Regression | Multiple Regression Package for PHP | Machine Learning library

Fun question! I haven't encountered it yet, but there might be a package to help do this automatically. Here's a manual approach using two hacks:

1. the clip = "off" parameter of the coord_* functions, to allow us to add annotations outside the plot area.
2. building a density plot, extracting its coordinates, and then rotating and translating those.

First, we can make a density plot of the change from initial to final, seeing a left skewed distribution:

This is a known bug. I am following up with product group to see if there any update for this bug. For the workaround you mentioned, it need you to go to the node failing with the JobConfigurationMaxSizeExceeded exception and manually set AZUREML_COMPUTE_USE_COMMON_RUNTIME:false in their Environment JSON field.

The node is as below screenshot.

The merits of lmtest::coeftest is that it is possible to use a different covariance matrix than computed by lm().

IIUC, you can use group_by and group_modify, with a map inside that iterates over predictors.

If you can isolate your predictor variables in advance, it'll make it easier, as with ivs in this solution.

You end up with the error with precision because some of your penalization is too strong for this model, if you check the results, you get 0 for f1 score when C = 0.001 and C = 0.01

Note that I was unable to replicate the errors you were having with destroying the subwindows. However, you are potentially having the issue as you are trying to destroy the individual windows, when you could just destroy the main window (as said by @furas). Just call self.parent.destroy() in your Destroy_subwindow function.

Yes, I think we can prove that a == 1 || b == 1 is always true. Most of the ideas here were worked out in comments by zwhconst and Peter Cordes, so I just thought I would write it up as an exercise.

(Note that X, Y, A, B below are used as the dummy variables in the standard's axioms, and may change from line to line. They do not coincide with the labels in your code.)

Suppose b = x.load() in thread2 yields 0.

We do have the coherence ordering that you asked about. Specifically, if b = x.load yields 0, then I claim that x.load() in thread2 is coherence ordered before x.store(1) in thread1, thanks to the third bullet in the definition of coherence ordering. For let A be x.load(), B be x.store(1), and X be the initialization x{0} (see below for quibble). Clearly X precedes B in the modification order of x, since X happens-before B (synchronization occurs when the thread is started), and if b == 0 then A has read the value stored by X.

(There is possibly a gap here: initialization of an atomic object is not an atomic operation (3.18.1p3), so as worded, the coherence ordering does not apply to it. I have to believe it was intended to apply here, though. Anyway, we could dodge the issue by putting x.store(0, std::memory_order_relaxed); in main before starting the threads, which would still address the spirit of your question.)

Now in the definition of the ordering S, apply the second bullet with A = x.load() and B = x.store(1) as before, and Y being the atomic_thread_fence in thread1. Then A is coherence-ordered before B, as we just showed; A is seq_cst; and B happens-before Y by sequencing. So therefore A = x.load() precedes Y = fence in the order S.

Now suppose a = y.load() in thread1 also yields 0.

By a similar argument to before, y.load() is coherence ordered before y.store(1), and they are both seq_cst, so y.load() precedes y.store(1) in S. Also, y.store(1) precedes x.load() in S by sequencing, and likewise atomic_thread_fence precedes y.load() in S. We therefore have in S:

• x.load precedes fence precedes y.load precedes y.store precedes x.load

which is a cycle, contradicting the strict ordering of S.

Looks great, as you have already followed most of the solutions to resolve gradient exploding problem. Below is the list of all solutions you can try

Solutions to avoid Gradient Exploding problem

1. Appropriate Weight initialization: utilise appropriate weight Initialization based on the activation function used.

   Initialization Activation Function He ReLU & variants LeCun SELU Glorot Softmax, Logistic, None, Tanh

2. Redesigning your Neural network: use fewer layers in neural network and/or use smaller batch size

3. Choosing Non Saturation activation function: choose the right activation function with reduced learning rates

   • ReLU
   • Leaky ReLU
   • randomized leaky ReLU (RReLU)
   • parametric leaky ReLU (PReLU)
   • exponential linear unit (ELU)

4. Batch Normalisation: Ideally using batch normalisation before/after each layer, based on what works best for your dataset.

   • after each layer Paper reference

The orignial error is occuring because you are not asking for the same input$id as the id you assign in textInput.