refract | Harness the power of reactive programming to supercharge | Reactive Programming library

I wouldn't use a class for this, and making this a method really doesn't make sense. Use another object instead of a static method:

I am focussing on _mie_S1_S2 since it appear to be the most expensive function on the provided example dataset.

First of all, you can use the parameter fastmath=True to the JIT to accelerate the computation if there is no values like +Inf, -Inf, -0 or NaN computed.

Then you can pre-compute some expensive expression containing divisions or implicit integer-to-float conversions. Note that (2 * n + 1) / n = 2 + 1/n and (n + 1) / n = 1 + 1/n. This can be useful to reduce the number of precomputed array but did not change the performance on my machine (this may change regarding the target architecture). Note also that such a precomputation have a slight impact on the result accuracy (most of the time negligible and sometime better than the reference implementation).

On my machine, this strategy make the code 4.5 times faster with fastmath=True and 2.8 times faster without.

The k-based loop can be parallelized using parallel=True and prange of Numba. However, this may not be always faster on all machines (especially the ones with a lot of cores) since the loop is pretty fast.

Here is the final code:

I found my mistake for anyone else that might have this same issue: I was setting the location of the cubemap textures to be the same as my sampler2D textures. I simply modified the glActiveTexture for the cubemap textures to be using GL_TEXTURE1 instead of GL_TEXTURE0 and initialized the uniforms to point to 1 and everything works now.

mix(x, y, a) can be expressed as x*(1.0−a) + y*a.

The last argument of mix is a constant. Hence the expression can be optimized by the compiler.

c, l and r columns don't wrap their contents. You'll have to use a fixed-width p{} column for your tabular, or consider using tabularx.

Here's an option with a paragraph-style column specification:

As I don't think this is possible yet, you could write some methos like those:

Here it is. Fix your conditionals as follows:

The original problem was caused by a spurious comma after input$Fe. This then led to a second problem caused by the mismatch between the names of the test dataframe required by the code and the construction of this from the input values. I also needed add a call to library(caret) when building the model. It runs now and I can see that Type is being passed into the prediction call. Not sure why this is needed since the prediction is trying to come up with the Type, so I removed that. I also removed the creation of the input.csv file and just created the test data frame directly.

Here is the full app.R.

You can use