I have this simple example in my DB
The relationship between each Grid-Node has a distance and time property.
The red node, represents a user. I would like to retrieve the closest Taxi for the user.
First i have a query for knowing in which grids i have taxis
MATCH (u:User)-[r:PICK_UP]->(g:Grid)-[r2:TO*1..3]-(g2:Grid)<-[r3:TRIP|:IS_ON]-(t:Taxi)
RETURN g2
As a result i got Grid3, 7, 8 and 11.
But i would like to retrieve the grids that satisfy the condition r2.time <= 5
in this case how can i use the reduce operation:
reduce(totalTime = 0, x IN ---?|totalTime + x.time) AS totalTime
WHERE totalTime <= 5
Any suggestions?
Thank you in advance
I'd try something like this:
MATCH (:User)-[:PICK_UP]->(g1:Grid),
p = (g1)-[:TO*..2]-(g2:Grid),
(g2)<-[:TRIP|IS_ON]-(:Taxi)
WITH g2, REDUCE(totalTime = 0, x IN RELATIONSHIPS(p) | totalTime + x.time) AS totalTime
WHERE totalTime <= 5
RETURN g2;
Related
This is the R script that I am attempting to recreate using a CASE WHEN statement in SQL:
dat[ ,X_1_7_Spline := pmax(1,pmin(ifelse(is.na(X),1,X),7))]
It seems that this command is telling the parser to return the parallel maxima of a vector containing a conditional statement as long as the value of variable X lies between 1 and the parallel minima of some value and 7 (as long as the value is not null). It then seems to join the new column containing these values back to the original dataset (dat). I am having some troubles representing the "pmax(1,pmin(ifelse(is.na(X),1,X),7))" portion of the code in my SQL query and would appreciate any ideas on how I might be able to do this effectively.
I have something very remedial right now, which I know does not express this above statement properly:
CASE WHEN MAX(IF(ISNOTNULL(X) AND MIN(X)=1 AND MAX(X)=7) then 1 else X end as X_1_7_Spline
Any thoughts/feedback would be greatly appreciated as I am still trying to understand the R script. Thanks in advance for any insight on this issue.
ifelse(is.na(X),1,X) can be translated into SQL's COALESCE(X, 1); and
pmin and pmax logic can be placed in a CASE WHEN (as you've started)
Perhaps this?
CASE WHEN X < 1 THEN 1
WHEN X > 7 THEN 7
ELSE coalesce(X, 1) END as NewX
We don't need to worry about coalesceing the X < 1 or X > 7 because null < 1 does not resolve as true, so it does not accept that case.
Demo in R using sqldf:
library(data.table)
dat <- data.table(X = c(-1,5,9,NA))
dat[, X_1_7_Spline := pmax(1,pmin(ifelse(is.na(X),1,X),7)) ]
sqldf::sqldf("select *, (CASE WHEN X < 1 THEN 1 WHEN X > 7 THEN 7 ELSE coalesce(X,1) END) as NewX from dat")
# X X_1_7_Spline NewX
# 1 -1 1 1
# 2 5 5 5
# 3 9 7 7
# 4 NA 1 1
I am trying to define the trigger point when wt1(Moving average 1) crosses over wt2(moving average 2) and add it to the column ['side'].
So basically add 1 to side at the moment wt1 crosses above wt2.
This is the current code I am using but doesn't seem to be working.
for i in range(len(df)):
if df.wt1.iloc[i] > df.wt2.iloc[i] and df.wt1.iloc[i-1] < df.wt2.iloc[i-1]:
df.side.iloc[1]
If I do the following:
long_signals = (df.wt1 > df.wt2)
df.loc[long_signals, 'side'] = 1
it return the value of 1 the entire time wt1 is above wt2, which is not what i am trying to do.
Expected outcome is when wt1 crosses above wt2 side should be labeled as 1.
Help would be appreciated!
Use shift in your condition:
long_signals = (df.wt1 > df.wt2) & (df.wt1.shift() <= df.wt2.shift())
df.loc[long_signals, 'side'] = 1
df
if you do not like NaNs in 'side', use df.fillna(0) at the end
Your first piece of code also works with the following small modification
for i in range(len(df)):
if df.wt1.iloc[i] > df.wt2.iloc[i] and df.wt1.iloc[i-1] <= df.wt2.iloc[i-1]:
df.loc[i,'side'] = 1
I know it was an answer to my question but I can't find it if you can help me with the link (here was the answer on the site). I want to display in Textbox (if I have for example)
Textbox1.Text=3,4,8,17,19,23,24,27,31,32,41,42,48,60,63,66,69,75,78,79
I Want Output:
Textbox2.Lines(0) = 3 - Count Number of interval 1-10
Textbox2.Lines(1) = 2 - Count Number of interval 10-20
Textbox2.Lines(2) = 3 - Count Number of interval 20-30
Textbox2.Lines(3) = 2 - Count Number of interval 30-40
Textbox2.Lines(4) = 3 - Count Number of interval 40-50
Textbox2.Lines(5) = 1 - Count Number of interval 50-60
Textbox2.Lines(6) = 4 - Count Number of interval 60-70
Textbox2.Lines(7) = 3 - Count Number of interval 70-80
Here is some help:
You need to convert your list of numbers from a string(s) into integers:
Dim lst As New List(Of Integer)
For Each item As String In Textbox1.Split(","c)
lst.Add(Convert.ToInt32(Item))
Next
Then you can use LINQ to query for ranges:
Dim count = lst.AsEnumerable().Count(Function(x) x>= 1 AndAlso x < 10)
You need to use AsEnumerable otherwise the standard Count() hides the LINQ extension method Count(Func)
Best of luck!
I'd like to calculate how many different variations of a certain amount of numbers are possible. The number of elements is variable.
Example:
I have 5 elements and each element can vary between 0 and 8. Only the first element is a bit more defined and can only vary between 1 and 8. So far I'd say I have 8*9^4 possibilities. But I have some more conditions. As soon as one of the elements gets zero the next elements should be automatically zero as well.
E.G:
6 5 4 7 8 is ok
6 3 6 8 0 is ok
3 6 7 0 5 is not possible and would turn to 3 6 7 0 0
Would somebody show me how to calculate the amount of combinations for this case and also in general, because I'd like to be able to calculate it also for 4 or 8 or 9 etc. elements. Later on I'd like to calculate this number in VBA to be able give the user a forecast how long my calculations will take.
Since once a 0 is present in the sequence, all remaining numbers in the sequence will also be 0, these are all of the possibilities: (where # below represents any digit from 1 to 8):
##### (accounts for 8^5 combinations)
####0 (accounts for 8^4 combinations)
...
#0000 (accounts for 8^1 combinations)
Therefore, the answer is (in pseudocode):
int sum = 0;
for (int x = 1; x <= 5; x++)
{
sum = sum + 8^x;
}
Or equivalently,
int prod = 0;
for (int x = 1; x <= 5; x++)
{
prod = 8*(prod+1);
}
great thank you.
Sub test()
Dim sum As Single
Dim x As Integer
For x = 1 To 6
sum = sum + 8 ^ x
Next
Debug.Print sum
End Sub
With this code I get exactly 37488. I tried also with e.g. 6 elements and it worked as well. Now I can try to estimate the calculation time
I am new to R and am trying to find a better solution for accomplishing this fairly simple task efficiently.
I have a data.frame M with 100,000 lines (and many columns, out of which 2 columns are relevant to this problem, I'll call it M1, M2). I have another data.frame where column V1 with about 10,000 elements is essential to this task. My task is this:
For each of the element in V1, find where does it occur in M2 and pull out the corresponding M1. I am able to do this using for-loop and it is terribly slow! I am used to Matlab and Perl and this is taking for EVER in R! Surely there's a better way. I would appreciate any valuable suggestions in accomplishing this task...
for (x in c(1:length(V$V1)) {
start[x] = M$M1[M$M2 == V$V1[x]]
}
There is only 1 element that will match, and so I can use the logical statement to directly get the element in start vector. How can I vectorize this?
Thank you!
Here is another solution using the same example by #aix.
M[match(V$V1, M$M2),]
To benchmark performance, we can use the R package rbenchmark.
library(rbenchmark)
f_ramnath = function() M[match(V$V1, M$M2),]
f_aix = function() merge(V, M, by.x='V1', by.y='M2', sort=F)
f_chase = function() M[M$M2 %in% V$V1,] # modified to return full data frame
benchmark(f_ramnath(), f_aix(), f_chase(), replications = 10000)
test replications elapsed relative
2 f_aix() 10000 12.907 7.068456
3 f_chase() 10000 2.010 1.100767
1 f_ramnath() 10000 1.826 1.000000
Another option is to use the %in% operator:
> set.seed(1)
> M <- data.frame(M1 = sample(1:20, 15, FALSE), M2 = sample(1:20, 15, FALSE))
> V <- data.frame(V1 = sample(1:20, 10, FALSE))
> M$M1[M$M2 %in% V$V1]
[1] 6 8 11 9 19 1 3 5
Sounds like you're looking for merge:
> M <- data.frame(M1=c(1,2,3,4,10,3,15), M2=c(15,6,7,8,-1,12,5))
> V <- data.frame(V1=c(-1,12,5,7))
> merge(V, M, by.x='V1', by.y='M2', sort=F)
V1 M1
1 -1 10
2 12 3
3 5 15
4 7 3
If V$V1 might contain values not present in M$M2, you may want to specify all.x=T. This will fill in the missing values with NAs instead of omitting them from the result.