Getting The Most Out of HashMaps
10 Jan 2017HashMap
is perhaps the most popular implementation of the Map
interface in Java Collections Framework (JCF). If you want to fully understand the internal workings of HashMap
, the best tutorial I have found online is here.
For this article, we are only going to focus on the performance specific concepts. HashMap
offers us a very fast, well-optimized data structure solution for many problem scenarios.
Good News
HashMap
is a great advantage for us because it is so versatile and fits in a lot of use cases:
- Want to load objects from an SQL result set and marshal them over the network? use a
HashMap
. - Want to group some log file content according to a certain field?
HashMap
is available. - Want to move some arbitrary data around without the restriction of creating a POJO or DTO? Oh! Hail!
HashMap
. What can’t you do withHashMap
, plus the API is extremely easy to use.
Bad News
The caveat is that, it is easy to use HashMap
in Java but not exactly easy to use it optimally or efficiently if you may. A few years back, when I was new to Java and programming in general, we were building a school information system.
We would query some SQL database somewhere and then need to marshal the objects over the network using RPC. I can still recall the excitement I experienced seeing HashMap
at work:
Test data would average around a thousand records and we would not realize any performance dip. Awesome feat right? WRONG.
Even without any test, a seasoned developer can look at the above code and immediately become uncomfortable — the rat smell is too strong. The happy go lucky developer, like myself a few years ago, would not lose their smile though.
If you are among the less comfortable with the above code, feel free to stop reading at this point. However, if you are like me a few years ago and are very comfortable with the above code, endeavor to read the post up to the end and you will surely gain the competence to refactor my code for me.
Initial Capacity
HashMap
uses an array as its primary storage. Every put
operation inserts a Key-Value pair at an index in this array, and every get operation retrieves a Key-Value pair from an index of this array.
Capacity refers to the size of this underlying array. Initial capacity is, therefore, the size of this array when the HashMap is created:
The above code resides inside java.util.HashMap
class. The above value(1<<4 // 16
) is used as the initial capacity of the hashmap if we initialize it the way I did:
Now that the hashmap has an initial capacity of 16 and my records run in the hundreds:
What happens when the number of elements being added to the underlying array are exhausting the initial capacity?
It so happens that HashMap is a self-regulating data structure. It has an internal mechanism for detecting when its current capacity is being exhausted and resizing its internal array.
What happens is that when the number of entries is found to have reached a certain threshold, a new array is created with twice the size of the previous one and all the entries are transferred from the old array to the new array. A process called rehashing.
Rehashing is quite an expensive process and should be mercilessly minimized during design, or even totally avoided if possible.
The HashMap
class contains another constructor that enable us to pass in a custom value for initial capacity. In the bad example that we saw in the Bad News section, we could simply have anticipated the size of the ResultSet and used that to create a well-dimensioned hashmap, say we expect about 1,000 records:
With the above refactored code, we would have saved ourselves at least six rehashing operations. Wondering how? Just picture that every time the number of entries reaches the current capacity, a rehashing operation doubles the capacity, starting from 16, then 32, 64, up until 1024.
The above explanation suffices, for now, however, we will realize later that the decision to rehash is a little more complicated than this.
The take-home point here is: Choose an initial capacity high enough to minimize the frequency of rehashing.
Collision
As a simple reminder to ourselves. HashMap
works on the principle of hashing. For reasons beyond the scope of this article, hash code collisions are a common occurrence, where different keys end up at the same array index. More about this can be found in the articles linked at the beginning of this post.
It sounds like an anomaly, but it is quite normal, specifically because of the equals
and hashCode
contract of java.lang.Object
, the mother of all objects in Java.
It suffices to only know that where there are collisions, HashMap performance is degraded.
When a collision occurs at a given array index, instead of a K-V pair being stored there, a linked list is created in place and the colliding entries are chained together in this list.
This way, if another entry hashes to the same location, each element of the linked list is compared with the new entry. If a match is found, the match is replaced, but if none is found, the new entry is placed at the head of the list.
Something similar happens during retrieval. When the key in hashMap.get(key);
hashes to an index that has collisions, the key is compared to all other keys in the linked list, if a match is found, the matched key’s value is returned, but if no match is found, null is returned.
One of the strengths of a hashmap is lightning speed storage (put) and retrieval (get) operations. In more technical terms, they occur in constant time, O(1)
. This means that regardless of the size of the hashmap or its capacity, a put or a get will return instantly.
However, in the event of collisions, we can no longer guarantee O(1)
performance. Because the performance will now be affected by the length of the linked list at a location where there is collision.
Specifically, the time to retrieve an entry will be the constant time to hash to the array index, then the linear time O(n) to iterate over the linked list, where n
is the size of the linked list.
We can not do much about this problem, apart from ensuring that the hashmap has a big enough capacity to minimize collisions and if possible, the hash function is good enough to disperse entries with equal distribution across the array.
Load Factor
Load Factor, abbreviated as LF, is the ratio of the hashmap’s size to its capacity. It is a float between 0 and 1 and the default value is 0.75f
:
The LF is the ratio of the hashmap size (number of entries stored so far) to capacity (total array length regardless of entries) at which a rehash operation occurs. The default LF of 0.75 implies that if our hashmap is initialized with 100 initial capacity, then as soon as we make 75 entries in
The default LF of 0.75 implies that if our hashmap is initialized with 100 initial capacity, then we can make 75 entries in it comfortably. The 76th entry will trigger a rehash. We can use a custom LF by creating hashmap with another constructor which takes LF along side initial capacity:
Through some tests and calculations, it can be proved that in the case of collisions, the average number size of the linked lists in the array is about the same as the LF. We have already seen that collision degrades the performance of hashmap in most operation.
This can only mean that when LF which is too high, it will degrade performance due to its relationship with the linked list size. When LF is too low, it will also degrade performance due to frequency of rehashes. However, 0.75f offers a good optimum value.
Iteration Performance
Iterating over a hashmap is affected by its size and capacity. Read the preceding sentence again and again until you think you have understood and then realize it’s very confusing and then accept that you need more explanation!
The time complexity of hashmap iteration is O(n) where n= capacity + size. This implies that if the hashmap’s initial capacity is 100 and it only has a single entry so far, iteration will still levy a worst case performance time of O(n) where n=101.
To picture this, we need to remember that iteration starts from the beginning of the array and proceeds sequentially to its end. There is no search efficiency in hashmap as there is in binary search trees.
It is possible that the value being searched is close to index 0 in the array, in which case the time will be a fraction of worst case run time. However, as computer scientists, we are better off planning for the worst case scenario.
We also need to remember how hashing works. The first entry in the map may hash to index 95 in the array and the second to index 34, etc. There is absolutely no order.
It is now obvious that a high initial capacity will result in poor iteration performance. This means that we should choose a high initial capacity where we know there will be a lot of entries but little iteration needed.
As we saw in the previous section, LF is approximately the average length of linked lists in the hashmap, so a low LF is a good thing for iteration performance.
Conclusion
In this article, we have explored HashMap class and its internal implementation details that are critical to performance. One should now be able to refactor the initial Bad News snippet to pass as optimized code. Afterall initial capacity is just one of the several implementation details that we have seen in this post.
It is very important to understand these intricacies so that we can become better designers and decision makers.


--- title: "AUC and accuracy considered harmful to model selection" author: "Einar" date: 2017-08-24 tags: ["auc", "model_selection", "prediction", "logistic_regression", "simulation", "brier score"] math: true ---
This post had bugs that messed with my build process and I can’t be bothered fixing it.


[% MACRO stable_id_mapping BLOCK %]
Stable ID Mapping
Of the
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%]%)
received their stable id by mapping an existing one from the old regulatory build and
-
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Of the
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were found in the new build and their stable ids were transferred.
-
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%]%) were lost.
[% END %]


{% extends 'annotationweb/one_column_layout.html' %} {% block content %}
Go back to dataset - Add an image sequence - Delete subject
| Format | Nr. of frames | Nr. of annotations | Delete |
|---|---|---|---|
| {{ sequence.format }} | {{ sequence.nr_of_frames }} | {{ sequence.imageannotation_set.count }} | Delete |


| Name | Description |
|---|---|
| Tank_adiabaticWall | Tank with wall |
| Tank_exchangingWall | Tank with wall |
Tank with wall
Extends from Interfaces.Water.PartialTwoPort_water (Partial component with two ports).
| Type | Name | Default | Description |
|---|---|---|---|
| Volume | V | 1 | max contained water volume [m3] |
| Volume | Vstart | 0.001 | initial water volume [m3] |
| Area | S | 1 | exchanging surface (inner and outer) [m2] |
| Area | Sb | 0.2 | base area for level/pressure [m2] |
| Temperature | Tstart | 273.15 + 25 | initial temp (fluid and metal) [K] |
| Pressure | pin | 0 | mean pressure at the flanges(used in case of a boiler) [Pa] |
| Type | Name | Description |
|---|---|---|
| WaterFlange | water_flange2 | |
| WaterFlange | water_flange1 | |
| HeatPort | fluid | |
| output RealOutput | oM | Mass of water |
| output RealOutput | oT | Temperature of water |
Tank with wall
Extends from Interfaces.Water.PartialTwoPort_water (Partial component with two ports).
| Type | Name | Default | Description |
|---|---|---|---|
| Volume | V | 1 | max contained water volume [m3] |
| Volume | Vstart | 0.001 | initial water volume [m3] |
| Area | S | 1 | exchanging surface (inner and outer) [m2] |
| Area | Sb | 0.2 | base area for level/pressure BEFORE Sb=0.01 [m2] |
| Mass | Mm | 10 | metal mass [kg] |
| SpecificHeatCapacity | cm | 500 | metal specific heat [J/(kg.K)] |
| CoefficientOfHeatTransfer | gwm | 5 | heat transfer coeff (fluid/metal) [W/(m2.K)] |
| Temperature | Tstart | 273.15 + 25 | initial temp (fluid and metal) [K] |
| Pressure | pin | 0 | mean pressure at the flanges(used in case of a boiler) [Pa] |
| Type | Name | Description |
|---|---|---|
| WaterFlange | water_flange2 | |
| WaterFlange | water_flange1 | |
| HeatPort | fluid | |
| HeatPort | wall | |
| output RealOutput | oM | Mass of water |
| output RealOutput | oT | Temperature of water |


Extremoduro are a Spanish hard rock band from Plasencia, Extremadura. The band is led by Roberto Iniesta, also known as "Robe", who is often referred as "El rey de Extremadura" (The king of Extremadura). The name Extremoduro is a pun on the band's home region in western Spain, Extremadura, and roughly translates as "extremely tough".
The band was created in 1987 and is regarded as one of the most influential bands of the Spanish hard rock scene, besides being a complete symbol for their region.
Well-known songs by Extremoduro include "So payaso", "Golfa", "Salir", "Extremaydura", "Puta" and "Jesucristo García". The highest moment in the band's career was probably reached with the release of their album Agila, which was awarded a prize for best video in the first edition of the Spanish Music Awards in 1997.
It is considered one of the best Spanish bands ever by several music magazines. They were ranked number 6 on Rolling Stone's "50 Greatest Spanish rock bands".
Extremoduro are a Spanish hard rock band from Plasencia, Extremadura. The band is led by Roberto Iniesta, also known as "Robe", who is often referred as "El rey de Extremadura" (The king of Extremadura). The name Extremoduro is a pun on the band's home region in western Spain, Extremadura, and roughly translates as "extremely tough".
The band was created in 1987 and is regarded as one of the most influential bands of the Spanish hard rock scene, besides being a complete symbol for their region.
Well-known songs by Extremoduro include "So payaso", "Golfa", "Salir", "Extremaydura", "Puta" and "Jesucristo García". The highest moment in the band's career was probably reached with the release of their album Agila, which was awarded a prize for best video in the first edition of the Spanish Music Awards in 1997.
It is considered one of the best Spanish bands ever by several music magazines. They were ranked number 6 on Rolling Stone's "50 Greatest Spanish rock bands".


Based on
Gridcannon
by Tom Francis.
Check link for instructions.
options
v1.0.5
X
OPTIONS
Display
Highlight playable cards
Show help
Show hover tips
Show animations
Rules
Maximum value card:
10
9
8
Number of Joker pairs:
2
1
0
Number of Blue Jokers:
2
1
0
While building, place cards
anywhere
in order
While building, skip
center
nothing
Place enemies
anywhere
near most similar card
When adding cards to deck,
don't shuffle
shuffle new cards
shuffle whole deck
On pile reset, jokers
remain
are removed
On pile reset, aces
remain
are removed
Jokers try to go
anywhere
to same color
Aces try to go
anywhere
to same color
to same suit
Attacks
can remove armor
must kill enemy
Appearance
Deck style:
Type 1
Type 2
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Automation
Auto-draw from main deck
Auto-build, if possible
Tap 'space' to make a forced move
Start New Game
Apply Changes


{% load static %}
Enter Title.
Which subjects/topics your study/training was about?
Where did you study/get Training?
Why did you choose this field(Background/Initial Motivation)?
How did you get the Fianancial Assistance(Family Conditions/Scholorships)?
How was the training/study/experience?
What did you gain from this activity(skill set) & its impact on your current/future life?
Satisfaction & suggestions for others.


Contrary to the announcement this morning, you should start to use safari with live stream and your login is your computer login, for example if the login is usr.name and your password is 732y4927q then that is your safari login.


{# This is an example home page template. It inherits and extends a layout template that lives in the top-level views/ folder for convenience #} {% extends "layout.html" %} {% block main %}
{{ data.user.title | string }} ({{ data.user.email | string }})
By accessing these files you agree to the following Terms and Conditions and Privacy Policy.
There are no videos available
{% endfor %}


class: center, middle, inverse, title-slide # Lecture 19 ## Introduction to Machine Learning ### Tyler Ransom ### ECON 6343, University of Oklahoma --- # Attribution Today's material is based on the two main Statistical Learning textbooks: .pull-left[ .center[  .small[James, Witten, Hastie, and Tibshirani (2013)] ] ] .pull-right[ .center[  .small[Hastie, Tibshirani, and Friedman (2009)] ] ] --- # Plan for the Day 1. What is Machine Learning (ML)? 2. Broad overview 3. How to do ML in Julia Next time, we'll discuss how ML is being used with econometrics --- # Machine Learning and Artifical Intelligence (AI) - .hi[Machine learning (ML):] Allowing computers to learn for themselves without explicitly being programmed - USPS: Computer to read handwriting on envelopes - Google: AlphaGo Zero, computer that defeated world champion Go player - Apple/Amazon/Microsoft: Siri, Alexa, Cortana voice assistants - .hi[Artificial intelligence (AI):] Constructing machines (robots, computers) to think and act like human beings - ML is a subset of AI --- # Prediction vs. Inference - Prediction and Inference are the two main reasons for analyzing data - .hi[Prediction:] We simply want to obtain `\(\hat{Y}\)` given the `\(X\)`'s - .hi[Inference:] Understanding how changing the `\(X\)`'s will change `\(Y\)` - Three types of inference, according to [Andrew Gelman](https://andrewgelman.com/2018/08/18/fallacy-excluded-middle-statistical-philosophy-edition/) 1. Generalizing from sample to population (statistical inference) 2. Generalizing from control state to treatment state (causal inference) 3. Generalizing from observed measurements to underlying constructs of interest - Philosophically, these can each be framed as prediction problems --- # Inference as Prediction - How can each type of inference be framed as a prediction problem? - Statistical inference: - Predict what `\(Y\)` (or `\(\beta\)`) would be in a different sample - Causal inference: - Predict what `\(Y\)` would be if we switched each person's treatment status - Measurement quality: - Predict what `\(Y\)` would be if we could better measure it (or the `\(X\)`'s) - e.g. personal rating in Harvard admissions (what does it measure?) --- # Vocabulary Econometrics and Machine Learning use different words for the same objects .pull-left[ .hi[Econometrics] - Dependent variable - Covariate - Observation - Objective function - Estimation ] .pull-right[ .hi[Machine Learning] - Target variable - Feature - Example (Instance) - Cost function (Loss function) - Training (Learning) ] --- # The goal of machine learning Machine learning is all about automating two hand-in-hand processes: 1. Model selection - What should the specification be? 2. Model validation - Does the model generalize to other (similar) contexts? - Want to automate these processes to maximize predictive accuracy - As defined by some cost function - This is .hi[different] than the goal of econometrics! (causal inference) --- # Training, validation and test data - In econometrics, we typically use the entire data set for estimation - In ML, we assess out-of-sample performance, so we should hold out some data - Some held-out data is used for validating the model, and some to test the model - Data used in estimation is referred to as .hi[training data] (60%-70% of sample) - Data we use to test performance is called .hi[test data] (10%-20%) - Data we use to cross-validate our model is called .hi[validation data] (10%-20%) - Division of training/validation/test sets should be .hi[random] --- # Model complexity and the bias/variance tradeoff - There is a trade-off between bias and variance `\begin{align*} \underbrace{\mathbb{E}\left(y-\hat{f}(x)\right)^2}_{\text{Expected }MSE\text{ in test set}} &= \underbrace{\mathbb{V}\left(\hat{f}(x)\right)}_{\text{Variance}} + \underbrace{\left[\text{Bias}\left(\hat{f}(x)\right)\right]^2}_{\text{Squared Bias}} + \underbrace{\mathbb{V}\left(\epsilon\right)}_{\text{Irreducible Error}} \end{align*}` - A model with high .hi[bias] is a poor approximation of reality - A model with high .hi[variance] is one that does not generalize well to a new data set - A model is .hi[overfit] if it has low bias and high variance - A model is .hi[underfit] if it has high bias and low variance --- # Visualizing the bias/variance tradeoff .center[![bias-variance](BVtradeoff.png)] - Optimal model complexity is at the minimum of the red line - Irreducible error means that the red line can't go below a certain level - maybe at 0.3 in this picture? - Image source: Hastie, Tibshirani, and Friedman (2009) --- # Continuous vs. categorical target variables - When the target variable is continuous, we use `\(MSE\)` to measure fit - When it is categorical, we use .hi[accuracy] or similar measures - or some combination of specificity and sensitivity - goal is to not have a good "fit" by randomly guessing - so each potential metric penalizes random guessing --- # Different types of learning algorithms - Just like in econometrics, the most basic "learning algorithm" is OLS - Or, if `\(Y\)` is categorical, logistic regression - But, we know there are many other ways to estimate models - e.g. non-parametric, semi-parametric, Bayesian, ... --- # Supervised and unsupervised learning - .hi[Supervised learning:] we predict `\(Y\)` from a set of `\(X\)`'s - .hi[Unsupervised learning:] we try to group observations by their `\(X\)`'s - Most of econometrics is about supervised learning (i.e. estimate `\(\hat{\beta}\)`) - But there are some elements of unsupervised learning - Particularly with regards to detecting unobserved heterogeneity types - e.g. factor analysis (detect types based on a set of measurements) - e.g. the EM algorithm (detect types based on serial correlation of residuals) --- # Supervised learning algorithms Examples of supervised learning algorithms: - Tree models - Basically a fully non-parametric bin estimator - Can generalize to "forests" that average over many "trees" - Neural networks - Model the human brain's system of axons and dendrites - "Input layer" is the `\(X\)`'s, "Output layer" is `\(Y\)` - "Hidden layers" nonlinearly map the input and output layers - Mapping ends up looking like a logit of logit of logits --- # Supervised learning algorithms (continued) - Bayesian models - Bayes' rule can be thought of as a learning algorithm - Use it to update one's prior - Support Vector Machine (SVM) - Originally developed for classification - Tries to divide 0s and 1s by as large of a margin as possible - Based on representing examples as points in space - Generalization of the maximal margin classifier --- # Unsupervised learning algorithms - We covered the EM algorithm and PCA in previous lectures - `\(k\)`-means clustering - Attempts to group observations together based on the `\(X\)`'s - Choose cluster labels to minimize difference in `\(X\)`'s among labeled observations `\begin{align*} & \min_{C_1,\ldots,C_K} \sum_{k=1}^K \frac{1}{N_k}\sum_{i\in C_k}\sum_{\ell=1}^L\left(x_{i\ell}-\overline{x}_{\ell j}\right)^2 \\ \end{align*}` `\(N_k\)` is the number of observations in cluster `\(k\)`, `\(L\)` is number of `\(X\)`'s - Can choose other metrics besides Euclidean distance --- # Active learning algorithms - .hi[Active learning:] algorithm chooses the next example it wants a label for - Balances "exploration" and "exploitation" - Two common examples of active learning: 1. .hi[Reinforcement learning] powers the world-beating chess engines - These algorithms use dynamic programming methods - Use Conditional Choice Probabilities for computational gains 2. .hi[Recommender systems] power social networks, streaming services, etc. --- # Back to the bias-variance tradeoff - The Bias-Variance Tradeoff applies to supervised learning - How do we ensure that our model is not overly complex (i.e. overfit)? - The answer is to penalize complexity - .hi[Regularization] is the way we penalize complexity - .hi[Cross-validation] is the way that we choose the optimal level of regularization --- # How cross-validation works (Adams, 2018) .center[![cross-validation](CVdiagram.jpg)] - Blue is the data that we use to estimate the model's parameters - We randomly hold out `\(K\)` portions of this data one-at-a-time (Green boxes) - We assess the performance of the model in the Green data - This tells us the optimal complexity (by "hyperparameters" if CV is automated) --- # Types of regularization - Regularization is algorithm-specific - in tree models, complexity is the number of "leaves" on the tree - in linear models, complexity is the number of covariates - in neural networks, complexity is the number/mapping of hidden layers - in Bayesian approaches, priors act as regularization - Whatever our algorithm, we can tune the complexity parameters using CV --- # Regularization of linear-in-parameters models There are three main types of regularization for linear-in-parameters models: 1. `\(L0\)` regularization (Subset selection) 2. `\(L1\)` regularization (LASSO) 3. `\(L2\)` regularization (Ridge) --- # `\(L0\)` regularization - Suppose you have `\(L\)` `\(X\)`'s you may want to include in your model - Subset selection automatically chooses which ones to include - This is an automated version of what is traditionally done in econometrics - Can use Adjusted `\(R^2\)` to penalize complexity - or AIC, BIC, or a penalized SSR - Algorithm either starts from 0 `\(X\)`'s and moves forward - Or it starts from the full set of `\(X\)`'s and works backward - But this won't work if `\(L>N\)`! (i.e. there are more `\(X\)`'s than observations) --- # `\(L1\)` and `\(L2\)` regularization - Consider two different penalized versions of the OLS model: `\begin{align*} &\min_{\beta} \sum_i \left(y_i - x_i'\beta\right)^2 + \lambda\sum_k \vert\beta_k\vert & \text{(LASSO)} \\ &\min_{\beta} \sum_i \left(y_i - x_i'\beta\right)^2 + \lambda\sum_k \beta_k^2 & \text{(Ridge)} \end{align*}` - .hi[LASSO:] Least Absolute Shrinkage and Selection Operator - sets some `\(\beta\)`'s to be 0, others to be attenuated in magnitude - .hi[Ridge:] - sets each `\(\beta\)` to be attenuated in magnitude --- # `\(L1\)` and `\(L2\)` regularization (continued) - We want to choose `\(\lambda\)` to optimize the bias-variance tradeoff - We choose `\(\lambda\)` by `\(k\)`-fold Cross Validation - We can also employ a weighted average of `\(L1\)` and `\(L2\)`, known as .hi[elastic net] `\begin{align*} &\min_{\beta} \sum_i \left(y_i - x_i'\beta\right)^2 + \lambda_1\sum_k \vert\beta_k\vert + \lambda_2\sum_k \beta_k^2 \\ \end{align*}` where we choose `\((\lambda_1,\lambda_2)\)` by cross-validation - `\(L1\)` and `\(L2\)` are excellent for problems where `\(L>N\)` (more `\(X\)`'s than observations) - We can apply `\(L1\)` and `\(L2\)` to other problems (logit, neural network, etc.) --- # How to estimate ML models - R, Python and Julia all have excellent ML libraries - Each language also has a "meta" ML library - [`mlr3`](https://mlr3.mlr-org.com/) (R), [`scikit-learn`](https://scikit-learn.org/stable/) (Python), [`MLJ.jl`](https://alan-turing-institute.github.io/MLJ.jl/dev/) (Julia) - In these libraries, the user specifies `\(Y\)` and `\(X\)` - With only a slight change in code, can estimate a completely different ML model - e.g. go from a logit to a tree model with minimal code changes - e.g. choose values of tuning parameters by `\(k\)`-fold CV - I'll go through a quick example with `MLJ.jl` --- # `MLJ` example After installing the required packages: ```julia using MLJ, Tables, DataFrames, MLJDecisionTreeInterface, MLJLinearModels models() ``` will list all of the models that can interface with `MLJ`: ```julia 151-element Array{NamedTuple{(:name, :package_name, :is_supervised, :docstring, :hyperparameter_ranges, :hyperparameter_types, :hyperparameters, :implemented_methods, :is_pure_julia, :is_wrapper, :load_path, :package_license, :package_url, :package_uuid, :prediction_type, :supports_online, :supports_weights, :input_scitype, :target_scitype, :output_scitype),T} where T<:Tuple,1}: (name = ARDRegressor, package_name = ScikitLearn, ... ) (name = AdaBoostClassifier, package_name = ScikitLearn, ... ) (name = AdaBoostRegressor, package_name = ScikitLearn, ... ) ⋮ (name = XGBoostClassifier, package_name = XGBoost, ... ) (name = XGBoostCount, package_name = XGBoost, ... ) (name = XGBoostRegressor, package_name = XGBoost, ... ) ``` --- # `MLJ` example (continued) .scroll-box-18[ ```julia # use house price data from US Census Bureau df = OpenML.load(574) |> DataFrame X = df[:,[:P1,:P5p1,:P6p2,:P11p4,:P14p9,:P15p1,:P15p3,:P16p2,:P18p2,:P27p4,:H2p2,:H8p2,:H10p1,:H13p1,:H18pA,:H40p4]] X = Tables.rowtable(X) y = log.(df.price) models(matching(X,y)) # declare a tree and lasso model tree_model = @load DecisionTreeRegressor pkg=DecisionTree lasso_model = @load LassoRegressor pkg=MLJLinearModels # initialize "machines" where results can be reported tree = machine(tree_model, X, y) lass = machine(lasso_model, X, y) # split into training and testing data train, test = partition(eachindex(y), 0.7, shuffle=true) # train the models MLJ.fit!(tree, rows=train) MLJ.fit!(lass, rows=train) # predict in test set yhat = MLJ.predict(tree, X[test,:]); yhat = MLJ.predict(lass, X[test,:]); # get RMSE across validation folds MLJ.evaluate(tree_model, X, y, resampling=CV(nfolds=6, shuffle=true), measure=rmse) MLJ.evaluate(lasso_model, X, y, resampling=CV(nfolds=6, shuffle=true), measure=rmse) ``` ] --- # References Adams, R. P. (2018). _Model Selection and Cross Validation_. Lecture Notes. Princeton University. URL: [https://www.cs.princeton.edu/courses/archive/fall18/cos324/files/model-selection.pdf](https://www.cs.princeton.edu/courses/archive/fall18/cos324/files/model-selection.pdf). Hastie, T, R. Tibshirani, and J. Friedman (2009). _The Elements of Statistical Learning: Data Mining, Inference, Prediction_. 2nd. New York: Springer. URL: [https://web.stanford.edu/~hastie/Papers/ESLII.pdf](https://web.stanford.edu/~hastie/Papers/ESLII.pdf). James, G, D. Witten, T. Hastie, et al. (2013). _An Introduction to Statistical Learning with Applications in R_. New York: Springer. DOI: [10.1007/978-1-4614-7138-7](https://doi.org/10.1007%2F978-1-4614-7138-7). URL: [https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR_Seventh_Printing.pdf](https://faculty.marshall.usc.edu/gareth-james/ISL/ISLR_Seventh_Printing.pdf).


Clouds, fog, rain, snow, drizzle - K-12 Education Online - USA Today
Q: Is there any way of knowing or estimating how many drops of rain fall in an . There's plenty more about fall nights and fog formation on this USA TODAY .
http://usatoday30.usatoday.com/weather/askjack/archives-clouds-precip.htm
Treatment for Herniated or Bulging Discs
Why Does the Windshield in a Car Fog Up? | eHow.com
Why Does the Windshield in a Car Fog Up?. Temperature differences between the inside of the car and outside create moisture that can build up on the inside of .
http://www.ehow.com/how-does_4925478_does-windshield-car-fog-up.html
Fog - Geography For Kids - By KidsGeo.com
Fog Clouds On The Ground. There is absolutely no difference between fog and clouds found high in the air. Fog is simply a cloud that has formed near the .
http://www.kidsgeo.com/geography-for-kids/0110-fog.php
hoe is fog formed Herniated or Bulging Discs
How to make your own fog machine The Blogs at HowStuffWorks
May 21, 2010 . See also: Making Fog Juice and How do fog machines work? . The first video on this page, the "Mighty Mini Fog Maker" (made of the coffee .
http://blogs.howstuffworks.com/2010/05/21/how-to-make-your-own-fog-machine/
HowStuffWorks "How do fog machines work?"
How do fog machines work? . According to a Seattle Times article, "The fog is created using propylene glycol and triethylene glycol, mixed with 20 percent .
http://entertainment.howstuffworks.com/arts/theater/question240.htm
How to Get Fog Out of a Watch | eHow.com
How to Get Fog Out of a Watch. Removing moisture from fog-laden watches may seem like a daunting task, but certain techniques help dry any liquid that has .
http://www.ehow.com/how_8458988_fog-out-watch.html
Official Impairment Listing for Spinal Disorders
Acid Rain - How is Acid Rain Formed? - Learn More - Our Changing ...
How is Acid Rain Formed? The primary cause of 'acid rain,' . as acid deposition. This precipitation can be wet (rain, snow, or fog) or dry (gases or acidic salts).
http://www.umac.org/ocp/HowisAcidRainFormed/info.html
- lg monitor smells funny (narrowing of the spine)
Above the Fog. Short Poems by Michael P. Garofalo.
Above the Fog . Life made our hearts cry, and it lifted our spirits high. The ordinary, the exceptional, . If you don't have a hoe, She will take it away. Last day of .
http://www.gardendigest.com/zen/above.htmKeep Car Windows From Fogging with a Potato
Mar 4, 2010 . Over at the how-to site wikiHow they've shared a guide to using a cut . This will clean it and leave a layer that will prevent fog from forming on .
http://lifehacker.com/5485831/keep-car-windows-from-fogging-with-a-potato
Fog Hoe Momma EP [Vamos Music] :: Beatport
Aug 29, 2012 . Download Fog Hoe Momma EP by Skydiver, D.F.K., UBRE on Beatport, the world's largest music store for DJs.
http://www.beatport.com/release/fog-hoe-momma-ep/954019
how to make fog juice - YouTube
Sep 28, 2007 . how to make home made fog fluid. . Versie 1 by warkeyhacks 9,407 views · Thumbnail Re: how to make fog juice by 5u6311f7tjt 7,526 views .
http://www.youtube.com/watch?v=3yO-tNfWb9s
Medical-Vocational Allowance for Herniated Disc
WH QUESTIONS
Question words: who, what, when, where, which, how. Directions: Change the statements to questions using the question word in parenthesis. Check verb tense.
http://fog.ccsf.cc.ca.us/~mbibliow/whquestions3-3.html
What are clouds made of
Advection fog is formed whenever a current of relatively warm, moist air passes over a colder body of land or water. Fog of . How are clouds formed? Clouds .
http://wiki.answers.com/Q/What_are_clouds_made_of
How to Make Fog in a Bottle: 16 steps - wikiHow
3 days ago . There are many different types of fog, depending on how and where they form, but they all are made in the same way: hot water vapor passing .
http://www.wikihow.com/Make-Fog-in-a-Bottle
Appealing a Denial of Benefits
Maori Creation Myth - Crystalinks
The Maori creation myth tells how heaven and earth were once joined as . When mist rises from the forests, these are Papa's sighs as the warmth of her body yearns . or creation myth, to explain how the world was created: Heaven and Earth .
http://www.crystalinks.com/maoricreation.html
Vision of Britain | William Cobbett | Oct. 30th to Nov. 8th, 1821 ...
FOG that you might cut with a knife all the way from London to Newbury. . by a piece of hill that went across them and formed a sort of bridge from one long hill to another. . I wonder how long this sickly, this childish, taste is to remain?
http://www.visionofbritain.org.uk/text/chap_page.jsp?t_id=Cobbett&c_id=1


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| Interface Summary | |
|---|---|
| ImageDecodeParam | An empty (marker) interface to be implemented by all image decoder parameter classes. |
| ImageDecoder | An interface describing objects that transform an InputStream into a BufferedImage or Raster. |
| ImageEncodeParam | An empty (marker) interface to be implemented by all image encoder parameter classes. |
| ImageEncoder | An interface describing objects that transform a BufferedImage or Raster into an OutputStream. |
| StreamSegmentMapper | An interface for use with the SegmentedSeekableStream
class. |
| Class Summary | |
|---|---|
| BMPCodec | |
| BMPEncodeParam | An instance of ImageEncodeParam for encoding images in
the BMP format. |
| BMPImageDecoder | |
| BMPImageEncoder | An ImageEncoder for the various versions of the BMP image file format. |
| ByteArraySeekableStream | A subclass of SeekableStream that takes input from an
array of bytes. |
| ComponentSampleModelJAI | This class represents image data which is stored such that each sample of a pixel occupies one data element of the DataBuffer. |
| DataBufferDouble | An extension of DataBuffer that stores data internally
in double form. |
| DataBufferFloat | An extension of DataBuffer that stores data internally in
float form. |
| FileCacheSeekableStream | A subclass of SeekableStream that may be used to wrap
a regular InputStream . |
| FileSeekableStream | A subclass of SeekableStream that takes its input
from a File or RandomAccessFile . |
| FloatDoubleColorModel | A ColorModel class that works with pixel values that
represent color and alpha information as separate samples, using
float or double elements. |
| ForwardSeekableStream | A subclass of SeekableStream that may be used
to wrap a regular InputStream efficiently. |
| FPXCodec | |
| FPXDecodeParam | An instance of ImageDecodeParam for decoding images
in the FlashPIX format. |
| FPXImage | |
| FPXImageDecoder | |
| FPXUtils | |
| GIFCodec | |
| GIFImageDecoder | |
| ImageCodec | An abstract class allowing the creation of image decoders and encoders. |
| ImageDecoderImpl | A partial implementation of the ImageDecoder interface
useful for subclassing. |
| ImageEncoderImpl | A partial implementation of the ImageEncoder interface useful for subclassing. |
| JPEGCodec | |
| JPEGEncodeParam | A class which encapsulates the most common functionality required for the parameters to a Jpeg encode operation. |
| JPEGImageDecoder | |
| JPEGImageEncoder | An ImageEncoder for the JPEG (JFIF) file format. |
| MemoryCacheSeekableStream | A subclass of SeekableStream that may be used to wrap
a regular InputStream . |
| PNGCodec | |
| PNGDecodeParam | An instance of ImageDecodeParam for decoding images in
the PNG format. |
| PNGEncodeParam | An instance of ImageEncodeParam for encoding images in
the PNG format. |
| PNGEncodeParam.Gray | |
| PNGEncodeParam.Palette | |
| PNGEncodeParam.RGB | |
| PNGImageDecoder | |
| PNGImageEncoder | An ImageEncoder for the PNG file format. |
| PNGSuggestedPaletteEntry | A class representing the fields of a PNG suggested palette entry. |
| PropertyUtil | |
| RasterFactory | A convenience class for the construction of various types of
WritableRaster and SampleModel objects. |
| SeekableStream | An abstract subclass of java.io.InputStream that
allows seeking within the input, similar to the
RandomAccessFile class. |
| SegmentedSeekableStream | A SegmentedSeekableStream provides a view of a
subset of another SeekableStream consiting of a series
of segments with given starting positions in the source stream and
lengths. |
| SimpleRenderedImage | A simple class implemented the RenderedImage
interface. |
| SingleTileRenderedImage | A simple class that provides RenderedImage functionality given a Raster and a ColorModel. |
| StreamSegment | A utility class representing a segment within a stream as a
long starting position and an int
length. |
| StructuredStorage | |
| TIFFCodec | |
| TIFFDecodeParam | An instance of ImageDecodeParam for decoding images in
the TIFF format. |
| TIFFDirectory | A class representing an Image File Directory (IFD) from a TIFF 6.0 stream. |
| TIFFEncodeParam | An instance of ImageEncodeParam for encoding images in
the TIFF format. |
| TIFFField | A class representing a field in a TIFF 6.0 Image File Directory. |
| TIFFImage | |
| TIFFImageDecoder | A baseline TIFF reader. |
| TIFFImageEncoder | A baseline TIFF writer. |
| TIFFLZWDecoder | A class for performing LZW decoding. |
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Scope and Interests
1st SME NITK Convention will provide a platform for researchers and practitioners to interact with one another and discuss the state-of-the-art developments in the field. The topics of the conference will cover all aspects of research and application in mining, including but not limited to:
- Ventilation problems in deep open cast mines
- Locating trapped miners underground from surface
- Ventilation of long tunnels
- Artificial Intelligence in Mining Industry
- Latest developments in Rock Mechanics
- Bulk Material Handling
- Coal & Energy
- Mining & Exploration
- Mine Economics
- Geo-Mechanics & Ground Control
- Geological Modelling & Resource Estimation
- Sustainable Mining
- Health, Safety & Environment
- Surface & Underground Hazards
- Acid Mine Drainage
- Mine Ventilation
- Mine Tailings
- Application of IT in Mining Industry (Smart Mine)
- Advanced Mining Methods & Machinery
- Disaster Management


{% autoescape true %}
New York Corporate League Standings
New York Corporate Soccer League
Team
Points
GP
W
T
L
GF
GA
{% for team in teams %}
{{team.name}}
{{team.points}}
{{team.wins + team.ties + team.losses}}
{{team.wins}}
{{team.ties}}
{{team.losses}}
{{team.goals_for}}
{{team.goals_against}}
{% endfor %}
{% endautoescape %}


You have an appointment to donate blood at {{appointment.hospital.name}}.
Your appointment time is at {{appointment.time | date:'medium'}}. Way to go!
You donated blood at {{appointment.hospital.name}}. The clot thickens!
You participated in an event {{appointment.hospital.name}}. Way to pool your efforts!
You drew blood from a stone and helped a blood-buddy get over his fear of needles!


Oblivion to the Infidelos
Galaxy: Hegus’dra
Satellite Galaxy: Imro’eth* (the galaxy of iron)
Sector: Set-dihex 1141 (protectorate designation)
Planet: Demptoria
City: Pezemtpa —Capital and seat of government for the Semme’tatra Federation, homeworld to the Ekta’ra
Daemos Rexus*
How long did it take these people to build those towers? Very fine works of stone and iron. Taller than mountains.
Only generations of blood and will can raise such things; the blood of ancestors, spent to give a birth right to their descendants.
And now I come —the Razer Prince*— to ply my trade, to burn that which they have forfeited, and ensure the glory, and honor of the Hyperg’enot.
In the distance, siege artillery shells, strike a shield that has stood for a million zera’ton (~ 500,000 years). Bom. Bom. Bom. Every impact lighting up the protective dome with angry branching sparks that split and crawl into the night.
Each shell, brings this city closer to its destiny, a fate these arrogant Ekta’ra sealed themselves, when they defied the Emperor Diatas —my father. Bom. Bom. Bom. The vengeance of my Lion Knights will be brutal. As is fitting for these Ekta’ra who dishonor us with their insolence. My father does not like repeating himself, in his wisdom, mercy leads to repetition.
Bom. Bom. Bom.
The dynast aide, Lotax’vet —the grey— speaks, his translated voice electronic and flat to the prince’s ears. “Lord Prince General, the city shields fall.” As he foretells, a blinding flash encompasses the city; blue-green arcs of energy snap into the air and ground. The sky above the city becomes an iridescent dome which then vanishes with a loud bang.
SNNCRAAACKSHPOPoppop
Lotax turns slightly to face his master, his metallic form glinting from beneath his hooded robe. “The city is defenseless my lord.” he said.
“The garrison will fight to the last.”
“They will my lord. Your Star-Lions* will accommodate their courage. Shall the army continue as planned my Lord Prince?”
“Yes…tell the arch’battalions* to cease bombardment. I want every nook and escape tunnel intact so that the razka* hunters can flush out all the infidelos* vermin.”
“As you wish my lord” Doors swosh open as Lotax exits to disseminate his orders. Waiting acolyte-adjutants* stand still and silent beyond in the adjutant enclave.
The ‘eternal’ shields fell within a dunestar (~2 weeks), the Lord Generals will not give Lotax problems carrying out my plan. Ladros, Uthmar, Vi’kete, and the rest of the Lord argent’generals* didn’t think the shields would fall that quickly. They were right. Until my little mouse that is. Had she not completed her mission, the shields would still stand. I haven’t decided if I should spare her?…
Lotax returns from the adjutant enclave were he made the Lord Prince’s will known, the adjutants now in great activity beyond the enclave doors. “All Generals are affirming your orders Lord Prince” he said.
“As they should. Sev’attendexi*.” Vanix’ferri —the sev’attendexi— replies as she was trained to —in elegant and distinguished Hai’Dre’Cledeshem*. “Yes my glorious Prince?” Such a fine sev’attendexi.
“I will address the army and my knights.”
Vanix works the consoles in front of her, with her four arms, opening audio channels to all Hyperg’enot military elements on the planet. Her task complete she says “All-hands, two-way audio established. My radiant Lord Prince may speak when he pleases”
Such a good pet, and such a good investment —those extra arms. It is always a thrill to project ones voice to so many champions. Remember what father always says about these times. They fight for blood-honor.
“Glorious Knights!” The rhythmic pounding of god-hammer gauntlets, war-lances, and his own voice —echoing off of the battlements and the land— fill the command enclave.
“I CAN HEAR YOU KNIGHTS” The pounding gets louder. The monstrous crashes of the knight’s twenty eight bestere (12 meter) long war-lances against their god-hammer gauntlets, was deafening.
“These Ekta’ra have no faith in the Diatas!” The din of the war-lances drowns out the chants from the army. The arch’battalions and glorious infantry instead make themselves heard, with bronze and brass war horns, which growled and spit menacing tones to mix with the knight’s crashing of steel.
“They are ungrateful for the gifts they have on this world, they have rejected my fathers generosity, they dishonor us, by refusing to surrender what is ours.” Shells no longer fall on the city, but the din echoing off the walls alone seems to threaten to flatten the city.
“Their unbreakable shield is broken, their garrison awaits SLAUGHTER” Hydaaa Hydaaa Hydaaa Rexus Revmet Rexus Revmet Amplified chants for glory of the Hyperg’enot, begin to mix with the pounding god-hammers and blaring siege horns.
“Arch’battalions! Bring down that wall!”
Bom Bom Bom Bom Bom Bom bom
The battalion’s shells land true, and an entire let’con (~2 mile) stretch of ancient wall, blossoms into grey dusty fireballs. As the dust clears it reveals shining buildings of glass and steel beyond.
“I Lord Princes Daemos Rexus decree that the Ekta’ra are shaka’infidelos*. Storm this city! Crush what petulant resistance remain! All Ekta’ra; be they adult, infirm, child, or infant! Put them to flame and blade! Tear into them! Until they are but meat for dogs! Put them to the flame until their bones are but wisps of ash! Give them death! Give them the freedom they so desire! When you are done my Knights, whatever is left can beg for slavery or death! Put this city Pezemtpa to righteous fire! May its name never again be spoken! Hyperg’enot eternal legion! Advance!” Even after Vanix cut the feed, the war cries of the eternal legion still filled the command enclave, only fading with distance as they advanced on the doomed city.
They could have given it up piece meal. Instead we will burn everything until the name Ekta’ra is only a memory. The disgraced ground of this planet —Demptoria— will feed many colonies; its mountains will build many shard-cruisers, chariots, war-lances, and god-hammers. Its core will armor them all. We will use whatever is left as slaves and fertilizer. We will take all of it as we please. All of it.
“The Lion-Knights are engaging the city garrison. The infidelos fall quickly. They fall to the last.” Intoned Lotax.
I want to see them. I want to watch as the legacy of this place dies. “Ready my guard and thunder-talon. I want to be there when the last of them die.”
“By your will, my Prince.”
Glory eternal to the Hyperg’enot. Death and oblivion to the infidelos
Razer Prince: The War-call* name given to Daemos
War-call: A name given to Star-Lion knigts
Daemos Rexus: Full name, Ret’mie Hypa Daemos Tragateus* Razer Lexo* Rexus
Tragateus: Brutal
Lexo: Of the lion*
Lion: Refers to the ancient lions of the Dre’cledeshem birthworld known as Burai. Burai lions look much like terran lions and filled much of the same niche.
Sev’attendexi: A disgraced member of a Hyperg’enot dynast —not all dynasts have sev’attendexi—, who must serve the rest of their life as the personal assistant of dynast royalty. They are complete slaves to their master, having only as many rights as they are given.
Infidelos: The name given to enemies which must be obliterated on sight.
Hai’Dre’Cledeshem: The high dialect of the Cledeshem ancestral language
Imro’eth: Long ago —according to the Hyperg’enot Astro’magi— during a galactic collision were at least one galaxy was ancient, iron rich, almost dead, galaxies, the stars that make up Imro’eth were tossed away from the colliding galaxies.
Razka: Elite commandos with smaller statures than other warriors and knights. They are famed for the war hounds that aid them in routing out the deepest of entrenched foes.
Hetr’steel: High strength composite steel.


Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'WeightIt', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with longitudinal treatments.
| Version: | 3.6.0 |
| Depends: | R (≥ 3.3.0) |
| Imports: | ggplot2 (≥ 3.0.0), ggstance, crayon, methods |
| Suggests: | MatchIt, WeightIt (≥ 0.5.0), twang, Matching, optmatch, ebal, CBPS (≥ 0.17), designmatch, optweight, mice, knitr, rmarkdown |
| Published: | 2018-11-25 |
| Author: | Noah Greifer [aut, cre] |
| Maintainer: | Noah Greifer <noah.greifer at gmail.com> |
| BugReports: | https://github.com/ngreifer/cobalt/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/ngreifer/cobalt |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | MissingData |
| CRAN checks: | cobalt results |
| Reference manual: | cobalt.pdf |
| Vignettes: |
Covariate Balance Tables and Plots: A Guide to the cobalt Package Appendix 1: Using cobalt with Other Preprocessing Packages Appendix 2: Using cobalt with Clustered and Multiply Imputed Data Appendix 3: Using cobalt with Longitudinal Treatments |
| Package source: | cobalt_3.6.0.tar.gz |
| Windows binaries: | r-devel: cobalt_3.6.0.zip, r-release: cobalt_3.6.0.zip, r-oldrel: cobalt_3.5.0.zip |
| OS X binaries: | r-release: cobalt_3.6.0.tgz, r-oldrel: cobalt_3.5.0.tgz |
| Old sources: | cobalt archive |
| Reverse imports: | WeightIt |
| Reverse suggests: | optweight |
Please use the canonical form https://CRAN.R-project.org/package=cobalt to link to this page.


menu
Notifications
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close
Threshold
Between 0 - 100%
Frequency
In Minutes
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In Minutes
Toast
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Notification


Multiple Notifications
Press Button 3 and Button 4 on the nRF51 DK to send notifications.
CONNECT
DISCONNECT
Button 3 value
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|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface MqttCallback
Asynchronous message listener. Classes implementing this interface
can be passed to MqttClient.setCallback(MqttCallback)
,
which will create a call back on this interface.
| Method Summary | |
|---|---|
void |
connectionLost(Throwable cause)
This method is called when the connection to the server is lost. |
void |
deliveryComplete(MqttDeliveryToken token)
Called when delivery for a message has been completed, and all acknowledgements have been received. |
void |
messageArrived(MqttTopic topic,
MqttMessage message)
This method is called when a message arrives from the server. |
| Method Detail |
|---|
void connectionLost(Throwable cause)
cause
- the reason behind the loss of connection.void messageArrived(MqttTopic topic, MqttMessage message) throws Exception
This method is invoked synchronously by the MQTT client. An acknowledgment on the network is not sent back to the server until this method returns cleanly.
If an implementation of this method throws an Exception
, then the
client will be shut down. When the client is next re-connected, any QoS
1 or 2 messages will be redelivered.
Any additional messages which arrive while an implementation of this method is running, will build up in memory, and will then back up on the network.
If an application needs to persist data, then it should ensure the data is persisted prior to returning from this method, as after returning from this method, the message is considered to have been delivered, and will not be reproducable.
It is possible to send a new message within an implementation of this callback (for example, a response to this message), but the implementation must not disconnect the client, as it will be impossible to send an acknowledgement for the message being processed, and a deadlock will occur.
topic
- the topic on which the message arrived.message
- the actual message.
Exception
- if a terminal error has occurred, and the client should be
shut down.void deliveryComplete(MqttDeliveryToken token)
MqttTopic.publish(MqttMessage)
.
token
- the delivery token associated with the message.
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
Auxiliary utility for the HROM training. More...
Public Member Functions | |
| def | __init__ (self, solver, custom_settings) |
| def | AppendCurrentStepResiduals (self) |
| def | CalculateAndSaveHRomWeights (self) |
| def | CreateHRomModelParts (self) |
| def | AppendHRomWeightsToRomParameters (self) |
Auxiliary utility for the HROM training.
This class encapsulates all the functions required for the HROM training. These are the ROM residuals collection, the calculation and storage of the HROM weights and the creation and export of the HROM model parts.
| def hrom_training_utility.HRomTrainingUtility.__init__ | ( | self, | |
| solver, | |||
| custom_settings | |||
| ) |
| def hrom_training_utility.HRomTrainingUtility.AppendCurrentStepResiduals | ( | self | ) |
| def hrom_training_utility.HRomTrainingUtility.AppendHRomWeightsToRomParameters | ( | self | ) |
| def hrom_training_utility.HRomTrainingUtility.CalculateAndSaveHRomWeights | ( | self | ) |
| def hrom_training_utility.HRomTrainingUtility.CreateHRomModelParts | ( | self | ) |
| hrom_training_utility.HRomTrainingUtility.echo_level |
| hrom_training_utility.HRomTrainingUtility.element_selection_svd_truncation_tolerance |
| hrom_training_utility.HRomTrainingUtility.hrom_output_format |
| hrom_training_utility.HRomTrainingUtility.hrom_visualization_model_part |
| hrom_training_utility.HRomTrainingUtility.hyper_reduction_element_selector |
| hrom_training_utility.HRomTrainingUtility.include_condition_parents |
| hrom_training_utility.HRomTrainingUtility.include_conditions_model_parts_list |
| hrom_training_utility.HRomTrainingUtility.include_elements_model_parts_list |
| hrom_training_utility.HRomTrainingUtility.include_minimum_condition |
| hrom_training_utility.HRomTrainingUtility.projection_strategy |
| hrom_training_utility.HRomTrainingUtility.rom_basis_output_folder |
| hrom_training_utility.HRomTrainingUtility.rom_basis_output_name |
| hrom_training_utility.HRomTrainingUtility.rom_settings |
| hrom_training_utility.HRomTrainingUtility.solver |
| hrom_training_utility.HRomTrainingUtility.time_step_residual_matrix_container |


Lady Speed Stick Lemon was created for a big celebration of Berlin Wall Demolition after families from East and West parts of Berlin finally could freely meet and Germany was united to one country. This symbolized fresh start for something very old. And of course, it cleans your body as well as our other two types of beer. Ingredients: Water, Dew, Hops, Barley, Yeast, Lemon. Enjoy!
Lady Speed Stick Lemon was created for a big celebration of Berlin Wall Demolition after families from East and West parts of Berlin finally could freely meet and Germany was united to one country. This symbolized fresh start for something very old. And of course, it cleans your body as well as our other two types of beer. Ingredients: Water, Dew, Hops, Barley, Yeast, Lemon. Enjoy!
This is original beer that came to us from 1383. Lady Speed Stick Power is famous for its ability to illuminate sweat smell. It has a reach taste and very refreshing at the same time. Ingredients: Water, Dew, Hops, Barley, Yeast. Enjoy!


Show legend
Annotate build-chain
Build flows origin
Build flows product
Compile debug
Compile production
Compile release
Coverity
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Flash image to STB
Incremental Job
Package, bundled artifacts
Promote build-chain
Seed configuration
Sign
Tag repositories
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{% extends 'base.html' %} {% block lead %}
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--- layout: page description: "A developers logbook" header-img: "img/home-bg.jpg" ---
Posted on {{ post.date | date: "%B %-d, %Y" }}
Tweets von @jangalinski


{% extends 'invitro/ivexperiment_detail.html' %} {% block content %} {{ block.super }} {% include "hawc/_delete_block.html" with name="experiment" notes="This will remove all content associated with this experiment." %} {% endblock content %}


Welcome to Oakleaf University!
At Oakleaf University, you can pursue all of your interests
through the many major programs we offer!
Check out the campus!
Academic Calendar
Check out the Academic Calendar so you don't miss any important deadlines or events!
Fall 2020 Football Games
Spend your fall Saturday's at the football field! Here is the fall football schedule.
New Dining Hall
We are excited to unveil our newest dining hall! Check out the new facility and new menu.
Clubs on Campus
Don't miss out on joining clubs this fall! Here are the new clubs on campus this semester.
Campus Contact Information
Find all of the important contact information for campus here.
Info for Incoming Freshmen
Starting college can be overwhelming. Make sure to get all of the details before coming to campus this fall!


'I feel like I haven't played in months!' - Downes raring to go against Blackpool
by Stuart WatsonIpswich Town midfielder Flynn Downes says the club's players are itching to get back to league action at Portman Road this weekend.
It's been 17 days since Paul Lambert's men won 1-0 at Rochdale, with the majority of the team that played at Spotland having sat out subsequent cup ties against Lincoln (twice in the FA Cup) and Colchester (EFL Trophy).
The hope is that key men return to action looking rested rather than rusty for two big home games in the space of four days - fifth-place Blackpool the visitors tomorrow afternoon, followed by new League One leaders Wycombe on Tuesday night.
"I feel like I haven't played in months! I can't wait to get back out there," said Downes, who was suspended for the win at Rochdale, then started the first match against Lincoln before playing twice for England Under-20s during the recent international break.
"I played the full 90 against Portugal and then 20 minutes against Iceland, but that's a bit different to club football.
"It's not as physical. When you're playing in the league you're playing against 30-year-old big geezers, but when you're playing for England it's against people your own age. It's more technical I'd say and obviously the atmosphere is a bit different."
With Luke Chambers, Luke Woolfenden, Luke Garbutt, Cole Skuse, Jon Nolan, Kayden Jackson and James Norwood among those to have been rested for three straight knockout matches, Downes added: "We're ready to. We're raring to go. We'll be back at it again. From what I hear the boys have been training hard. We'll be good.
"Me personally, I'm feeling good. I've been saying it for a while now that I need to add goals to my game and I feel like one is coming. I need that feeling again!"
MORE: Dave Gooderham: Win in FA Cup doesn't mean we shouldn't question Lambert's rotation policy
Asked if he wanted to have started the initial FA Cup clash with Lincoln two weeks ago, Downes replied: "Of course I would. I want to play every game. If the gaffer picks me for the next one (at Coventry) I'll be happy."
And when quizzed as to whether the Blues can compete on three fronts this season, an EFL Trophy first round clash at Peterborough coming up, Downes said: "Yeah, why not? We've just got to take each game as it comes. Whether it's league or cup we've just got to go out there and try and get the wins.
"These next two games are against two title challengers so it will be a big week."


| Rule | Description | Parameters | Example |
|---|---|---|---|
required |
Required | ||
required_file |
Required (file upload) | ||
required_if |
Required if another field is present or another field equals a specific value | another_field[=value] |
required_if[type=corporation] |
required_unless |
Required unless another field is present or another field equals a specific value | another_field[=value] |
required_unless[type=corporation] |
values |
Value is one of the values specified using the values parameter | ||
matches |
Value matches another field’s value | another_field |
matches[password2] |
min_value |
Field must be >= specified value | value |
min_value[3] |
max_value |
Field must be <= specified value | value |
max_value[5] |
exact_value |
Field must equal specified value | value |
exact_value[15] |
min_length |
Length of field value must be >= specified value | value |
min_value[3] |
max_length |
Length of field value must be <= specified value | value |
max_value[5] |
exact_length |
Length of field value must equal specified value | value |
exact_length[2] |
alpha |
Letters only | ||
alpha_numeric |
Letters and numbers only | ||
alpha_numeric_space |
Letters, numbers, and spaces only | ||
alpha_dash |
Letters and dashes only | ||
alpha_dash_dot |
Letters, dashes, and periods (.) only | ||
numeric_dash |
Numbers and dashes only | ||
numeric |
Numbers only | ||
boolean |
0 or 1 only | ||
valid_email |
Valid e-mail address | ||
valid_phone |
Valid phone (N-NNN-NNN-NNNN ) |
||
valid_url |
Valid URL | ||
valid_username |
Letters, numbers, spaces, dashes, underscores, and periods only | ||
valid_ip |
Valid IPv4 address (NNN.NNN.NNN.NNN ) |
||
valid_base64 |
Valid Base64-encoded data | ||
valid_image |
Valid image (all formats supported by GD) | ||
filetype |
Checks MIME type of file (equires the Finfo PEAR extension) |
mime_type[,mime_type...] |
filetype[image_png,image_png] |
valid_month |
Valid month (number, 1-12) | ||
valid_day |
Valid day of month (number, 1-31) | month_field,year_field |
valid_day[month,year] |
valid_year |
Valid four-digit year | ||
valid_date |
Valid date or timestamp (checks via strtotime() |
||
after |
Checks if date falls after another date field | another_date_field |
after[event_start] |
on_or_after |
Checks if date falls on or after another date field | another_date_field |
on_or_after[event_start] |
valid_time |
Valid time (HH:MM:SS or H:MM:SSAA ) |
||
valid_creditcard_type |
VISA, MC, AMEX, DISC | ||
valid_creditcard |
Checks CC using Luhn’s algorithm | ||
valid_creditcard_exp |
Checks format and expiration of CC exp date (MMYY ) |
Occasionally you will need a validation rule that performs some sort of special application logic, such as a login validation rule.
Follow this pattern when writing custom validation functions:
/** * custom_validation_function - example form validation callback * * @param string $str Submitted value * @param string $parameters Rule configuration options passed in square brackets [] * @param string $field Name of field being validated * @param Form $form Form instance * @return boolean */ function custom_validation_function($str, $parameters, $field, Form $form) { # Register the error message for this rule. # # The first occurrence of %s in your error message will be replaced with # the human-friendly field label # The second occurrence of %s will be replaced with whatever parameters # you passed the rule in square brackets $form->set_error_message('custom_validation_function', '%s is not an accepted value.'); if(empty($str)) { # Important - assume valid if no value passed. # Let the require rule handle empty strings! return TRUE; } else { if(/* validation expression */) { // invalid! # flag an error for the current field # pass the field and the rule name $form->set_error($field, 'custom_validation_function'); return FALSE; } else { // valid! return TRUE; } } }
To call this rule, simply add custom_validation_function
to the list of rules for the desired field (see “Setting Up Your Form” on the Forms page).


public class LotPath
extends java.lang.Object
The class to hold a path for the program to use to traverse the graph.
Includes miscellaneous methods/functions for info about the path.
Started: 11/18/15 TODO:: deal with edges to avoid.
| Modifier and Type | Field and Description |
|---|---|
boolean |
infSizeFlag
Flag to denote this should be treated as a path with infinite size.
|
java.util.LinkedList<LotEdge> |
path
the path of edges held by this object
|
| Constructor and Description |
|---|
LotPath()
Basic constructor.
|
LotPath(java.util.Collection<LotEdge> collectionIn)
Constructor that takes in a collection of edges.
|
LotPath(java.util.Collection<LotNode> nodeListIn,
java.util.Collection<LotEdge> edgesToAvoid)
Creates a lot path out of a list of nodes.
|
LotPath(LotPath pathIn)
Constructor that takes in a LotPath.
|
| Modifier and Type | Method and Description |
|---|---|
LotPath |
append(java.util.Collection<LotEdge> edgeListIn)
Appends a collection of LotEdges to the current list.
|
LotPath |
append(LotEdge edgeIn)
Appends an edge to the path.
|
LotPath |
append(LotNode nodeIn)
Appends an edge from the last node pointed to in the set to the given node
|
LotPath |
append(LotPath pathIn)
Appends a LotPath to this LotPath.
|
boolean |
equals(java.lang.Object obj) |
double |
getEdgesMetricRatio()
Gets the ratio of the number of individual edges to the total path metric.
|
LotNode |
getEndNode()
Gets the node at the end of the path.
|
double |
getNormalizedPathMetric()
Gets the metric for traveling the whole path.
|
double |
getPathMetric()
Gets the metric for traveling the whole path.
|
int |
hashCode() |
boolean |
hasLoops()
Determines if this path contains any loops.
|
boolean |
hasWaypoint(LotNode nodeIn)
Determines if the path hits the node given during its travel.
|
boolean |
hasWaypoint(LotNode nodeIn,
boolean countLast)
Determines if the path hits the node given during its travel.
|
boolean |
isLonger(LotPath pathIn)
Determines if this path is longer than the path given.
|
boolean |
isShorter(LotPath pathIn)
Determines if this path is shorter than the path given.
|
boolean |
pathIsContinuous()
Determines if the path held by this object is valid.
|
static boolean |
pathIsContinuous(LotPath pathIn)
Determines if the path given is valid.
|
void |
removeLoops()
Removes all loops from the path.
|
LotPath |
setPath(java.util.Collection<LotEdge> collectionIn)
Sets the
path to a collection of edges. |
LotPath |
setPathWithNodes(java.util.Collection<LotNode> nodeListIn)
Sets the current path to one given via a collection of nodes.
|
LotPath |
setPathWithNodes(java.util.Collection<LotNode> nodeListIn,
java.util.Collection<LotEdge> edgesToAvoidIn)
Sets the current path to one given via a collection of nodes.
|
int |
size()
Gets the number of 'hops' in the path.
|
java.lang.String |
toString() |
public java.util.LinkedList<LotEdge> path
public boolean infSizeFlag
public LotPath(LotPath pathIn)
pathIn
- The LotPath given.public LotPath(java.util.Collection<LotEdge> collectionIn) throws LotPathException
collectionIn
- The collection of edges.LotPathException
- If the given set of edges is not continuous.public LotPath(java.util.Collection<LotNode> nodeListIn, java.util.Collection<LotEdge> edgesToAvoid) throws LotPathException
nodeListIn
- The list of nodes we are getting.edgesToAvoid
- Edges we do not want to go down. Simply give this param as an empty subclass of Collection to negate this. (this param is required to overload the other constructor with a collection)LotPathException
- If there is no actual path described by this list.public LotPath()
public LotPath setPath(java.util.Collection<LotEdge> collectionIn) throws LotPathException
path
to a collection of edges.collectionIn
- The collection of edges.LotPathException
- If the given set of edges is not continuous.public LotPath setPathWithNodes(java.util.Collection<LotNode> nodeListIn) throws LotPathException
nodeListIn
- The list of nodes to turn into the path.LotPathException
- If something went wrong.public LotPath setPathWithNodes(java.util.Collection<LotNode> nodeListIn, java.util.Collection<LotEdge> edgesToAvoidIn) throws LotPathException
nodeListIn
- The list of nodes in.edgesToAvoidIn
- Edges we do not want to go down.LotPathException
- If something goes wrong.public double getPathMetric()
public double getEdgesMetricRatio()
returns: (# of edges) / (total path metric)
public double getNormalizedPathMetric()
TODO:: do I want to do this this way? Figure that out.
public int size()
Essentially a wrapper for path.size()
public LotNode getEndNode()
public void removeLoops()
public boolean hasLoops()
public static boolean pathIsContinuous(LotPath pathIn)
pathIn
- The path to check.public boolean pathIsContinuous()
public LotPath append(java.util.Collection<LotEdge> edgeListIn) throws LotPathException
edgeListIn
- The edge list to append.LotPathException
- If the given list breaks the path.public LotPath append(LotPath pathIn) throws LotPathException
pathIn
- The path to append.LotPathException
- If the given path breaks the path.public LotPath append(LotEdge edgeIn) throws LotPathException
edgeIn
- The edge to add.LotPathException
- If the edge given would not make a continuous path if appended.public LotPath append(LotNode nodeIn) throws LotPathException
nodeIn
- The node to add an edge to.LotPathException
- If something went wrong.public boolean isShorter(LotPath pathIn)
pathIn
- The path we are testing against.public boolean isLonger(LotPath pathIn)
Wrapper for isShorter(LotPath)
pathIn
- The path we are testing against.public boolean hasWaypoint(LotNode nodeIn)
nodeIn
- The node to check for.public boolean hasWaypoint(LotNode nodeIn, boolean countLast)
nodeIn
- The node to check for.countLast
- If you want to account for the last node.public int hashCode()
hashCode
in class java.lang.Object
public boolean equals(java.lang.Object obj)
equals
in class java.lang.Object
public java.lang.String toString()
toString
in class java.lang.Object


The filter CSS property lets you apply graphical effects like blurring or color shifting to an element.


This is a very basic example for the Recorder.js. Checkout GitHub for details and have a look at the source for this file. Start by clicking record:


| Constant | Defined by | ||
|---|---|---|---|
| FORMAT_BOOLEAN : String = "jcss_format_boolan" [static]
Constant for the boolean formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_CLASS : String = "jcss_format_class" [static]
Constant for the class formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_HTML_COLOR : String = "jcss_format_html_color" [static]
Constant for the color formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_HTML_COLOR_LIST : String = "jcss_format_html_color_list" [static]
Constant for the color list formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_NUMBER : String = "jcss_format_number" [static]
Constant for the number formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_SKIN : String = "jcss_format_skin" [static]
Constant for the skin formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_STRING : String = "jcss_format_string" [static]
Constant for the string formatter.
| JCSS_StyleValueFormat | ||
| FORMAT_STRING_LIST : String = "jcss_format_string_list" [static]
Constant for the string list formatter.
| JCSS_StyleValueFormat |
| FORMAT_BOOLEAN | constant |
public static const FORMAT_BOOLEAN:String = "jcss_format_boolan"
Constant for the boolean formatter.
The boolean formatter casts true
, 1, "1" and "true" to true
and the rest to false.
| FORMAT_CLASS | constant |
public static const FORMAT_CLASS:String = "jcss_format_class"
Constant for the class formatter.
If the given value is already of type class, the value is returned. All other
values are tried to convert into a class by using flash.utils.getDefinitionByName()
.
| FORMAT_HTML_COLOR | constant |
public static const FORMAT_HTML_COLOR:String = "jcss_format_html_color"
Constant for the color formatter.
The color formatter accepts uint values or HTML color values such as "#FF0000". uint values are simply returned while HTML color values are converted into uint.
| FORMAT_HTML_COLOR_LIST | constant |
public static const FORMAT_HTML_COLOR_LIST:String = "jcss_format_html_color_list"
Constant for the color list formatter.
If the value is passed as String, the string list formatter splits
the given value on the comma char and returns an array of uint values.
A valid example value is: color: #FFFFFF, #FF0000, #FF00FF;
If the value is passed as Array, the formatter returns that array.
| FORMAT_NUMBER | constant |
public static const FORMAT_NUMBER:String = "jcss_format_number"
Constant for the number formatter.
The number formatter simply casts the given value to Number.
| FORMAT_SKIN | constant |
public static const FORMAT_SKIN:String = "jcss_format_skin"
Constant for the skin formatter.
If the given value is already of type JCSS_SkinReference
, the value is returned.
If the value is of type String, the formatter creates a new JCSS_SkinReference
using
the following pattern:
com.path.to.the.SkinClass [cache=CACHENAME, construct=CONSTRUCTOR_PARAMETER]
The parameters in the square brackets are optional. Use use them to create cached stateful skins where the client is supposed to cache a skin instance using the value of the cache parameter and to pass the value of the construct parameter right to the constructor of the skin. This enables different states for skins of the same class.
You can set a skin style programatically as shown in the next listing:
myButton.setStyle(":over", "skin", new JCSS_SkinReference(SkinClass, "over", "over"));
The first parameter of JCSS_SkinReference
is a reference to the class
to be instantiated as the skin. The second is a cache identifier, the last parameter is
a value that gets passed to the skin class constructor.
| FORMAT_STRING | constant |
public static const FORMAT_STRING:String = "jcss_format_string"
Constant for the string formatter.
The string formatter simply casts the given value to String.
| FORMAT_STRING_LIST | constant |
public static const FORMAT_STRING_LIST:String = "jcss_format_string_list"
Constant for the string list formatter.
If the value is passed as String, the string list formatter splits the given value on the comma char and returns an array of values.
If the value is passed as Array, the formatter returns that array.


GTK-4.2.1
Introduction to GTK 4
The GTK 4 package contains
libraries used for creating graphical user interfaces for
applications.
This package is known to build and work properly using an LFS-10.1
platform.
Package Information
GTK 4 Dependencies
Required
FriBidi-1.0.9, gdk-pixbuf-2.42.6,
graphene-1.10.6, ISO
Codes-4.6.0, libepoxy-1.5.8, libxkbcommon-1.3.0, Pango-1.48.5, wayland-protocols-1.21,
Recommended
adwaita-icon-theme-40.1.1 (default
for some gtk4 settings keys), FFmpeg-4.4,
gst-plugins-bad-1.18.4, hicolor-icon-theme-0.17 (needed for
tests and for defaults), and librsvg-2.50.7
Recommended (Required if building GNOME)
gobject-introspection-1.68.0
Optional
colord-1.4.5, Cups-2.3.3op2,
GTK-Doc-1.33.2, Highlight-4.1
(runtime, only used by gtk4-demo for syntax highlighting
of demo source code), JSON-GLib-1.6.2, rest-0.8.1, sassc-3.6.2, Tracker-3.1.2,
gi-docgen,
libcloudproviders,
and vulkan
User Notes: https://wiki.linuxfromscratch.org/blfs/wiki/gtk4
Installation of GTK 4
Install GTK 4 by running the
following commands:
mkdir build &&
cd build &&
meson --prefix=/usr --buildtype=release -Dbroadway_backend=true .. &&
ninja
Running the test suite is not recommended unless you are in a
Wayland session. If you are not in a Wayland session, around 625
tests will fail. Under a Wayland session, the number decreases to
around 70. If you are in a Wayland session and wish to run the
tests, issue: ninja
test.
Now, as the root
user:
ninja install
Command Explanations
--buildtype=release
:
Specify a buildtype suitable for stable releases of the package, as
the default may produce unoptimized binaries.
-Dbroadway_backend=true
:
This switch enables the HTML5 GDK backend.
-Dcloudproviders=enabled
: Use this
switch if you have libcloudproviders
installed and wish to enable support for cloud providers in a file
chooser window.
-Dsysprof=enabled
: Use this switch if
you have sysprof-3.40.1 installed and wish to enable
tracing support for GTK4-based applications.
-Dtracker=enabled
: Use this switch if
you have Tracker-3.1.2 installed and wish to use search
functionality when running a file chooser.
-Dcolord=enabled
: Use this switch if
you have colord-1.4.5 installed and wish to use colord
with the CUPS printing backend.
-Dsassc=enabled
: Use this switch if you
have sassc-3.6.2 installed and wish to rebuild the
themes.
-Dgtk_doc=true
: Use this switch if you
have GTK-Doc-1.33.2 installed and wish to generate
the API reference documentation.
Configuring GTK 4
Config Files
~/.config/gtk-4.0/settings.ini
and
/usr/share/gtk-4.0/settings.ini
Configuration Information
GTK 4 themes change the way a
GTK 4 application looks. An icon
theme can be used to change the icons that appear on the
application's toolbar. If you have installed a GTK 4 theme (e.g. the Adwaita theme built in
GTK 4), an icon theme (such as
oxygen-icons5-5.83.0) and/or a font
(Dejavu fonts), you can set your
preferences in ~/.config/gtk-4.0/settings.ini
, or the default
system-wide configuration file (as the root
user), in /usr/share/gtk-4.0/settings.ini
. For the local
user, an example is:
mkdir -pv ~/.config/gtk-4.0
cat > ~/.config/gtk-4.0/settings.ini << "EOF"
[Settings]
gtk-theme-name = Adwaita
gtk-icon-theme-name = oxygen
gtk-font-name = DejaVu Sans 12
gtk-cutsor-theme-size = 18
gtk-xft-antialias = 1
gtk-xft-hinting = 1
gtk-xft-hintstyle = hintslight
gtk-xft-rgba = rgb
gtk-cursor-theme-name = Adwaita
EOF
There are many settings keys, some with default values. You can
find them at Settings:
GTK 4 Reference Manual.
Contents
Installed Programs:
gtk4-broadwayd, gtk4-builder-tool,
gtk4-demo, gtk4-demo-application, gtk4-encode-symbolic-svg,
gtk4-icon-browser, gtk4-launch, gtk4-print-editor,
gtk4-query-settings, gtk4-update-icon-cache, and
gtk4-widget-factory
Installed Libraries:
libgtk-4.so
Installed Directories:
/usr/include/gtk-4.0, /usr/lib/gtk-4.0,
and /usr/share/gtk-4.0
Short Descriptions
|
gtk4-broadwayd
|
provides support for displaying GTK 4 applications in a
web browser using HTML5 and web sockets
|
|
gtk4-builder-tool
|
performs various operations on GtkBuilder .ui files
|
|
gtk4-demo
|
is a simple program that demonstrates some of the tasks
that can be done with GTK 4
|
|
gtk4-demo-application
|
is a simple GTK 4 application which is useful for testing
|
|
gtk4-encode-symbolic-svg
|
converts symbolic SVG icons into special PNG files. GTK 4
can load and recolor these PNGs, just like original SVGs,
but loading them is much faster
|
|
gtk4-icon-browser
|
is a utility to explore the icons in the current icon
theme. It shows icons in various sizes, their symbolic
variants when available, as well as a description of the
icon and its context
|
|
gtk4-launch
|
launches an application using the given name. The name
should match the application .desktop file name (as seen
in /usr/share/applications ), with or
without the '.desktop' extension
|
|
gtk4-print-editor
|
is a simple program to demonstrate printing using GTK 4
applications
|
|
gtk4-query-settings
|
provides a complete listing of all settings related to
GTK 4
|
|
gtk4-update-icon-cache
|
is an icon theme caching utility that creates mmap()able
cache files for icon themes
|
|
gtk4-widget-factory
|
is a program to view GTK 4 themes and widgets
|
|
libgtk-4.so
|
contains functions that provide an API to implement
graphical user interfaces
|
Last updated on Fri Jun 4 20:57:26 2021 +


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| DOSE: T+ 0:00 |
10 g |
oral |
Nutmeg |
(powder / crystals) |
| T+ 6:00 |
10 g |
oral |
Nutmeg |
(powder / crystals) |
My boyfriend and I took about 10 grams of Nutmeg in fresh powdered form at about 6pm one Friday night. We just mixed it up with some coke and attempted to drink it. It really doesn't taste bad at all! Just that its like trying to swallow sand! It is really tricky to swallow, but you get over it 1/2 way through and just deal with it.
It wasn't until about 9:30 pm that it kicked in, just as we were waiting for a train into the city for a night out.
We started off with the strange feeling we get when we smoke pot, like something's happening, but not (!?!?) Its not physical or mental, but something's happening somewhere. Anyway, shortly following, our pulses really started to race and we got very sweaty. We stared at lights which looked much brighter and prettier than usual. We stared a lot, but barely talked. The train ride became more and more thrilling as the minutes passed. It seemed to go faster and faster, then seemed as if it were going down into the centre of the earth!! Our sense of direction was pretty warped at this stage, yet we were perfectly capable of speech and movement without looking suspicious.
Some time later, maybe 1/2 hour, we were in a friend's car in the back seat looking up at the tall buildings against the night sky. Our sense of reality was again a little warped and it seemed that the whole city was just floating in space...like a space ship or something. Even at the time it seemed ridiculous but still very possible (the same thing happens to us when we're stoned).
At about 12 midnight we each had about another 10 grams and got significantly worse after that. I found that standing up and being still were very difficult, I'd stumble and trip over a lot. I'd shuffle about and talk very very slowly, but wouldn't shut up (!) like when I'm completely drunk or I guess really really stoned!
The night went on and I was so out of it! I could barely move I was so heavy and tired and just brain dead I think!! I enjoyed food, and found things tasted really different.  The taste of the pizza really did remind me of that maze book!
Anyway, as it got to about 6am, on the train ride back home, I felt as if I were asleep, or in a dream, but I wasn't asleep! Walking home from the station seemed very surreal also!
Unfortunately the next day I slept in until about 4pm and felt pretty lazy for most of the next day also, but it was worth it for an interesting experience!! I'd recommend it, just keep an open mind!
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.


Installing Mysql on Ubuntu linux
SQL server is considered the best database for .NET on Windows. In my personal opinion, the analogy with Mono is MySql.
In this post, we’ll see how to setup it and it’s connector on Ubuntu.
I was trying to do some Mysql, to support a project which need it and can run on .NET on Ubuntu linux. In an effort to get everything out of Windows.
During the process, the software center had some fever:
Initially, I tried individual installations of Workbench and Mysql from APT. However, since I was not 100% sure (and the above eclipse), I just purged everything and followed the sure way.
Setup Mysql on Ubuntu
Installed mysql server using command sudo apt-get install mysql-server
Gave password (and forgot):
Set up terminal client using sudo apt-get install mysql-client
, which can be accesed as root by using command mysql -u root -p
And/Or from UI using sudo apt-get install mysql-workbench
Setting up Mysql connector
Go here to download the connector. Select .NET & Mono
platform and initiate download process.
Extract the downloaded file and move it to your directory of preference.
An inside view of the directory will look like this:
cd
to the directory and ensure that MySql.Data.dll
indeed exists by using ls
command. I went to v4.5
to make sure that I have all the new patches.
Use command sudo gacutil /i MySql.Data.dll
to register it to Global Assembly Cache (GAC)
To confirm registration:
- Go to directory
/usr/lib/mono/gac
by using commandcd /usr/lib/mono/gac
- List it’s contents by using command
ls
You will find MySql.Data
if the registration has been successful.
I tried following official installation instruction but didn’t found it perfect, the above steps were tried on 64bit Ubuntu 14.6.


Prabhu Vignesh Kumar Rajagopal
Certified
DevSecOps Consultant
Few Works in Public Repos. Private Repos on request.
Click here to see all my reposAn Experianced DevSecOps Consultant who understands and architect the melding of operations, Security, Devlopment and Business to quickly deliver the best Product and services. With the experiance of Cloud, DevOps and agile Making DevOps infinity lifecycle is easy. My Solutions will meet the future demand and adopt for new technoloy and methodology changes. Certified by EMC and Rackspace's CloudU for Cloud Solution Architect
Have Good knowledge and experience in Ruby, Python, Ruby on Rails, Django, PostGres, MySQL, JavaScripts, REST API, LDAP & SSO, OWASP Top 10, GIT, Ansible, Chef, Jenkins, Docker, Hashicorp suit(Terraform, Packer, Nomad, Consul, Vagrant.), AWS, Google Cloud, OpenStack, IBM Bluemix, ELK stack, Android and Cordova. Extremely comfortable with Agile Methodology for SDLC and having experience with Atlassian suit and all Agile Management tools including ServiceNow, BMCRemedy, TFS, CA Suit etc. and a lot more..


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Trump says U.S. has eye on Baghdadi's third in command
WASHINGTON (Reuters) - President Donald Trump said on Tuesday the United States knows the location of the third in command to Islamic State leader Abu Bakr al-Baghdadi, who was killed last month in a U.S.-led raid.
“We have our eye on his third,” Trump said during the question and answer session following a speech at the Economic Club of New York. “His third has got a lot of problems because we know where he is too.”


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Services
In keeping with the spirit of St. Francis (known as the Father of the Poor) there is no charge for any of the below services.
Food Service
- Monday to Saturday inclusive (including Bank Holidays)
- Breakfast served each day 9.00 – 11.30 am
- Lunch served each day 1.00 – 3.00 pm
Food Parcels
- Distributed every Wednesday morning from 9.00 to 11.30 am.
Family Facilities
- We have a designated family area where families can enjoy a nutritious meal in comfort and safety.
Clothing
- Distributed when available.
Showers
- Shower Facilities and fresh underwear and personal hygiene products. With the exception of Wednesday morning. Showers and fresh underclothing are available each morning from 9.00 to 11.30 am.
Advocacy
- Friendship, Advocacy and general social supports are provided as far as possible.


--- layout: post title: PGDay 2011 date: '2011-09-16T16:31:00.000+02:00' author: Luca Ferrari tags: - itpug - pgday modified_time: '2011-09-16T16:31:00.271+02:00' blogger_id: tag:blogger.com,1999:blog-1836481905487384887.post-5521042797251415071 blogger_orig_url: http://fluca1978.blogspot.com/2011/09/pgday-2011.html permalink: /:year/:month/:day/:title.html ---


The linear least squares problem is:
In the most usual case and , and in this case the solution to problem (2.1) is unique, and the problem is also referred to as finding a least squares solution to an overdetermined system of linear equations.
When m < n and , there are an infinite number of solutions x which exactly satisfy b-Ax=0. In this case it is often useful to find the unique solution x which minimizes |x|2, and the problem is referred to as finding a minimum norm solution to an underdetermined system of linear equations.
The driver routine xGELS solves problem (2.1) on the assumption that -- in other words, A has full rank -- finding a least squares solution of an overdetermined system when m > n, and a minimum norm solution of an underdetermined system when m < n. xGELS uses a QR or LQ factorization of A, and also allows A to be replaced by AT in the statement of the problem (or by AH if A is complex).
In the general case when we may have -- in other words, A may be rank-deficient -- we seek the minimum norm least squares solution x which minimizes both |x|2 and .
The driver routines xGELSX, xGELSY, xGELSS, and xGELSD, solve this general formulation of problem 2.1, allowing for the possibility that A is rank-deficient; xGELSX and xGELSY use a complete orthogonal factorization of A, while xGELSS uses the singular value decomposition of A, and xGELSD uses the singular value decomposition of A with an algorithm based on divide and conquer.
The subroutine xGELSY is a faster version of xGELSX, but requires more workspace since it calls blocked algorithms to perform the complete orthogonal factorization. xGELSX has been retained for compatibility with Release 2.0 of LAPACK, but we omit references to this routine in the remainder of this users' guide.
The subroutine xGELSD is significantly faster than its older counterpart xGELSS, especially for large problems, but may require somewhat more workspace depending on the matrix dimensions.
The LLS driver routines are listed in Table 2.3.
All four routines allow several right hand side vectors b and corresponding solutions x to be handled in a single call, storing these vectors as columns of matrices B and X, respectively. Note however that problem 2.1 is solved for each right hand side vector independently; this is not the same as finding a matrix X which minimizes .


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