A list of frameworks, libraries and software for the Java Swing GUI toolkit. stars. 17 forks. Star. Watch. Code. Issues 0.  Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. Sign up for free. Dismiss. master. 1 branch. 0 tags. Go to file. Выберите переключатель "URL-адрес WSDL" и вставьте в соответствующее поле URL-адрес файла WSDL. По умолчанию URL-адресом является http://localhost/FlowerAlbumService/FlowerServiceService?wsdl. URL-адрес можно найти в браузере, выполнив тестирование веб-службы и заменив текст?Tester на?wsdl в конце URL-адреса. Примите все прочие значения по умолчанию, включая пустое имя пакета. Нажмите кнопку "Завершить".  В заключение описывается привязка компонентов Swing к коду клиента веб-службы. При отсутствии необходимости самостоятельного проектирования формы JFrame можно загрузить готовый файл JFrame Java здесь. The way you move a Swing Frame is by clicking on its title bar and dragging it around. No title bar implies no movement. How did you plan to drag it around without a title bar?  Just copy below codes to your project, and extend it, you can make your Frame window draggable! Extend MoveJFrame like extend a JFrame, you can directly drag your window: public class ContactUi extends MoveJFrame implements Runnable {. The MoveJFrame class code, just copy it and extend it like extend JFrame: import - *; import - *; import - dapter; import - vent; import - ption  Browse other questions tagged java swing or ask your own question. The Overflow Blog. Choosing Java instead of C++ for low-latency systems. Our V and M models are designed to be trained efficiently with the backpropagation algorithm using modern GPU accelerators, so we would like most of build your own swing frame url model's complexity, and model parameters to reside in V and M. It would retract when either knob was turned, but not when attempting to close the door without turning a knob. I work with BlueJ and when I try to compile the triangle-class from the 2nd code example it says, that it cannot find the class Color. This is a nice addition to a spacious garden! Thanks for sharing these! The M model serves as a predictive model of the future z z z vectors that V is expected to produce.

I want to build one so badly. Also love the hanging squash idea! Thanks for sharing these! Oh my gosh. I have always liked the idea of green houses. They are so simple and organic. There is nothing better than growing things in your own backyard. I never have owed a property to have one of my own. Thanks for sharing!! The gourd tunnel and the geo domes are my favorite but all of these are amazing!

What a great round-up; I have pinned it for later. Oh I just love this post. We were just talking about how we needed to do this. Following you! Wow, the cd case one blew my mind. I actually had to stare at it for a while to try and figure it out. So creative. These are all really great ideas! I love how organized everything was in your post — and I really love your blog design, too! These are all great DIY Greenhouses. Please let me know if you come across any swing set greenhouses.

These are some great ideas for greenhouses! I think they fit with my traditional ideas as to what a greenhouse should look like.

What should I be looking for in greenhouse windows? Is being double-glazed important, for example? Amazing piece of content, breath-taking images.

Wanted to write an article about DIY greenhouse but with this kind of ideas who needs more! Urdu with Arabic alphabets is widely spoken language of this country.

I hope to one day build my very own greenhouse in a garden. Depending on the garden it would either be a lean on green house or that beautiful geo dome greenhouse. And of all the greenhouses of options, the most reasonable design seems Straw Bale. Its main advantage is that it gives heat plants, and this is important for the gardener.

This is a nice addition to a spacious garden! Thanks for the tip about the bottle mini greenhouses. This might just be the solution. Wow, I never thought about those cold boxes!

Will do some more digging! Thanks for the wonderful ideas! I love the idea of repurposing old windows. I love these, the Hoop Houses look amazing and something I might have to try and create in my own back garden if I ever get the time.

Oh yes! I love these ideas because my heart is into greens. This will definitely help me with my landscaping business. Your blog provided us with valuable information. Thanks a lot for sharing. What an amazing article for gardening and landscaping! Thanks for all the best ideas! I really like these greenhouses because they are different in appearance but still as effective. Thank you so much for sharing these great ideas on DIY greenhouses!

It will be so helpful when we start ours! These greenhouses look great! We are going to build some smaller cold frames using old windows we just found! It really looks so easy to do. I have a big garden and everything grow, just as nature intended. Thanks for sharing. These greenhouses are super cool and could even last year round in Washington where I live! This idea also saves our country from pollution. I will follow this idea and make an awesome greenhouse.

Thanks a lot for sharing those amazing greenhouse ideas with us. Thank you for sharing this wide and informative guide on DIY greenhouses, As I found your blog, now it Build Your Own Swing Frame Game seems like I can do it myself. Keep sharing good things! You can use a similar concept.

Below is the ouput of above code-snippet:. There's something really simple that you might be overlooking after trying to center the window using either setLocationRelativeTo null or setLocation x,y and it ends up being a little off center. Make sure that you use either one of these methods after calling pack because the you'll end up using the dimensions of the window itself to calculate where to place it on screen. Until pack is called, the dimensions aren't what you'd think thus throwing off the calculations to center the window.

Hope this helps. Example: Inside myWindow on line 3 is the code you need to set the window in the center of the screen. The following code center the Window in the center of the current monitor ie where the mouse pointer is located. Actually frame. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. How to center a Window in Java? Ask Question.

Asked 12 years, 5 months ago. Active 7 months ago. Viewed k times. Andrew Swan Andrew Swan The title should be "in Swing" not "in Java", it would be more clear that way. Add a comment. Active Oldest Votes. From this link If you are using Java 1.

Tudor 1 1 gold badge 6 6 silver badges 18 18 bronze badges. As kleopatra said on another answer, setLocationRelativeTo null has to be called after pack in order to work. As explained below, setLocationRelativeTo null has to be called after any call of pack or setSize. Eusebius Odd, I followed a tutorial that made me set it before pack and it put the topleft corner of the frame at the center of my screen.

After moving the line to below pack it got properly centered. Well pack sets the correct size based on the contents and layout, and you can't centre something unless you know its size, so it is indeed odd that the tutorial had you packing it after centering it. I know that this is pretty old but this works fine, provided the frame size is set before calling this function — S. Krishna May 27 '16 at Yep, make sure the size is applied before using pack for example — Myoch Feb 23 '17 at Jee Mok 4, 7 7 gold badges 32 32 silver badges 63 63 bronze badges.

Dzmitry Sevkovich Dzmitry Sevkovich 1 1 gold badge 7 7 silver badges 16 16 bronze badges. You're right. There is extensive literature on learning a dynamics model, and using this model to train a policy.

Many concepts first explored in the s for feed-forward neural networks FNNs and in the s for RNNs laid some of the groundwork for Learning to Think. The more recent PILCO is a probabilistic model-based search policy method designed to solve difficult control problems. Using data collected from the environment, PILCO uses a Gaussian process GP model to learn the system dynamics, and then uses this model to sample many trajectories in order to train a controller to perform a desired task, such as swinging up a pendulum, or riding a unicycle.

While Gaussian processes work well with a small set of low dimensional data, their computational complexity makes them difficult to scale up to model a large history of high dimensional observations. Other recent works use Bayesian neural networks instead of GPs to learn a dynamics model.

These methods have demonstrated promising results on challenging control tasks , where the states are known and well defined, and the observation is relatively low dimensional. Here we are interested in modelling dynamics observed from high dimensional visual data where our input is a sequence of raw pixel frames. In robotic control applications, the ability to learn the dynamics of a system from observing only camera-based video inputs is a challenging but important problem.

Early work on RL for active vision trained an FNN to take the current image frame of a video sequence to predict the next frame , and use this predictive model to train a fovea-shifting control network trying to find targets in a visual scene.

To get around the difficulty of training a dynamical model to learn directly from high-dimensional pixel images, researchers explored using neural networks to first learn a compressed representation of the video frames. Recent work along these lines was able to train controllers using the bottleneck hidden layer of an autoencoder as low-dimensional feature vectors to control a pendulum from pixel inputs.

Learning a model of the dynamics from a compressed latent space enable RL algorithms to be much more data-efficient. Video game environments are also popular in model-based RL research as a testbed for new ideas. Guzdial et al. Learning to predict how different actions affect future states in the environment is useful for game-play agents, since if our agent can predict what happens in the future given its current state and action, it can simply select the best action that suits its goal.

This has been demonstrated not only in early work when compute was a million times more expensive than today but also in recent studies on several competitive VizDoom environments. The works mentioned above use FNNs to predict the next video frame. We may want to use models that can capture longer term time dependencies. RNNs are powerful models suitable for sequence modelling. He trained RNNs to learn the structure of such a game and then showed that they can hallucinate similar game levels on its own.

Using RNNs to develop internal models to reason about the future has been explored as early as in a paper called Making the World Differentiable , and then further explored in. A more recent paper called Learning to Think presented a unifying framework for building a RNN-based general problem solver that can learn a world model of its environment and also learn to reason about the future using this model. Subsequent works have used RNN-based models to generate many frames into the future , and also as an internal model to reason about the future.

In this work, we used evolution strategies ES to train our controller, as it offers many benefits. For instance, we only need to provide the optimizer with the final cumulative reward, rather than the entire history. ES is also easy to parallelize -- we can launch many instances of rollout with different solutions to many workers and quickly compute a set of cumulative rewards in parallel.

Recent works have confirmed that ES is a viable alternative to traditional Deep RL methods on many strong baseline tasks. Before the popularity of Deep RL methods , evolution-based algorithms have been shown to be effective at finding solutions for RL tasks. Evolution-based algorithms have even been able to solve difficult RL tasks from high dimensional pixel inputs. We have demonstrated the possibility of training an agent to perform tasks entirely inside of its simulated latent space world.

This approach offers many practical benefits. For instance, video game engines typically require heavy compute resources for rendering the game states into image frames, or calculating physics not immediately relevant to the game. We may not want to waste cycles training an agent in the actual environment, but instead train the agent as many times as we want inside its simulated environment.

Agents that are trained incrementally to simulate reality may prove to be useful for transferring policies back to the real world. Our approach may complement sim2real approaches outlined in previous work. Furthermore, we can take advantage of deep learning frameworks to accelerate our world model simulations using GPUs in a distributed environment.

The benefit of implementing the world model as a fully differentiable recurrent computation graph also means that we may be able to train our agents in the dream directly using the backpropagation algorithm to fine-tune its policy to maximize an objective function. The choice of implementing V as a VAE and training it as a standalone model also has its limitations, since it may encode parts of the observations that are not relevant to a task.

After all, unsupervised learning cannot, by definition, know what will be useful for the task at hand. For instance, our VAE reproduced unimportant detailed brick tile patterns on the side walls in the Doom environment, but failed to reproduce task-relevant tiles on the road in the Car Racing environment.

By training together with an M that predicts rewards, the VAE may learn to focus on task-relevant areas of the image, but the tradeoff here is that we may not be able to reuse the VAE effectively for new tasks without retraining. Learning task-relevant features has connections to neuroscience as well. Primary sensory neurons are released from inhibition when rewards are received, which suggests that they generally learn task-relevant features, rather than just any features, at least in adulthood.

Another concern is the limited capacity of our world model. While modern storage devices can store large amounts of historical data generated using an iterative training procedure, our LSTM-based world model may not be able to store all of the recorded information inside of its weight connections.

While the human brain can hold decades and even centuries of memories to some resolution , our neural networks trained with backpropagation have more limited capacity and suffer from issues such as catastrophic forgetting. Future work will explore replacing the VAE and MDN-RNN with higher capacity models , or incorporating an external memory module , if we want our agent to learn to explore more complicated worlds.

Like early RNN-based C--M systems , ours simulates possible futures time step by time step, without profiting from human-like hierarchical planning or abstract reasoning, which often ignores irrelevant spatial-temporal details. However, the more general Learning To Think approach is not limited to this rather naive approach. Instead it allows a recurrent C to learn to address "subroutines" of the recurrent M, and reuse them for problem solving in arbitrary computable ways, e.

A recent One Big Net extension of the C--M approach collapses C and M into a single network, and uses PowerPlay-like behavioural replay where the behaviour of a teacher net is compressed into a student net to avoid forgetting old prediction and control skills when learning new ones. Experiments with those more general approaches are left for future work. If you would like to discuss any issues or give feedback, please visit the GitHub repository of this page for more information.

The interative demos in this article were all built using p5. Deploying all of these machine learning models in a web browser was made possible with deeplearn. A special thanks goes to Nikhil Thorat and Daniel Smilkov for their support. We would like to thank Chris Olah and the rest of the Distill editorial team for their valuable feedback and generous editorial support, in addition to supporting the use of their distill.

Any errors here are our own and do not reflect opinions of our proofreaders and colleagues. If you see mistakes or want to suggest changes, feel free to contribute feedback by participating in the discussion forum for this article.

The instructions to reproduce the experiments in this work is available here. In this section we will describe in more details the models and training methods used in this work. In the following diagram, we describe the shape of our tensor at each layer of the ConvVAE and also describe the details of each layer:. As the environment may give us observations as high dimensional pixel images, we first resize each image to 64x64 pixels and use this resized image as V's observation.

Each pixel is stored as three floating point values between 0 and 1 to represent each of the RGB channels. Each convolution and deconvolution layer uses a stride of 2. All convolutional and deconvolutional layers use relu activations except for the output layer as we need the output to be between 0 and 1.

We use this network to model the probability distribution of the next z z z in the next time step as a Mixture of Gaussian distribution. The only difference in the approach used is that we did not model the correlation parameter between each element of z z z , and instead had the MDN-RNN output a diagonal covariance matrix of a factored Gaussian distribution.

This approach is very similar to previous work in the Unconditional Handwriting Generation section and also the decoder-only section of SketchRNN. We would sample from this pdf at each time step to generate the environments. Given that death is a low probability event at each time step, we find the cutoff approach to be more stable compared to sampling from the Bernoulli distribution.

For instance, in the Car Racing task, the steering wheel has a range from In the Doom environment, we converted the discrete actions into a continuous action space between Following the approach described in Evolving Stable Strategies , we used a population size of 64, and had each agent perform the task 16 times with different initial random seeds. The agent's fitness value is the average cumulative reward of the 16 random rollouts.

The figure below charts the best performer, worst performer, and mean fitness of the population of 64 agents at each generation:. Since the requirement of this environment is to have an agent achieve an average score above over random rollouts, we took the best performing agent at the end of every 25 generations, and tested it over random rollout scenarios to record this average on the red line.

After generations, an agent was able to achieve an average score of We used random rollouts rather than because each process of the 64 core machine had been configured to run 16 times already, effectively using a full generation of compute after every 25 generations to evaluate the best agent times.

In the figure below, we plot the results of same agent evaluated over rollouts:. These results are shown in the two figures below:. Please note that we did not attempt to train our agent on the actual VizDoom environment, but only used VizDoom for the purpose of collecting training data using a random policy.

DoomRNN is more computationally efficient compared to VizDoom as it only operates in latent space without the need to render an image at each time step, and we do not need to run the actual Doom game engine. The best agent managed to obtain an average score of over random rollouts. This is the highest score of the red line in the figure below:. This page requires Javascript.

Interactive demo: Tap screen to override the agent's decisions. NIPS What we see is based on our brain's prediction of the future. We learn to perceive time spatially when we read comics. Flow diagram of a Variational Autoencoder. The MDN outputs the parameters of a mixture of Gaussian distribution used to sample a prediction of the next latent vector z z z.

We use a similar model to predict the next latent vector z z z. Flow diagram of our Agent model. Our agent learning to navigate a top-down racing environment. Actual observations from the environment.

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