weighted | A dead-simple module for picking a random item with weights | Machine Learning library

I have 4 columns of data in a dataframe, where most of the time all 4 columns have data.

In some cases, there are empty cells in the dataframe.

How can I multiply the first 3 occurring values together regardless of their position? I have tried things like df.loc to no avail.

Data below.

I need to create a slider for a game that you can set skills to each player,

The rules are :

1. Each skill starts at 0.
2. The skills cannot total more than 100 points at any time.
3. It should always be possible to assign any 0-100 value to a given skill. Given rule (2), if this gets us over 100 total points, the excess automatically, immediately, removed from the other skills, according to their current values.
4. It's not required to use all 100 points (or any).
5. A skill's value is always an integer.

For example :

• We start with:
  Stamina: 0 | Speed: 0 | Armor: 0 | Strength: 0 | Remaining: 100

• The player adds 50 Speed.
  Stamina: 0 | Speed: 50 | Armor: 0 | Strength: 0 | Remaining: 50

• The player adds 25 Armor.
  Stamina: 0 | Speed: 50 | Armor: 25 | Strength: 0 | Remaining: 25 - 115

• The player now adds 40 Stamina. The excess is automatically reduced from the other skills, weighted by their current values.
  Stamina: 40 | Speed: 40 | Armor: 20 | Strength: 0 | Remaining: 0

• The player then reduces Speed to 10.
  Stamina: 40 | Speed: 30 | Armor: 20 | Strength: 0 | Remaining: 10

• Finally, the player sets Strength to 100.
  Stamina: 0 | Speed: 0 | Armor: 0 | Strength: 100 | Remaining: 0

To do so i've created a function the receives 3 arguments :

1. An array of values of the slider

let arrToCalc = [14,24,55,0]

1. The index number of the skill (0 for Stamina, 1 for Speed ...etc)

let newValueIndex = 2

1. New value for base the calculation on

let newVal = 64.

Im not sure my calculations are accurate so i'm getting partial good results.

when set to

I have a complicated Elasticsearch query like the following example. This query has two sub queries: a weighted bool query and a decay function. I am trying to understand how Elasticsearch aggregrates the scores from each sub queries. If I run the first sub query alone (the weighted bool query), my top score is 20. If I run the second sub query alone (the decay function), my score is 1. However, if I run both sub queries together, my top score is 15. Can someone explain this?

My second related question is how to weight the scores from the two sub queries?

I am making an inter-city route planning program where the graph that is formed has string-type nodes (e.g. LHR, ISB, DXB). It's undirected but weighted, and is initialized as:

I am using lmfit for solving a non-linear optimization problem. It works fine to the point where I am trying to implement a measurement error as the standard deviation of the dependent variable y (sigma_y). My problem is, that I cannot interpret the Information criteria properly. When implementing the return (model - y)/(sigma_y) they just raise from really low to very high values.

i.e. [left: return (model - y) -weighting-> right: return (model - y)/(sigma_y)]:

• chi-square 0.00159805 -> 47.3184972
• reduced chi-square 1.7756e-04 -> 5.25761080 expectation value is 1 || SO discussion
• Akaike info crit -93.2055413 -> 20.0490661 the more negative, the better
• Bayesian info crit -92.4097507 -> 20.8448566 the more negative, the better

My guess is, that this is somehow connected to bad usage of lmfit (wrong calculation of Information criteria, bad error scaling) or to a general lack of understanding these criteria (to me reduced chi-square of 5.258 (under-estimated) or 1.776e-4 (over-estimated) sounds like a really poor fit, but the plot of residuals etc. looks okay for me...)

Here is my example code that reproduces the problem:

I am trying to add a column with the weighted average of 4 columns with 4 columns of weights

I am trying to extract lines that match two different patterns from test.txt.
First I want to extract line that match >> fbat -v1 and then match the corresponding line just below p-value(2-sided).

This is the code I tried, but it only extracts the first match.

I have a dataframe that looks something like this. The tfs column is a map of String to Long and the weights are floats

I am newly learning Python, and I am trying to create a bfs algorithm that can take vertices of a weighted graph and return the bfs. Eventually, I will need to add the weighted edges to the vertices so that I can calculate the distance travelled, however I am able to get the bfs to work with my vertices alone. This is the code so far: