c3d | project implements action recognition algorithm | Machine Learning library

Here is a way to pivot longer with the {tidyr} package.

This is the function signature:

We could use a regex replacement with a callback function:

You are missing the necessary include

We could use strsplit to split the 'c' column by ';', then loop over the list with map, get the pair of combnations, convert to data.frame, and unnest the list of 'data.frame' column

NOTE: Now, that you added the XSD tag, note you cannot use a lookahead in XML Schema regex: "Particularly noteworthy is the complete absence of anchors like the caret and dollar, word boundaries, and lookaround.".

XML Schema 1.1 supports xs:assertions. With the following, you can assure 123, 345, 567 text matches and (123, 345, 567) text and (123, 345, 567) 123, 345, 567 text do not:

Rather than trying to find which points are within a contour, I would try to evaluate the density at each point, and colour the points according to how that value compares to the level of the contour. It might come to a different decision for a few points near the boundary, but should be pretty close.

To do that evaluation, you could use the oce::approx3d function on the density estimate.

The other thing I'd do is to choose the contour based on the quantiles of the observed densities, rather than trying to simulate a 3-d integral of the estimated density.

Here's code to do all of that:

Nice application for minimization of Gibbs Free Energy! Some of the things that needed to be fixed:

• Use m.log instead of np.log when defining Gekko equations. This allows automatic differentiation to provide exact first and second derivatives to the solver (IPOPT).
• Don't override nA-nH with numeric values. Use the nA.value property.
• You can add equations such as nA>=0 or just add a lower bound by setting nA.lower=0. Using upper and lower limits on the variables is more efficient than adding inequality constraints.
• The indentation doesn't appear to be correct in your posted code. I assumed that everything after the for T in T: statement should be indented as part of that loop. Please check this.
• I added an upper bound for your variables of 5.0. Otherwise, the solution is unbounded. You shouldn't typically have a variable at a bound for Gibbs Free Energy minimization problems so please check this as well. I also set the lower bound to 0.01 so that the m.log terms would not be zero and cause an evaluation error.