How do i find an element wise product of two tensors? Both my tensors are of the same dimension and i want to find their product?
q
1 2 3
2 4 6
w
1 2 3
2 4 6
It should yield:
1 4 9
4 16 36
You can do this by using cmul.
th> torch.cmul(q,w)
1 4 9
4 16 36
As a side note:
q:cmul(w): will multiply them and store the value back into q,
z=torch.cmul(q,w): will multiply them and return a new tensor which will be stored in z.
Related
guys:
I have two matrix as following:
d <- cbind(c(1,2,3,4),c(1,1,1,1),c(1,2,4,8))
v <- cbind(c(2,2,2,2),c(3,3,3,3))
But I want to get a matrix consisted of divj as following:
d1v1 d1v2 d2v1 d2v2 d3v1 d3v2
2 3 2 3 2 3
4 6 2 3 4 6
6 9 2 3 8 12
8 12 2 3 16 24
This is an example of my question,I wonder if you can tell me how to write codes to solve this question.Many thanks.
matrix(apply(v,2,function(x){x*d}),4,6)
I am a R noob, and hope some of you can help me.
I have two data sets:
- store (containing store data, including location coordinates (x,y). The location are integer values, corresponding to GridIds)
- grid (containing all gridIDs (x,y) as well as a population variable TOT_P for each grid point)
What I want to achieve is this:
For each store I want loop over the grid date, and sum the population of the grid ids close to the store grid id.
I.e basically SUMIF the grid population variable, with the condition that
grid(x) < store(x) + 1 &
grid(x) > store(x) - 1 &
grid(y) < store(y) + 1 &
grid(y) > store(y) - 1
How can I accomplish that? My own take has been trying to use different things like merge, sapply, etc, but my R inexperience stops me from getting it right.
Thanks in advance!
Edit:
Sample data:
StoreName StoreX StoreY
Store1 3 6
Store2 5 2
TOT_P GridX GridY
8 1 1
7 2 1
3 3 1
3 4 1
22 5 1
20 6 1
9 7 1
28 1 2
8 2 2
3 3 2
12 4 2
12 5 2
15 6 2
7 7 2
3 1 3
3 2 3
3 3 3
4 4 3
13 5 3
18 6 3
3 7 3
61 1 4
25 2 4
5 3 4
20 4 4
23 5 4
72 6 4
14 7 4
178 1 5
407 2 5
26 3 5
167 4 5
58 5 5
113 6 5
73 7 5
76 1 6
3 2 6
3 3 6
3 4 6
4 5 6
13 6 6
18 7 6
3 1 7
61 2 7
25 3 7
26 4 7
167 5 7
58 6 7
113 7 7
The output I am looking for is
StoreName StoreX StoreY SUM_P
Store1 3 6 479
Store2 5 2 119
I.e for store1 it is the sum of TOT_P for Grid fields X=[2-4] and Y=[5-7]
One approach would be to use dplyr to calculate the difference between each store and all grid points and then group and sum based on these new columns.
#import library
library(dplyr)
#create example store table
StoreName<-paste0("Store",1:2)
StoreX<-c(3,5)
StoreY<-c(6,2)
df.store<-data.frame(StoreName,StoreX,StoreY)
#create example population data (copied example table from OP)
df.pop
#add dummy column to each table to enable cross join
df.store$k=1
df.pop$k=1
#dplyr to join, calculate absolute distance, filter and sum
df.store %>%
inner_join(df.pop, by='k') %>%
mutate(x.diff = abs(StoreX-GridX), y.diff=abs(StoreY-GridY)) %>%
filter(x.diff<=1, y.diff<=1) %>%
group_by(StoreName) %>%
summarise(StoreX=max(StoreX), StoreY=max(StoreY), tot.pop = sum(TOT_P) )
#output:
StoreName StoreX StoreY tot.pop
<fctr> <dbl> <dbl> <int>
1 Store1 3 6 721
2 Store2 5 2 119
Hi everybody I am trying to solve a little problem in R. I want to compute the difference between rows in a dataframe in R. My dataframe looks like this:
df <- data.frame(ID=1:8, x2=8:1, x3=11:18, x4=c(2,4,10,0,1,1,9,12))
I want to create a new column named diff.var. This column saves the results of differences from rows in variable. One posibble solution is using diff() function. When I used this function I got this:
diff(df$x4)
[1] 2 6 -10 1 0 8 3
That works fine but when I try to apply in my dataframe using df$diff.var=diff(df$x4) I got this:
Error in `$<-.data.frame`(`*tmp*`, "diff.var", value = c(2, 6, -10, 1, :
replacement has 7 rows, data has 8
Due to the fact that the firs row doesn't have a previous row to compute the difference I want to set this in zero. I would like to get something this:
ID x2 x3 x4 diff.var
1 8 11 2 0
2 7 12 4 2
3 6 13 10 6
4 5 14 0 -10
5 4 15 1 1
6 3 16 1 0
7 2 17 9 8
8 1 18 12 3
Where the first element of diff.var is zero due to this element doesn't have a previous element. I would like to build a function to set firts element of diff.var is zero and that makes the differences for the next rows. I wish to create a new dataframe with all variables and diff.var because ID is used por posterior analysis with diff.var. diff() doesn't allow to create this new variable. Thanks for your help.
This question was already asked before in this forum and can be found elsewhere. Anyway, do what Frank suggests
df <- data.frame(ID=1:8, x2=8:1, x3=11:18, x4=c(2,4,10,0,1,1,9,12))
df$vardiff <- c(0, diff(df$x4))
df
ID x2 x3 x4 vardiff
1 1 8 11 2 0
2 2 7 12 4 2
3 3 6 13 10 6
4 4 5 14 0 -10
5 5 4 15 1 1
6 6 3 16 1 0
7 7 2 17 9 8
8 8 1 18 12 3
Given a data frame as follows:
id<-c(1,1,1,1,1,1,2,2,2,2,2,2)
t<-c(6,8,9,11,12,14,55,57,58,60,62,63)
p<-c("a","a","a","b","b","b","a","a","b","b","b","b")
df<-data.frame(id,t,p)
row id t p
1 1 6 a
2 1 8 a
3 1 9 a
4 1 11 b
5 1 12 b
6 1 14 b
7 2 55 a
8 2 57 a
9 2 58 b
10 2 60 b
11 2 62 b
12 2 63 b
I want to create a new variable 'ta' such that the value of ta is:
Zero for the row in which 'p' changes from a to b for a given ID (rows 4 and 9) (this I can do)
Within each unique id, when p is 'a', the value of ta should count down from zero by the change in t between the row in question and the row above it. For example, for row 3, the value of ta should be 0 - (11-9) = -2.
Within each unique id, when p is 'b', the value of ta should count up from zero by the change in t between the row in question and the row below it. For example, for row 5, the value of ta should be 0 + (12-11) = 1.
Thus, when complete, the data frame should look as follows:
row id t p ta
1 1 6 a -5
2 1 8 a -3
3 1 9 a -2
4 1 11 b 0
5 1 12 b 1
6 1 14 b 3
7 2 55 a -3
8 2 57 a -1
9 2 58 b 0
10 2 60 b 2
11 2 62 b 4
12 2 63 b 5
I've been playing around with loops and cumsum() and head() and tail() and can't quite make this kind of within id/within condition summing work. There are a number of other questions about working with values from previous or following rows, but I can't quite reshape any of those techniques to work here. Your thoughts are greatly appreciated.
Here you go. This is a split-apply-combine strategy of breaking everything up by id, establishing the transition point between p=='a' and p=='b' and then subtracting values above and below that. It only works if your data are actually ordered in the way you show them here.
do.call('rbind',
lapply(split(df, id), function(x) {
# save values of `0` at transition points in `p`
x <- cbind.data.frame(x, ta=ifelse(c(0,diff(as.numeric(as.factor(x$p))))==1, 0, NA))
# identify indices for those points
w <- which(x$ta==0)
# handle `ta` values for `p=='b'`
x$ta[(w+1):nrow(x)] <- x$ta[w] + (x$t[(w+1):nrow(x)] - x$t[w])
# handle `ta` values for `p=='a'`
x$ta[1:(w-1)] <- x$ta[w] - (x$t[w] - x$t[1:(w-1)])
return(x)
})
)
Result:
id t p ta
1.1 1 6 a -5
1.2 1 8 a -3
1.3 1 9 a -2
1.4 1 11 b 0
1.5 1 12 b 1
1.6 1 14 b 3
2.7 2 55 a -3
2.8 2 57 a -1
2.9 2 58 b 0
2.10 2 60 b 2
2.11 2 62 b 4
2.12 2 63 b 5
I have a table that I routinely compute with R that has three dimensions. I would like to add some tables within the (here 5) marginal tables. What I usually do is like:
A=sample(LETTERS[1:5],100, rep=T)
b=sample(letters[1:2],100, rep=T)
numbers=sample(1:3,100, rep=T)
( tab=table(A,b,numbers) )
( tab1=ftable(addmargins(tab)) )
I would like to add the sum of the first few marginal tables and then the sum of the remaining tables at the bottom, then the grand total. I can imagine that in the resulting ftable I would want the As,Bs,Cs, then the sum of those three, then the Ds, Es, and the sum of those two, then the sum of all of the tables, like:
numbers 1 2 3 Sum
A b
A a 1 5 0 6
b 4 2 2 8
Sum 5 7 2 14
B a 2 6 6 14
b 5 4 5 14
Sum 7 10 11 28
C a 3 2 5 10
b 1 2 2 5
Sum 4 4 7 15
sumac a 6 13 11 30 #### how do i add these three lines
b ....
sum ....
D a 2 1 1 4
b 4 5 3 12
Sum 6 6 4 16
E a 5 3 4 12
b 4 3 8 15
Sum 9 6 12 27
sumde a 7 4 5 20 #### and these
b ....
sum ....
sumae a 13 17 16 46
b 18 16 20 54
Sum 31 33 36 100
As usual I think the solution is prolly many fewer lines than the question. Thanks
Seth Latimer
You could do something like this:
isABC<-ifelse(A %in% c("A", "B", "C"), "ABC", "CD")
( tab=table(isABC,A,b,numbers) )
( tab1=ftable(addmargins(tab)) )
Now you have a larger table which holds even more rows than you want, but those should be easy to drop...