| DiscreteWavelets Toolbox |
|
| AudioList | Lists all audio files included with the package and their attributes. |
| AudioNames | Creates a cell array of absolute paths and names to all audio files included with the package. |
| WaveletVectorPlay | Plays portions of wavelet transformations of vectors. |
| DonohoSure | Compute the SureShrink tolerance. |
| Heavisine | The Heavisine function. |
| MAD | Calculate the Median Absolute Deviation of a vector or matrix. |
| NoiseEstimate | Estimate the noise level in a vector or matrix. |
| ShrinkageFunction | The piecewise linear function used to quantize portions of the wavelet transform in denoising. |
| SureShrink | Perform denoising of a vector or matrix using the SureShrink method. |
| TestSparseness | Test a vector or matrix to determine whether or not it is sparse. |
| UniversalThreshold | Compute the universal threshold used to perform denoising via the VisuShrink method. |
| WaveletShrinkage | A generic function for performing denoising using wavelet shrinkage. |
| CDF97 | The Cohen-Daubechies-Feauveau 9/7 biorthogonal filter pair. |
| Coif | An orthogonal Coiflet filter. |
| Daub | An orthogonal Daubechies filter. |
| Haar | The Haar filter. |
| LeGall | The biorthogonal LeGall filter pair. |
| SplineFilters | A biorthogonal spline filter pair. |
| Cs | Cosine in terms of complex exponentials. |
| FiniteFourier | Creates a finite Fourier series from a given input vector of coefficients. |
| PolySin2j | Creates the trig polynomial P(t)=sum_{k=0}^{K-1} binomial(K-1+j,j)sin^(2j)(w/2). |
| Sn | Sine in terms of complex exponentials. |
| HuffmanTree | Plots a tree of Huffman codes. |
| ImageList | Lists all image files included with the package and their attributes. |
| ImageNames | Creates a cell array of absolute paths and names to all image files included with the package. |
| ImagePlot | Plot a grayscale or color image. |
| ImageRead | Read an image from disk or the internet and modify it as directed. |
| ShowThumbnails | Plot thumbnail sketches of images that are inclued with the package. |
| WaveletDensityPlot | Plot the two-dimensional wavelet transformation. |
| WaveletVectorPlot | Plot the one-dimensional wavelet transformation. |
| GammaCorrection | Apply gamma correction to an image. |
| HistogramEQ | Apply histogram equalization to an image. |
| MakeHuffmanCodes | Create Huffman codes for an input string or image. |
| RGBToYCbCr | Convert from RGB space to YCbCr space. |
| YCbCrToRGB | Convert from YCbCr space to RGB space. |
| CE | Compute the cumulative energy vector for a vector or matrix. |
| Comp | Quantize a wavelet transformation. |
| Entropy | Compute the entropy of a vector or matrix. |
| MSE | Compute the mean square error of two vectors or two matrices. |
| nCE | Determine the number of elements of a wavelet transformation to convert to zero by Comp. |
| PSNR | Compute the peak signal-to-noise ratio between two images. |
| ChopVector | Remove end elements of a vector so that its length is divisible by 2^n. |
| DataList | Lists all data files included with the package and their attributes. |
| DataNames | Creates a cell array of absolute paths and names to all data files included with the package. |
| GetCorner | Get the upper left corner of a matrix. |
| Make3D | Convert three matrices to a 3-D array used for plotting. |
| MaxIterations | Determine the largest j so that 2^j divides the dimensions (length) of a matrix (vector). |
| PutCorner | Replace the corner of a matrix with another matrix. |
| Split3D | Split a 3-D array into three two-dimensional matrices. |
| WaveletListToMatrix | Takes a cell array holding various portions of a wavelet transform and reformats it as a matrix. |
| WaveletListToVector | Takes a structure holding various portions of a wavelet transform and reformats it as a vector. |
| WaveletMatrixToList | Reformat a wavelet transformation matrix as a structure whose elements are various parts of the transformation. |
| WaveletVectorToList | Reformat a wavelet transformation vector as a structure whose elements are various parts of the transformation. |
| Categories … |
|---|
|
Orthogonal Wavelet Transformations |
| HWT1D1 | |
| HWT1D | |
| HWT2D1 | |
| HWT2D | |
| IHWT1D1 | |
| IHWT1D | |
| IHWT2D1 | |
| IHWT2D | |
| LeftHWT | |
| LeftIHWT | |
| RightHWT |
| WT1D1 | |
| WT1D | |
| WT2D1 | |
| WT2D | |
| IWT1D1 | |
| IWT1D | |
| IWT2D1 | |
| IWT2D | |
| IWTht | |
| LeftWT | |
| LeftIWT | |
| RightWT |
| BWT1D1 | |
| BWT1D | |
| BWT2D1 | |
| BWT2D | |
| IBWT1D1 | |
| IBWT1D | |
| IBWT2D1 | |
| IBWT2D | |
| IBWTht |
| LWT1D1 | |
| LWT1D | |
| LWT2D1 | |
| LWT2D | |
| ILWT1D1 | |
| ILWT1D | |
| ILWT2D1 | |
| ILWT2D |
| Examples | Functions by Alphabetical List |
© 2007-2008 Patrick Van Fleet


Paddle On Kubernetes¶
In this article, we will introduce how to run Paddle training job on single CPU machine using Kubernetes. In next article, we will introduce how to run Paddle training job on distributed cluster.
Build Docker Image¶
In distributed Kubernetes cluster, we will use Ceph or other shared storage system for storing training related data so that all processes in Paddle training can retrieve data from Ceph. In this example, we will only demo training job on single machine. In order to simplify the requirement of the environment, we will directly put training data into Paddle’s Docker Image, so we need to create a Paddle Docker image that already includes the training data.
Paddle’s Quick Start Tutorial introduces how to download and train data by using script from Paddle’s source code.
And paddledev/paddle:cpu-demo-latest
image has the Paddle source code and demo. (Caution: Default Paddle image paddledev/paddle:cpu-latest
doesn’t include the source code, Paddle’s different versions of image can be referred here: Docker installation guide), so we run this container and download the training data, and then commit the whole container to be a new Docker image.
Run Docker Container¶
$ docker run --name quick_start_data -it paddledev/paddle:cpu-demo-latest
Download Training Data¶
Getting into /root/paddle/demo/quick_start/data
Directory，using get_data.sh
to download training data.
Then getting into /root/paddle/demo/quick_start
Directory, using preprocess.sh
to pre-process training data.
$ root@fbd1f2bb71f4:~/paddle/demo/quick_start/data# ./get_data.sh
Downloading Amazon Electronics reviews data...
--2016-10-31 01:33:43-- http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/reviews_Electronics_5.json.gz
Resolving snap.stanford.edu (snap.stanford.edu)... <IP_ADDRESS>
Connecting to snap.stanford.edu (snap.stanford.edu)|<IP_ADDRESS>|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 495854086 (473M) [application/x-gzip]
Saving to: 'reviews_Electronics_5.json.gz'
10% [=======> ] 874,279 64.7KB/s eta 2h 13m
Modify Startup Script¶
After downloading the data，modify /root/paddle/demo/quick_start/train.sh
file contents are as follows (one more cd cmd):
set -e
cd /root/paddle/demo/quick_start
cfg=trainer_config.lr.py
#cfg=trainer_config.emb.py
#cfg=trainer_config.cnn.py
#cfg=trainer_config.lstm.py
#cfg=trainer_config.bidi-lstm.py
#cfg=trainer_config.db-lstm.py
paddle train \
--config=$cfg \
--save_dir=./output \
--trainer_count=4 \
--log_period=20 \
--num_passes=15 \
--use_gpu=false \
--show_parameter_stats_period=100 \
--test_all_data_in_one_period=1 \
2>&1 | tee 'train.log'
Commit Docker Image¶
$ docker commit quick_start_data mypaddle/paddle:quickstart
Use Kubernetes For Training¶
We will use Kubernetes job for training process, following steps shows how to do the training with Kubernetes.
Create Yaml Files¶
The output result in container will be demolished when job finished (container stopped running), so we need to mount the volume out to the local disk when creating the container to store the training result. Using our previously created image, we can create a Kubernetes Job, the yaml contents are as follows:
apiVersion: batch/v1
kind: Job
metadata:
name: quickstart
spec:
parallelism: 1
completions: 1
template:
metadata:
name: quickstart
spec:
volumes:
- name: output
hostPath:
path: /home/work/paddle_output
containers:
- name: pi
image: mypaddle/paddle:quickstart
command: ["bin/bash", "-c", "/root/paddle/demo/quick_start/train.sh"]
volumeMounts:
- name: output
mountPath: /root/paddle/demo/quick_start/output
restartPolicy: Never
Start Paddle Job¶
Using the above yaml file to start the Kubernetes job.
$ kubectl create -f paddle.yaml
Get the detailed status of the job:
$ kubectl get job
NAME DESIRED SUCCESSFUL AGE
quickstart 1 0 58s
$ kubectl describe job quickstart
Name: quickstart
Namespace: default
Image(s): registry.baidu.com/public/paddle:cpu-demo-latest
Selector: controller-uid=f120da72-9f18-11e6-b363-448a5b355b84
Parallelism: 1
Completions: 1
Start Time: Mon, 31 Oct 2016 11:20:16 +0800
Labels: controller-uid=f120da72-9f18-11e6-b363-448a5b355b84,job-name=quickstart
Pods Statuses: 0 Running / 1 Succeeded / 0 Failed
Volumes:
output:
Type: HostPath (bare host directory volume)
Path: /home/work/paddle_output
Events:
FirstSeen LastSeen Count From SubobjectPath Type Reason Message
--------- -------- ----- ---- ------------- -------- ------ -------
1m 1m 1 {job-controller } Normal SuccessfulCreate Created pod: quickstart-fa0wx
Get Training Result¶
We can use kubectl command to take a look at the status of related pod.
$ kubectl describe pod quickstart-fa0wx
Name: quickstart-fa0wx
Namespace: default
Node: paddle-demo-let02/<IP_ADDRESS>
Start Time: Mon, 31 Oct 2016 11:20:17 +0800
Labels: controller-uid=f120da72-9f18-11e6-b363-448a5b355b84,job-name=quickstart
Status: Succeeded
IP: <IP_ADDRESS>
Controllers: Job/quickstart
Containers:
quickstart:
Container ID: docker://b8561f5c79193550d64fa47418a9e67ebdd71546186e840f88de5026b8097465
Image: registry.baidu.com/public/paddle:cpu-demo-latest
Image ID: docker://18e457ce3d362ff5f3febf8e7f85ffec852f70f3b629add10aed84f930a68750
Port:
Command:
bin/bash
-c
/root/paddle/demo/quick_start/train.sh
QoS Tier:
cpu: BestEffort
memory: BestEffort
State: Terminated
Reason: Completed
Exit Code: 0
Started: Mon, 31 Oct 2016 11:20:20 +0800
Finished: Mon, 31 Oct 2016 11:21:46 +0800
Ready: False
Restart Count: 0
Environment Variables:
Conditions:
Type Status
Ready False
Volumes:
output:
Type: HostPath (bare host directory volume)
Path: /home/work/paddle_output
We can also ssh to Kubernetes node to take a look at the training result.
[root@paddle-demo-let02 paddle_output]# ll
total 60
drwxr-xr-x 2 root root 4096 Oct 31 11:20 pass-00000
drwxr-xr-x 2 root root 4096 Oct 31 11:20 pass-00001
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00002
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00003
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00004
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00005
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00006
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00007
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00008
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00009
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00010
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00011
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00012
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00013
drwxr-xr-x 2 root root 4096 Oct 31 11:21 pass-00014


Task management and a calendar for all
“Task management. Calendars. Email. They all have one thing in common…literally no one enjoys managing them. Thanks to years of research, we can now predict every single thing you'll ever have to do or go to. Yeah, we're that good.”
Mark Otto, Creator of Mochi
We've been working for over 100 years to build the best possible app for all your needs. We're glad that others agree.
With over 20 years of collective experience, we take the unthinkable and make it just a couple quick taps away.
We focused on design relentlessly. We loved it so much, we spent just as much time taking these crazy high resolution photos. Here are some more stats on that.
Join over 900,000 nerds already using Mochi.
By clicking "Get started" I agree to Mochi's Terms of service


---
layout: null
section-type: about
title: About
---
## About
Welcome to GATO, a clothing store founded by Plaza LLama. This is a clothing store where the geeks become ballers and the ballers become geeks. It’s inspired mainly by New York City’s multi-ethnic urban culture. We hope you enjoy our apparel. [via].
Octocat is Github's logo and trademark and here is used to illustrate a fictional character (John Smith) that used the *{ Personal }* Jekyll Theme to build his website.
Support the repo by starring or forking it!


Coming up with features is difficult, time-consuming, requires expert knowledge. "Applied machine learning" is basically feature engineering.
X_i_train, X_i_test, y_train, y_test = train_test_split(
X_interaction, y, random_state=0)
logreg3 = LogisticRegressionCV().fit(X_i_train, y_train)
logreg3.score(X_i_test, y_test)
0.960
age articles_bought gender spend$ time_online + Male * (age articles_bought spend$ time_online ) + Female * (age articles_bought spend$ time_online ) + (age > 20) * (age articles_bought gender spend$ time_online) + (age <= 20) * (age articles_bought gender spend$ time_online) + (age <= 20) * Male * (age articles_bought gender spend$ time_online)
PolynomialFeatures()
adds polynomials and interactions.make_pipeline
!from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures()
X_bc_poly = poly.fit_transform(X_bc_scaled)
print(X_bc_scaled.shape)
print(X_bc_poly.shape)
(379, 13) (379, 105)
scores = cross_val_score(RidgeCV(), X_bc_scaled, y_train, cv=10)
print2(np.mean(scores), np.std(scores))
(0.693, 0.111)
scores = cross_val_score(RidgeCV(), X_bc_poly, y_train, cv=10)
print2(np.mean(scores), np.std(scores))
(0.829, 0.071)


Adds browser specific prefixes and returns resulting css.
w2utils.cssPrefix(prop, val, returnString)
| prop |
string, css property |
| val |
string, property value |
| returnString |
boolean, return as string instead of object |
This method is used internally in grid, popup, etc. But you can use it as well. It will add prefixes "-webkit", "-moz", "-ms", "-o" and return resulting object.


Here you can see how predictable your favorite team is. You can change between countries, leagues and bookies.
Insights: Our research shows that the most predictable teams are either the strongest or the weakest ones as they perform consistently well or bad.
Not sure which country's league you should bet on? Countries on this map have a darker color if they are very unpredictable and a bright one otherwise. Naturally, the brighter a country is, the most predictable its league is.
Insights: As you might have observed, in most cases there's a 50/50 chance you'll lose. Imagine if you were betting on the English League on 2015, when Leicester won the league, you'd regret it instantly! So why waste your money on betting? Go drink a lemonade instead!🍋
Who doesn't love bubbles? We certainly do! Check these bad boys out!
Click here to change countries:Easter 🥚 : As a little bonus we added a minigame! Drag the different bubbles around the screen if you want to compare the sizes more closely or help your favorite team bump all its rivals away! 😎
Wondering how your money would evolve if you betted every week? We've got you covered! This graph shows you what would happen if you betted 10CHF every week and followed a specific strategy!
Insights: You can't beat the bank. Even if you do make a profit (and that's a big if), it never exceeds 10% of your investement. To put it simply: if you bet 1000$ you'll never make more than 100$ of profit.
And now the 🍒 on the 🎂
Ever wondered which team has been consistently the best?
Choose your favorite country and let the games begin!


Carnegie Financial Services (CFS) offers premier online banking services.our products run the gamut from free checking and saving accounts, certificates of deposit, business loans, personal loans, home mortgages.
Carnegie Financial Services (CFS) is the best provider of echecking accounts. Our checking accounts can be accessed online and completely free.
Checkings ServiceCFS offers the best high-yield online savings accounts service and it is a safe place to deposit your money while earning top high-yield money market account rates.
Savings Account Service(CFS) is the leading provider of best rate certificate of deposit. Our bank provides high yield, low risk investment eCDs
CDs ServiceCFS offers the best eMortgages Service with competitive home loan rates. Lending Advisor provids more specific information that massage and data rates increaseing the mortgage affordability. Tools and calculators are provided as a courtesy to help you estimate your mortgage needs
Mortgages Service


Javascript
This is about to come pretty awesome ,descriptive blog about JS, but super short with time , so expect by the end of this week to read something cool :)))
This is about to come pretty awesome ,descriptive blog about JS, but super short with time , so expect by the end of this week to read something cool :)))


Syntax errors in games
The goal here is that syntax errors are shown to the user, if the user allows that (by y configuration parameter).
Wrong syntax
The expectation here is, that the syntax error is shown instead of the normal game (so the div is used to display
the errror instead).


Notifies application of button press events for buttons to which the application is subscribed. SDL supports two button press events defined as follows:
SHORT - Occurs when a button is depressed, then released within two seconds. The event is considered to occur immediately after the button is released.
LONG - Occurs when a button is depressed and held for two seconds or more. The event is considered to occur immediately after the two second threshold has been crossed, before the button is released.
HMI Status Requirements:
HMILevel:
The application will receive OnButtonPress notifications for all subscribed buttons when HMILevel is FULL.
The application will receive OnButtonPress notifications for subscribed media buttons when HMILevel is LIMITED. Media buttons include SEEKLEFT, SEEKRIGHT, TUNEUP, TUNEDOWN, and PRESET_0-PRESET_9.
The application will not receive OnButtonPress notification when HMILevel is BACKGROUND or NONE.
AudioStreamingState: Any
SystemContext: MAIN, VR. In MENU, only PRESET buttons. In VR, pressing any subscribable button will cancel VR.
@since SDL 1.0
The button’s name
Required
@property (nonatomic, strong) SDLButtonName _Nonnull buttonName;
var buttonName: SDLButtonName { get set }
Indicates whether this is a LONG or SHORT button press event
Required
@property (nonatomic, strong) SDLButtonPressMode _Nonnull buttonPressMode;
var buttonPressMode: SDLButtonPressMode { get set }
If ButtonName is “CUSTOM_BUTTON”, this references the integer ID passed by a custom button. (e.g. softButton ID)
@since SDL 2.0
Optional, Integer 0 - 65536
@property (nonatomic, strong, nullable) NSNumber<SDLInt> *customButtonID;
var customButtonID: (NSNumber & SDLInt)? { get set }


This API can be used by anyone to query the (wow!) achievements of Alex Fan.
This API uses XML to query requests from a secure SQL database containing Alex Fan Club's list of Alex Fan's achievements. Additionally, requests expire after about a minute because that is cool!
To query the API, you have to make a POST request to {{ apiurl }} with two parameters. The time
parameter contains a base64 urlsafe representation of the epoch time. For example, the current epoch time is {{ epochtime }}
, and the urlsafe base64 encoded representation is {{ basesftime }}
. The search
parameter contains the search query for the API enclosed in <search></search>
. The API returns the results of the search separated by newlines.
An example of a python script that queries the string "cool" to the API is the following
import requests
import base64
import time
api = "{{ apiurl }}"
req_time = int(time.time())
data = {
"time": base64.urlsafe_b64encode(req_time.to_bytes((req_time.bit_length() + 7) // 8, byteorder="big")),
"search": "<search>cool</search>"
}
x = requests.post(api, data=data)
print(x.text)
The above code snippet will print the following
Too cool for #include <bits/stdc++.h>
Is super cool
Has a cool profile picture


Seek first to understand, then to be understood - Stephen R. Covey
Based on Chapter 7 - Empathy and the monkey business of brain tangos - 06/03/16
Have you tried using any of the insight and techniques for growing trust and sincerity? How did it go?
I think naturally I have been using the basis of these techniques anyways.Perhaps not in the structured way they are presented in this chapter but in a round about way I have been achieving the end result already. It is always good to think the best of people and to want the best for them
Try empathetic listening - how did it go
Naturally, I am a very empathic person so again, I think subconsciously I have already been doing this all along. But it was nice to pay attention to it and really focus on the what the other person was saying and take notice of the feelings that I picked up from our conversation.
How did you find the loving kindness meditation?
I really liked the loving kindness meditation exercise and I thought it was very kind and thoughtful but above all very compassionate and along with the just like me meditation I think it can be very valuable when dealing with difficult situations and people.
Complete the political awareness exercise here in writing
Context - negotiating my work load with my boss for the duration of the bootcamp phase at EDA. My side - I am still going to work part-time while I am at EDA, my role is remote and not time dependent so I can fit work in around my EDA schedule. Their side - My employee is doing a full-time course and will fit their work around that course first and foremost. They will not be able to get back to phone calls and emails until after office hours so I think this could be a problem. Outcome - If both myself and my employer feel as though the workload is too much whilst at EDA full-time, we will review the situation and off load some of my work if necessary


| DOSE: |
0.5 bowls |
smoked |
Cannabis |
(tar / resin) |
It was monday night, I was bored and had some california poppy extract (extracted with 60% alcohol). It was time to smoke before going to bed, but I was not very tired at all. So I scraped some resin out of my broken pipe and put it in a bowl, along with 4 drops of the california poppy extract.
The first few hits sizzled like cooking eggs and produced a pleasant tasting smoke. About 10 mins later I had toasted the hell out of the bowl (very hot pipe!). I noticed a pleasant relaxing opiate-like high. Only opiate-like not really like any opiates I have tried. It was less euphoric and more bodily relaxing, my limbs felt very heavy, and i was very relaxed. I also had an unexplanable head buzz that was cool and lasted for about 15 mins. The effects were far beyond placebo, the two definatley mixed together well. For brief moments I could feel myself floating away into anasthetic and colorful realm. This is when the head buzz was acting.
In short it was a pleasant experience. Very relaxing for the day after a being nervous for no reason. I can't say I recommend people putting liquid extracts on weed and smoking it, but I had researched califonia poppy and found no known interaction or overdose cases. Just allergic reactions. I still took a gamble, but in this case it did not harm me at all. It was a very pleasant weed buzz mixed in with a odd opiate-like (without extreme euphoria) head buzz. But, still i would rather take hydrocodone or smoke opium than this stuff.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


{% extends "base.html" %} {% block content %}
If your language is not on the list, please report an Issue on GitHub.


{% extends 'layout.html' %} {% block style %} {% endblock %} {% block content %} {% include 'navbar.html' %}
Indibidual's Infection Probability is {{Probability}}%.
Thanks and Stay at Home!


Simple GDPR manager
This library helps you build a GPDR compliant system by providing you easy to manipulate interfaces.
If you need any help, you're more than welcome on my official Discord server dedicated to my open-source projects.
Using ES6-style imports:
import {
//most useful
GdprStorage,
GdprManagerBuilder,
GdprDeserializer,
//helpers
makeGuard,
GdprGuardGroup,
GdprManager,
GdprSerializer,
GdprSaviorAdapter,
} from "gdpr-guard"
Using node style require:
const {
//most useful
GdprStorage,
GdprManagerBuilder,
GdprDeserializer,
//helpers
makeGuard,
GdprGuardGroup,
GdprManager,
GdprSerializer,
GdprSaviorAdapter,
} = require("gdpr-guard");
Directly from your browser:
const {
//most useful
GdprStorage,
GdprManagerBuilder,
GdprDeserializer,
//helpers
makeGuard,
GdprGuardGroup,
GdprManager,
GdprSerializer,
GdprSaviorAdapter,
} = gdprGuard;
The name
used for guards and groups must be unique! This is the identifier/key that binds it.
The wide concept of guard
is that a guard
is an entity that can be toggled to allow/deny some functionalities.
You can check the documentations here
This is an enum-like type that lists the available storage options, these include:
GdprManagerBuilder
provides a nice and easy to write/read way to create a GdprManager
object from the groun up.
For instance you can use it like this:
const manager = GdprManagerBuilder.make()
.startRequiredGroup(GdprStorage.Cookie, "Functionalities", "Information purely used for the user's experience")
// This is a group that by default uses cookies for storage, every option and the group itself is required
.withEnabledGuard("PHP_SESSID", "Server session identifier")
.startGuard()
.withName("theme")
.withDescription("User's current colors' theme")
.storedIn(GdprStorage.LocalStorage)
.endGuard()
.endGroup()
.startGroup(GdprStorage.Cookie, "Advertisement", "Tracking-based avertisement informations")
.startGroup(GdprStorage.Cookie, "Advertisement : Local", "Sitewide advertisement informations")
// [...]
.endGroup()
.startGroup(GdprStorage.Cookie, "Advertisement : 3rd-party", "3rd-party advertisement informations")
// [...]
.endGroup()
.build();
console.log(manager.raw()); // inspect useful information
GdprDeserializer
allows you to retrieve a gdpr object from its raw
representation.
import { GdprManagerBuilder, GdprDeserializer } from "gdpr-guard"
// [...]
const manager = GdprManagerBuilder.make()
// [...]
.build();
const raw = manager.raw();
//store in local storage
const raw = //get from local storage
const manager = GdprDeserializer.manager(raw);
if(manager === null){ //failed deserialization
//handle error
return;
}
// here, both managers are equivalent
A GdprManager
manages a list of GdprGuardGroup
. You can :
raw()
)addGroup(guardGroup)
and createGroup(name, description)
)hasGuard(name)
)getGuard(name)
)hasGroup(name)
)getGroup(name)
)isEnabled(name)
)enable()
)disable()
)toggle()
)enableForStorage(gdprStorage)
)disableForStorage(gdprStorage)
)toggleForStorage(gdprStorage)
)A GdprGuardGroup
manages a list of GdprGuard
(which includes raw guards, GdprGuardGroup
and GdprManager
although one would not recommend to put managers inside managers).
You can:
makeRequired()
)raw()
)hasGuard(name)
)getGuard(name)
)isEnabled(name)
)enable()
)disable()
)toggle()
)enableForStorage(gdprStorage)
)disableForStorage(gdprStorage)
)toggleForStorage(gdprStorage)
)makeGuard
is a function that creates the simplest guard possible, it has the following signature:
declare function makeGuard(name: string, description: string, storage?: GdprStorage, required?: boolean, enabled?: boolean | null): GdprGuard;
A class that implements most of the behavior for the Savior API.
abstract class GdprSaviorAdapter implements GdprSavior{
public abstract restore(shouldUpdate?: boolean): Promise<GdprManager|null>;
public abstract store(manager: GdprManagerRaw): Promise<boolean>;
public abstract updateSharedManager(manager: GdprManager): Promise<void>;
}
This API helps saving and restoring the manager state. It is exposed mainly for library authors as it helps creating various bindings for frameworks.
This is the interface:
interface GdprSavior{
restore(shouldUpdate?: boolean): Promise<GdprManager|null>;
exists(shouldUpdate?: boolean): Promise<boolean>;
restoreOrCreate(factory: GdprManagerFactory): Promise<GdprManager>;
store(manager: GdprManagerRaw): Promise<boolean>;
storeIfNotExists(manager: GdprManagerRaw): Promise<boolean>;
updateSharedManager(manager: GdprManager): Promise<void>;
check(): Promise<void>;
}


Fonts
Use
spans
to mix fonts in paragraphs.
For example, this is Times New Roman.
And this is Courier New.


Under the hood, Nest makes use of Express, but as mentioned already, provides compatibility with a wide range of other libraries, like e.g. Fastify. How does it work? In fact, what Nest needs to use your favorite library, is a compatible adapter, that primarily proxies corresponding handlers to appropriate library-specific methods. Additionally, your library has to provide at least similar request-response cycle management as express.
A Fastify fits here very well because it solves design issues in a similar manner as express does. However, fastify is much faster, achieving almost two times better benchmarks results. The question, why Nest still uses express as a default HTTP provider then? Because express is widely-used, well-known, and has a huge set of compatible middleware.
But we don't lock people in a single paradigm. We let them use whatever they
need. Fastify is a better choice when you care about really good
performance, that's why we provide a built-in
FastifyAdapter
that helps to integrate this library with Nest.
Firstly, we need to install required package:
{{ packages }}
Once fastify platform is installed, we can use the
FastifyAdapter
.
{{ bootstrap }}
If you're running your Nest instance inside of a
docker container you will need to specify the host, like so:
{{ docker }}
And that's all. Also, you are able to pass options into fastify constructor
through FastifyAdapter
constructor. Keep in mind that now, Nest
uses fastify as an HTTP provider, meaning, each recipe that
relays on express won't work anymore. You should rather use fastify
equivalent packages.
A working example is available here.


THtmlViewer components
property BorderStyle: THTMLBorderStyle;
The BorderStyle property works slightly different from that for most Delphi components. BorderStyle may be assigned the following values:
htFocusedThe border will appear when the viewer has the focus.
htNoneNever any border.
htSingleA single line border will always be present.


"CHURL "in Isaiah 32:5 (R.V. marg., “crafty”), means a deceiver. In 1 Samuel 25:3, the word churlish denotes a man that is coarse and ill-natured, or, as the word literally means, “hard.” The same Greek word as used by the LXX. here is found in Matthew 25:24, and there is rendered “hard.”
"CHURL "in Isaiah 32:5 (R.V. marg., “crafty”), means a deceiver. In 1 Samuel 25:3, the word churlish denotes a man that is coarse and ill-natured, or, as the word literally means, “hard.” The same Greek word as used by the LXX. here is found in Matthew 25:24, and there is rendered “hard.”


Fine after dog bites woman, Perth Sheriff Court hears
Animal subsequently put down
by Perthshire Advertiser, https://www.dailyrecord.co.uk/authors/perthshire-advertiser/A woman suffered at least eight deep puncture wounds after a Staffordshire bull terrier sank its teeth into her knee while she was out for an early morning run.
The animal was subsequently put down by its owner, 34-year-old Debbie Bennett, of Myrtle Park, Blairgowrie.
But she was ordered to pay victim Alison Lee £600 in compensation when she appeared for sentence at Perth Sheriff Court.
The accused was also given a community payback order, with 18 months’ supervision, after she admitted the animal was “dangerously out of control” in Rie-Achan Road, Pitlochry, on February 23 this year.
She also pled guilty to failing to comply with a September 4, 2014, dog control notice requiring the dog to be muzzled when it was outside.


Constructs a new MdmResource
.
Optionally provide configuration options to this resource class. If not provided, suitable defaults will be used.
Optionally provide a specific IMdmRestHandler. If not provided, the DefaultMdmRestHandler implementation will be used.
Stores the base API endpoint for all REST requests.
Stores the default options to apply to every REST request.
Generate a query string for a URL based on object property values provided.
A query string parameters object to convert to a string.
A URL encoded string containing each parameter and value, or an empty string if no properties were provided.
Test if a string represents a UUID.
The string to test.
True if value
represents a UUID.
Send a HTTP DELETE
request to the given resource.
The full URL to the REST resource.
Optional query string parameters, if required.
Optional REST handler options, if required.
The result of the DELETE
request.
Send a HTTP GET
request to the given resource.
The full URL to the REST resource.
Optional query string parameters, if required.
Optional REST handler options, if required.
The result of the GET
request.
Send a HTTP POST
request to the given resource.
The full URL to the REST resource.
The data to include in the POST
request body.
Optional REST handler options, if required.
The result of the POST
request.
Send a HTTP PUT
request to the given resource.
The full URL to the REST resource.
The data to include in the PUT
request body.
Optional REST handler options, if required.
The result of the PUT
request.
Send a generic HTTP request to the given resource.
The full URL to the REST resource.
Optional query string parameters, if required.
Optional REST handler options, if required.
The result of the request.
All details of HTTP request must be included in the options
parameter, particularly the HTTP method:
simpleRequest('http://localhost/api/test', { }, { method: 'GET' });
Generated using TypeDoc
Controller: folder | POST | /api/folders/${folderId}/folders | Action: save | GET | /api/folders/${folderId}/folders | Action: index | GET | /api/folders/${folderId}/search | Action: search | POST | /api/folders/${folderId}/search | Action: search | DELETE | /api/folders/${folderId}/readByAuthenticated | Action: readByAuthenticated | PUT | /api/folders/${folderId}/readByAuthenticated | Action: readByAuthenticated | DELETE | /api/folders/${folderId}/readByEveryone | Action: readByEveryone | PUT | /api/folders/${folderId}/readByEveryone | Action: readByEveryone | DELETE | /api/folders/${folderId}/folders/${id} | Action: delete | PUT | /api/folders/${folderId}/folders/${id} | Action: update | GET | /api/folders/${folderId}/folders/${id} | Action: show | POST | /api/folders | Action: save | GET | /api/folders | Action: index | DELETE | /api/folders/${id} | Action: delete | PUT | /api/folders/${id} | Action: update | GET | /api/folders/${id} | Action: show


3D by Fisayo Adeyeye
You can call it a dream
if you want, if it makes you feel better
to peel paint off the walls / gently lower
pieces into your mouth / Rainbow smile
in the oil slick on the driveway / kids playing
eviction / In summer these dollar bills
melted on our chests, and peppered mango
made our lips / bleed red / When you told me
how this could feel / a Big Gulp cup full
of bullets / your tongue wet / against
the cigarette paper / I twisted in pleasure,
until the whole of my chest turned,
every possible color / It was always so hard
to sleep in the city / I never knew
what it meant if I didn’t tremble, and you
didn’t tremble / and neither of us trembled.
Fisayo Adeyeye is an MFA candidate at SFSU, and has had works published by Little River, Potluck Magazine, and Have U Seen My Whale. You can find him at tinybluewhales@gmail.com.


|
A collection of supporting classes used by all other components.
Contains things like collection classes, geometry class, runtime typing, etc. Click here for more
information.
This component is a collection of supporting routines and classes used by the rest of
the SDK. It is modeled after the MFC (Microsoft Foundation Classes) to provide a
shallow learning curve for programmers familiar with that MS Windows based C++
library. This component contains three sub-groupings of classes as follows:
| Collection classes: A group of classes providing standard C++
collection classes. This component includes the following classes: Array (supports
all MF field types), DoublyLinkedList (intrusive and non-intrusive), and Stack |
| Geometric classes: A group of classes providing support for various
geometric related functions. This component includes the following classes:
AxisAlignedBox (for bounding box calculations), Matrix, Plane, Ray, Rect2D, Rotation,
Color, Vector2 and Vector3. These classes support the Node
Library as well as the Solid Modeling library. |
| Utility classes: A group of classes supporting various other
functionality. This component includes the following classes: BaseNode (base class
of all node classes - implements reference counting), Dump (support for writing to files),
ImageUtils (support for reading texture and other image files), Runtime (support for
runtime typing), SoundUtils (support for sound files -- not completed), TimeUtils (support
for runtime execution environment - VEM) and WebUtils (support for
fetching files over the internet). |
|


{% load staticfiles %}
Register - In 1 easy step
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Hooks
- Actions: functions that are executed at specific points throughout the WordPress core.
- Filters: functions that intercept, manage, and return specific data throughout the WordPress core.
APIs
- Shortcode API
- Widget API
- Settings API
- JSON REST API
The WordPress Plugin Boilerplate
Custom Post Types and Custom Taxonomies


ir_datasets
: MSMARCO (passage)A passage ranking benchmark with a collection of 8.8 million passages and question queries. Most relevance judgments are shallow (typically at most 1-2 per query), but the TREC Deep Learning track adds deep judgments. Evaluation typically conducted using MRR@10.
Note that the original document source files for this collection contain a double-encoding error that cause strange sequences like "å¬" and "ðºð". These are automatically corrrected (properly converting previous examples to "公" and "🇺🇸").
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Official dev set.
scoreddocs are the top 1000 results from BM25. These are used for the "re-ranking" setting. Note that these are sub-sampled to about 1/8 of the total avaialable dev queries by the MSMARCO authors for faster evaluation. The BM25 scores from scoreddocs are not available (all have a score of 0).
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Subset of msmarco-passage/dev that only includes queries that have at least one qrel.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/judged')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/judged')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/judged')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/judged')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Official "small" version of the dev set, consisting of 6,980 queries (6.9% of the full dev set).
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/small')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/small')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/dev/small')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Official eval set for submission to MS MARCO leaderboard. Relevance judgments are hidden.
scoreddocs are the top 1000 results from BM25. These are used for the "re-ranking" setting. Note that these are sub-sampled to about 1/8 of the total avaialable eval queries by the MSMARCO authors for faster evaluation. The BM25 scores from scoreddocs are not available (all have a score of 0).
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/eval')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/eval')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/eval')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Official "small" version of the eval set, consisting of 6,837 queries (6.8% of the full eval set).
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/eval/small')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/eval/small')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Official train set.
Not all queries have relevance judgments. Use msmarco-passage/train/judged for a filtered list that only includes documents that have at least one qrel.
scoreddocs are the top 1000 results from BM25. These are used for the "re-ranking" setting. Note that these are sub-sampled to about 1/8 of the total avaialable train queries by the MSMARCO authors for faster evaluation. The BM25 scores from scoreddocs are not available (all have a score of 0).
docpairs provides access to the "official" sequence for pairwise training.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train')
for docpair in dataset.docpairs_iter():
docpair # namedtuple<query_id, doc_id_a, doc_id_b>
Subset of msmarco-passage/train that only includes queries that have at least one qrel.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/judged')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/judged')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/judged')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/judged')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/judged')
for docpair in dataset.docpairs_iter():
docpair # namedtuple<query_id, doc_id_a, doc_id_b>
Subset of msmarco-passage/train that only includes queries that have a layman or expert medical term. Note that this includes about 20% false matches due to terms with multiple senses.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/medical')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/medical')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/medical')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/medical')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/medical')
for docpair in dataset.docpairs_iter():
docpair # namedtuple<query_id, doc_id_a, doc_id_b>
Subset of msmarco-passage/train without 200 queries that are meant to be used as a small validation set. From various works.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-train')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-train')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-train')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-train')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-train')
for docpair in dataset.docpairs_iter():
docpair # namedtuple<query_id, doc_id_a, doc_id_b>
Subset of msmarco-passage/train with only 200 queries that are meant to be used as a small validation set. From various works.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-valid')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-valid')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 1 | Labeled by crowd worker as relevant |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-valid')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-valid')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/train/split200-valid')
for docpair in dataset.docpairs_iter():
docpair # namedtuple<query_id, doc_id_a, doc_id_b>
Queries from the TREC Deep Learning (DL) 2019 shared task, which were sampled from msmarco-passage/eval. A subset of these queries were judged by NIST assessors, (filtered list available in msmarco-passage/trec-dl-2019/judged).
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Subset of msmarco-passage/trec-dl-2019, only including queries with qrels.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019/judged')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019/judged')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019/judged')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2019/judged')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Queries from the TREC Deep Learning (DL) 2020 shared task, which were sampled from msmarco-passage/eval. A subset of these queries were judged by NIST assessors, (filtered list available in msmarco-passage/trec-dl-2020/judged).
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
Subset of msmarco-passage/trec-dl-2020, only including queries with qrels.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020/judged')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020/judged')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020/judged')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-2020/judged')
for scoreddoc in dataset.scoreddocs_iter():
scoreddoc # namedtuple<query_id, doc_id, score>
A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold1')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold1')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold1')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold2')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold2')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold2')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold3')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold3')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold3')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold4')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold4')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold4')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>
A more challenging subset of msmarco-passage/trec-dl-2019 and msmarco-document/trec-dl-2020.
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold5')
for query in dataset.queries_iter():
query # namedtuple<query_id, text>
Language: en
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold5')
for doc in dataset.docs_iter():
doc # namedtuple<doc_id, text>
Relevance levels
| Rel. | Definition |
|---|---|
| 0 | Irrelevant: The passage has nothing to do with the query. |
| 1 | Related: The passage seems related to the query but does not answer it. |
| 2 | Highly relevant: The passage has some answer for the query, but the answer may be a bit unclear, or hidden amongst extraneous information. |
| 3 | Perfectly relevant: The passage is dedicated to the query and contains the exact answer. |
Example
import ir_datasets
dataset = ir_datasets.load('msmarco-passage/trec-dl-hard/fold5')
for qrel in dataset.qrels_iter():
qrel # namedtuple<query_id, doc_id, relevance, iteration>


A figure that has bilateral symmetry can be cut exactly into two identical mirror halves.
The line along which a shape is cut is called the line of symmetry.
The line along which a solid is cut is called the plane of symmetry.


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| load_all {pkgload} | R Documentation |
load_all
loads a package. It roughly simulates what happens
when a package is installed and loaded with library()
.
load_all( path = ".", reset = TRUE, compile = NA, export_all = TRUE, export_imports = export_all, helpers = TRUE, attach_testthat = uses_testthat(path), quiet = FALSE, recompile = FALSE, warn_conflicts = TRUE )
path |
Path to a package, or within a package. |
reset |
clear package environment and reset file cache before loading
any pieces of the package. This largely equivalent to running
|
compile |
If |
export_all |
If |
export_imports |
If |
helpers |
if |
attach_testthat |
If |
quiet |
if |
recompile |
DEPRECATED. force a recompile of DLL from source code, if
present. This is equivalent to running |
warn_conflicts |
If |
Currently load_all
:
Loads all data files in data/
. See load_data()
for more details.
Sources all R files in the R directory, storing results in
environment that behaves like a regular package namespace. See
below and load_code()
for more details.
Compiles any C, C++, or Fortran code in the src/
directory
and connects the generated DLL into R. See pkgload::compile_dll()
for more details.
Runs .onAttach()
, .onLoad()
and .onUnload()
functions at the correct times.
If you use testthat, will load all test helpers so
you can access them interactively. devtools sets the
DEVTOOLS_LOAD
environment variable to "true"
to
let you check whether the helpers are run during package loading.
The namespace environment <namespace:pkgname>
, is a child of
the imports environment, which has the name attribute
imports:pkgname
. It is in turn is a child of
<namespace:base>
, which is a child of the global environment.
(There is also a copy of the base namespace that is a child of the empty
environment.)
The package environment <package:pkgname>
is an ancestor of the
global environment. Normally when loading a package, the objects
listed as exports in the NAMESPACE file are copied from the namespace
to the package environment. However, load_all
by default will
copy all objects (not just the ones listed as exports) to the package
environment. This is useful during development because it makes all
objects easy to access.
To export only the objects listed as exports, use
export_all=FALSE
. This more closely simulates behavior when
loading an installed package with library()
, and can be
useful for checking for missing exports.
load_all
also inserts shim functions into the imports environment
of the loaded package. It presently adds a replacement version of
system.file
which returns different paths from
base::system.file
. This is needed because installed and uninstalled
package sources have different directory structures. Note that this is not
a perfect replacement for base::system.file
.
## Not run: # Load the package in the current directory load_all("./") # Running again loads changed files load_all("./") # With reset=TRUE, unload and reload the package for a clean start load_all("./", TRUE) # With export_all=FALSE, only objects listed as exports in NAMESPACE # are exported load_all("./", export_all = FALSE) ## End(Not run)