gonum | numeric libraries for the Go programming language | Math library

In

f# is a statically typed language - we simply often don't notice because of its excellent type inferencing. But when deserializing from a JSON file, before writing any code, it is useful to determine whether the JSON has a fixed schema, and if so, create or choose an appropriate data model to which the JSON can be mapped automatically.

In your case, your JSON looks like:

if you check the docs of Unmarshal. You will find that the know type of Numbers in Unmarshal is float64

To unmarshal JSON into an interface value, Unmarshal stores one of these in the interface value:

The authors of the repository must have migrated to a different repository.

The official repository of these packages is: github.com/go-gota/gota

It looks like you'll need to calculate it manually, and there are multiple ways to do this, depending on assumptions you can make about your data. If you really go this route, I'd strongly encourage you to also test against existing implementations - for example R's cor.test - to ensure that you're not doing something wrong.

If the observed values are each approximately normal, then the value

where r is the calculated correlation coefficient and n is the number of observations, will follow Student's t distribution with n-2 degrees of freedom. Hence, you can use Student's t distribution as implemented in GoNum to compute the p-value. This is what cor.test in R does.

It should go something like (please note I've never used Go):

As Adrian told in the comments, the issue was that the go version I was running was too old for gonum to install correctly. This was due to the fact that the go-golang package installed on my computer via apt-get was giving me the 1.6 version of go. By removing the package and making sure I had a recent go release installed on my computer I managed to install gonum.

In Golang it is described for promoted methods as

Promoted fields act like ordinary fields of a struct except that they cannot be used as field names in composite literals of the struct.

Given a struct type S and a defined type T, promoted methods are included in the method set of the struct as follows:

• If S contains an embedded field T, the method sets of S and *S both include promoted methods with receiver T. The method set of *S also includes promoted methods with receiver *T.
• If S contains an embedded field *T, the method sets of S and *S both include promoted methods with receiver T or *T.

The problem is that you are passing pointer type arguments to AddVec function. But you are using pointer type fields in second case.

NumPy source code can be tricky to navigate, because it has so many functions for so many data types. You can find the C-level source code for the absolute value function in the file scalarmath.c.src. This file is actually a template with function definitions that are later replicated by the build system for several data types. Note each function is the "kernel" that is run for each element of the array (looping through the array is done somewhere else). The functions are always called _ctype_absolute, where is the data type it applies to and is generally templated. Let's go through them.

I have confirmed my suspicions and now have a proper answer. The essential problem is that there was an error in my use of the C++ API (which would have led to only writing 1/2 of a double in certain cases) and I was essentially trying to repeat that error in GO. In fact, the solution is very simple.

The attribute type that is passed into the attribute read method, is not the type of the attribute, it is the type that you want it converted to when stored in memory. That means that my C++ code should be much simpler as there is no need to check the attribute type, nor to static_cast it to the result. To read and store the attribute value, relying on HDF5 to perform the conversion and for a suitable exception to be thrown if the attribute is not convertible to a double, is as simple as