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Nmap "safe" vs "default" scripts
awk -F: '{print $1}' #-F is the field separator, so separate with : and print first column
Execute shell commands in Empire with> shell <cmd>


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#{extends 'main.html' /} #{set title:'Team'/}
“Obviously, you're not a golfer.”
“Too close for missiles, I'm switching to guns.”
“It does not matter how slowly you go as long as you do not stop.”


Noodles
Price: 200
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Price: 200
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Price: 200
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Noodles
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Noodles
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Meal Qty Unit Price Total Noodles 2 200 400 Cake 2 200 400 Coca Cola 2 200 400 Order Total 1200 Order ID: 17 Status: Waiting Customer: James Bond Recipient Name: John Doe Recipient Address: 24 Araromi Yaba Lagos Recipient Phone Number:<PHONE_NUMBER>4 Time of Order: 9:34am, Sept. 10, 2018 Time of Acceptance: 9:40am, Sept. 10, 2018 Time of Delivery: 10:00am, Sept. 10, 2018 -
Meal Qty Unit Price Total Noodles 2 200 400 Cake 2 200 400 Coca Cola 2 200 400 Order Total 1200 Order ID: 18 Status: Completed Customer: James Bond Recipient Name: John Doe Recipient Address: 24 Araromi Yaba Lagos Recipient Phone Number:<PHONE_NUMBER>4 Time of Order: 9:34am, Sept. 10, 2018 Time of Acceptance: 9:40am, Sept. 10, 2018 Time of Delivery: 10:00am, Sept. 10, 2018 -
Meal Qty Unit Price Total Noodles 2 200 400 Cake 2 200 400 Coca Cola 2 200 400 Order Total 1200 Order ID: 19 Status: Completed Customer: James Bond Recipient Name: John Doe Recipient Address: 24 Araromi Yaba Lagos Recipient Phone Number:<PHONE_NUMBER>4 Time of Order: 9:34am, Sept. 10, 2018 Time of Acceptance: 9:40am, Sept. 10, 2018 Time of Delivery: 10:00am, Sept. 10, 2018 -
Meal Qty Unit Price Total Noodles 2 200 400 Cake 2 200 400 Coca Cola 2 200 400 Order Total 1200 Order ID: 20 Status: Completed Customer: James Bond Recipient Name: John Doe Recipient Address: 24 Araromi Yaba Lagos Recipient Phone Number:<PHONE_NUMBER>4 Time of Order: 9:34am, Sept. 10, 2018 Time of Acceptance: 9:40am, Sept. 10, 2018 Time of Delivery: 10:00am, Sept. 10, 2018 -
Meal Qty Unit Price Total Noodles 2 200 400 Cake 2 200 400 Coca Cola 2 200 400 Order Total 1200 Order ID: 21 Status: Declined Customer: James Bond Recipient Name: John Doe Recipient Address: 24 Araromi Yaba Lagos Recipient Phone Number:<PHONE_NUMBER>4 Time of Order: 9:34am, Sept. 10, 2018 Time of Acceptance: 9:40am, Sept. 10, 2018 Time of Delivery: 10:00am, Sept. 10, 2018 -
Meal Qty Unit Price Total Noodles 2 200 400 Cake 2 200 400 Coca Cola 2 200 400 Order Total 1200 Order ID: 22 Status: Completed Customer: James Bond Recipient Name: John Doe Recipient Address: 24 Araromi Yaba Lagos Recipient Phone Number:<PHONE_NUMBER>4 Time of Order: 9:34am, Sept. 10, 2018 Time of Acceptance: 9:40am, Sept. 10, 2018 Time of Delivery: 10:00am, Sept. 10, 2018


---
title: Giris
keywords: fastai
sidebar: home_sidebar
summary: "Giris hesaplara ulasim icin ana modul olarak calisir"
description: "Giris hesaplara ulasim icin ana modul olarak calisir"
nb_path: "00_card.ipynb"
---
Finds and returns the class object that will provide
the definition of method_name (as a string) if it is
invoked on obj.
obj: any python object
method_name: string method name
{% endraw %}
{% raw %}
<class '__main__.Deck'>
2 of Diamonds
3 of Diamonds
Ace of Hearts
9 of Hearts
3 of Spades


-
Accessibility Developer Tools
-
ChromeVox
Adapted from the slides presented at Google I/O on June 29, 2012.
Press right-arrow to advance, 'F' for fullscreen, 'P' for presenter notes.
Accessibility Developer Tools
ChromeVox
Accessible Text Editor
Accessible Terminal Emulator
PlusVox ChromeVox Extension
We can invert all content on this page, including images, with the following css:
html {
-webkit-filter: invert()
}
Many web apps are not fundamentally inaccessible in design.
Simple errors can cause big problems for users!
accesskey
,
longdesc
,
noscript
,
over newer alternatives from ARIA or HTML5
| Category | Some things we check for |
|---|---|
| Labels and Alternative Content | Images and form fields are labeled |
| Keyboard Accessibility | Focusable UI controls |
| ARIA | Valid ARIA roles |
| Low-vision Accessibility | Foreground/background contrast ratio |
| Video Accessibility | Captions and fallback content |
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<button>Click here</button>
<input type=range></input>
<textarea></textarea>

...

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<textarea></textarea>

 ...

cvox.Api.setSpeechForNode(targetNode, nodeDescriptions)
function generateSpeechForThread(domObj) { var op = new gPlusPost(); op.author = authorFromDomObj(domObj); ... var commentCountNode = getCommentCount(domObj); var nodeDescs = new Array(); nodeDescs.push(generateNodeDescriptionFromPost(op)); cvox.Api.setSpeechForNode(domObj, nodeDescs); }
cvox.Api.syncToNode(targetNode, speakNode)
cvox.Api.click(targetElement, shiftKey)
#element1 { -webkit-filter: invert() } #element1 { -webkit-filter: brightness(20%) } #element1 { -webkit-filter: grayscale() } #element1 { -webkit-filter: invert() contrast(80%) }
We can invert all content on this page, including images, with the following css:
html {
-webkit-filter: invert()
}
We can use a simple rule to avoid inverting JPEG images:
html {
-webkit-filter: invert()
}
img[src*="jpg"] {
-webkit-filter: invert()
}
Note that the I/O logo at the bottom is still inverted.
(This often works better for UI images, as opposed to photos.)
<input x-webkit-speech>
function checkStart() { if (chrome.extension.lastError) { alert("Couldn't start speech input: " + chrome.extension.lastError.message); } } function recognitionFailed(error) { alert("Speech input failed: " + error.code); } function recognitionSucceeded(result) { alert("Recognized '" + result.hypotheses[0].utterance + "' with confidence " + result.hypotheses[0].confidence); } chrome.experimental.speechInput.onError.addListener(recognitionFailed); chrome.experimental.speechInput.onResult.addListener(recognitionSucceeded); chrome.experimental.speechInput.start({ "language": "en" }, checkStart);
{hypotheses: [{confidence:0.836, utterance: "open email from larry page"}, {confidence:0, utterance: "open the email from larry page"}, {confidence:0, utterance: "open email to mary paint"}, {confidence:0, utterance: "open email merry paint"}, {confidence:0, utterance: "open email flary paint"}]}
Install and run TTS Demo from the Chrome Web Store.
Users can install new text-to-speech voices with just a click!
var speakListener = function(utterance, options, sendTtsEvent) { sendTtsEvent({'event_type': 'start', 'charIndex': 0}) // (start speaking) sendTtsEvent({'event_type': 'end', 'charIndex': utterance.length}) }; var stopListener = function() { // (stop all speech) }; chrome.ttsEngine.onSpeak.addListener(speakListener); chrome.ttsEngine.onStop.addListener(stopListener);
The voice you've been listening to just now is now available on the Chrome Web Store!
One cool TTS API feature is the ability to listen for events and synchronize your UI with speech...
Install and run Animated TTS Demo from the Chrome Web Store.


{% extends 'base.html' %} {% load static %} {% block content %}
Machine Learning, dominated by artificial intelligence, is an area that allows predicting results without the code explicitly containing the answer. For the machine to learn by itself it needs two important things:
When we talk about supervised learning, we can easily understand that the machine needs help from humans. In this case the Machine must train with data labeled at the input and predefined output data. This kind of learning makes it possible to solve problems of regression (output variable is a continuous value, it can take any value) and of classification (output variable is a discrete value, related to a class, the number of possibility is limited).
Unlike supervised learning, unsupervised learning gives the machine a lot more flexibility. In fact, the machine receives a non-labeled data set and does not know the output values. Like a human being left in an unknown environment without a guide, he will try to regroup the data, make subsets, to classify a potential future unknown data. To achieve these subsets, we use learning algorithms for clustering (grouping of data according to their similarities) and association (finding relationships according to the attributes of the data).
This kind of method is special, and it is not the most used due to its complexity. The algorithms of this method are based on a system of reward and punishment. Obviously, the aim of the algorithm is to obtain the best reward for a given objective. Most generally, the algorithm creates its own dataset by exploring a specific environment.
The SVM algorithm is a machine learning algorithm with supervised learning used to meet classification needs. It belongs to the category of linear classifiers because it uses linear separation of data. It is much more efficient in classification, especially binary classification. The algorithm can also handle regression problems.
The goal of an SVM algorithm is to find an optimal boundary by maximizing the distance between training points and the boundary thanks to support vectors located on the limit of each class. It will be necessary to use a "one-vs-one" or "one-vs-all" trick to be able to use the SVM in the multi-class classification.
We can use the "kernel trick" if the data is not separable in a linear way. However, it sometimes happens that the data model is not necessarily representable in the form of two linearly separable classes. It is a matter of common sense and in this case, it will be necessary to choose another type of algorithm such as the decision tree.
With more than two dimensions it is difficult to choose the right kernel. You have to test. Here are the different kernels that we can use:
You will also see below the mathematical formulas of the kernels, taken from scikit-learn, allowing to understand the hyperparameters with which we can play such as the sigma and the polynomial degree.
SVM is one of the most powerful linear classification algorithms. Its strong point is that it is very efficient with little data and becomes less efficient with too much data. (For once I have an advantage over Google). KVM algorithms are known for their solid theoretical guarantees, their great flexibility and their ease of use. This algorithm cannot be applied to too complex problems and it is better to stay on simple use of binary classification. For example, to classify emails as SPAM or NON-SPAM it is effective.
KNN is also a supervised learning algorithm that can solve both classification and regression problems. It is very easy to implement and study. There are no ambiguous settings or tips. The hyperparameters mainly concern the definition of the weight of the features, the way in which the distance is calculated (Euclidean, manhattan or minkowski) and the algorithm used for the search for neighbors. Here are the steps to follow:
KNN is a powerful algorithm that is very easy to understand and use. It is much more reliable for classification problems, but it can be used for regression. In our case of tumor detection (benign / malignant classification) it turned out to be very efficient. You still have to be careful with this kind of algorithm because it is quite sensitive to noise. The other useful information is related to K. The lower the K, the more there is a risk of having an overfitting problem (over-trained: the model is trained too narrowly). Conversely, the higher the K the more there is the risk of underfitting (under-trained: the model is treated too broadly).
Before talking about the random forest, we must look at the decision tree of which it is composed.
The decision tree uses the same principle as for managerial decision trees. Obviously, a manager will make a small decision tree depending on what he knows it will be quick and efficient for a small subject. The computer can do a lot better by having a lot more data.
The decision tree is a supervised algorithm because they need labels, but the target variable can be real (regression problem) or categorical (classification problem). Respectively we call regression trees and classification trees.
As a reminder, a tree is an undirected, acyclic graph. For our algorithm we still start from the root to go to a leaf but if we draw the tree nothing prevents us from starting from a leaf to find how we arrived at the conclusion (See the example below).
The principle of the decision tree is to decide at each level of the tree until you find the leaf that corresponds to our data. for each case we have a data set. For the root and the intermediate nodes, it is necessary to answer a question (generally binary) with one of the data. Depending on the answer, we go to one of the intermediate nodes of the next level and we start interrogating again the data with a new question. The procedure is repeated until we reach a leaf that will allow us to obtain the result. Please take a look at our breast tumor decision tree below (one of possible results).
Decision trees are very easy to implement and represent, they do not need a lot of parameters, and they require little data preprocessing.
One of the big advantages is that graphical visualization is possible and allows you to better understand certain errors (see the graph above). However, the problem is that a slight bias in the data can easily corrupt the response. We must also pay attention to the depth of the tree, as for the K of KNN, because the algorithm can quickly become underfitted (too small depth) or overfitted (too huge depth).
Now we can talk about the random forest. It is simply several decision trees then at the end we average the results of all the trees. Yes it is that easy.
| SVM Polynomial Degree : 2 Sigma : 1/ ( n features * X.val() ) |
SVM Linear |
SVM Polynomial Degree : 2 Sigma : 1/ n features |
SVM Radial Basis |
KNN | Decision Tree | Random Forest | |
|---|---|---|---|---|---|---|---|
| Stability of the result | |||||||
| Speed | |||||||
| Best Accuracy | |||||||
| Zero False Negative | |||||||
| Details of result |


README¶
TabNet : Attentive Interpretable Tabular Learning¶
This is a pyTorch implementation of Tabnet (Arik, S. O., & Pfister, T. (2019). TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442.) https://arxiv.org/pdf/1908.07442.pdf. Please note that some different choices have been made overtime to improve the library which can differ from the orginal paper.
Any questions ? Want to contribute ? To talk with us ? You can join us on Slack
Installation¶
Easy installation¶
You can install using pip
or conda
as follows.
with pip
pip install pytorch-tabnet
with conda
conda install -c conda-forge pytorch-tabnet
Source code¶
If you wan to use it locally within a docker container:
git clone git@github.com:dreamquark-ai/tabnet.git
cd tabnet
to get inside the repository
CPU only¶
make start
to build and get inside the container
GPU¶
make start-gpu
to build and get inside the GPU container
poetry install
to install all the dependencies, including jupytermake notebook
inside the same terminal. You can then follow the link to a jupyter notebook with tabnet installed.
What is new ?¶
from version > 4.0 attention is now embedding aware. This aims to maintain a good attention mechanism even with large number of embedding. It is also now possible to specify attention groups (using
grouped_features
). Attention is now done at the group level and not feature level. This is especially useful if a dataset has a lot of columns coming from on single source of data (exemple: a text column transformed using TD-IDF).
Contributing¶
When contributing to the TabNet repository, please make sure to first discuss the change you wish to make via a new or already existing issue.
Our commits follow the rules presented here.
What problems does pytorch-tabnet handle?¶
TabNetClassifier : binary classification and multi-class classification problems
TabNetRegressor : simple and multi-task regression problems
TabNetMultiTaskClassifier: multi-task multi-classification problems
How to use it?¶
TabNet is now scikit-compatible, training a TabNetClassifier or TabNetRegressor is really easy.
from pytorch_tabnet.tab_model import TabNetClassifier, TabNetRegressor
clf = TabNetClassifier() #TabNetRegressor()
clf.fit(
X_train, Y_train,
eval_set=[(X_valid, y_valid)]
)
preds = clf.predict(X_test)
or for TabNetMultiTaskClassifier :
from pytorch_tabnet.multitask import TabNetMultiTaskClassifier
clf = TabNetMultiTaskClassifier()
clf.fit(
X_train, Y_train,
eval_set=[(X_valid, y_valid)]
)
preds = clf.predict(X_test)
The targets on y_train/y_valid
should contain a unique type (e.g. they must all be strings or integers).
Default eval_metric¶
A few classic evaluation metrics are implemented (see further below for custom ones):
binary classification metrics : ‘auc’, ‘accuracy’, ‘balanced_accuracy’, ‘logloss’
multiclass classification : ‘accuracy’, ‘balanced_accuracy’, ‘logloss’
regression: ‘mse’, ‘mae’, ‘rmse’, ‘rmsle’
Important Note : ‘rmsle’ will automatically clip negative predictions to 0, because the model can predict negative values.
In order to match the given scores, you need to use np.clip(clf.predict(X_predict), a_min=0, a_max=None)
when doing predictions.
Custom evaluation metrics¶
You can create a metric for your specific need. Here is an example for gini score (note that you need to specifiy whether this metric should be maximized or not):
from pytorch_tabnet.metrics import Metric
from sklearn.metrics import roc_auc_score
class Gini(Metric):
def __init__(self):
self._name = "gini"
self._maximize = True
def __call__(self, y_true, y_score):
auc = roc_auc_score(y_true, y_score[:, 1])
return max(2*auc - 1, 0.)
clf = TabNetClassifier()
clf.fit(
X_train, Y_train,
eval_set=[(X_valid, y_valid)],
eval_metric=[Gini]
)
A specific customization example notebook is available here : https://github.com/dreamquark-ai/tabnet/blob/develop/customizing_example.ipynb
Semi-supervised pre-training¶
Added later to TabNet’s original paper, semi-supervised pre-training is now available via the class TabNetPretrainer
:
# TabNetPretrainer
unsupervised_model = TabNetPretrainer(
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
mask_type='entmax' # "sparsemax"
)
unsupervised_model.fit(
X_train=X_train,
eval_set=[X_valid],
pretraining_ratio=0.8,
)
clf = TabNetClassifier(
optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2),
scheduler_params={"step_size":10, # how to use learning rate scheduler
"gamma":0.9},
scheduler_fn=torch.optim.lr_scheduler.StepLR,
mask_type='sparsemax' # This will be overwritten if using pretrain model
)
clf.fit(
X_train=X_train, y_train=y_train,
eval_set=[(X_train, y_train), (X_valid, y_valid)],
eval_name=['train', 'valid'],
eval_metric=['auc'],
from_unsupervised=unsupervised_model
)
The loss function has been normalized to be independent of pretraining_ratio
, batch_size
and the number of features in the problem.
A self supervised loss greater than 1 means that your model is reconstructing worse than predicting the mean for each feature, a loss bellow 1 means that the model is doing better than predicting the mean.
A complete example can be found within the notebook pretraining_example.ipynb
.
/!\ : current implementation is trying to reconstruct the original inputs, but Batch Normalization applies a random transformation that can’t be deduced by a single line, making the reconstruction harder. Lowering the batch_size
might make the pretraining easier.
Data augmentation on the fly¶
It is now possible to apply custom data augmentation pipeline during training.
Templates for ClassificationSMOTE and RegressionSMOTE have been added in pytorch-tabnet/augmentations.py
and can be used as is.
Easy saving and loading¶
It’s really easy to save and re-load a trained model, this makes TabNet production ready.
# save tabnet model
saving_path_name = "./tabnet_model_test_1"
saved_filepath = clf.save_model(saving_path_name)
# define new model with basic parameters and load state dict weights
loaded_clf = TabNetClassifier()
loaded_clf.load_model(saved_filepath)
Useful links¶
Model parameters¶
n_d
: int (default=8)Width of the decision prediction layer. Bigger values gives more capacity to the model with the risk of overfitting. Values typically range from 8 to 64.
n_a
: int (default=8)Width of the attention embedding for each mask. According to the paper n_d=n_a is usually a good choice. (default=8)
n_steps
: int (default=3)Number of steps in the architecture (usually between 3 and 10)
gamma
: float (default=1.3)This is the coefficient for feature reusage in the masks. A value close to 1 will make mask selection least correlated between layers. Values range from 1.0 to 2.0.
cat_idxs
: list of int (default=[] - Mandatory for embeddings)List of categorical features indices.
cat_dims
: list of int (default=[] - Mandatory for embeddings)List of categorical features number of modalities (number of unique values for a categorical feature) /!\ no new modalities can be predicted
cat_emb_dim
: list of int (optional)List of embeddings size for each categorical features. (default =1)
n_independent
: int (default=2)Number of independent Gated Linear Units layers at each step. Usual values range from 1 to 5.
n_shared
: int (default=2)Number of shared Gated Linear Units at each step Usual values range from 1 to 5
epsilon
: float (default 1e-15)Should be left untouched.
seed
: int (default=0)Random seed for reproducibility
momentum
: floatMomentum for batch normalization, typically ranges from 0.01 to 0.4 (default=0.02)
clip_value
: float (default None)If a float is given this will clip the gradient at clip_value.
lambda_sparse
: float (default = 1e-3)This is the extra sparsity loss coefficient as proposed in the original paper. The bigger this coefficient is, the sparser your model will be in terms of feature selection. Depending on the difficulty of your problem, reducing this value could help.
optimizer_fn
: torch.optim (default=torch.optim.Adam)Pytorch optimizer function
optimizer_params
: dict (default=dict(lr=2e-2))Parameters compatible with optimizer_fn used initialize the optimizer. Since we have Adam as our default optimizer, we use this to define the initial learning rate used for training. As mentionned in the original paper, a large initial learning rate of
0.02
with decay is a good option.scheduler_fn
: torch.optim.lr_scheduler (default=None)Pytorch Scheduler to change learning rates during training.
scheduler_params
: dictDictionnary of parameters to apply to the scheduler_fn. Ex : {”gamma”: 0.95, “step_size”: 10}
model_name
: str (default = ‘DreamQuarkTabNet’)Name of the model used for saving in disk, you can customize this to easily retrieve and reuse your trained models.
verbose
: int (default=1)Verbosity for notebooks plots, set to 1 to see every epoch, 0 to get None.
device_name
: str (default=’auto’) ‘cpu’ for cpu training, ‘gpu’ for gpu training, ‘auto’ to automatically detect gpu.mask_type: str
(default=’sparsemax’) Either “sparsemax” or “entmax” : this is the masking function to use for selecting features.grouped_features: list of list of ints
(default=None) This allows the model to share it’s attention accross feature inside a same group. This can be especially useful when your preprocessing generates correlated or dependant features: like if you use a TF-IDF or a PCA on a text column. Note that feature importance will be exactly the same between features on a same group. Please also note that embeddings generated for a categorical variable are always inside a same group.n_shared_decoder
: int (default=1)Number of shared GLU block in decoder, this is only useful for
TabNetPretrainer
.n_indep_decoder
: int (default=1)Number of independent GLU block in decoder, this is only useful for
TabNetPretrainer
.
Fit parameters¶
X_train
: np.array or scipy.sparse.csr_matrixTraining features
y_train
: np.arrayTraining targets
eval_set
: list of tupleList of eval tuple set (X, y).
The last one is used for early stoppingeval_name
: list of str
List of eval set names.eval_metric
: list of str
List of evaluation metrics.
The last metric is used for early stopping.max_epochs
: int (default = 200)Maximum number of epochs for trainng.
patience
: int (default = 10)Number of consecutive epochs without improvement before performing early stopping.
If patience is set to 0, then no early stopping will be performed.
Note that if patience is enabled, then best weights from best epoch will automatically be loaded at the end of
fit
.weights
: int or dict (default=0)/!\ Only for TabNetClassifier Sampling parameter 0 : no sampling 1 : automated sampling with inverse class occurrences dict : keys are classes, values are weights for each class
loss_fn
: torch.loss or list of torch.lossLoss function for training (default to mse for regression and cross entropy for classification) When using TabNetMultiTaskClassifier you can set a list of same length as number of tasks, each task will be assigned its own loss function
batch_size
: int (default=1024)Number of examples per batch. Large batch sizes are recommended.
virtual_batch_size
: int (default=128)Size of the mini batches used for “Ghost Batch Normalization”. /!\
virtual_batch_size
should dividebatch_size
num_workers
: int (default=0)Number or workers used in torch.utils.data.Dataloader
drop_last
: bool (default=False)Whether to drop last batch if not complete during training
callbacks
: list of callback function
List of custom callbackspretraining_ratio
: float/!\ TabNetPretrainer Only : Percentage of input features to mask during pretraining. Should be between 0 and 1. The bigger the harder the reconstruction task is.
warm_start
: bool (default=False) In order to match scikit-learn API, this is set to False. It allows to fit twice the same model and start from a warm start.compute_importance
: bool (default=True)Whether to compute feature importance


For my website design and coding assesment, I was given two 'clients' to choose from. Each client had their own idea of what they wanted their website to be about and what images/logo to feature on the website. I chose 'Content Living', a building company which specialises in modern houses.
Sitemap and Wireframe: A sitemap is where you draw a sort of tree which connects each page on a website to another. It visualises what buttons lead to the next pages. A wireframe is a rough sketch of the appearance of a website. both of these were necessary for me to get a good idea and visual understanding on what to include on my website.
Final Design: This is where I digitally draw what I want my website to look like. This would include the images, the colours, the fonts, everything I wanted to include on my website is right here as a visual. This definitely helped me when it came to Adobe Dreamweaver, as all I needed to do was code this!
HTML and CSS: Once I had my complete plan for my website, it was time to begin working on Adobe Dreamweaver, where I would (slowly but surely) learn how to write HTML and CSS. Through Dreameaver, and after many hours of frustration, I'd completed the Content Living website!


Creates a custom event value for a trigger. The trigger will then only affect an elevator class with this event value set in its event_mask. The custom value should be a bit value (i.e. a power of 2) so that it can be added with the other custom and preset bits (this works fine). LEC always uses 65536 onwards so I suggest you do this too. However, it seems that you do not HAVE to use a bit value because in EXECUTOR.INF LEC uses 2621444 (note the extra 4) and it works OK. But I don't suggest you do this.
Event: is needed with multi-class elevators or triggers where each class is controlled by a separate trigger. For example in the Research Facility (level 4), the sector called "Corecat" (spins around the Phrik metal) is two classes of elevator - elevator move_fc and elevator morph_spin2. Two switches control these classes individually. If the "event" variable was not used, both switches would move both classes of elevator to its next stop at the same time. Utilising the event variable, it is made possible to have one switch control the spinning and the other control the moving floor/ceiling.
Usage:
| event: [value]


Element 1
Element 2
Element 3
Email:
Element1
Your browser does not support HTML video.
Element3
Hello World!, we are software developers!


This test case demonstrates functionality of the Web Forms 2.0 specification and tests the compliance of this cross-browser Web Forms 2.0 implementation.
The following demonstrates using the required
attribute on controls within a repetition template. The autofocus
attribute is also included an input
.


Space Age Calculator
Enter your birthday:
Year
Month
January
Feburary
March
April
May
June
July
August
September
October
November
December
Day
Select your planet
Mercury
Venus
Mars
Jupiter
Submit


HTTrack
https://www.httrack.com/html/fcguide.html
advanced website cloner
Switches
-O output folder
-c4 number of multiple connections
-%c10 maximum number of connections per second
-A2000 maximum transfer rate in bytes/seconds
-m2000,5000 maximum file length for a non-html file, for html file (50000000 = 50mb)
-M10000 maximum mirror size which can be downloaded<PHONE_NUMBER> = 10Gb)
-r25 mirror depth
-%e1 external links depth
-H2 host is abandonned if: 0=never, 1=timeout, 2=slow, 3=timeout or slow
-i continue an interrupted mirror using the cache
-P proxy use (-P proxy:port or -P user:pass@proxy:port)
-GN pause transfer if N bytes reached, and wait until lock file is deleted (may be used with cron)
-RN number of retries, in case of timeout or non-fatal errors (*R1)
-n get non-html files near an html file
-t test all URLs (even forbidden ones)
-F user-agent field sent in HTTP headers (-F "user-agent name")
Examples
Before mirroring check target site MIME types and create mappings in .httrackrc or add --assume switch
--assume asp=text/html,php3=text/html,cgi=image/gif,zip=application/x-zip,mpg=application/x-mp3
Mirror given site, mirror all subdomains, output to given directory
httrack "http://www.hackthissite.org/" "+*.hackthissite.org/*" -O "/tmp/www.hackthissite.org"
Mirror given site and skip specific locations
httrack www.hackthissite.org -O /root/Desktop/httrack -c4 -%c10 -A20000000 -m50000000,1000000 -M1000000000 -r25 -R3 -H2 -n -t -www.hackthissite.org/forums/* -www.hackthissite.org/user/*


Open Field
This pipline is developed to detect laser pulses received at the optic fibers on the animal’s head when moving freely in an open field. See example below.
It is necessary for to provide the stim_duration
, inter_stim_interval
in the config file.
Here’s an example for a 30-second ON followed by 30-second OFF protocol.
You can see that the 4th pulse is barely captured by the side camera. Therefore, to make consistent detections the detection of these pulses are extrapolated from the solid detection (with durations complying with the stim_duration
in specified in the config file). That is why it is important to input the specifics of the protocol in the config file.
Note
note that if none of the detected pulses are complient with the protocol but the detected pulse durations have very low variability, they are reported as they are without any correction applied.


This is the game template you will be using to create your game. The game consists of four levels - a math question, drag and drop, text input, and a puzzle game. Your task is to modify each level to make it your own!
Click this link for the full set of tasks you will need to complete in order to create your game.


As a node module:
const semver = require('semver')
semver.valid('1.2.3') // '1.2.3'
semver.valid('a.b.c') // null
semver.clean(' =v1.2.3 ') // '1.2.3'
semver.satisfies('1.2.3', '1.x || >=2.5.0 || 5.0.0 - 7.2.3') // true
semver.gt('1.2.3', '9.8.7') // false
semver.lt('1.2.3', '9.8.7') // true
semver.minVersion('>=1.0.0') // '1.0.0'
semver.valid(semver.coerce('v2')) // '2.0.0'
semver.valid(semver.coerce('<IP_ADDRESS>.3-alpha')) // '42.6.7'
As a command-line utility:
$ semver -h
A JavaScript implementation of the https://semver.org/ specification
Copyright Isaac Z. Schlueter
Usage: semver [options] <version> [<version> [...]]
Prints valid versions sorted by SemVer precedence
Options:
-r --range <range>
Print versions that match the specified range.
-i --increment [<level>]
Increment a version by the specified level. Level can
be one of: major, minor, patch, premajor, preminor,
prepatch, or prerelease. Default level is 'patch'.
Only one version may be specified.
--preid <identifier>
Identifier to be used to prefix premajor, preminor,
prepatch or prerelease version increments.
-l --loose
Interpret versions and ranges loosely
-p --include-prerelease
Always include prerelease versions in range matching
-c --coerce
Coerce a string into SemVer if possible
(does not imply --loose)
Program exits successfully if any valid version satisfies
all supplied ranges, and prints all satisfying versions.
If no satisfying versions are found, then exits failure.
Versions are printed in ascending order, so supplying
multiple versions to the utility will just sort them.
A “version” is described by the v2.0.0
specification found at https://semver.org/.
A leading "="
or "v"
character is stripped off and ignored.
A version range
is a set of comparators
which specify versions that satisfy the range.
A comparator
is composed of an operator
and a version
. The set of primitive operators
is:
<
Less than<=
Less than or equal to>
Greater than>=
Greater than or equal to=
Equal. If no operator is specified, then equality is assumed, so this operator is optional, but MAY be included.For example, the comparator >=1.2.7
would match the versions 1.2.7
, 1.2.8
, 2.5.3
, and 1.3.9
, but not the versions 1.2.6
or 1.1.0
.
Comparators can be joined by whitespace to form a comparator set
, which is satisfied by the intersection of all of the comparators it includes.
A range is composed of one or more comparator sets, joined by ||
. A version matches a range if and only if every comparator in at least one of the ||
-separated comparator sets is satisfied by the version.
For example, the range >=1.2.7 <1.3.0
would match the versions 1.2.7
, 1.2.8
, and 1.2.99
, but not the versions 1.2.6
, 1.3.0
, or 1.1.0
.
The range 1.2.7 || >=1.2.9 <2.0.0
would match the versions 1.2.7
, 1.2.9
, and 1.4.6
, but not the versions 1.2.8
or 2.0.0
.
If a version has a prerelease tag (for example, 1.2.3-alpha.3
) then it will only be allowed to satisfy comparator sets if at least one comparator with the same [major, minor, patch]
tuple also has a prerelease tag.
For example, the range >1.2.3-alpha.3
would be allowed to match the version 1.2.3-alpha.7
, but it would not be satisfied by 3.4.5-alpha.9
, even though 3.4.5-alpha.9
is technically “greater than” 1.2.3-alpha.3
according to the SemVer sort rules. The version range only accepts prerelease tags on the 1.2.3
version. The version 3.4.5
would satisfy the range, because it does not have a prerelease flag, and 3.4.5
is greater than 1.2.3-alpha.7
.
The purpose for this behavior is twofold. First, prerelease versions frequently are updated very quickly, and contain many breaking changes that are (by the author’s design) not yet fit for public consumption. Therefore, by default, they are excluded from range matching semantics.
Second, a user who has opted into using a prerelease version has clearly indicated the intent to use that specific set of alpha/beta/rc versions. By including a prerelease tag in the range, the user is indicating that they are aware of the risk. However, it is still not appropriate to assume that they have opted into taking a similar risk on the next set of prerelease versions.
Note that this behavior can be suppressed (treating all prerelease versions as if they were normal versions, for the purpose of range matching) by setting the includePrerelease
flag on the options object to any functions that do range matching.
The method .inc
takes an additional identifier
string argument that will append the value of the string as a prerelease identifier:
command-line example:
Which then can be used to increment further:
Advanced range syntax desugars to primitive comparators in deterministic ways.
Advanced ranges may be combined in the same way as primitive comparators using white space or ||
.
X.Y.Z - A.B.C
Specifies an inclusive set.
1.2.3 - 2.3.4
:= >=1.2.3 <=2.3.4
If a partial version is provided as the first version in the inclusive range, then the missing pieces are replaced with zeroes.
1.2 - 2.3.4
:= >=1.2.0 <=2.3.4
If a partial version is provided as the second version in the inclusive range, then all versions that start with the supplied parts of the tuple are accepted, but nothing that would be greater than the provided tuple parts.
1.2.3 - 2.3
:= >=1.2.3 <2.4.0
1.2.3 - 2
:= >=1.2.3 <3.0.0
1.2.x
1.X
1.2.*
*
Any of X
, x
, or *
may be used to “stand in” for one of the numeric values in the [major, minor, patch]
tuple.
*
:= >=0.0.0
(Any version satisfies)1.x
:= >=1.0.0 <2.0.0
(Matching major version)1.2.x
:= >=1.2.0 <1.3.0
(Matching major and minor versions)A partial version range is treated as an X-Range, so the special character is in fact optional.
""
(empty string) := *
:= >=0.0.0
1
:= 1.x.x
:= >=1.0.0 <2.0.0
1.2
:= 1.2.x
:= >=1.2.0 <1.3.0
~1.2.3
~1.2
~1
Allows patch-level changes if a minor version is specified on the comparator. Allows minor-level changes if not.
~1.2.3
:= >=1.2.3 <1.(2+1).0
:= >=1.2.3 <1.3.0
~1.2
:= >=1.2.0 <1.(2+1).0
:= >=1.2.0 <1.3.0
(Same as 1.2.x
)~1
:= >=1.0.0 <(1+1).0.0
:= >=1.0.0 <2.0.0
(Same as 1.x
)~0.2.3
:= >=0.2.3 <0.(2+1).0
:= >=0.2.3 <0.3.0
~0.2
:= >=0.2.0 <0.(2+1).0
:= >=0.2.0 <0.3.0
(Same as 0.2.x
)~0
:= >=0.0.0 <(0+1).0.0
:= >=0.0.0 <1.0.0
(Same as 0.x
)~1.2.3-beta.2
:= >=1.2.3-beta.2 <1.3.0
Note that prereleases in the 1.2.3
version will be allowed, if they are greater than or equal to beta.2
. So, 1.2.3-beta.4
would be allowed, but 1.2.4-beta.2
would not, because it is a prerelease of a different [major, minor, patch]
tuple.^1.2.3
^0.2.5
^0.0.4
Allows changes that do not modify the left-most non-zero digit in the [major, minor, patch]
tuple. In other words, this allows patch and minor updates for versions 1.0.0
and above, patch updates for versions 0.X >=0.1.0
, and no updates for versions 0.0.X
.
Many authors treat a 0.x
version as if the x
were the major “breaking-change” indicator.
Caret ranges are ideal when an author may make breaking changes between 0.2.4
and 0.3.0
releases, which is a common practice. However, it presumes that there will not be breaking changes between 0.2.4
and 0.2.5
. It allows for changes that are presumed to be additive (but non-breaking), according to commonly observed practices.
^1.2.3
:= >=1.2.3 <2.0.0
^0.2.3
:= >=0.2.3 <0.3.0
^0.0.3
:= >=0.0.3 <0.0.4
^1.2.3-beta.2
:= >=1.2.3-beta.2 <2.0.0
Note that prereleases in the 1.2.3
version will be allowed, if they are greater than or equal to beta.2
. So, 1.2.3-beta.4
would be allowed, but 1.2.4-beta.2
would not, because it is a prerelease of a different [major, minor, patch]
tuple.^0.0.3-beta
:= >=0.0.3-beta <0.0.4
Note that prereleases in the 0.0.3
version only will be allowed, if they are greater than or equal to beta
. So, 0.0.3-pr.2
would be allowed.When parsing caret ranges, a missing patch
value desugars to the number 0
, but will allow flexibility within that value, even if the major and minor versions are both 0
.
^1.2.x
:= >=1.2.0 <2.0.0
^0.0.x
:= >=0.0.0 <0.1.0
^0.0
:= >=0.0.0 <0.1.0
A missing minor
and patch
values will desugar to zero, but also allow flexibility within those values, even if the major version is zero.
^1.x
:= >=1.0.0 <2.0.0
^0.x
:= >=0.0.0 <1.0.0
Putting all this together, here is a Backus-Naur grammar for ranges, for the benefit of parser authors:
range-set ::= range ( logical-or range ) *
logical-or ::= ( ' ' ) * '||' ( ' ' ) *
range ::= hyphen | simple ( ' ' simple ) * | ''
hyphen ::= partial ' - ' partial
simple ::= primitive | partial | tilde | caret
primitive ::= ( '<' | '>' | '>=' | '<=' | '=' ) partial
partial ::= xr ( '.' xr ( '.' xr qualifier ? )? )?
xr ::= 'x' | 'X' | '*' | nr
nr ::= '0' | ['1'-'9'] ( ['0'-'9'] ) *
tilde ::= '~' partial
caret ::= '^' partial
qualifier ::= ( '-' pre )? ( '+' build )?
pre ::= parts
build ::= parts
parts ::= part ( '.' part ) *
part ::= nr | [-0-9A-Za-z]+
All methods and classes take a final options
object argument. All options in this object are false
by default. The options supported are:
loose
Be more forgiving about not-quite-valid semver strings. (Any resulting output will always be 100% strict compliant, of course.) For backwards compatibility reasons, if the options
argument is a boolean value instead of an object, it is interpreted to be the loose
param.includePrerelease
Set to suppress the default behavior of excluding prerelease tagged versions from ranges unless they are explicitly opted into.Strict-mode Comparators and Ranges will be strict about the SemVer strings that they parse.
valid(v)
: Return the parsed version, or null if it’s not valid.inc(v, release)
: Return the version incremented by the release type (major
, premajor
, minor
, preminor
, patch
, prepatch
, or prerelease
), or null if it’s not valid
premajor
in one call will bump the version up to the next major version and down to a prerelease of that major version. preminor
, and prepatch
work the same way.prerelease
will work the same as prepatch
. It increments the patch version, then makes a prerelease. If the input version is already a prerelease it simply increments it.prerelease(v)
: Returns an array of prerelease components, or null if none exist. Example: prerelease('1.2.3-alpha.1') -> ['alpha', 1]
major(v)
: Return the major version number.minor(v)
: Return the minor version number.patch(v)
: Return the patch version number.intersects(r1, r2, loose)
: Return true if the two supplied ranges or comparators intersect.parse(v)
: Attempt to parse a string as a semantic version, returning either a SemVer
object or null
.gt(v1, v2)
: v1 > v2
gte(v1, v2)
: v1 >= v2
lt(v1, v2)
: v1 < v2
lte(v1, v2)
: v1 <= v2
eq(v1, v2)
: v1 == v2
This is true if they’re logically equivalent, even if they’re not the exact same string. You already know how to compare strings.neq(v1, v2)
: v1 != v2
The opposite of eq
.cmp(v1, comparator, v2)
: Pass in a comparison string, and it’ll call the corresponding function above. "==="
and "!=="
do simple string comparison, but are included for completeness. Throws if an invalid comparison string is provided.compare(v1, v2)
: Return 0
if v1 == v2
, or 1
if v1
is greater, or -1
if v2
is greater. Sorts in ascending order if passed to Array.sort()
.rcompare(v1, v2)
: The reverse of compare. Sorts an array of versions in descending order when passed to Array.sort()
.diff(v1, v2)
: Returns difference between two versions by the release type (major
, premajor
, minor
, preminor
, patch
, prepatch
, or prerelease
), or null if the versions are the same.intersects(comparator)
: Return true if the comparators intersectvalidRange(range)
: Return the valid range or null if it’s not validsatisfies(version, range)
: Return true if the version satisfies the range.maxSatisfying(versions, range)
: Return the highest version in the list that satisfies the range, or null
if none of them do.minSatisfying(versions, range)
: Return the lowest version in the list that satisfies the range, or null
if none of them do.minVersion(range)
: Return the lowest version that can possibly match the given range.gtr(version, range)
: Return true
if version is greater than all the versions possible in the range.ltr(version, range)
: Return true
if version is less than all the versions possible in the range.outside(version, range, hilo)
: Return true if the version is outside the bounds of the range in either the high or low direction. The hilo
argument must be either the string '>'
or '<'
. (This is the function called by gtr
and ltr
.)intersects(range)
: Return true if any of the ranges comparators intersectNote that, since ranges may be non-contiguous, a version might not be greater than a range, less than a range, or satisfy a range! For example, the range 1.2 <1.2.9 || >2.0.0
would have a hole from 1.2.9
until 2.0.0
, so the version 1.2.10
would not be greater than the range (because 2.0.1
satisfies, which is higher), nor less than the range (since 1.2.8
satisfies, which is lower), and it also does not satisfy the range.
If you want to know if a version satisfies or does not satisfy a range, use the satisfies(version, range)
function.
coerce(version)
: Coerces a string to semver if possibleThis aims to provide a very forgiving translation of a non-semver string to semver. It looks for the first digit in a string, and consumes all remaining characters which satisfy at least a partial semver (e.g., 1
, 1.2
, 1.2.3
) up to the max permitted length (256 characters). Longer versions are simply truncated (<IP_ADDRESS>.2-alpha2
becomes 4.6.3
). All surrounding text is simply ignored (v3.4 replaces v3.3.1
becomes 3.4.0
). Only text which lacks digits will fail coercion (version one
is not valid). The maximum length for any semver component considered for coercion is 16 characters; longer components will be ignored<PHONE_NUMBER>0000<IP_ADDRESS>
becomes 4.7.4
). The maximum value for any semver component is Number.MAX_SAFE_INTEGER || (2**53 - 1)
; higher value components are invalid<PHONE_NUMBER>9999<IP_ADDRESS>
is likely invalid).


{% extends 'layout.html' %} {% block pageTitle %} State Pension Activation {% endblock %} {% block content %} {% include 'includes/parts/phase-banner.html' %} {{country}} When did you work here? From Month Year To Put the current month and year if you work in this country. Month Year Have you worked here on another occasion? Yes No {% include 'includes/parts/helpinternational.html' %} {% endblock %} {% block pageScripts %} {% include 'includes/scripts/country-dates-focus.html' %} {% endblock %}
Put the current month and year if you work in this country.


RT @JRubyConfEU: The conference is happening in exactly 2 months! Get your ticket now: http://tickets.eurucamp.org/ We’re looking forward to welc…
5/31/2015, 10:27:49 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @JRubyConfEU: The conference is happening in exactly 2 months! Get your ticket now: http://tickets.eurucamp.org/ We’re looking forward to welc…
5/31/2015, 10:27:49 AM
Favs: 0
Retweets: 0
link


Gino Ureta
About me
Visualizations
SVG
Source
Flow Vis
Fluid Flow Visualization (Three.js)
Credits / Acknowledgements
Three.js Library and Documentation
D3.js
EVL HW03 boilerplate code
DataSet
Bootstrap
Mozilla MDN
Code samples from CodePen.io
Code samples from w3schools.com


--- title: "Training with CloudML" output: rmarkdown::html_vignette: default vignette: > %\VignetteIndexEntry{Training with CloudML} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} type: docs repo: https://github.com/rstudio/cloudml menu: main: name: "Training with CloudML" identifier: "tools-cloudml-training" parent: "cloudml-top" weight: 20 aliases: - /tools/cloudml/training.html ---
Training models with CloudML uses the following workflow:
Develop and test an R training script locally
Submit a job to CloudML to execute your script in the cloud
Monitor and collect the results of the job
Tune your model based on the results and repeat training as necessary
CloudML is a managed service where you pay only for the hardware resources that you use. Prices vary depending on configuration (e.g. CPU vs. GPU vs. multiple GPUs). See https://cloud.google.com/ml-engine/pricing for additional details.
Working on a CloudML project always begins with developing a training script that runs on your local machine. This will typically involve using one of these packages:
keras — A high-level interface for neural networks, with a focus on enabling fast experimentation.
tfestimators — High-level implementations of common model types such as regressors and classifiers.
tensorflow — Lower-level interface that provides full access to the TensorFlow computational graph.
There are no special requirements for your training script, however there are a couple of things to keep in mind:
When you train a model on CloudML all of the files in the current working directory are uploaded. Therefore, your training script should be within the current working directory and references to other scripts, data files, etc. should be relative to the current working directory. The most straightforward way to organize your work on a CloudML application is to use an RStudio Project.
Your training data may be contained within the working directory, or it may be located within Google Cloud Storage. If your training data is large and/or located in cloud storage, the most straightforward workflow for development is to use a local subsample of your data. See the article on Google Cloud Storage for a detailed example of using distinct data for local and CloudML execution contexts, as well as reading data from Google Cloud Storage buckets.
Once your script is working the way you expect you are ready to submit it as a job to CloudML.
The core unit of work in CloudML is a job. A job consists of a training script and related files (e.g. other scripts, data files, etc. within the working directory). To submit a job to CloudML you use the cloudml_train()
function, passing it the name of the training script to run. For example:
Note that the very first time you submit a job to CloudML the various packages required to run your script will be compiled from source. This will make the execution time of the job considerably longer that you might expect. It’s only the first job that incurs this overhead though (since the package installations are cached), and subsequent jobs will run more quickly.
The cloudml_train()
function returns a job
object. This is a reference to the training job which you can use later to check it’s status, collect it’s output, etc. For example:
$ createTime : chr "2017-12-18T20:35:21Z"
$ etag : chr "2KRqIbAhzvM="
$ jobId : chr "cloudml_2017_12_18_203510175"
$ startTime : chr "2017-12-18T20:35:52Z"
$ state : chr "RUNNING"
$ trainingInput :List of 3
..$ jobDir : chr "gs://cedar-card-791/r-cloudml/staging"
..$ region : chr "us-central1"
..$ runtimeVersion: chr "1.4"
$ trainingOutput:List of 1
..$ consumedMLUnits: num 0.04
View job in the Cloud Console at:
https://console.cloud.google.com/ml/jobs/cloudml_2017_12_18_203510175?project=cedar-card-791
View logs at:
https://console.cloud.google.com/logs?resource=ml.googleapis.com%2Fjob_id%2Fcloudml_2017_12_18_203510175&project=cedar-card-791
To interact with jobs you don’t need the job
object returned from cloudml_train()
. If you call job_status()
or with no arguments it will act on the most recently submitted job:
You can call job_collect()
at any time to download a job:
Note also that if you are using RStudio v1.1 or higher you’ll be given the to monitor and collect submitted jobs in the background using an RStudio terminal:
In this case you don’t need to call job_collect()
explicitly as this will be done from within the background terminal after the job completes.
Once the job is complete it’s results will be downloaded and a report will be automatically displayed:
Each training job will produce one or more training runs (it’s typically only a single run, however when doing hyperparmeter turning there will be multiple runs). When you collect a job from CloudML it is automatically downloaded into the runs
sub-directory of the current working directory.
You can list all of the runs as a data frame using the ls_runs()
function:
Data frame: 6 x 37
run_dir eval_loss eval_acc metric_loss metric_acc metric_val_loss metric_val_acc
6 runs/cloudml_2018_01_26_135812740 0.1049 0.9789 0.0852 0.9760 0.1093 0.9770
2 runs/cloudml_2018_01_26_140015601 0.1402 0.9664 0.1708 0.9517 0.1379 0.9687
5 runs/cloudml_2018_01_26_135848817 0.1159 0.9793 0.0378 0.9887 0.1130 0.9792
3 runs/cloudml_2018_01_26_135936130 0.0963 0.9780 0.0701 0.9792 0.0969 0.9790
1 runs/cloudml_2018_01_26_140045584 0.1486 0.9682 0.1860 0.9504 0.1453 0.9693
4 runs/cloudml_2018_01_26_135912819 0.1141 0.9759 0.1272 0.9655 0.1087 0.9762
# ... with 30 more columns:
# flag_dense_units1, flag_dropout1, flag_dense_units2, flag_dropout2, samples, validation_samples,
# batch_size, epochs, epochs_completed, metrics, model, loss_function, optimizer, learning_rate,
# script, start, end, completed, output, source_code, context, type, cloudml_console_url,
# cloudml_created, cloudml_end, cloudml_job, cloudml_log_url, cloudml_ml_units, cloudml_start,
# cloudml_state
You can view run reports using the view_run()
function:
There are many tools available to list, filter, and compare training runs. For additional information see the documentation for the tfruns package.
You can enumerate previously submitted jobs using the job_list()
function:
JOB_ID STATUS CREATED
1 cloudml_2017_12_18_203510175 SUCCEEDED 2017-12-18 15:35:21
2 cloudml_2017_12_18_202228264 FAILED 2017-12-18 15:22:39
3 cloudml_2017_12_18_201607948 SUCCEEDED 2017-12-18 15:16:18
4 cloudml_2017_12_18_132620918 SUCCEEDED 2017-12-18 08:26:30
5 cloudml_2017_12_15_182614794 SUCCEEDED 2017-12-15 13:26:29
6 cloudml_2017_12_14_183247626 SUCCEEDED 2017-12-14 13:33:04
You can use the JOB_ID
field to interact with any of these jobs:
The job_stream_logs()
function can be used to view the live log of a running job:
The job_cancel()
function can be used to cancel a running job:
Tuning your application typically requires choosing and then optimizing a set of hyperparameters that influence your model’s performance. This could include the number and type of layers, units within layers, drop rates, regularization, etc.
You can experiment with hyperparameters on an ad-hoc basis, but in general it’s better to explore them more systematnically. The key to doing this with CloudML is by defining training flags within your script and the parameterizing runs using those flags.
For example, you might define the following training flags:
FLAGS <- flags( flag_integer("dense_units1", 128), flag_numeric("dropout1", 0.4), flag_integer("dense_units2", 128), flag_numeric("dropout2", 0.3),Then use the flags in a script as follows:
predictions <- input %>% layer_dense(units = FLAGS$dense_units1, activation = 'relu') %>% layer_dropout(rate = FLAGS$dropout1) %>% layer_dense(units = FLAGS$dense_units2, activation = 'relu') %>% layer_dropout(rate = FLAGS$dropout2) %>% layer_dense(units = 10, activation = 'softmax') model <- keras_model(input, predictions) %>% compile( loss = 'categorical_crossentropy', optimizer = optimizer_rmsprop(lr = 0.001), metrics = c('accuracy') ) history <- model %>% fit( x_train, y_train, batch_size = 128, epochs = 30, verbose = 1, validation_split = 0.2Note that instead of literal values for the various hyperparameters we want to vary we now reference members of the FLAGS list returned from the flags()
function.
You can try out different flags by passing a named list of flags
to the cloudml_train()
function. For example:
These flags are passed to your script and are also retained as part of the results recorded for the training run.
You can also more systematically try combinations of flags using CloudML hyperparameter tuning.
By default, CloudML utilizes “standard” CPU-based instances suitable for training simple models with small to moderate datasets. You can request the use of other machine types, including ones with GPUs, using the master_type
parameter of cloudml_train()
.
For example, the following would train the same model as above but with a Tesla K80 GPU:
To train using a Tesla P100 GPU you would specify "standard_p100"
:
To train on a machine with 4 Tesla P100 GPU’s you would specify "complex_model_m_p100"
:
See the CloudML website for documentation on available machine types. Also note that GPU instances can be considerably more expensive that CPU ones! See the documentation on CloudML Pricing for details.
You can provide custom configuration for training by creating a cloudml.yml
file within the working directory from which you submit your training job. This file can be used to customize various aspects of training behavior including the virtual machines used as well as the runtime version of CloudML used in the job.
For example, the following config file specifies a custom scale tier with a master type of “large_model”. It also specifies that the CloudML runtime version should be 1.2.
cloudml.yml
You can also pass a named configuration file (i.e. one for a hyperparameter tuning job) via the config
parmater of cloudml_train()
. For example:
Note that trainingInput
is used as the top level key in the config file (this is required). Additional documentation on available fields in the configuration file is available here https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#TrainingInput.
The following articles provide additional documentation on training and deploying models with CloudML:
Hyperparameter Tuning explores how you can improve the performance of your models by running many trials with distinct hyperparameters (e.g. number and size of layers) to determine their optimal values.
Google Cloud Storage provides information on copying data between your local machine and Google Storage and also describes how to use data within Google Storage during training.
Deploying Models describes how to deploy trained models and generate predictions from them.


| Interface | Description |
|---|---|
| DatabaseManager.OnHabitEventsListener | |
| DatabaseManager.OnHabitsListener | |
| DatabaseManager.OnSaveListener | |
| DatabaseManager.OnUserDataListener | |
| LoginManager.OnLoginCompleteListener |
| Class | Description |
|---|---|
| AddHabitActivity |
The activity allows the user to add a habit
|
| AddHabitActivityTest | |
| AddHabitEventActivity |
The activity allows the user to add a habit event
|
| BuildConfig | |
| DatabaseManager |
The class for database
|
| EditHabitActivity |
The activity allows the user to edit a habit
|
| EditHabitEventActivity |
The activity allows the user to edit a habit event
|
| FeedActivity |
Feed activity in the main menu page
|
| FilterActivity |
Filter activity for filter page, could filter habits by name and comment
|
| FilterActivityTest | |
| FilterAdapter |
The defined filter layout adapter
|
| FilterAdapter.ViewHolder | |
| FollowingHabitActivity |
The activity for following a user
|
| FollowingHabitAdapter |
The defined adapter for the people layout
|
| FollowingHabitAdapter.ViewHolder | |
| FollowNotification |
The class for the follow notification
|
| FollowUserActivity |
The activity for follow a user
|
| Global |
Could be used in globally
|
| gpsActivity |
The gps activity for map activity
|
| gpsTracker |
Could track a location
|
| Habit |
The class for a habit, has all the properties for a habit
|
| HabitEvent |
The class for habit event, it has date and other properties for habit event
|
| HabitEventListAdapter | |
| HabitEventUnitTest | |
| HabitsAdapter |
The adapter for defined habit view
|
| HabitsAdapter.ViewHolder | |
| HabitStatsActivity |
The activity for habit statistic
|
| HabitUnitTest | |
| historyActivity |
The activity for history page
|
| LastCompleteActivity |
The activity for the the feed function, it shows the most recent completed activity
|
| LikeNotification |
The like notification for habitevent
|
| LoginActivityTest | |
| LoginManager |
Login manager could manage different users
|
| MainActivity |
The main activity for the app, could allow user to login or sign up
|
| MainActivityTest | |
| Manifest | |
| Manifest.permission | |
| Map |
The class map, it could add, remove, and get location
|
| MapActivity |
The activity for map, could show all habits events in the map
|
| MapUnitTest | |
| MarkDoneActivityTest | |
| MenuActivity |
The main menu page after user loged into the app, has six different options
|
| MenuActivityTest | |
| MyHabitActivity |
The activity for user's habit when user select it
|
| Notification |
The notification class for a signal notification
|
| NotificationActivity |
The activity for the notification, could send and receive a notification
|
| NotificationAdapter |
The defined notification adapter
|
| NotificationAdapter.ViewHolder | |
| PeopleActivity |
The activity for people option in the main menu
|
| PeopleAdapter |
The defined adapter for the people layout
|
| PeopleAdapter.ViewHolder | |
| R | |
| R.anim | |
| R.array | |
| R.attr | |
| R.bool | |
| R.color | |
| R.dimen | |
| R.drawable | |
| R.id | |
| R.integer | |
| R.interpolator | |
| R.layout | |
| R.mipmap | |
| R.string | |
| R.style | |
| R.styleable | |
| SignupActivity |
The activity for signup when user don't have an account
|
| SignUpActivityTest | |
| TodayActivity |
Today activity for today page
|
| TodayAdapter |
The defined adapter for today page layout
|
| TodayAdapter.ViewHolder | |
| User |
The class for a signal user, has all user's property, like name, and user's habit
|
| UserUnitTest | |
| ViewHabitActivityTest |


Get more resources
Number of vesselsEnvironment
You have acquired {{ over_vessel_credits }} more vessels than we would normally let you. This usually means that some of your donations have gone offline.
We won't remove the extra acquired vessels from your account, but you won't be able to acquire more vessels or renew your vessels until you remove or let expire {{ over_vessel_credits }} of them. (Or, of course, until more donations are made on behalf of your account or those donations that have gone offline come back online.)


--- layout: structure--pattern title: Distribute with Cards (Bottom Heavy) ---
The bottom-most items are larger.


" Skills are more important than the Attitude, then the Past, than Education, than Money, than Circumstances, than Appearance, than what people do or say "
Akash Singh
An aspiring lad from NIT HAMIRPUR always in the search of some new always wants to contribute to the society and make fame. Always wants to help others and beleives in shaping future without thinkng of the past. Cent percent loyal and dedicated towards his works, friends and well wishers.
" Skills are more important than the Attitude, then the Past, than Education, than Money, than Circumstances, than Appearance, than what people do or say "


New Website
Finally managed to finish my new website! I've been working hard on this new website since a few months now. So what's new you might ask? Pretty much everything.
- Migrated from Wordpress to Laravel
- Custom backend (which still needs some tweaking)
- Bootstrap 3
- Moved to Fortrabbit for hosting
- Much more
I use both static Markdown posts as well as posts which are saved in the database for my blog posts and static pages. This allows me to provide a way for people to contribute to blog posts over Github as well as keep drafts of unfinished posts in the database. The static posts and pages can be found in the posts/
and pages/
folders. Dayle Rees' flat file Markdown parser proofed to be perfect for the static Markdown posts.
For the blog posts itself I really wanted a clean approach which was focused on making the reading as easy as possible. Blog posts which aren't too wide, easy to use font, clear syntax highlighting for code blocks. And most of all: nothing else that gets in the way. Medium obviously was a big inspiration for this.
Now that my website is done I should have more time to spend on new blog posts. I hope to get a couple out soon. For starters, check out the blog post with tips I collected on setting up Laravel 4 on a shared host.
I've also started to use Wercker to handle my site's deployments. Pretty great deployment tool if you ask me.
There's probably still some quirks here and there so don't hesitate to let me know in the comments if you find something.
For more info check out the Colophon page I made. If you'd like to see the source code, you can view it on Github.
As always, thanks for reading :)


Professional Skills
Android
Android Studio, Java, JUnit, Espresso, MVP, Gradle, RxJava, Retrofit, Dagger, Butterknife
iOS
Xcode, Objective-C, Swift, XCTest, KIF, MVC, Cocoapods, AFNetworking, Alamofire
Web / Server
RubyMine, Ruby-on-Rails, HTML, Javascript (CoffeeScript, AngularJS), CSS, some Java Web (Spring)
Tools
Git, Continuous Integration (Jenkins), JIRA, Confluence
Other
Agile, Scrum, Extreme Programming, Object Oriented Programming, Architecture Designs


Introduction to Programming
Testing Web Applications
Testing Tools
Mobile/App Testing
Testing Principles
Elective courses
Object Oriented Programming I
Professional Software Testing
Software Test Automation
Structured Query Language
Cloud Application Development
Data Structures and Algorithms
BI Application Development


My Name is Ahmad Zaid, I have a becholer degree in industrial engineering from one of the top Univerities in
Saudi Arbia, King Saud University, I graduated in jun,2020.
I have multiple training course:
Smart Methods Company
I have training for 10 weeks in desging robots using Cinima 4D software
-
louna Al-sham company
I have 3 monthes training in luna Al-Sham in CNC machies for alaminum cutting.
- being a professional programming so that will give me an advantage as i have an engineering background
- have a degree in business administration
- handle a big projects in the future
Top 10 Universites in the World
- Massachusetts Institute of Technology (MIT)
- University of Oxford
- Stanford University
- University of Cambridge
- Harvard University
- California Institute of Technology (Caltech)
- Imperial College London
- ETH Zurich (Swiss Federal Institute of Technology)
- UCL (University College London)
- University of Chicago


PAPA KD's PIZZA PARLOR
What size pizza would you like?
Small (16" pie) -
$16
Medium (19" pie) -
$19
Large - (22" pie) -
$22
What base would you like for your pizza?
Olive Oil -
$0
Red Sauce -
$1
White Sauce -
$2
What toppings would you like to add?
Meat -
$3/each
Pepperoni
Sausage
Bacon
Veggies -
$1/each
Bell Peppers
Onions
Black Olives
Mushrooms
Pineapple
Tomatoes
Jalapeños
CLEAR
GET TOTAL
Total:
$0


A flexible, intuitive ToDo list app for keeping track of your cars' maintenance needs.
Built, programmed, and drove a robot that got the top seed and went undefeated at the VEX World Championships.
Open source C/C++ development for the VEX Cortex and V5 microcontrollers.
An assortment of VEX Robotics utilities, including an AI-driven PID autotuner.
Github |


Fully deployed website that maps locations of breweries using multiple API’s. Uses HTML5, Bootstrap, CSS3, Javascript, Jquery, and Firebase.
*NOTE* CORS extension must be enabled for the API to work. Please enable Allow-Control-Allow-Origin extension.
Food Truck Review Application for the RDU area. Technologies used include HTML5, Bootstrap, CSS3, Javascript, Node.JS, MySQL, Twitter API, and AWS for storing menus.
A full remote App based on React-Native. This app leverages the Spotify SDK plus Facebook and Google logins via Oauth which then allows a Client View to add songs to a play list that is then played via a Host View..


Ripple (XRP) Price Primed For Additional Losses, $0.2450 Next?
- Ripple price is following a downtrend below the $0.2700 support area against the US dollar.
- The price is currently trading near the $0.2620 support and it could continue to decline.
- This week’s crucial bearish trend line is active with resistance near $0.2700 on the hourly chart of the XRP/USD pair (data source from Kraken).
- Similarly, Ethereum is trading near a key support and it could break down below $180.
Ripple price is trading with a bearish bias below $0.2700 against the US Dollar, similar to bitcoin. XRP price remains at a risk of more losses below the $0.2620 and $0.2600 supports.
Ripple Price Analysis
In the past few days, ripple mostly followed a bearish pattern below the $0.2800 and $0.2750 supports against the US Dollar. Moreover, XRP/USD settled below the $0.2750 support area and the 100 hourly simple moving average.
The recent decline pushed the price below the $0.2700 support area. A new monthly low was formed near $0.2630 and the price is currently consolidating losses. The price corrected above the 23.6% Fib retracement level of the recent drop from the $.2749 high to $0.2630 low.
However, there are many resistances on the upside near the $0.2680 and $0.2700 levels. The first key resistance is near the $0.2690 level. Besides, the 50% Fib retracement level of the recent drop from the $.2749 high to $0.2630 low is near the $0.2690 level.
The main resistance is near the $0.2700 and $0.2720 levels. More importantly, this week’s crucial bearish trend line is active with resistance near $0.2700 on the hourly chart of the XRP/USD pair.
Above the trend line, ripple could struggle near the 100 hourly SMA and $0.2720. Therefore, a successful close above the $0.2720 resistance is needed for upside acceleration towards the $0.2800 level.
On the downside, an initial support is near the $0.2630 and $0.2620 levels. If there is a clear break below the $0.2620 support, the price may perhaps decline further below the $0.2600 and $0.2550 levels. In the mentioned case, the next major support is near the $0.2450 level, where the bulls might take a stand.
Looking at the chart, ripple price is facing an uphill task near the $0.2700 and $0.2720 levels. Unless the bulls manage to push the price above $0.27202, the bears remain in control. It seems like they are sighting a downside break below the $0.2600 and $0.2500 support levels in the near term.
Technical Indicators
Hourly MACD – The MACD for XRP/USD is currently gaining momentum in the bearish zone.
Hourly RSI (Relative Strength Index) – The RSI for XRP/USD is now well below the 50 level.
Major Support Levels – $0.2620, $0.2600 and $0.2500.
Major Resistance Levels – $0.2690, $0.2700 and $0.2720.


--- layout: post status: publish published: true title: "哭笑" author: Leask author_login: leask author_email<EMAIL_ADDRESS>author_url: https://leaskh.com wordpress_id: 414 wordpress_url: http://leaskh.wordpress.com/2006/11/11/%e5%93%ad%e7%ac%91 date: '2006-11-11 15:24:19 +0800' date_gmt: '2006-11-11 15:24:19 +0800' categories: - Life tags: [] comments: [] ---


The scene displayed at the galaxy view - handles the control over all systems, loading, and content management for the scene.
More...
|
| | GalaxyScene (EntityMap map, ContentManager content) |
| | Initializes a new instance of the T:MB2D.GalaxyScene class. Loads all resources and sets up the galaxy for generation. More...
|
| |
| override void | Initialize () |
| | Initializes the galaxy generation and background music - sets up the players ship and the collision bounds. More...
|
| |
| override void | HandleInput () |
| | Handles all input for the player ship and moves between galaxy and star system scenes if the player enters a star system. More...
|
| |
| override void | Update () |
| | Updates the galaxy view and fuel consumption if not loading. More...
|
| |
| override void | Draw (SpriteBatch spriteBatch, SpriteBatch uiSpriteBatch) |
| | Draw the game world and UI to the specified spriteBatch and uiSpriteBatch. More...
|
| |
| override void | Exit () |
| | Exit this scene instantly. More...
|
| |
| override void | Pause () |
| | Fades the sound out when transitioning to another scene unless the next scene is the star system scene. More...
|
| |
| override void | Resume () |
| | Resets the physics environment when returning to the scene and rebuilds the galaxy from its cache. More...
|
| |
| | Scene (EntityMap gameObjects, ContentManager content) |
| | Initializes a new instance of the T:MB2D.Scenes.Scene class with a pre-existing EntityMap More...
|
| |
|
void | UpdateTransition () |
| |
| void | Cleanup () |
| | Cleans up the scene and unloads content. More...
|
| |
The scene displayed at the galaxy view - handles the control over all systems, loading, and content management for the scene.
§ GalaxyScene()
| MidnightBlue.GalaxyScene.GalaxyScene |
( |
EntityMap |
map, |
|
|
ContentManager |
content |
|
) |
| |
|
inline |
Initializes a new instance of the T:MB2D.GalaxyScene class. Loads all resources and sets up the galaxy for generation.
- Parameters
-
| map | Game objects to use. |
| content | Content manager to use. |
§ Draw()
| override void MidnightBlue.GalaxyScene.Draw |
( |
SpriteBatch |
spriteBatch, |
|
|
SpriteBatch |
uiSpriteBatch |
|
) |
| |
|
inlinevirtual |
Draw the game world and UI to the specified spriteBatch and uiSpriteBatch.
- Parameters
-
| spriteBatch | Sprite batch for world-based entities. |
| uiSpriteBatch | User interface sprite batch. |
Implements MB2D.Scenes.Scene.
§ Exit()
| override void MidnightBlue.GalaxyScene.Exit |
( |
| ) |
|
|
inlinevirtual |
§ HandleInput()
| override void MidnightBlue.GalaxyScene.HandleInput |
( |
| ) |
|
|
inlinevirtual |
Handles all input for the player ship and moves between galaxy and star system scenes if the player enters a star system.
Implements MB2D.Scenes.Scene.
§ Initialize()
| override void MidnightBlue.GalaxyScene.Initialize |
( |
| ) |
|
|
inlinevirtual |
Initializes the galaxy generation and background music - sets up the players ship and the collision bounds.
Implements MB2D.Scenes.Scene.
§ Pause()
| override void MidnightBlue.GalaxyScene.Pause |
( |
| ) |
|
|
inlinevirtual |
Fades the sound out when transitioning to another scene unless the next scene is the star system scene.
Implements MB2D.Scenes.Scene.
§ Resume()
| override void MidnightBlue.GalaxyScene.Resume |
( |
| ) |
|
|
inlinevirtual |
Resets the physics environment when returning to the scene and rebuilds the galaxy from its cache.
Implements MB2D.Scenes.Scene.
§ Update()
| override void MidnightBlue.GalaxyScene.Update |
( |
| ) |
|
|
inlinevirtual |
Updates the galaxy view and fuel consumption if not loading.
Implements MB2D.Scenes.Scene.
The documentation for this class was generated from the following file:
- Shared/src/Game/Scenes/GalaxyScene.cs


Generate the main markup used from the datepicker This means that here we are generating input sliders for hours, minutes, months, dates and years and a radio input for swithing the time AM/PM Source: mtr-datepicker.js, line 177


Welcome! I'm excited to hear about your interest into the world of Computer Programming. At first glance, the amount of different coding languages and information available can be quite daunting. But have no fear! I will be offering you different explanations of a few different languages, and then I will ask you a few questions which will make it a little easier in deciding which is best for you.
While this might all seem a little overwhelming, I have good news! It ties back to the founding father of coding, Herbie Hancock. Herbie created the first THREE computer languages, but in trying to teach others, learned that it was overwhelming for the user to decide which was preferred. So, he enlisted the help of psychologist-extraordinairre, Good Will Hunting, to devise a simple yet efficient way to help in programming language selction! This is a modern-day version, of course.
The Zodiac signs you feel most compatible with are:
Your favorite seasons are:
Your favorite genres of movies are:
Your favorite household pets are:
Your favorite Ice cream flavors are:


There are 24 bananas in a basket. How many bananas are there in 32 baskets?
What is asked for in the problem? What operation are you going to use? How will you solve the given problem?
To solve the problem , you need to multiply 323 by 32. The number sentence is 32 x 323 = n . Study the solution below using the place value chart.
Step 1 : Multiply 3 hundreds , 2 tens , and 3 ones by 2 ones.
Step 2 : Multiply 3 hundreds , 2 tens and 3 ones by 3 tens.
Step 3 : add the partial products.
So there are 10 336 bananas in 32 baskets.


R/build-reference.R
By default, pkgdown will generate an index that simply lists all
the functions in alphabetical order. To override this, provide a
reference
section in your _pkgdown.yml
as described
below.
build_reference(pkg = ".", lazy = TRUE, examples = TRUE, run_dont_run = FALSE, mathjax = TRUE, seed = 1014, override = list(), preview = NA) build_reference_index(pkg = ".")
| pkg | Path to package. |
|---|---|
| lazy | If |
| examples | Run examples? |
| run_dont_run | Run examples that are surrounded in \dontrun? |
| mathjax | Use mathjax to render math symbols? |
| seed | Seed used to initialize so that random examples are reproducible. |
| override | An optional named list used to temporarily override
values in |
| preview | If |
To tweak the index page, you need a section called reference
which provides a list of sections containing, a title
, list of
contents
, and optional description
.
For example, the following code breaks up the functions in pkgdown into two groups:
reference: - title: Render components desc: Build each component of the site. contents: - starts_with("build_") - init_site - title: Templates contents: - render_page
Note that contents
can contain either a list of function names,
or if the functions in a section share a common prefix or suffix, you
can use starts_with("prefix")
and ends_with("suffix")
to
select them all. For more complex naming schemes you can use an aribrary
regular expression with matches("regexp")
. You can also use a leading
-
to exclude matches from a section. By default, these functions that
match multiple topics will exclude topics with keyword "internal". To
include, use (e.g.) starts_with("build_", internal = TRUE)
.
Alternatively, you can selected topics that contain specified concepts with
has_concept("blah")
. Concepts are not currently well-supported by
roxygen2, but may be useful if you write Rd files by hand.
pkgdown will check that all non-internal topics are included on this page, and will generate a warning if you have missed any.
You can optionally supply an icon for each help topic. To do so, you'll
need a top-level icons
directory. This should contain .png files
that are either 40x40 (for regular display) or 80x80 (if you want
retina display). Icons are matched to topics by aliases.
# This example illustrates some important output types # The following output should be wrapped over multiple lines a <- 1:100 a#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 #> [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 #> [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 #> [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 #> [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 #> [91] 91 92 93 94 95 96 97 98 99 100cat("This some text!\n")#> This some text!message("This is a message!")#>warning("This is a warning!")#> Warning: This is a warning!# This is a multi-line block { 1 + 2 2 + 2 }#> [1] 4# NOT RUN { stop("This is an error!", call. = FALSE) # }# This code won't generally be run by CRAN. But it # will be run by pkgdown b <- 10 a + b#> [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 #> [19] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 #> [37] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 #> [55] 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 #> [73] 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 #> [91] 101 102 103 104 105 106 107 108 109 110


{% include head.html %}
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{% include description.html %} {% include buttons-front.html %}
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{% include footer.html %}


Works Cited
Christies. “Samuel Morse with His Recorder.” Wikimedia Commons, upload.wikimedia.org/wikipedia/commons/thumb/5/53/Samuel_Morse_with_his_Recorder_by_Brady%2C_1857.png/275px-Samuel_Morse_with_his_Recorder_by_Brady%2C_1857.png. Accessed 23 May 2017.
“Electromagnetic Telegraph.” Edubilla, uploads.edubilla.com/inventions/39/38/electromagnetictelegraph2.png. Accessed 23 May 2017.
Gardner, Alexander. “Plate 73. U.S. Military Telegraph Battery Wagon.” National Museum of American History, Smithsonian, americanhistory.si.edu/collections/search/object/nmah_1294290. Accessed 22 May 2017.
Hochfelder, David. “The Telegraph.” Essential Civil War Curriculum, Virginia Tech, www.essentialcivilwarcurriculum.com/the-telegraph.html. Accessed 22 May 2017.
Hydrargyrum. “SOS Morse Code.” Wikimedia Commons.
Ilikelegos. “Morse Code (100% Pen).” Scratch, MIT, 11 Feb. 2017, scratch.mit.edu/projects/144359518/. Accessed 23 May 2017.
“International Morse Code.” The Art of Manliness, content.artofmanliness.com/uploads/2008/10/morse-code.jpg. Accessed 22 May 2017.
Kent, Robert. “Calculating Morse Code Speed.” KENT Morse Keys, R. A. Kent Engineers, www.kent-engineers.com/codespeed.htm. Accessed 22 May 2017.
“Map of U.S. Military Telegraph Lines, 1885.” U.S. Army Center Of Military History, www.history.army.mil/books/30-17/notes/53.jpg. Accessed 23 May 2017.
Mountjoy, Shane. Technology and the Civil War. New York, Chelsea House Publishers, 2009.
Phillips, Stephen C. “International Morse Code.” Stephen C Phillips, morsecode.scphillips.com/morse.html. Accessed 22 May 2017.
Sauerberg, Jim. “Route Ciphers in the Civil War.” Mathematics Awareness Month, JPBM, www.mathaware.org/mam/06/Sauerberg_route-essay.html. Accessed 22 May 2017.
“The US Civil War, the First Modern War.” Æ Aeragon, www.aeragon.com/03/#telegraph. Accessed 22 May 2017.
“Telegraph Wagon.” Jalopnik, Gizmodo Media, i.kinja-img.com/gawker-media/image/upload/s--PRpQ-iPc--/c_scale,f_auto,fl_progressive,q_80,w_800/18bw9gbihsu8djpg.jpg. Accessed 23 May 2017.
“Western Union 22-A Telegraph Relay.” Telegraph, Scientific Instrument & Radio Museums, w1tp.com/2582.jpg. Accessed 23 May 2017.


--- layout: post title: "Quarkus on OpenShift: Getting rid of succesful liveness and readyness logs" date: 2021-06-14 07:00:00 author: "Bennet Schulz" tags: - Java - Quarkus - OpenShift - Kubernetes ---
It's been a while since my last blog post and I think it's time to write a new one. Some days ago we had the situation of too many useless successful live- and readyness check messages in our application logs as well as in Kibana and we didn't want to filter these logs afterwards in Kibana, so we decided to implement a filter in our application to get rid of these logs as early as possible and log errors, only. There isn't much in the web about it, so I decided to write this blogpost. Maybe it's useful for others as well.
In our OpenShift Logs there are tons of log messages like these:
<IP_ADDRESS> - - 09/Jun/2021:16:07:07 +0200 "GET /q/health HTTP/1.1" 200 46 "-" "Mozilla/5.0 (X11; Fedora; Linux x86_64; rv:88.0) Gecko/20100101 Firefox/88.0
As much as I like that the application is healthy, messages like these are not useful. They blow up the log files and you always have to filter these messages in Kibana to see the really interesting logs.
That's why we want to filter these messages.
First you have to add three entries to your application.properties
file:
The first entry enables the Quarkus internal access logging, the second one declares the name of the logging category which is the same as the loggers name and the last entry is a standard format which is also used by Apache Tomcat (see).
By doing this, the application starts printing access logs. You can test it by calling the /health
endpoint of your application.
After enabling the general access logging we have to add the filter itself. This can be done by adding a filter to the existing
logging category. The name of the logger has to be the same as declared in the applications.properties file.
As you can see here in the initAccessLogFilter()
method, we get the Logger with the same name
as the quarkus.http.access-log.category=access_log
entry in the applications.properties
file.
In the next step we set a filter which filters every log entry with a http status code of 200 and containing the health, health/live, health/ready or metrics endpoint.
By adding this handler you can get rid of these non helpful successful access log entries. Unfortunately you don't find much in the web to do this, so I wanted to write this blog post.
Hope this blog post is useful for someone and you learned something new :-)
Bye,
Bennet


To get started using the "jim" stack to create a static web site, you will need to:
npm install
or npm update
in the root of the project.modules\config.js
as appropriate.templates
directory.img
directory, scripts in js
, stylesheets in css
,
and "root assets" (files to be directly copied to the root of the output
folder, such as favicon.ico
)
in rootassets
.Write some content in the posts
and/or pages
directories (feel free to delete this post).
You can use GitHub-Flavored Markdown
or HTML or both for formatting content. The main differences between posts and pages are:
sitemap.xml
and atom.xml
are implemented as pages for this reason.Run iojs process
in the root of the project. The results will be in the output
directory.
See the second answer (and its comments) to this StackOverflow question for how metadata is added to Markdown using nothing but Markdown-legal syntax (and hence no need for a preprocessor). Basically, you set metadata like this:
[author]: # (Jim Lehmer)
[title]: # (Getting started)
[created]: # (2015-04-13)
[tags]: # (jade iojs markdown)
output
directory (yes,
that could get unwieldy at thousands of posts - I am not worried about it for now).post.jade
template is used.
Note that this means you can create a "fake" page or post and use that solely to drive a template
getting rendered (e.g., the atom.md
page is a placeholder file that does nothing more than
cause the atom.jade
template to be used to render atom.xml
).


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Scan me to join!
Open Firefox Developer Edition
Tools -> WebIDE
Click "Remote Runtime"
Enter "<IP_ADDRESS>:52222"
Click to join!
Click to take snapshot!


Personal stories and visuals are powerful. They illustrate in a way abstract ideas can't demonstrate the value of openness. Our stories are generated by real people trying access real research, and try to demonstrate how we'd benefit from making research open.
Our stories, requests and maps are embedable and searchable. You can search requests here. On each request page you will find code to paste into a HTML editor to display your request.
The Open Access Button needs fantastic resources for engaging authors around Open Access to be successful. We're compiling resources we use below. If we've missed a resource, let us know.
The 2002 Budapest Open Access Initiative outlines the principles of Open Access, and defines Open Access as the free availability and full re-use of research articles.
Read the BOAIThis video by PhD Comics with narration by Nick Shockey and Jonathan Eisen provides an illustrated explanation of Open Access.
Watch the VideoOpen Access publications that are re-usable typically utilise a Creative Commons license.
Find out moreOur friends at SPARC have put together a clear, concise factsheet explaining Open Access.
Get the factsheetOpen means anyone can freely access, use, modify, and share for any purpose (subject, at most, to requirements that preserve provenance and openness).
Read the definitionThe Panton Principles are a set of recommendations that address how best to make published data from scientific studies available for re-use. In this context, "published" means "made public" and is not restricted to formal publication in the scholarly literature.
ReadOur friends at SPARC have put together a clear, concise factsheet explaining Open Data.
Get the factsheetFigshare is an online digital repository where researchers can preserve and share their research outputs, including figures, datasets, images, and videos. It is free to upload content and free to access.
Check it outZenodo is an open dependable home for the long-tail of science, enabling researchers to share and preserve any research outputs in any size, any format and from any science.
Check it outSHERPA/RoMEO is a resource that lists copyright and archiving policies for journals. Use it to learn if you're able to archive your research.
Check it outThe DOAJ is an online directory that lists nearly 9,000 highly quality, peer reviewed Open Access journals.
Check it outThis handbook provides concrete information on how to produce open data.
Read theFrom increased access and visibility to higher citation counts Open Access benefits researchers and readers.
Learn moreAcross all fields, Open Access articles in repositories have a higher citation rate than non-Open Access articles.
Read moreSHERPA/JULIET is a resource that lists the Open Access policies of funders. Discover if your funder requires Open Access.
Check it out10 steps you can take to make sure you contribute to a culture shift towards open science.
Find out moreScrutinising arguments expressing concerns against Open Data.
Read theIf you don't share your data, you are breaking professional standards in research, and are thus committing scientific misconduct.
Read more"In order to tap into the vast potential that is attributed to academic data sharing we need to forge new policies that follow the guiding principle reputation instead of obligation".
Read moreThere is "a statistically well-supported citation benefit from open data".
Check the figuresIf you don't share your data, you are breaking professional standards in research, and are thus committing scientific misconduct
Read more"Datasets should be deposited in an appropriate, recognised, publicly available repository. Where no data-specific repository exists, authors should deposit their datasets in a general repository such as Dryad or Figshare, or include it in the supplementary material."
Read the PolicyThis checklist is designed to help you understand what someone outside your research project (or you in 5-10 years) would need to know about your data in order to build on your work.
Check the list