I want to keep the combination that contains 8 values from 1:30, 1 or 2 values from 31:60, and 3 values from 61:70,
and I have the following combinations :
15 6 10 26 7 27 19 51 54 61 64 69 70
# do not keep this b/c there are 4 values from 61:70
23 2 7 29 3 17 4 20 60 56 61 66 68 # keep this one
17 30 24 3 25 5 15 11 43 49 66 67 68 # keep this one
25 13 14 9 29 16 15 4 56 63 66 67 70
# do not keep this b/c there are 4 values from 61:70
14 24 3 17 11 15 27 25 31 59 62 65 69
20 28 8 24 1 18 25 3 44 45 69 61 70
... (32 in totals)
how can i do this ?
edit.
I am not sure how you want to "keep" the required combinations, but to find the combinations you are looking for you can do something like
v <- c(15,6,10,26,7,27,19,51,54,61,64,69,70)
if(sum(v>=1 & v<= 30) == 8 &
sum(v>=31 & v<= 60) %in% c(1L, 2L) &
sum(v>=61 & v<= 70) == 3){TRUE}
else{FALSE}
Thanks to #thelatemail for pointing out that the second condition should accept multiple values.
Related
How can I translate this geometric law problem to numpy ?
Products produced by a machine has a 3% defective rate.
What is the probability that the first defective oc-curs in the fifth item inspected?
P(X= 5) =P(1st 4 non-defective )P( 5th defective)=(0.974)(0.03)
In R > dgeom (x= 4, prob = .03)[1] 0.02655878T
The convention in R is to record X as the number of failures that occur
before the first success.
Is this my numpy code ok ? :
result = np.random.geometric(p=0.03, size=1000)
print(result);
result = (result == 5).sum() / 1000.
print(result * 1000,"%");
I get 17 % as a result with numpy , is it ok ? Seem wrong because there is only 3% defect rate.
This is the numpy result Array :
""" [ 31 20 37 9 47 31 22 7 44 15 52 15 4 14 36 45 26 27
9 48 30 5 7 17 7 24 121 22 23 49 2 26 25 8 4 5
3 27 70 71 3 1 19 22 103 18 14 20 34 45 8 169 11 63
29 71 30 79 75 19 56 9 5 8 15 44 8 12 40 29 46 2
144 69 65 1 4 90 20 187 100 52 46 76 3 105 12 110 31 3
113 18 6 15 127 22 6 7 3 18 123 41 69 104 13 18 2 8
52 35 54 27 74 22 31 27 3 15 21 26 13 3 32 10 131 20
I guess that 31 is the number of integrity checks before a failure .... 20 , 37 etc ...
This is what I would do:
np.random.seed(1)
tests = np.random.choice([0,1], size=(1000,5), p=[0.7,0.3])
((np.argmax(tests, axis=1) == 4) & tests[:,4]==1).mean()
# 0.073
Good evening,
I need to solve a location problem in R and I'm stuck in one of the first steps.
From a .txt file I need to create a distance matrix using the euclidean method.
datos <- file.choose()
servidores <- read.table(datos)
servidores
From which I obtain the following information:
X50 shows the total number of servers.
x5 the number of hubs required.
x120 the total capacity.
The first column shows the distance of x.
The second column shows the distance of y.
The third column shows the requirements of the node.
X50 X5 X120
1 2 62 3
2 80 25 14
3 36 88 1
4 57 23 14
5 33 17 19
6 76 43 2
7 77 85 14
8 94 6 6
9 89 11 7
10 59 72 6
11 39 82 10
12 87 24 18
13 44 76 3
14 2 83 6
15 19 43 20
16 5 27 4
17 58 72 14
18 14 50 11
19 43 18 19
20 87 7 15
21 11 56 15
22 31 16 4
23 51 94 13
24 55 13 13
25 84 57 5
26 12 2 16
27 53 33 3
28 53 10 7
29 33 32 14
30 69 67 17
31 43 5 3
32 10 75 3
33 8 26 12
34 3 1 14
35 96 22 20
36 6 48 13
37 59 22 10
38 66 69 9
39 22 50 6
40 75 21 18
41 4 81 7
42 41 97 20
43 92 34 9
44 12 64 1
45 60 84 8
46 35 100 5
47 38 2 1
48 9 9 7
49 54 59 9
50 1 58 2
I tried to use the dist() function:
distance_matrix <-dist(servidores,method = "euclidean",diag = TRUE,upper = TRUE)
but since x and y are on different columns I am not sure what to do to get a 50x50 matrix with all the distances.
Anybody knows how could I create such matrix?.
Many thanks in advance.
This question already has answers here:
Create integer sequences defined by 'from' and 'to' vectors
(2 answers)
Closed 5 years ago.
Let's say, I created two vectors like:
Ncla = 10
CC.1 = seq(2,((Ncla *Ncla)-Ncla),(Ncla+1))
CC.2 = seq(Ncla,((Ncla *Ncla)-Ncla),(Ncla))
and, I tried to create the following sequence:
#[1] 2 3 4 5 6 7 8 9 10 13 14 15 16 17 18 19 20 24 25 26
# 27 28 29 30 35 36 37 38 39 40 46 47 48 49 50 57 58 59 60 68 69 70 79 80 90
using the statement:
for(i in 1:(Ncla-1)) A.1[i]={c(seq(CC.1[i],CC.2[i],length = 1))}
but it doesn't work.
Any help is greatly appreciated.
Try
unlist(Map(seq, CC.1, CC.2))
# [1] 2 3 4 5 6 7 8 9 10 13 14 15 16 17 18 19 20 24 25 26 27 28 29 30 35
#[26] 36 37 38 39 40 46 47 48 49 50 57 58 59 60 68 69 70 79 80 90
Or
unlist(sapply(seq_along(CC.1), function(i) seq(CC.1[i], CC.2[i])))
Or
A.1 <- list()
for(i in seq_along(CC.1)) A.1[[i]] <- seq(CC.1[i], CC.2[i])
unlist(A.1)
# [1] 2 3 4 5 6 7 8 9 10 13 14 15 16 17 18 19 20 24 25 26 27 28 29 30 35
#[26] 36 37 38 39 40 46 47 48 49 50 57 58 59 60 68 69 70 79 80 90
test<-NULL
for(i in 1:(Ncla-1)) {
A.1=c(seq(CC.1[i],CC.2[i],1))
test<-c(test,A.1)
}
test
Your mistake: You were not saving your results.
I Have a sequence below that has a pattern that does not change. I can create a vector to represent the missing variables to this pattern(below). But I can't seem to figure out a way to print this specific sequence below as a vector. How would one create a vector that shows the sequence below but stops at a specific row(the last row in the pattern), instead of 51? Thanks
bad <- seq(1,51,by=3)
2,3,5,6,8,9,11,12,14,15,17,18,20,21,23,24,26,27,29,30,32,33,35,36,38,39,41,42,44,45,47,48,50,51
The most straightforward way I can think of is to use "recycling" of a logical vector:
(1:51)[c(FALSE, TRUE, TRUE)]
# [1] 2 3 5 6 8 9 11 12 14 15 17 18 20 21 23 24 26 27 29 30 32 33 35 36 38 39
# [27] 41 42 44 45 47 48 50 51
> bad <- 2:51
> bad[!bad %% 3 == 1]
[1] 2 3 5 6 8 9 11 12 14 15 17 18 20 21 23 24 26 27 29 30 32 33 35 36 38 39 41 42 44 45 47 48 50 51
cumsum(rep(c(2,1), 51/3))
probably inefficient though.
a = 2:51
b = seq(1, 51, by=3)
setdiff(a,b)
Say I have vector:
x <- c(11,6,5,3,2,1,25,10,16,12,22,24,19,14,18,32,17,15,8,7,
33,4,27,9,29,13,30,23,20,31,26,21,28)
x
[1] 11 6 5 3 2 1 25 10 16 12 22 24 19 14 18 32 17 15 8 7 33 4 27 9 29 13 30 23 20
[30] 31 26 21 28
I want to identify which elements are not ascending. So, for example, elements 2 to 5 (values 6,5,3,2,1) are out of order because they are less than element 1 (11). Then element 6 is in order because its greater than 11, then all elements until element 16 (32) are out of order. I want to remove those elements.
Vectorized/shortcut way of doing this?
Create some data:
set.seed(1)
x <- sample(100, 30)
x
[1] 27 37 57 89 20 86 97 62 58 6 19 16 61 34 67 43 88 83 32 63 75 17 51 10 21 29 1 28 81 25
Select only those elements that are greater than or equal to the cumulative maximum:
x[x >= cummax(x)]
[1] 27 37 57 89 97