I have problem in my new rails project.I want to implement a function which can show the user's info completeness by a bar like Linkedin.
I think I can use a variable to record the completeness,but I don't have any idea about how to calculate it.
P.S I have two Model,one is the User Model,another is the Info Model.
This is, in fact, completely arbitrary. It's based entirely on which activities on the site you want to encourage.
A couple of mechanisms you can consider:
Model "accomplishments" with a completed/not completed status. Count up the ones you care about. Store the accomplishments based on activity either as they happen or at the end of the day in some batch job. For each user, calculate the percentage with the usual math (accomplishments completed/sum of available accomplishments) * 100 = percentage.
A variation of the same, but weighted based on what you consider more valuable contributions. In this case, the math is basically sum of (weight n * accomplishment n)/total weight.
The previous Careers.stackoverflow.com model made a geeky joke about Spinal Tap by making it possible to have counts greater than 100%. You can do that simply by undercounting the maximum accomplishments.
Related
I'm setting up a Google Sheet that will calculate the most effective purchase size of specific agricultural inputs (fertilizer, chemical, etc). I set up the price data in its own tab with a separate row for each input name + size.
To keep it easy for the user I'd like to require only the input name, # of gallons per acre, and acres and then have a formula spit out the total cost and most effective purchase (bulk if > X gallons, X # of 250 gallon containers + X 55 drums, etc). How can I use the input name plus a wildcard to find the appropriate purchase size?
https://docs.google.com/spreadsheets/d/1bMOPuk2qhmVuJT7vE_ni3KFxfcgKvwTwkM4p50xQF_0/edit?usp=sharing
I tried:
=ArrayFormula(iferror(INDEX('Data (Current)'!H2:H,SMALL(IF($A2&"*"='Data (Current)'!A2:A,ROW('Data (Current)'!A2:A)-1),1))))
...but it returns blank so I'm guessing the reference $A2&"*" to the input name isn't working properly. When I replace it with a string found in the 'Data (Current)' tab then it works fine.
=ArrayFormula(iferror(INDEX('Data (Current)'!H2:H,SMALL(IF($A2&"*"='Data (Current)'!A2:A,ROW('Data (Current)'!A2:A)-1),1))))
I expected the output to be the smallest value (in this case I think it's 5). Then when I change the last number to 2 or 3 it will find the next smallest value, in this case, 55 or 250. Then I can use simple formulas to interact with that and finish the spreadsheet.
Unfortunately, the actual output is nothing, or "".
Sorry if this isn't what you're looking for, as I had some trouble understanding your question.
Presuming what you want is essentially this:
I want to buy Y quantity of item.
I can buy item at cheaper prices if I buy in higher quantities, although sometimes they have a minimum order quantity.
What is the most optimal combination of the options I have to minimize the price I pay?
I'm unsure if there's a simple solution for this within Google Sheets alone. This might be treading more into Apps Script territory.
However, that's not to say that it's not impossible. I've "brute-forced" the above solution above with an iterative-like approach, for the "Chelated Calcium" product: https://docs.google.com/spreadsheets/d/1YSBiSx0IMr4T0R11Dqb-tqOhH4AOTTAWeH2yQfT4X5w
First, list the data in a standardized manner. This includes giving each same product something easy to look it up by. For example, on the Data (Current) tab, I've added 3 columns:
Product Common Name - This is used so that all items of different quantities can be found easily, without needing wildcards.
Gallons - Much easier to parse the data if it it's explicitly laid out.
Minimum Order Gallons - This is your threshold for Bulk. I've set it at an arbitrary 20,000 gallons for Chelated Calcium.
The data here is ordered least-effective first. How you do this will be up to you. In this case, I sorted by the Retail Cost Per Ounce parameter from your sheet, highest first. This eliminates any guesswork about which of the options are most effective, since you can just traverse your options in order. Note: The way I've laid out the formulas will only work IFF the same products are directly next to each other. It won't work if there are other products between them.
On the Field Level Tool tab, standardize your inputs to the Gallons unit. I do this in Total Gallons Needed column (I multiply anything with a "GAL" with 1, and "QUART" with 0.25).
For each item, determine the row numbers where the product begins and ends. This is marked by columns L (Least Efficient Index) and M (Most Efficient Index). I got these results by using the MATCH function.
Set up the iterations, from 0 to N-1. On this sheet, I've set up N=5 iterations, which means that it can traverse 5 different options of the same product only. Since Chelated Calcium only has 4 different options (5 Gal, 30 Gal, 250 Gal, Bulk), 5 is more than enough for this product. If you have products with more options, you may want to have more iterations.
The iterations are on the right side of the Field Level Tool tab.
In your case, you might want to put it on a different tab since the place I put it makes the file look very messy.
In each iteration, I perform the following steps:
To Fulfill - How many gallons still need to be purchased by this iteration?
ThisIndex - What is the row number of this iteration? This is determined by Most Efficient Index - Iteration Number. Remember that since we sorted in order of ascending efficiency, this means that the iteration starts with the most efficient option it can find first. There is a check to make sure that it only outputs a value if it is between the range [Least Efficient Index, Most Efficient Index]. Otherwise, it will be blank to avoid miscalculations by intruding into another product in the Data (Current) tab.
Retail Price, Minimum Gals, Gallons per Order - Simple data extraction for easy usage in the iteration, using INDEX (and indirectly, MATCH by virtue of ThisIndex).
Order - This formula does a couple of things, outlined below:
It checks whether there still remains a valid choice of product at this iteration. It does this by checking whether ThisIndex still exists. If the product doesn't exist, then it will be nulled. This is accomplished by using the IF function.
It will determine if there is a minimum threshold that must be met to purchase this choice. You can see in the 0th iteration, for example, that there is a minimum quantity of 20,000 gallons. If To Fulfill quantity is greater than or equal to the threshold OR there is no threshold, then a purchase is quantified by this column. The mathematics are simply to divide the To Fulfill amount by the Gallons per Order amount to determine the number of orders of this particular product choice. If there is a threshold but the To Fulfill amount doesn't meet it, then this iteration is skipped with a 0 order value.
If the item is already on its least efficient choice (ThisIndex == Least Efficient Index), it will do a CEILING function to ensure that the order is fulfilled. If not, it will do a FLOOR function instead. This is because you cannot order 3.5 units of an item, so they have to be rounded either up or down.
Expenditure - This is simply Order multiplied by the Retail Price, or how much money you spend in this iteration.
Remaining - How much of the product is left unfulfilled at the end of this iteration, to be used as To Fulfill for the next iteration.
Note: If you see formulas that are of the form =IF(ThisIndex, [calculations_here],), that is simply a check to nullify that calculation if ThisIndex is invalid.
Copy the iterations as many times as you want to the right. Something nice to do is to force the iterations to do a CEILING on the very last one to ensure that you never under-buy.
Generate a user-readable string for the purchase suggestion. You can see this on the Suggested Purchase column.
Calculate the Gallons Bought with a simple SUMPRODUCT over all the iterations.
Calculate the total expenditure with a simple SUM over all the iterations.
I hope this is what you were looking for. Regardless, it's at least a fun exercise on how much you can abuse Sheets. ;)
I am predicting stock price of a company , I have used everyday changes in time series, but the negative changes are needed and I cant use log transformation on it. So is it ok if I model the sign as one more variable.
If you normalize and have a single variable, time-series has the property to take care of crests and troughs . There wont be any need of having the signs.
I faced this same dilema sometime back. I figured its not that needed unless you want to give pointer on weather the prices will go up or down also in future.Otherwise no.
Can you give a more clear picture on whats the objective and what data are you considering !
I'm trying to build a per-visit average page load time metric in Adobe Analytics for use in analysis such as average page load time versus conversion rate (Example: Users with avg load time between 0-1s convert at X rate, users between 1-2s convert at Y rate, users between 2-3s convert at Z rate, etc).
We currently have page load time implemented as an event and an eVar on all pages, being captured in ms (ex: On loading the home page, we'll see eVar10=1782 and event10=1782). The eVar is set as a text string set to expire on hit with most recent allocation, while the event is set as an "up is bad" always record numeric with participation enabled.
My first instinct was simply a calculated metric with the Event divided by Page Views, but that ends up aggregating at too high a level (the grand total of all load speeds is divided by the grand total of page views). I tried throwing in various summation functions, but it all ended up equally garbage.
Is it possible to build Average-Per-Visit metrics in AA? Is my implementation even going in the right direction?
Could you do something like this? This might not be exactly what you want, but the calculated metric might help. If you divide your page load time metric by "page load time instances" you can get an average. Which if you have an evar for session id or username, etc, you can find the average load time per session or user. In the example below I use Day as the dimension, but you could use whatever evar you wanted, like username or session id.

Came across this thread as I was looking for something similar. The way I went about this is:
Check the CM here along with the breakdown
You can create a Pageload time/Pageview for each ECID and then further break it down by Visits. This will give you the average Page laod time for a user for a visit.
This you can cross tab with Orders. In my case visits which had orders typically
Occasionally FedEx or UPS may be unavailable to my app servers, or I need to process 100s of packages for a single transaction.
In these cases an estimate is better than nothing.
Currently I cache results for the exact parameters and "rounded" parameters, eg from_zip[:2], round(weight, 10)
What techniques should I look at to do better than this?
I think that a better approach would be to use some kind or interpolation to perform a proximity search of the target price. It can be as simple as finding 2 bounding "points" and interpolating the price of the target point, probably also using a "distance" threshold to not generate too "wild" guesses.
Either way, it's very important to inform the users that the prices are estimates, and subject to change.
Rather than using distance, just use a zone lookup table. You should be able to download a zone chart for your account plus associated rates to create a simpler lookup.
My goal here is to generate a system similar to that of the front page of reddit.
I have things and for the sake of simplicity these things have votes. The best system I've generated is using time decay. With a halflife of 7 days, if a vote is worth 20 points today, then in seven days, it it worth 10 points, and in 14 days it will only be worth 5 points.
The problem is, that while this produces results I am very happy with, it doesn't scale. Every vote requires me to effectively recompute the value of every other vote.
So, I thought I might be able to reverse the idea. A vote today is worth 1 point. A vote seven days from now is worth 2 points, and 14 days from now is worth 4 points and so on. This works well because for each vote, I only have to update one row. The problem is that by the end of the year, I need a datatype that can hold fantastically huge numbers.
So, I tried using a linear growth which produced terrible rankings. I tried polynomial growth (squaring and cubing the number of days since site launch and submission) and it produced slightly better results. However, as I get slightly better results, I'm quickly re-approaching unmaintainable numbers.
So, I come to you stackoverflow. Who's got a genius idea or link to an idea on how to model this system so it scales well for a web application.
I've been trying to do this as well. I found what looks like a solution, but unfortunately, I forgot how to do math, so I'm having trouble understanding it.
The idea is to store the log of your score and sort by that, so the numbers won't overflow.
This doc describes the math.
https://docs.google.com/View?id=dg7jwgdn_8cd9bprdr
And the comment where I found it is here:
http://blog.notdot.net/2009/12/Most-popular-metrics-in-App-Engine#comment-25910828
Okay, thought of one solution to do that on every vote. The catch is that it requires a linked list with atomic pop/push on both sides to store votes (e.g. Redis list, but you probably don't want it in RAM).
It also requires that decay interval is constant (e.g. 1 hour)
It goes like this:
On every vote, update the score push the next time of decay of this vote to the tail of the list
Then pop the first vote from the head of the list
If it's not old enough to decay, push it back to the head
Otherwise, subtract the required amount from the total score and push the updated information to the tail
Repeat from step 2 until you hit a fresh enough vote (step 3)
You'll still have to check the heads in background to clear the posts that no one votes on anymore, of course.
It's late here so I'm hoping someone can check my math. I think this is equivalent to exponential decay.
MySQL has a BIGINT max of 2^64
For simplicity, lets use 1 day as our time interval. Let n be the number of days since the site launched.
Create an integer variable. Lets call it X and start it at 0
If an add operation would bring a score over 2^64, first, update every score by dividing it by 2^n, then set X equal to n.
On every vote, add 2^(n-X) to the score.
So, mentally, this makes better sense to me using base 10. As we add things up, our number gets longer and longer. We stop caring about the numbers in the lower digit places because the values we're incrementing scores by have a lot of digits. Which means that the lower digits kind of stop counting for very much. So if they don't count, why not just slide the decimal place over to a point that we care about and truncate the digits below the decimal place at some point. To do this, we need to slide the decimal place over on the amount we're adding each time as well.
I can't help but feel like there's something wrong with this.
Here are two possible pseudo queries that you could use. I know that they don't really address scalability, but I think that they do provide methods so that you can
SELECT article.title AS title, SUM(vp.point) AS points
FROM article
LEFT JOIN (SELECT 1 / DATEDIFF(NOW(), vote.created_at) as point, article_id
FROM vote GROUP BY article_id) AS vp
ON vp.article_id = article.id
or (not in a join, which will be a bit faster I think, but harder to hydrate),
SELECT SUM(1 / DATEDIFF(NOW(), created_at)) AS points, article_id
FROM vote
WHERE article_id IN (...) GROUP BY article_id
The benefit of these queries is that they can be run at any time with the same data and they will always return the same answers. They don't destroy any data.
If you need to, you can also run the queries in a background job and they will still give the same result.