How can I calculate an inner product with an arbitrary number of columns using ddply?

问题

I want to perform an inner product of the first D columns for each row in a data frame with a given array, W. I am trying the following:

W = (1,2,3);
ddply(df, .(id), transform, inner_product=c(col1, col2, col3) %*% W);

This works but I typically may have an arbitrary number of columns. Can I generalize the above expression to handle that case?

Update:

This is an updated example as asked for in the comments:

libary(kernlab);
data(spam);
W = array();
W[1:3] = seq(1,3);
spamdf = head(spam);
spamdf$id = seq(1,nrow(spamdf));
df_out=ddply(spamdf, .(id), transform, inner_product=c(make, address, all) %*% W);

> W
[1] 1 2 3
> spamdf[1,]
  make address  all num3d  our over remove internet order mail receive will
1    0    0.64 0.64     0 0.32    0      0        0     0    0       0 0.64
  people report addresses free business email  you credit your font num000
1      0      0         0 0.32        0  1.29 1.93      0 0.96    0      0
  money hp hpl george num650 lab labs telnet num857 data num415 num85
1     0  0   0      0      0   0    0      0      0    0      0     0
  technology num1999 parts pm direct cs meeting original project re edu table
1          0       0     0  0      0  0       0        0       0  0   0     0
  conference charSemicolon charRoundbracket charSquarebracket charExclamation
1          0             0                0                 0           0.778
  charDollar charHash capitalAve capitalLong capitalTotal type id
1          0        0      3.756          61          278 spam  1
> df_out[1,]
  make address  all num3d  our over remove internet order mail receive will
1    0    0.64 0.64     0 0.32    0      0        0     0    0       0 0.64
  people report addresses free business email  you credit your font num000
1      0      0         0 0.32        0  1.29 1.93      0 0.96    0      0
  money hp hpl george num650 lab labs telnet num857 data num415 num85
1     0  0   0      0      0   0    0      0      0    0      0     0
  technology num1999 parts pm direct cs meeting original project re edu table
1          0       0     0  0      0  0       0        0       0  0   0     0
  conference charSemicolon charRoundbracket charSquarebracket charExclamation
1          0             0                0                 0           0.778
  charDollar charHash capitalAve capitalLong capitalTotal type id inner_product
1          0        0      3.756          61          278 spam  1           3.2

The above example performs a inner product of the first three dimensions with an array W=(1,2,3) of the spam data set available in kernlab package. Here I have explicity specified the first three dimensions as c(make, address, all). Thus df_out[1,"inner_product"] = 3.2.

Instead I want to perform the inner product over all the dimensions without having to list all the dimensions. The conversion to a matrix and back to a data frame seems to be an expensive operation?

回答1:

A strategy along the lines of the following should work:

Convert each chunk to a matrix
Perform a matrix multiplication
Convert results to data.frame

The code:

set.seed(1)
df <- data.frame(
    id=sample(1:5, 20, replace=TRUE),
    col1 = runif(20),
    col2 = runif(20),
    col3 = runif(20),
    col4 = runif(20)
    )

W <- c(1,2,3,4)
ddply(df, .(id), function(x)as.data.frame(as.matrix(x[, -1]) %*% W))

The results:

   id       V1
1   1 4.924994
2   1 5.076043
3   2 7.053864
4   2 5.237132
5   2 6.307620
6   2 3.413056
7   2 5.182214
8   2 7.623164
9   3 5.194714
10  3 6.733229
11  4 4.122548
12  4 3.569013
13  4 4.978939
14  4 5.513444
15  4 5.840900
16  4 6.526522
17  5 3.530220
18  5 3.549646
19  5 4.340173
20  5 3.955517

回答2:

If you want to append a column of cross-products, you could do this (assuming W had the right number of elements to match the non-"id" columns:

df2 <- cbind(df, as.matrix(df[, -grep("id", names(df))]) %*% W )

It does not appear that the .(id) serves any useful purpose, since you are not do a sum of crossproducts within id, and if you were then you wouldn't be using transform but some other aggregating function.

来源：https://stackoverflow.com/questions/8195745/how-can-i-calculate-an-inner-product-with-an-arbitrary-number-of-columns-using-d

标签

dataframe

plyr