- BREW INSTALL AQUATERM TERMINAL COMMAND HOW TO
- BREW INSTALL AQUATERM TERMINAL COMMAND FULL
- BREW INSTALL AQUATERM TERMINAL COMMAND SOFTWARE
Provides for uninstalls and upgrades for installed ports.Ĭonfines ported software to a private “ sandbox” that keeps it from intermingling with To install gnuplot with Aquaterm, first install Aquaterm then install gnuplot with brew install gnuplot -with-aquaterm.Installs automatically any required support software, known as dependencies, for a given port. It also supports retina displays, which is nice. Unlike the other graphics terminals available for use with gnuplot, Aquaterm will a) keep the plot window open after the Lua process finishes, and b) not hang the Lua process while the plotting window is open. Instead, the best I’ve been able to do on OSX is to use torch.gnuplot with Aquaterm. Although the hydrogen package gives you direct access to iTorch, it doesn’t return plots made there. You probably also want convenient plotting. Replace the command: "lua" lines with command: "th". To set atom-script up to use the former rather than the latter, go to Preferences > Packages > script > Settings > View Code and open lib/ffee.
If you’re coming from iTorch though, your interpreter is probably th rather than lua.
To run scripts from the editor, you’ll want the atom-script package. The language-lua and linter-lua packages are mandatory. Here’re some of the hiccups I’ve had when setting it up for use with Torch : If you’ve got the time and the mathematical foundations, it’s well worth your time to sit down and read it through.Ītom is looking to be my IDE of choice.
BREW INSTALL AQUATERM TERMINAL COMMAND FULL
Either way, it doesn’t affect the algorithm’s function - it’s just replacing a random sample with another random sample.įinally, my usual disclaimer: this post is a poor imitation of the full work. This seems unnecessary because $\hat T$ is set to $T + \gamma$ at the start of each step as well. Notesīoth in the arXiv paper and Science’s supplementary material, in case (2) the variable $\hat T$ is set to $T + \gamma$. It also differs from the algorithm proven in the paper (see §3 of the the supplementary materials), and doesn’t make any mention of budget or generalization error. Somewhat confusingly, it mixes code for the classifier in with the code for Thresholdout. If you’re looking for an implementation, code for the §5 experiments was released.
BREW INSTALL AQUATERM TERMINAL COMMAND HOW TO
Unfortunately, they don’t say how to calculate that budget explicitly. In the implementation (see below), Dwork et al use different random variable distributions to the ones they use in the proof, and claim that the new distributions give a more reasonable budget. If you’ve got a thousand element holdout set and you want a generalization error of less than $\tau = 0.1$, you end up with a budget of $B = 10$ queries.įortunately, in practice it seems you can do better. With maybe a W tensorflow/core/common_runtime/:1027] 0x19332b0 Compute status: Invalid argument: You must feed a value for placeholder tensor 'embeddings' with dtype double and shape dim \quad B \approx \tau^4 n^2$$ I tensorflow/core/kernels/fifo_:154] Skipping cancelled enqueue attempt , timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batch/fifo_queue, input_producer/Gather)]]
If you’re playing around with TensorFlow and trying to populate queues that depend on placeholders, you might run into errors like this: W tensorflow/core/common_runtime/:1027] 0x7f4dc800f810 Compute status: Cancelled: Enqueue operation was cancelled
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