I'd like to calculate the standard deviation over two fields from the same dataset.
example:
MyFields1 = 10, 10
MyFields2 = 20
What I want now, is the standard deviation for (10,10,20), the expected result is 4.7
In SSRS I'd like to have something like this:
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value)
Unfortunately this isn't possible, since (Fields!MyField1.Value + Fields!MyField2.Value) returns a single value and not a list of values. Is there no way to combine two fields from the same dataset into some kind of temporary dataset?
The only solutions I have are:
To create a new Dataset that contains all values from both fields. But this is very annoying because I need about twenty of those and I have six report parameters that need to filter every query. => It's probably getting very slow and annoying to maintain.
Write the formula by hand. But I don't really know how yet. StDevP is not that trivial to me. This is how I did it with Avg which is mathematically simpler:
=(SUM(Fields!MyField1.Value)+SUM(Fields!MyField2.Value))/2
found here: http://social.msdn.microsoft.com/Forums/is/sqlreportingservices/thread/7ff43716-2529-4240-a84d-42ada929020e
Btw. I know that it's odd to make such a calculation, but this is what my customer wants and I have to deliver somehow.
Thanks for any help.
CTDevP is standard deviation.
Such expression works fine for me
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value) but it's deviation from one value (Fields!MyField1.Value + Fields!MyField2.Value) which is always 0.
you can look here for formula:
standard deviation (wiki)
I believe that you need to calculate this for some group (or full dataset), to do this you need set in the CTDevP your scope:
=StDevP(Fields!MyField1.Value + Fields!MyField2.Value, "MyDataSet1")
Related
I have used the following 5 lines to achieve a kalman filter with your work for a smoothed pricing model, and it worked great.
mod = sm.tsa.UnobservedComponents(obs, 'local level')
lm = sm.OLS(obs, xlm, missing='drop').fit()
obs_noise = abs(lm.resid).mean()
params = [obs_noise, obs_noise / obs_noise_level]
mod_filter, mod_smooth = mod.filter(params), mod.smooth(params)
However currently I would like to adjust the filtering smoothness at certain time, for example, when unemployment rate or interest rate made a big surge, I would like to make the output (Kalman filtered/smoothed) value closer to the observed value, while in most other time I will keep the what it is from the model. So, I have created an array, while a few items greater than 1, and the others will be exactly 1.
e.g.: ir_coeff = np.array([1,1,1,1,1.345,1.23,1.78,1,1,1])
What could be the best approach to achieve this? Thank you a lot in advance.
I have tried to include it in the output file with a dot product operation, however it is not reasonable to do this.
I'm new to AMPL and I have data in a text file in matrix form from which I need to use certain values. However, I don't know how to use the matrices directly without having to manually add column and row indexes to them. Is there a way around this?
So the data I need to use looks something like this, with hundreds of rows and columns (and several more matrices like this), and I would like to use it as a parameter with index i for rows and j for columns.
t=1
0.0 40.95 40.36 38.14 44.87 29.7 26.85 28.61 29.73 39.15 41.49 32.37 33.13 59.63 38.72 42.34 40.59 33.77 44.69 38.14 33.45 47.27 38.93 56.43 44.74 35.38 58.27 31.57 55.76 35.83 51.01 59.29 39.11 30.91 58.24 52.83 42.65 32.25 41.13 41.88 46.94 30.72 46.69 55.5 45.15 42.28 47.86 54.6 42.25 48.57 32.83 37.52 58.18 46.27 43.98 33.43 39.41 34.0 57.23 32.98 33.4 47.8 40.36 53.84 51.66 47.76 30.95 50.34 ...
I'm not aware of an easy way to do this. The closest thing is probably the table format given in section 9.3 of the AMPL Book. This avoids needing to give indices for every term individually, but it still requires explicitly stating row and column indices.
AMPL doesn't seem to do a lot with position-based input formats, probably because it defaults to treating index sets as unordered so the concept of "first row" etc. isn't meaningful.
If you really wanted to do it within AMPL, you could probably put together a work-around along these lines:
declare a single-index param with length equal to the total size of your matrix (e.g. if your matrix is 10 x 100, this param has length 1000)
edit the beginning and end of your "matrix" data file to turn it into appropriate format for a single-index parameter indexed from 1 to n
then define your matrix something like this:
param m{i in 1..nrows,j in 1..ncols} := x[j+i*(ncols-1)];
(not tested, I won't promise that I have rows and columns the right way around there!)
But you're probably better off editing the input file into one of the standard AMPL matrix formats. AMPL isn't really designed for data wrangling - you can do it in a pinch but if you're doing this kind of thing repeatedly it may be less trouble to code it in a general-purpose language e.g. Python.
I am trying to reduce the size of my data and I cannot make it work. I have data points taken every minute over 1 month. I want to reduce this data to have one sample for every hour. The problem is: Some of my runs have "NA" value, so I delete these rows. There is not exactly 60 points for every hour - it varies.
I have a 'Timestamp' column. I have used this to make a 'datehour' column which has the same value if the data set has the same date and hour. I want to average all the values with the same 'datehour' value.
How can I do this? I have tried using the if and for loop below, but it takes so long to run.
Thanks for all your help! I am new to Julia and come from a Matlab background.
======= CODE ==========
uniquedatehour=unique(datehour,1)
index=[]
avedata=reshape([],0,length(alldata[1,:]))
for j in uniquedatehour
for i in 1:length(datehour)
if datehour[i]==j
index=vcat(index,i)
else
rows=alldata[index,:]
rows=convert(Array{Float64,2},rows)
avehour=mean(rows,1)
avedata=vcat(avedata,avehour)
index=[]
continue
end
end
end
There are several layers to optimizing this code. I am assuming that your data is sorted on datehour (your code assumes this).
Layer one: general recommendation
Wrap your code in a function. Executing code in global scope in Julia is much slower than within a function. By wrapping it make sure to either pass data to your function as arguments or if data is in global scope it should be qualified with const;
Layer two: recommendations to your algorithm
Statement like [] creates an array of type Any which is slow, you should use type qualifier like index=Int[] to make it fast;
Using vcat like index=vcat(index,i) is inefficient, it is better to do push!(index, i) in place;
It is better to preallocate avedata with e.g. fill(NA, length(uniquedatehour), size(alldata, 2)) and assign values to an existing matrix than to do vcat on it;
Your code will produce incorrect results if I am not mistaken as it will not catch the last entry of uniquedatehour vector (assume it has only one element and check what happens - avedata will have zero rows)
Line rows=convert(Array{Float64,2},rows) is probably not needed at all. If alldata is not Matrix{Float64} it is better to convert it at the beginning with Matrix{Float64}(alldata);
You can change line rows=alldata[index,:] to a view like view(alldata, index, :) to avoid allocation;
In general you can avoid creation of index vector as it is enough that you remember start s and end e position of the range of the same values and then use range s:e to select rows you want.
If you correct those things please post your updated code and maybe I can help further as there is still room for improvement but requires a bit different algorithmic approach (but maybe you will prefer option below for simplicity).
Layer three: how I would do it
I would use DataFrames package to handle this problem like this:
using DataFrames
df = DataFrame(alldata) # assuming alldata is Matrix{Float64}, otherwise convert it here
df[:grouping] = datehour
agg = aggregate(df, :grouping, mean) # maybe this is all what you need if DataFrame is OK for you
Matrix(agg[2:end]) # here is how you can convert DataFrame back to a matrix
This is not the fastest solution (as it converts to a DataFrame and back but it is much simpler for me).
Having a bit of an issue and unsure if it's actually possible to do.
I'm working on a file that I will enter target progression vs actual target reporting the % outcome.
PAGE 1
¦NAME ¦TAR 1 %¦TAR 2 %¦TAR 3 %¦TAR 4 %¦OVERALL¦SUB 1¦SUB 2¦SUB 3¦
¦NAME1¦ 114%¦ 121%¦ 100%¦ 250%¦ 146%¦ 2¦ 0¦ 0%¦
¦NAME2¦ 88%¦ 100%¦ 90%¦ 50%¦ 82%¦ 0¦ 1¦ 0%¦
¦NAME3¦ 82%¦ 54%¦ 64%¦ 100%¦ 75%¦ 6¦ 6¦ 15%¦
¦NAME4¦ 103%¦ 64%¦ 56%¦ 43%¦ 67%¦ 4¦ 4¦ 24%¦
¦NAME5¦ 87%¦ 63%¦ 89%¦ 0%¦ 60%¦ 3¦ 2¦ 16%¦
Now I already have it sorting all rows by the Overall % column so I can quickly see at a glance but I am creating a second page that I need to reference points.
So on the second page I would like to somehow sort and reference different columns for example
PAGE 2
TOP TAR 1¦Name of top %¦Top %¦
TOP TAR 2¦Name of top %¦Top %¦
Is something like this possible to do?
Essentially I'm creating an Employee of the Month form that automatically works out who has topped what.
I'm willing to drop a paypal donation for whoever can figure this out for me as I've been doing it manually every month and would appreciate the time saved
I don't think a complicated array formula is necessary for this - I am suggesting a fairly standard Index/Match approach.
First set up the row titles - you can just copy and transpose them from Page 1, or use a formula in A2 of Page 2 like
=transpose('Page 1'!B1:E1)
The use them in an index/match to get the data in the corresponding column of the main sheet and find its maximum (in C2)
=max(index('Page 1'!A:E,0,match(A2,'Page 1'!A$1:E$1,0)))
Finally look up the maximum in the main sheet to find the corresponding name:
=index('Page 1'!A:A,match(C2,index('Page 1'!A:E,0,match(A2,'Page 1'!A$1:E$1,0)),0))
If you think there could be a tie for first place with two or more people getting the same score, you could use a filter to get the different names:
So if the max score is in B8 this time (same formula)
=max(index('Page 1'!A:E,0,match(A8,'Page 1'!A$1:E$1,0)))
the different names could be spread across the corresponding row using transpose (in C8)
=ArrayFormula(TRANSPOSE(filter('Page 1'!A:A,index('Page 1'!A:E,0,match(A8,'Page 1'!A$1:E$1,0))=B8)))
I have changed the test data slightly to show these different scenarios
Results
Im new to Report Builder and having issues with some expressions that Im trying to implement in a report. I got the standard ones to work however as soon as I try any distinctions, I get error messages. Over the last couple weeks, Ive tried many combinations, read the expression help, google and looking at other questions at internet sites. To reduce my frustrations, I even would jump to other expressions and walk away hoping I would have different insight coming back.
Its probably something simple or something I dont know about writing expressions.
Im hoping that someone can help with these expressions; they are the versions I get the least errors with(usually just expression expected) and show what Im trying to accomplish.
=IIF((Fields!RECORDFLAG.Value)='D',COUNTDISTINCT(Fields!TICKETNUM.Value),0)
=IIF((Fields!TRANSTYPE.Value)='1' and (Fields!RECORDFLAG.VALUE)='A' or
'B',SUM(Fields!DOLLARS.Value),0)
=IIF((Fields!TRANSTYPE.Value)='1' and
(Fields!RECORDFLAG.VALUE)='P',SUM(Fields!DOLLARS.Value),0)
=Sum([DOLLARS] case when [RECORDFLAG]='P' then -1*[DOLLARS])
Thank You.
=IIF((Fields!RECORDFLAG.Value)=”D”,COUNTDISTINCT(Fields!TICKETNUM.Value))
The error message gives you the answer here - no false part of the iif() has been specified. Use =IIF((Fields!RECORDFLAG.Value)=”D”,COUNTDISTINCT(Fields!TICKETNUM.Value), 0)
=IIF((Fields!TRANSTYPE.Value)="1" and (Fields!RECORDFLAG.VALUE)="A" or "B",SUM(Fields!DOLLARS.Value),0)
This is not how an OR works in SSRS. Use:
=IIF((Fields!TRANSTYPE.Value)="1" and (Fields!RECORDFLAG.VALUE="A" or Fields!RECORDFLAG.Value = "B"),SUM(Fields!DOLLARS.Value),0)
The 0s are returned due to your report design. countdistinct() is an aggregate function - it's meant to be used on a set of data. However, your iif() is only testing on a per row basis - you're basically saying "if the current row is thing, count all the distinct values" which doesn't make sense. There are a couple of ways forward:
You can count the number of times a certain value occurs in a given condition using a sum(). This is not the same as the countdistinct(), but if you use =sum(iif(Fields!RECORDFLAG.Value = "D", 1, 0)) then you will get the number of times RECORDFLAG is D in that set. Note: this requires the data to be aggregated (so in SSRS, grouped in a tablix).
You can use custom code to count distinct values in a set. See https://itsalocke.com/aggregate-on-a-lookup-in-ssrs/. You can apply this even if you have only one dataset - just reference the same one twice.
You can change the way your report works. You can group on Fields!RECORDFLAG.Value and filter the group to where Fields!RECORDFLAG.Value = "D". Then in your textbox, use =countdistinct(Fields!TICKETNUM.Value) to get the distinct values for TICKETNUM when RECORDFLAG is D.