relative ease. With additional features to assist in the process of editing classes, Recaf is the most feature rich free bytecode editor available.
Useful Information
While ASM makes bytecode manipulation very simple it does not mean you should dive head-first into editing compiled java programs without understanding some basic programming concepts and the Java class file architecture. Here are some references for these topics:
- Specification: Chapter 4. The class File Format
- JVM Architecture 101: Get to Know Your Virtual Machine
- Java opcodes:
Download & Building
Downloadable jar binaries are provided through Github's release page.
Alternatively you can build from the source by cloning (or downloading) the repository and using maven to build the executable.
Requirements
Recaf requires Java 8 to run. To use a few of the features you will need the JDK rather than the JRE. For running on Java 9 and above please read the usage guide for how to do that. Editing capabilities are independent from the version of java you use to run Recaf.
Usage Guide & More Information
Contact & Support
For reporting bugs and suggesting new features please use the github repo's issue page and submit a new issue. For other inquiries join the discord server here: discord.gg/Bya5HaA


Use the DeepL Translator API to integrate the world's best translation technology.
The DeepL Translator API allows you to incorporate the DeepL Translator into your own products and services. Besides using it in your favourite CAT tools or integrating it into your intranet services, our API lends itself to many more of your in-house software applications.
The standardized programming interface makes integration into your own software extremely straightforward.
Your privacy and confidentiality are important to us - we don't save any texts sent to the DeepL Translator API, and we don't use them to improve the quality of the service.
Click here to contact our sales team for access.
The DeepL API is accessible through a REST interface. All functions support GET and POST methods, although POST requests are recommended. Request parameters are used to pass information to the API, results are returned in a JSON representation. Request parameters must be UTF-8 encoded. Authentication is based on fixed keys, SSL handling transport security.
The v1 API supports only the translate-function. See below for full documentation.
The translate function is available at https://api.deepl.com/v1/translate. The call supports the following parameters:
| Parameter | Description | |
|---|---|---|
| text | Required | Text to be translated. Only UTF8-encoded plain text is supported. The parameter may be specified multiple times and translations are returned in the same order as in the input. Each of the parameter values may contain multiple sentences. |
| source_lang | Optional | Language of the text to be translated. Can be one of the following
|
| target_lang | Required | The language into which you want to translate. Can be one of the following
|
| tag_handling | Optional | Sets which kind of tags should be handled. Comma-separated list of one or more of the following values:
|
| split_sentences | Optional | Sets whether the translation engine should first split the input into sentences. This is enabled
by default. Possible values are
|
| preserve_formatting | Optional | Sets whether the translation engine should preserve some aspects of the formatting, even if it would usually
correct some aspects. Possible values are
|
| auth_key | Required | The authentication key as provided to you. |
GET /v1/translate?text=Hallo%20Welt!&target_lang=EN&auth_key=123
{ "translations": [ { "detected_source_language": "DE", "text": "Hello World!" } ] }
The translate function returns a JSON representation of the translations in the order the text parameters have been specified.
| Return value | Description |
|---|---|
| detected_source_language | The language which has been detected for the source text. It reflects the value of the source_lang parameter if it has been specified. |
| text | The translated text. |
Errors are indicated by HTTP error codes. The following error codes are used:
| Code | Description |
|---|---|
| 400 | Wrong request, please check error message and your parameters. |
| 403 | Authorization failed. Please supply a valid auth_key parameter. |
| 429 | Too many requests. Please wait and send your request once again. |
| 456 | Quota exceeded. The character limit has been reached. |
Additional information may be provided by a JSON response that contains more details about the error:
{ "message": "Parameter 'target_lang' not specified." }
By default the translation engine does not take tags into account. By setting the tag_handling parameter accordingly, you can enable various sets of special tag handlers.
xml
All XML tags that do not contain any text are treated as placeholders. For example, the translation engine will try to copy tags "<x></x>" and "<y/>" into the output sentence, trying to place them at the proper position in the translation.
For XML tags which enclose words, the translation engine tries to find matching words in the translated sentence and enclose them with the same tags. If no matching words are found, the tags are removed.
All XML tags are allowed, there is no restriction. Attributes are copied together with tags.
The request size should not exceed 30kbytes. The maximum number of texts to be translated within one request should not exceed 50.
Please ensure your client does not exceed the limits as specified in the quota documentation delivered to you. Your client should be prepared to back-off if you receive the HTTP response code 429 and retry the request later.
When establishing the connection, you should verify that the the correct auth_key parameter has been provided using the API key provided. You should also verify whether the SSL certificate for api.deepl.com is valid.


Best charity Christmas gifts 2019 for presents that give back
by Precious AdesinaWhile Christmas is a time for giving and sharing with your family and friends, it can also be used as an opportunity to give to charity.
One way to make Christmas go further is by choosing unusual presents that have a charity attached to it in some way. This could be by gifting someone an item that, when bought, a donation is given to a specific UK or international charity. Another option is to donate to a cause in someone's name.
We certainly love the idea of using Christmas gifts to give back to the community. Here, you can find a list of our top charity Christmas gifts, including everything from jewellery to more specific forms of aid.
You can also get involved in The Daily Telegraph's Christmas Charity Appeal for 2019.
Clothing and accessories
1. Vivienne Westwood Save The Rainforest T-shirt
This T-shirt designed by Vivienne Westwood has been spotted on Kate Moss, Fearne Cotton and Ronnie Wood to name a few. It's the ideal gift for someone who's into fashion but who likes to know that what they're wearing is doing good. The Cool Earth foundation has already saved nearly a million acres of rainforest and you can help increase that number for just £25.
2. Ethical Stories Ethical Me Chaand Round Hammered Gold and Pearl Earring - India
£44.95, Ethical Stories Ethical Me
These extremely stylish earrings are hammered brass and faux pearls. Not only is this an eco-conscious buy for a fashion conscious family member. Best of all, 10 per cent of your purchase goes towards the charity Future Hope, which works to provide a classroom education for children who live on the streets and slums across India.
3. Blue Cross Man’s Best Friends Socks
Odd socks with a dog motif? Who wouldn't want to find this set in their Christmas stocking? Especially when the proceeds will help the Blue Cross care for and re-home over 40,000 pets every year.
4. Jade Beaded Bracelet With Elephant
This very pretty bracelet will make a lovely stocking filler. Made from jade, it was created by Thai artisan Somluck Komolmith and not only will you be supporting her business, you'll be supporting Unicef's programmes that provide nutrition, medicine, education, clean water and relief to children in danger.
5. Bottletop Luisa Mini Clutch Bag
Bottletop uses upcycled ring pulls and metal tabs to create gorgeous bags any style maven would want in their wardrobe. Each one is made in the label's atelier in Salvador, Brazil by local women who gain skills, full time employment and benefits they wouldn't otherwise have access to. Proceeds are used to fund smaller projects around the world to providing young people with health education.
6. Four Rivers Gold Plated Ring at ISHKAR
ISHKAR works with craftsmen in war torn countries, making their handmade products available to buy throughout the world. It helps them grow their business by providing fair wages while also contributing to local NGOs. This gorgeous ring made by Mehdi Ansar in Afghanistan is inspired by the four rivers of paradise and will make a lovely a present for a jewellery fan.
7. Sana Jardin Revolution De La Fleur Eau de Parfum
Sana Jardin is the world's first socially conscious luxury fragrance brand. The scents are gorgeous but also empower the women involved in their creation by giving them tools to upcycle floral waste into their own line of products, which are then sold all around the world. There are seven fragrances to choose from but we love Revolution de la Fleur with its blend of Madagascan ylang ylang, Moroccan jasmine, frangipani and rose.
Homeware
8. Bee Free Fine Bone China Mug
This pretty mug has a fun bee print and 100 per cent of the profit from every product sold by Marie Curie goes towards providing high quality care to people with terminal illnesses in their own homes.
9. The Dogs Trust The Rabble Treat Tin
If they've got a dog then they'll need some handy storage to stash away their treats and this Dogs Trust tin will be just the thing. Plus by buying it you'll be helping to save the lives of thousands of stray and abandoned dogs.
10. Thomas Street Lime & Basil Candle
A scented candle is always a welcome gift so choose one this year that will do more than create a fragrant home. This Thomas Street candle is available at Save the Children's online shop and 100 per cent of the profits will go towards helping children everywhere.
11. Chilly's X Refill Personalisable Water Bottle
For every bottle sold Chilly’s will donate £10 to City to Sea, the plastic pollution organisation behind the Refill campaign. City to Sea is tackling the single-use plastic items found most commonly on our beaches and in our oceans. The bottles can be made to look pretty fun.
12. Clic Sargent Reed Diffuser
The proceeds from this deliciously scented diffuser will go towards helping young people under 25 who are living with cancer as well as to supporting their families, to limit the damage cancer causes in all areas of their lives.
Direct donation
13. Centre Point New Year New Start Box
A generous gift but one which will truly make a difference in a homeless young person's life. This gift box will ensure they have everything they need for a new start; a place to stay, counselling, food, toiletries, school or college equipment, clothing and the essential support they don't have from their families. If £1,500 is out of your budget then how about buying a homeless person Christmas dinner for only £10, or a hamper that will provide food for a week for £50, or with £144 you could sponsor a room so they'll have somewhere safe to stay for up to a year.
14. Brooke Water All Year Round
Brooke helps working donkeys, mules and horses across Africa, Asia, Latin America and the Middle East and by picking this 'Water All Year Round' gift for a friend, you'll be supporting a community to build and maintain a water trough so their animals can remain hydrated, as well as creating change in the owners' attitudes to their animals.
15. Water Aid Hand Pump
Give a hand pump on your loved one's behalf to a whole community, ensuring they can all access safe, clean drinking water every day.
16. Help Refugees Hygiene Kit
A hygiene kit will provide a refugee with enough daily essentials for three months. From toothpaste to sanitary pads, these little things we take for granted take on huge significance when you're living in an inhospitable camp with no access to money or shops. Other gifts offered by Help Refugees include tents, socks, blankets, sleeping bags, coats for children, medical kits and meal ingredients, all guaranteed to have an immediate impact on the lives of the people who'll receive them.
17. Advent of Change Premium Advent Calendar
Everyone loves opening the doors of an Advent calendar, especially if there's a treat hidden behind each one. But the Advent of Change calendar is turning the idea upside down and instead of receiving something every day of December, you can help give to a whole host of charities instead.
Food, books and other items
18. Heartfelt Healthy Recipe Book
£14.99, British Heart Foundation
This cookbook written by Pippa Middleton in collaboration with the British Heart Foundation includes over 100 healthy recipe ideas for breakfast, family meals, romantic dinners and hosting. A great gift for the foodie in your family.
19. Migrateful Cookery Class
£35 for one or £70 for two, Migrateful
Migrateful is a London-based charity that helps migrants, refugees and asylum seekers integrate into society by empowering them to teach their cuisines to the general public. Its chef training programs has assisted chefs and home cooks from countries such as Syria, Pakistan, Nigeria, Iran, Cuba, and many more. A voucher gets them on the charity's open classes with a chef of their choice.
20. Spana Springwatch Garden Tin
Children will love creating their own little gardens with this tin filled with wooden animals, birds, trees, plants, a felt mat and instructions. You could buy this set elsewhere but by shopping at Spana you'll be helping the charity to care for working animals around the world.
21. The Book of Everyone and Battersea Dogs & Cats Home Wise(ish) Words For Dog Lovers
From £19.99, The Book Of Everyone
Create a personalised book for the dog lover in your life with The Book of Everyone and £5 from the price will go straight to Battersea Dogs and Cats Home. And don't worry cat lovers, there's one for you too.
For more Christmas features and gift guides, see www.telegraph.co.uk/christmas
The Telegraph makes a small amount of money by adding affiliate links to products mentioned in this article. All products are picked independently by our journalists.


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Fed: Sanguine about the US economy – RBS
by Sandeep KanihamaAnalysis team at the Royal Bank of Scotland points out that the minutes of the latest Federal Reserve meeting in October revealed the committee was pretty sanguine about the US economy, thanks largely to continued positive consumer spending and a firm labour market.
Key Quotes
“Risks remain “tilted to the downside”. In particular concerns persist that the ongoing global slowdown could percolate to weaker domestic demand via slower hiring, threatening household spending, the mainstay of US growth. The current level of elevated US corporate debt and a potential widening in credit spreads via worsening credit quality were also mentioned.”


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System developed for information sharing across internationally dispersed teams, originally aimed at the High Energy Physics community
Stores documents and other web resources, interlinked with each other and accessed via the Internet
In 1989, Tim Berners Lee invented the three fundamental technologies that are the foundation of the WWW
Not only that, he wrote the first web browser and first web server
After that, he created and still leads the World Wide Web Consortium (W3C)
A pointer to a resource on the World Wide Web. Can be something as simple as a file or a directory, or a reference to a more complicated object, such as a query to a database or to a search engine
A Web HTML document can contain a number of URLs, pointing to other Web resources (links)
HTTP is a stateless, application-layer protocol for communicating between distributed systems and is the foundation of the modern web. Communication between a host and a client occurs via a request/response pair. The client initiates an HTTP request message, which is serviced through an HTTP response message in return.
HTTP verbs define the action to perform on a specific web resource
The most popular verbs are:
With URLs and verbs, the client can initiate requests to the server. In return, the server responds with status codes and message payloads. The status code is important and tells the client how to interpret the server response
// Dump to screen the contents of the index page at www.google.com
$ curl http://www.google.com
// Create a mirror image of GNU WWW site (same directory structure the original has)
// with only one try per document, saving the log of the activities to a log file
$ wget -r -t1 http://www.gnu.com -o mirror.log
Immutable object representing an abstraction of a Uniform Resource Locator
Comes with the capability of retrieving the URL contents and support for proxy servers and multiple protocols, making use of the Strategy design pattern
java.net.URLConnection and its subclasses provide advanced features and control. They provide a way to use sockets in your code without having to know any of the underlying socket details
Built into Google Chrome, they provide web developers with deep access to the internals of the browser and web applications
Firebug extension provides similar functionality for FirefoxCreated by <Academia de Código_>
The part of the Web that we can see and interact with
The most basic building block of the Web
Used to describe the content and structure of a web page
Uses XML Markup to annotate text, images
and other content to display in a Web browser
HTML Elements consist of
Some HTML elements can use a self-closing tag syntax
the forward slash / is optionalIn HTML, the characters <, >,",' and & are special characters. They are part of the HTML syntax itself and should not appear as content
Special codes exist that represent these characters and can be used in these circumstances:
HTML elements are organized hierarchically on a document tree, and subsequently rendered on screen by the browser
Each element in the HTML document is represented as a rectangular box, with the box's content, padding, border, and margin built up around one another
Each of these box properties can be manipulated for adjusting their format and layout on screenWe can visualize the boxes associated with each HTML element on the Chrome Dev Tools
Two important categories of elements in HTML exist:
Blocks are formatted vertically one after the other. Inlines are formatted horizontally

The MDN is the official website for development documentation of web standards, explore it to take full advantage of the web's capabilities

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Elements with the float CSS property applied allow other elements to wrap around them
Blue text wraps around an image which is floated to the right

Blue text wraps around an image
which is floated to the right

Floats can also be used to create entire web layouts
/*
clears floats coming from the
left and from the right
*/
#footer {
clear: both;
}
Besides floats, other techniques can be used to layout HTML elements on a page
Comes from the necessity of designing websites that can be displayed on multiple devices with different screen sizes
Content must adapt and change its user interface depending on the browser or device accessing the site
The most popular front-end framework
Bootstrap can be downloaded from their website getbootstrap.com,
or alternatively used via a CDN:
<link rel="stylesheet"
href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">

To support small screens, the responsive design html5 meta tag should be used
<meta name="viewport" content="width=device-width, initial-scale=1.0">
Bootstrap requires a containing element to wrap the site contents
Two types of containers are available

Is a responsive mobile-first grid system that scales up to 12 columns as the viewport size increases
Content can span a different number of columnsScreen sizes are divided into 4 types with different css prefixes

one column on phones (default), two columns on tablets/desktops (size cascades up) and 3 on large desktops

hide this on phones

show this on large desktops

By default, Bootstrap offers great looking fonts and size ratios of headers and paragraphs
Bootstrap provides various table styles and functionalities
Bootstrap delivers various form control styles, layout features and custom made components for setting up a variety of forms
Many reusable components are available, proving iconography, dropdowns, input groups, navigation, alerts and much more


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Code Golf is a game designed to let you show off your code-fu by solving problems in the least number of characters.
Since this is your first time here, I suggest starting with something simple like Fizz Buzz.
Also visit our Discord server for some advice.


Numbers 9:11
The fourteenth day of the second month at even they shall keep it, and eat it with unleavened bread and bitter herbs.


{% extends "site/base.html" %} {% block content %}
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TechRock is a community of players, on multiple platforms, pushing the limits of Minecraft: Bedrock Edition. We try our best to learn the ins and outs of the game as well as farm everything we can. We encourage discussion and sharing of ideas and contraptions on the Discord. If you haven't already, hop on there and help us advance BE technology.


| KettleBuffer |
interface KettleBuffer
Interface for serialization buffers |
| KettleSerializable |
interface KettleSerializable
Interface for classes that can be serialized |
| SerializableComponent |
interface SerializableComponent : KettleSerializable, Component
Interface for components which can be serialized |


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