chemicals | Chemical database of Chemical Engineering Design Library | Dataset library

Process your data convert all values into % Ex: For Agriculture you have 120, 30, 30, total will be 180. Now, change values to % by dividing 120/180, 30/180, 30/180. Hence agriculture will be 100% when you sum it's targets

Repeat for all the groups you have, so your source should be equal to 100% when sum up, so heights of your source (left nodes) will be equal

You have a more complex comparison than you are doing. You have two primary conditions:

1. Is taxPayers.salesstaff(i) empty? (if so, you are done); or
2. Does each of the taxPayers.salesstaff(i) components, e.g. staff1:staff2:... appear in the salesStaff[] array?

It is this second condition that is a many-to-many relationship that has many subconditions. In order to make this determination, you must separate the taxPayers.salesstaff(i) entry on ':' (much like you did input from your data file on ','). Then you must loop over each entry and compare the entry against each in the salesStaff[] array. If ONE of the entries in taxPayers.salesstaff(i) does not match ANY of the entries in the salesStaff[] array. then you have a bad entry.

You can use a couple of bool flags to help you work through the checks. One way to do that is:

Parsing CSV file is an old topic. You will find at least 100 answers with code examples here on stackoverflow. Very often you will find a solution with the function std::getline. Please read the documentation here. It can read characters from a std::ifstream until or up to a delimiter (a comma , in our case), then store the result, without the comma, in a string and discard the comma from the stream. So, throwing that away. The characters will be stored in a std::string. If numbers or other types are needed, we need to convert the string to the other type, using the appropriate function.

Example: We have a string consisting of characters ‘1’, ‘2’ and ‘3’, so, “123”. The quotes indicate the string type. If we want to convert this string into an integer, we can use for example the function std::stoi.

In your case, you have 2 double values. So, we would split the input of the file into strings and then convert the 2 strings with the double values in it, using the function std::stod.

What you need to know additionally is, that often a 2 step approach is used. This is done to prevent potential problems arising from extracting all the string parts from one csv line. So,

• we first read a complete line,
• then put that line into a std::istringstream, and finally
• read input and split the CSV from there.

Then, the rest is simple. Just use std::getline to al the data that you need.

Last but not least, to read the file, we will simply open it, read line by line, create an “EmployeeAccount” and push that into a std::vector

At the end we do some debug output.

Please see below one of may potential implementation proposals:

BLUF or the more trendy TL;DR I think that the function ols_step_best_subset has limitations. However, there are other ways to get what you are looking for.

The long version:

Okay, I worked with the data you provided, but I did not run into any of the issues you ran into. I thought it may have been due to how few rows of data you had provided. (You provided a lot of information! There just isn't a lot for the model to work with.)

Instead of asking you for more data, since it seemed to be more of a question on the limitations of R, I found a built-in wide dataset. I still didn't run into the issues you ran into.

I used the data dhfr from the package caret. It has over 200 variables; I randomly chose 24.

I didn't clean it. I did look at the influential variables and that is likely going to be an issue. I did this as a way to look for multicollinearity which is a really big problem for linear regression. Since that wasn't a problem, I used this data.

As what Llama said I was using the wrong attribute for the Serializer that I'm using when I switched [JsonPropertyName] to [JsonProperty] from the JSON.NET library the results is as expected.

As the data is in no table format we have to extract text and extract the boiling temperature by matching pattern BoilingPoint.

Here's one way. You can just put a change event listener on the form tag - any changes to the form will trigger the script. The string that gets written into the hidden field is JSON and it can be easily decoded on the back end.

The RANK() analytic function would seem to be what you want here:

You can use a group concat in your query to get all the symbols in one string, then use a set to hold only the unique letters.
Create a set of the letters of the alphabet and do a symmetric difference operation on them.