linear | Least Squares Regression of CSV in Golang | Machine Learning library

I'm creating a project to learn React Native. I'm using typescript on this project. I added react-navigation : To make react-navigation working, I had to do :

I'm going through an example in A Taste of Linear Logic.

It first introduces the standard array with the usual operations defined (page 24):

Then suggests that a linear equivalent (using a linear logic for type signatures to restrict array copying) would have a slightly different type signature:

This is designed with the idea that array contains values that are cheap to copy but that the array itself is expensive to copy and thus should be passed along from use to use as a handle.

Question: The signatures for lookup and update correspond well to the standard signatures, but how do I interpret the signature for new?

In particular:

• The function new does not seem to return an array. How can I get an array to use if one is not provided?
• I think I do understand that Arr –o Arr x X is not derivable using linear logic and therefore a function to extract individual values without consuming the array is needed, but I don't understand why new doesn't provide that function directly

It was working fine before I have done nothing, no packages update, no gradle update no nothing just created new build and this error occurs. but for some team members the error occur after gradle sync.

The issue is that build is generating successfully without any error but when opens the app it suddenly gets crash (in both debug and release mode)

Error

I have a very simple snippet:

I've got data with time (seconds) on the x axis and intensity (in relative fluorescent units, or rfu) on the y-axis. It's generated by watching fragments of DNA pass a camera - the bigger the DNA fragment the bigger the time. There are 23 fragments of known size (in DNA base pair units, bp), and therefore there should be 23 peaks. As I know the size of the DNA fragments in bp, I want to recalibrate the x-axis from time (seconds) to base pairs (bp) using a linear model.

Unfortunately there is quite a lot of noise in the data that produces spurious peaks. The only way to confidently tell the true ones from the false ones is that the false ones don't fit the expected pattern in DNA base pairs.

I've provided data from one sample at this link in a data frame called demo. Unfortunately it's too large to paste below.

https://1drv.ms/t/s!AvBi5ipmBYfrhf0v_kvWuN2foLyBgg?e=RWfdXZ

I can pick out all the peaks as follows.

I am trying to create an std::set of pointers to data members. However, I can't find a method to sort or hash such pointers.

They can't be compared with operator<, they don't seem to be supported by std::less and there is no standard integer type that is guaranteed to hold their representation (they might not fit in std::uintptr_t).

This is what I tried first (https://godbolt.org/z/K8ajn3rM8) :

I want to generate a toy example to illustrate a convex piecewise linear function in python, but I couldn't figure out the best way to do this. What I want to do is to indicate the number of lines and generate the function randomly.

A convex piecewise-linear function is defined as:

For instance, if I want to have four linear lines, then I want to generate something as shown below.

Since there are four lines. I need to generate four increasing random integers to determine the intervals in x-axis.

To be able to see through to the other side what I want to do is make the circle area transparent so you are able to see through to the background image.

How would this be done?

Is there a way to do that?

https://jsfiddle.net/r95sy2fw/

This image is what I am trying to replicate in the code.

How do I make it transparent like that?

The snippet I provided currently looks like this:

I am trying to find a more efficient solution to a combinatorics problem than the solution I have already found.

Suppose I have a set of N objects (indexed 0..N-1) and wish to consider each subset of size K (0<=K<=N). There are S=C(N,K) (i.e., "N choose K") such subsets. I wish to map (or "encode") each such subset to a unique integer in the range 0..S-1.

Using N=7 (i.e., indexes are 0..6) and K=4 (S=35) as an example, the following mapping is the goal:
0 1 2 3 --> 0
0 1 2 4 --> 1
...
2 4 5 6 --> 33
3 4 5 6 --> 34

N and K were chosen small for the purposes of illustration. However, in my actual application, C(N,K) is far too large to obtain these mappings from a lookup table. They must be computed on-the-fly.

In the code that follows, combinations_table is a pre-computed two-dimensional array for fast lookup of C(N,K) values.

All code given is compliant with the C++14 standard.

If the objects in a subset are ordered by increasing order of their indexes, the following code will compute that subset's encoding: