I am new to Stack overflow but I am having logic issues with Google sheets and need some advice on how to proceed.
My goal is I have 3 cup sizes, small medium and large. Example: Small holds 50ML, Medium 100ML,Large 200ML,etc
I want to take a large number and evenly distribute it among the cups to show me how many cups I will need with the least amount of cups used. Example : 170ML = 1 Large, 0 Medium, 0 Small. I only need 1 large to hold everything. Also, 240ML would suggest 1 Large, 0 medium, 1 small. Since small can hold the remaining 40 and medium would be too big of a container.
Problem is I don't understand how to break down my original large number into the smaller number as I have to check and compare if it will fit, I also have to be able to add more if there's a remainder and as far as I know the Google sheet functions only run and represent numbers once.
I've already tried breaking it down to my large container first then in my second row with medium cups I take the first result and subtract from my large number to see if anything is left. If there is, all I can do is add 1 or set the number, I can't seem to scale it up if it requires more than 1 cup which isn't what I want.
I've been going crazy trying to find an easy solution to this but it seems to get more complex as if I need IF statements of some kind.
If anyone has any ideas I'd be happy to hear them out.
you could use IF statement for example like this:
=ARRAYFORMULA(IF(A2:A<>"",
IF(A2:A>=B1, QUOTIENT(A2:A, B1),
IF(A2:A> C1+D1, QUOTIENT(B1, A2:A), 0)), ))
Related
RE: Apache OpenOffice 4.1.7, AOO417m1(Build:9800) - Rev. 46059c9192, 2019-09-03 12:04.
I need to sum non-integer entries across a range of cells, but without including the decimal values (complicated by some cells being text). I started with ROUNDDOWN, then TRUNC, then FLOOR. I'm driving myself nuts trying to find a clean code (or even an arbitrarily extensible ugly code) for what would be the following:
=SUMIF(ISTEXT(R7:CL7);0;TRUNC(R7:CL7))
The above doesn't work, of course, since TRUNC() doesn't apply to ranges, but it conveys what I'm trying to do in a nutshell -- some of the cells contain text, which SUM() ignores (luckily), but they flummox TRUNC, so I needed to handle the text problem.
I started with ISNUMBER, just to get the ball rolling; ISTEXT has fewer characters, but it's not worth fixing that right now.
FLOOR was equally disappointing for ranges:
=SUM(FLOOR(R7:T7;1))
I tried variations of =SUM(IF(... and searches for ROUNDDOWN range (and variations on that) and such pseudocode as "IFTEXT" and "SUMTRUNC" (and variations on that). I found info on ROUNDDOWN(SUM(... and so forth, but not "SUM(ROUNDDOWN(..." or any equivalent.
In my delirium, I got silly and even tried:
=SUMIF(ISTEXT(S7:U7);0;AND(TRUNC(S7);TRUNC(T7);TRUNC(U7)))
To be clear: {2.9→2 + 2.9→2 + 2.9→2 = 6} ≠ {2.9+2.9+2.9 = 8.7→8}. I'm looking for a 6, not an 8 (I'd joke about sixes and sevens, but I'm way past pumpkin o'clock and 2.428571 takes up too much space).
My current test-kludge is:
=SUM(IF(ISNUMBER(R7);ROUNDDOWN(R7);0);IF(ISNUMBER(S7);ROUNDDOWN(S7);0);IF(ISNUMBER(T7);ROUNDDOWN(T7);0); ... ;IF(ISNUMBER(AX7);ROUNDDOWN(AX7);0))
It ends at AX7 only because of the char count. I hope to SUM the whole row in a single sweep, but that ain't gonna cut it. I could do it in large chunks in multiple cells, and then add those cells up, but oy gevalt.
Since it's already ugly anyway, I could use the following to save a few characters, but this would only mean being able to extend the range maybe 6 further cells (not much point in that):
=IF(ISTEXT(R7);0;TRUNC(R7))+IF(ISTEXT(S7);0;TRUNC(S7))+IF(ISTEXT(S7);0;TRUNC(S7))
I'm seriously considering simply going down a bunch of rows (to below my data cells) and entering the following, then copying the cell and pasting it to a complementary range, and telling the SUM cells to just sum up their respectively shadowed rows (instead of the data rows that they sit in):
=IF(ISTEXT(R7);0;TRUNC(R7))
Sorry for the rambling; I need sleep. This started as a need, then multiple failed attempts became a grudge match of principle and obstinacy, and now I'm just plugging away at it out of blind habit developed over the past 2-3 days (hopefully I won't forget what the purpose was).
In summary...: ++?????++ Out of Cheese Error +++DIVIDE BY CUCUMBER.
I'm comfortable enough with macros, though it's been ~7 years (and that was in Excel). Thanks in advance, even if the answer is that I'm stuck with one of these! 🙂
EDIT: I don't see a way to attach a .csv here (though I could open the .csv with Notepad, and copy-and-paste the contents if that would help anyone), so here's a set of pics:
This question already has answers here:
Given a set of intervals, how to find the maximum number of intersections among them,
(3 answers)
Closed 3 years ago.
On the input, we are given a number N stating how many presentations are to be given, which is followed by N rows of start and end times given in HHMM format
Example:
3
0800 0900
0830 1000
0900 1030
The code must calculate the maximum nuber of occupied rooms (one room can host only one presentation at a time, therefore expected output to the provided example is 2.
My first idea was to create a table of bools 1440 x N (number of minutes in a day by number of presentations) and fill each minute when a presentation is being held, and later go column by column and find the maximum value of presentations at a time. It works, but I'm sure it can be done faster and better. Can someone suggest how to do it in a better manner?
Pretty simple actually: we just simulate the process. First of all, it doesn't matter which presentations are going on right now. All we care about is the number of presentations happening. So we'll just have a counter which we update when a presentation starts or ends.
We could iterate over every minute for the simulation, but our counter only changes when a presentation starts or ends, so we can just make a big list of all the start and end events, sort the list by time, and iterate through the list adjusting our counter appropriately.
The way you propose needs 1440 x 3 = 4320 values. As you can tell, that's very inefficient. A better way is to store just the minutes that are actually required. To save even less values, divide the time into slots of 30 minutes.
Now use a dictionary (std::map) to keep a count of how many presentations are during the same time slot. For your example this gives:
std::map<std::string, int> slots =
{
{"0800", 1},
{"0830", 2},
{"0900", 2},
{"0930", 2},
{"1000", 1},
{"1030", 1},
}
I'll let you figure out how to implement this.
I've been doing some work for my exams in a few days and I'm going through some past papers but unfortunately there are no corresponding answers. I've answered the question and I was wondering if someone could tell me if I am correct.
My question is
(c) A transactional dataset, T, is given below:
t1: Milk, Chicken, Beer
t2: Chicken, Cheese
t3: Cheese, Boots
t4: Cheese, Chicken, Beer,
t5: Chicken, Beer, Clothes, Cheese, Milk
t6: Clothes, Beer, Milk
t7: Beer, Milk, Clothes
Assume that minimum support is 0.5 (minsup = 0.5).
(i) Find all frequent itemsets.
Here is how I worked it out:
Item : Amount
Milk : 4
Chicken : 4
Beer : 5
Cheese : 4
Boots : 1
Clothes : 3
Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:
{items} : Amount
{Milk, Chicken} : 2
{Milk, Beer} : 4
{Milk, Cheese} : 1
{Chicken, Beer} : 3
{Chicken, Cheese} : 3
{Beer, Cheese} : 2
Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?
I agree you should go for the Apriori Algorithm.
The Apriori algorithm is based on the idea that for a pair o items to be frequent, each individual item should also be frequent.
If the hamburguer-ketchup pair is frequent, the hamburger itself must also appear frequently in the baskets. The same can be said about the ketchup.
So for the algorithm, it is established a "threshold X" to define what is or it is not frequent. If an item appears more than X times, it is considered frequent.
The first step of the algorithm is to pass for each item in each basket, and calculate their frequency (count how many time it appears).
This can be done with a hash of size N, where the position y of the hash, refers to the frequency of Y.
If item y has a frequency greater than X, it is said to be frequent.
In the second step of the algorithm, we iterate through the items again, computing the frequency of pairs in the baskets. The catch is that
we compute only for items that are individually frequent. So if item y and item z are frequent on itselves,
we then compute the frequency of the pair. This condition greatly reduces the pairs to compute, and the amount of memory taken.
Once this is calculated, the frequencies greater than the threshold are said frequent itemset.
(http://girlincomputerscience.blogspot.com.br/2013/01/frequent-itemset-problem-for-mapreduce.html)
There are two ways to solve the problem:
using Apriori algorithm
Using FP counting
Assuming that you are using Apriori, the answer you got is correct.
The algorithm is simple:
First you count frequent 1-item sets and exclude the item-sets below minimum support.
Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.
The algorithm can go on until no item-sets are greater than threshold.
In the problem given to you, you only get 1 set of 2 items greater than threshold so you can't move further.
There is a solved example of further steps on Wikipedia here.
You can refer "Data Mining Concepts and Techniques" by Han and Kamber for more examples.
OK to start, you must first understand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Now, the amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database. Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.
Now you must understand that association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in real- life applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.
Given a set of transactions T (the database), the problem of mining association rules is to discover all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf).
I hope that once you understand the very basics of data mining that the answer to this question shall become apparent.
I'm currently implementing a histogram that will show a very large scale data using Qt and I have some doubts about which data structure(s) I should be using for my problem. I will be displaying the amount of queries received from users of an application and the way I should display is as follows -in a single application that will show different histograms upon clicking different "show me this data etc." buttons-
1) Display the histogram of total queries per every month -4 months of data here, I
kept four variables and incremented them as I caught queries belonging to those months
in the CSV file-
2) Display the histogram of total queries per every single day in a selected month -I was thinking of using 4 QVectors to represent the months for this one, incrementing every element of the vector (day), as I come by that specific day -e.g. the vector represents the month of August and whenever I come across a data with 2011-08-XY , I will increment the (XY + 1)th element of that vector by 1- my second alternative is to use 4 QLinkedList's for the sake of better complexity but I'm not sure if the ways I've come up with are efficient enough and I'm willing to listen to any other idea.
3) Here's where things get a bit complicated. Display the histogram of total queries per every hour in a selected day and month. The data represented is multiplied in a vast manner and I don't know which data structure -or combination of structures- I should use to implement this one. A list of lists perhaps?
Any ideas on my problems at 2) and 3) would be helpful, Thanks in advance.
Actually, it shouldn't be too unmanageable to always do queries per hour. Assuming that the number of queries per hour is never greater than the maximum int value, that's only 24 ints per day = 32 bits or 64 depending on your machine. Assuming 32 bits, then you could get up to 28 years worth of data per MB.
There's no need to transfer the month/year - your program can work that out. Just assign hour 0 to the earliest point in your data, which you keep as a constant, then work out the date based on hours passed since then.
This avoids having to have a list of lists or anything fancy - just use an array where each address contains the number of hours since hour 0, and the number of queries for that hour.
Why don't you simply use a classical database?
When you start asking these kind of question I think it is a good time to consider a more robust structure.There are multiple data structures implemented inside any DB, optimized either for different access type. You should considerate at least lookup, insertion, deletion, range queries. There is no structure which is better than the others in all costs, so there is always a trade-off.
Qt has some database classes you can use. I never used the Qt SQL library, but I think you should give it a shot. Fortunately, there is a Qt SQL programming guide at the end of the page linked.
Okay, I think I might be over-complicating this issue but I truly am stuck. Basically, I am trying to model a weight set, specifically an olympic weight set. So I have the bar which is 45 lbs, then I have 2 weights of 2.5 lbs, 4 of 5 lbs, and then 2 of 10, 25, 35, and 45 respectively. This makes a total of 300 lbs.
bar = 45 lbs
2 of 2.5
4 of 5
2 of 10
2 of 25
2 of 35
2 of 45
I want to model this weight set so that I have this information: the weight and the quantity of weights I have. I know I could hard-code this but I eventually want to let the user enter how many of each weight they may have.
Anyways, originally I thought I could simply have an NSDictionary with the key being the weight, such as 35, and the value being the quantity.
Naturally I cannot store primitives in an NSDictionary or other Cocoa collection, so I have to encapsulate each integer in an NSNumber. However, the point of my modeling this weight set is so that I can simulate the use of certain weights. For example, if I use a 35 lbs. weight that takes 2 off (one for each side), so I have to go and edit the value for the 35 lbs. weight to reflect the fact that I have deducted 2 from the quantity.
This involves the tedious task of unboxing the NSNumber, converting back to a primitive, doing the math, and then re-boxing into an NSNumber and assigning that new result to the appropriate location in the NSDictionary. After searching around a bit, I confirmed my initial premonition that this was not a good idea.
So I have a couple questions. First of all, is there a better way of modeling a weight set aside from using a dictionary-style solution? If not, what is the suggested way to go about doing this? Do I have to leave the cocoa-realm and resort to using some sort of C++ STL template such as a map?
I have seen some information on NSDecimalNumber, should I just use that?
Like I said, I wouldn't be surprised if I am over-complicating this. I would really appreciate any help, thanks.
EDIT: I am beginning to think that the weight set 'definition' as described should indeed be immutable, as it is a definition after all. Then when I use a certain weight, I can add to some sort of tally. The thing is that the tally will also be some form of collection whose values I will be modifying (adding to), so that I can correlate it to the specific weight. So I guess I am in the same problem.
I think where I am trying to get at is creating a 'clone' so to speak of the weight set definition which I can easily modify (to simulate the usage of individual weights).
Sorry, I'm burned out.
Storing this in a dictionary isn't a natural fit. I think the best approach would be to make a Weight class that represents the weights, and stick them in an NSCountedSet. You can get the individual kinds of Weight and the counts for each kind, and you can get the weight of the whole set with [weightSet valueForKeyPath:#"#sum.weightInPounds"] (assuming the Weights have a weightInPounds property that represents how heavy they are).
You could use NSNumbers in the NSCountedSet and sum them with #sum.integerValue if you wanted, but it seems a bit awkward to me. At any rate, NSCountedSet is definitely a more natural collection than an NSDictionary for storing — well, a counted set.
There's nothing wrong with storing your numbers in an NSDictionary! The question you referenced was referring to complicated, frequent math. Converting from NSNumber and back is slow compared to simple int addition, but is still super-fast compared to human perception. I think your dictionary idea is EDIT: not as good as Chuck's NSCountedSet idea. :)