The problem that I need to solve is almost like the basic LP warehouse problem, where you have n warehouses, each one with a certain amount of a product, and m shops, each one demanding a certain amount of that product. So the goal is to minimize the amount of Km made by the trucks that have to deliver the products from the warehouses to the shops.
This was the easy part, I already identified the constraints and the objective function.
The part that I can't get my head around, is that the truck that delivers the products has a certain capacity, C. Every single truck has the same capacity. I can't tell if that piece of information is really relevant and should be included in some kind of constraint or something. I would really apreciate a hint, cause I've been stuck on this part for a while now and couldn't fine any example of this exact type of problem on the Internet
The number of trucs needed can be bounded by
numtrucs(i,j)*capacity >= shipment(i,j)
Add a term to the objective that minimizes the number of trucs.
Related
I am trying to predict match winner based on the historical data set as shown below,
The data set comprises of IPL seasons and Team_Name_id vs Opponent Team are the team names in IPL. I have set the match id as Row id and created the model. When running realtime testing, the result is not as expected (shown below)
Target is set as Match_winner_id.
Am I missing any configurations? Please help
The model is working perfectly correctly. There's just two problems:
Your input data is not very good
There's no way for the model to know that only one of those two teams should win
Data Quality
A predictive model needs good quality input data on which to reverse-engineer a model that explains a given result. This input data should contain information that can be used to predict a result given a different set of input data.
For example, when predicting house prices, it would need to know the suburb (category), number of bedrooms/bathrooms/parking spaces, age of the building and selling price. It could then predict the selling price for other houses with a slightly different mix of variables.
However, based on your screenshot, you are giving the following information (and probably more) on which to make your prediction:
Teams: Not great, because you are separating Column C and Column D. The model will assume they are unrelated information. It doesn't realise that those two values could be swapped.
Match date: Useless information unless the outcome varies in proportion to time (eg a team continually gets better)
Season: As with Match Date, this is probably useless because you're always predicting the future -- you won't be predicting for a past season
Venue: Only relevant if a particular team always wins at a given venue
Toss Decision: Would this really influence the outcome? Also, it's only known once the game begins, so not great for predicting a future game.
Win Type: You won't know the win type until a game is over, so it's not suitable for predicting a future game.
Score: Again, not known until the actual game, so no good for future predictions.
Man of the Match: Not known for future games.
Umpire: How does an umpire influence the result of a game?
City: Yes, given that home teams often have an advantage.
You have provided very little information that could be used to predict a future game. There is really only the teams and the venue. Everything else is either part of the game itself or irrelevant.
Picking only one of the two teams
When the ML model looks at your data and tries to make a prediction, it will look at all the data you have provided. For example, it might notice that for a given venue and season, Team 8 has a higher propensity to win. Therefore, given that venue and season, it will favour a win by Team 8. The model has no concept that the only possible outcome is one of the two teams given in columns C and D.
You are predicting for two given teams and you are listing the teams in either Column C or Column D and this makes no sense -- the result is the same if you swapped the teams between columns, but the model has no concept of this. Also, information about Team 1 vs Team 2 is totally irrelevant for Team 3 vs Team 4.
What you should do is create one dataset per team, listing all their matches, plus a column that shows the outcome -- either a boolean (Win/Lose) or a value that represents the number of runs by which they won (where negative is a loss). You would then ask them model to predict the result for that team, given the input data, which would be win/lose or a points above/below the other team.
But at the core, I think that your input data doesn't have enough rich content to be able to make a sensible prediction. Just ask yourself: "What data would I like to know if I were to guess which team would win?" It would probably be past results, weather conditions, which players were on each team, how many matches they played in the last week, etc. None of this information is being provided as input on each line of your input data.
I have a booking system for something where the price can change based on the day. The admins for the site can make these changes. If a booking crosses the boundary of a daily rate, they pay pro-rata for the rates they used.
I'm losing confidence in how this is implemented. There are at least two ways:
Having Rates that specify their validity (start, end fields) and then working out which of those apply. But which overlapping ones take priority? Etc. Nasty. This is what we're trying to do and cannot currently answer sufficiently well.
The same except that there is some form of unique quality to date so that no two rates can overlap. The problem here is we'd need to split existing Rates on insert and rejoin two on delete/edit, etc if they had the same value. We'd need to make sure there were no gaps. It requires some heavy ORM overriding.
Keeping a DayRate table with every day defined. This means keeping a load of extra data around but most bookings are for tens of days, not thousands so I'm not worried about the database bandwidth requirements here. Date would be primary-unique and I'd just do a range filter for grabbing which ones I need to factor in.
The problem is generating these dates ahead of time. I know that as soon as I implement this, somebody will make a booking for 2032. Is there a good way around this or should we limit them?
None of these answers seems great and I have to imagine that I'm not the first guy with a booking system. Is there a better way of keeping track of a rate over a contiguous (possibly infinite) amount of time?
Few days now I've got new project to do related with a "real world modelling" program.
Here's how it looks like:
A visit to a psychologist (Use queue). Experts provides psychologist's advice, some of them (n) forms therapeutic groups of k people (GrT - duration of group therapy in hours), other experts (m) takes individual patients (InT - duration of individual therapy in hours). Each newly came patient (new patient's appearance probability is p1, recurring patients comes after period of time (h)) can choose to go to a psychologist providing individual therapies, or to group therapies. If group therapy session is full, patients who are wishing to participate in group sessions must wait. Recurring patients wishing to go to group sessions can start a session with smaller group, but can't go to same session with newly came patients. It has been observed that patients who took individual therapy are recovering faster than those, who chose group sessions(they will need less sessions), but there are exceptions - due to social interaction factor, some patients (probability p2) recover h percent faster than those, who choose individual therapy. Individual session costs InC, group session GrC. You need to assess what therapeutic approach patient should choose optimizing with their resources, and how many and what specialists should hire a health care facility.
Here's my approach to this problem:
Read text file containing Names, Surnames, money willing to spend and place everything in queue structure.
Find which group is better for patient by generating random number for p2probability and using it, we'll find if patient recover faster in individual or group therapy. IMO factor sequence here: Money(looking, if patient can afford individual therapy sessions) > p2 (should patient take group sessions if it's better for him).
By looking how many patients there are in queue, we can find how many psychologists we'll need. (Are there any other factors here? What if we are short of experts?)
Problems that I can't understand: how do I implement p1 probability of new patients appearance if I write every patient into a text file and put them in a queue? How many therapy sessions does it take for patient to recover (static number?)?
Am I missing something? Basically it's open question and there could be no right answer. If anyone have any suggestions how to build this program to better one, I'd be glad to take it!
Programming language I'm using: C++
If you want to break up a task, analyse it and prepare it for coding, you could :
Firstly make a Block diagram, representing program flow control.
Followed by Pseudo code implementation.
P.S. update the question following the above and when you reach the "code stage", there, definitely, will be more help.
Suppose I want to do some data mining on the database of a supermarket. What does that actually mean?
1) What will the output/results be like?
2) Will the output be different every day or change over time?
3) Before applying data mining, do I need to know what I want or will data mining give everything I want automatically?
Data Mining is a general category of techniques that can be applied to different kinds of datasets, just like programming is a general category of techniques that can be applied using different languages to do different things.
None of your questions make any sense.
A1: Data mining will give us an accurate reports about your queries of database of supermarket.
A2: Sure, because Data mining depend on analyzing during time, in this case it depend on your problems or goals that you want to reach it. if your database was very big also you built data warehouse in right way you will get the different output over time.
A3: yes you should determine what are the problems you have to mine then use tools of Data mining to get the results or indicators automatically.
To answer your first question: For the case of supermarket customer data, I could image the following questions:
how many products X are usually sold on Fridays ?
(helps you to determine how many X you should have in stock)
which customers bought product X often in the last month/year ?
Useful when when you introduce a new X-like product: send advertising material (which has a given cost) only to those customers.
given a customer buys product X (e.g. beer) what's the probability that he/she also buys product Y (e.g. chips) ?
useful for the following: make sure X and Y never are on promotional offer at the same time (X and Y are bought together often). Get the customers into the store by offering a rebate on X knowing they'll also by Y at the same time. Or: put a high price X-like product right next to Y, putting the cheaper X somewhere else.
which neighborhoods have the smallest number of customers ?
helps to find out which neighborhoods you could target with advertising to bring more customers into the store.
Often, by 'asking certain questions to the data' one discovers some features and comes up with new questions.
Data mining is a set of techniques. It refers to discovering interesting and unexpected patterns in data.
If you want to apply some data mining techniques, you need to know which one and you should know why. The answer to questions 1, 2 and 3 depends on the techniques that you choose.
For example, if i want to find associations between items sold in a supermarket, i may use association rule mining. If i want to find groups of similar customers, I might use a clustering algorithm. etc.
There is not just ONE technique in data mining.
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.