spl | Solaris Porting Layer for OpenZFS on OS X

I need to prepare my data for modelling and I want to create a dataframe with 0-1 values for the columns. I have a list with different columns which i want to one hot encode into a dataframe.

I am trying to query a dataframe and store the results into another dataframe.

The dataframe sample I have is:

I'm trying to implement an SPL loss in keras. All I need to do is pretty simple, I'll write it in numpy to explain what I need:

After making my Sankey Diagram work I wanted to change some stuff in the JSON input file, I added two more values to have them displayed when I over a nodes but I run into an issue which leads me here. So, After adding these two values my diagram simply won't work again, first with the two values added it's output the same error in the title here's the code for both

In JavaScript, for some research I'm trying to simulate the process of following some x86-64 Assembly instructions from scratch. The first step is to properly initiate and be able to perform basic math with any two registers as operators. Since each smaller register in the General Purpose Registers is a piece of a larger register, I initiated the 16 GPR registers as ArrayBuffers and then used a Register class to create the correct views on the 16 buffers.

But my math operations must be able to handle 64-bit and greater register sizes, so my getOperand method tried to create a BigUint64Array, with any parts of the ArrayBuffer that shouldn't be included in the operation zeroed out. The BigUInt64Array is initializing as a much larger value than it should be.

You'll see when you run the example. I'm not even sure I'm going about this right. Could someone explain the best way to improve this, or what's wrong with what I'm doing?

Note: The typed arrays and buffers being logged in this snippet are much easier to read if you F12 the Dev Console rather than the logs rendered by SO.

I'm fairly new to keras and tensorflow. I'm trying to figure out why running my code gives me an error when using dense layer = 2 and not dense = 1. This is how I assigned the classes based on a dir structure:

i want to convert "hello,world" into vector as "hello","world"

i tried splitting as below:

I am working of customizing the standard model.fit() method to calculate frame-wise accuracy of each sample during validation. Sample dimension is 512 time frames and 128 frequency bins. As of now I am calculating frame wise binary accuracy by looping through each frame of the sample but this is taking more time (~ 7 seconds per sample). Due to which validation process takes lot of time. How can I speedup the process by vectorizing the process or by other methods?

Sample code I am using is as below: