i have a database that contain some measure. The problem is that the measure are saved in the main units (for example, 1 mV is saved as 0.001 V).
Now I use crytal report to generate a report with these measure, but I want to convert 0.001V into 1mV.
How can i do this?
I tryed with:
if lcase({database.result_type})="eval" then
{#Test_Result}
else
(
if {database.measure}<0.001 then
{database.measure}*1000000 & "uV"
else if {database.measure}<1 then
{database.measure}*1000 & "mV"
else if {database.measure}<1000 then
{database.measure} & "V"
);
but it doesn't work. The first IF is to understand if it's a PASS/FAIL test or a measurement.
How can i do?
You formula needs to produce a consistent result, either a string or a number, but not both:
if lcase({database.result_type})="eval" then
// assuming that this is a string
{#Test_Result}
else
(
// assuming that `{database.measure}` is a number
if {database.measure}<0.001 then
ToText({database.measure}*1000000) & "uV"
else if {database.measure}<1 then
ToText({database.measure}*1000) & "mV"
else if {database.measure}<1000 then
ToText({database.measure}) & "V"
);
Related
I have a data source in tableau that looks something similar to this:
SKU Backup_Storage
A 5
A 1
B 2
B 3
C 1
D 0
I'd like to create a calculated field in tableau that performs a SUM calculation IF the SKU column contains the string 'A' or 'D' , and to perform an AVERAGE calculation if the SKU column contains the letters 'C' or 'B'
This is what I am doing:
IF CONTAINS(ATTR([SKU]),'A') or
CONTAINS(ATTR([SKU]),'D')
THEN SUM([Backup_Storage])
ELSEIF CONTAINS(ATTR([SKU]),'B') or
CONTAINS(ATTR([SKU]),'C')
THEN AVG([Backup_Storage])
END
UPDATE - desired output would be:
SKU BACKUP
A, D 6 (This is the SUM OF A and D)
B, C 2 (This is the AVG of B and C)
The calculation above shows as valid, however, I see NULLS in my data source table.
Any suggestion is appreciated.
I have named the calculated field:
SKU_FILTER_CALCULATION
Basically, IF THEN ELSE condition works when one test that is either TRUE/FALSE. Your specified condition is not a proper use case of IF THEN ELSE because SKUs can take all possible values. See it like this..
your data
SKU Backup_Storage
A 5
A 1
B 2
B 3
C 1
D 0
Let's name your calc field as CF, then CF will take value A in first row and will output SUM(5) = 5. For second row it will output sum(1) = 1, for third and onward rows it will output as avg(2) = 2, avg(3) = 3, avg(1) and sum(0) respectively. all these values just equals [Backup_storage] only and I'm sure that this you're not trying to get.
If instead you are trying to get sum(5,1,0) + avg(2,3,1) (obviously i have assumed + here) which equals 8 i.e. one single value for whole dataset, please proceed with this calculated field..
SUM(IF CONTAINS([SKU], 'A') OR CONTAINS([SKU], 'D')
THEN [Backup storage] END)
+
AVG(IF CONTAINS([SKU], 'B') OR CONTAINS([SKU], 'C')
THEN [Backup storage] END)
This will return an 8 when put to view
Needless to say, if you want any other operator instead of + you have to change that in CF accordingly
As per your edited post, I suggest a different methodology. Create diff groups where you want to perform different aggregations
Step-1 Create groups on SKU field. I have named this group as SKUG
Step-2 create a calculated field CF as
SUM(ZN(IF CONTAINS([SKU], 'A') OR CONTAINS([SKU], 'D')
THEN [Backup storage] END))
+
AVG(ZN(IF CONTAINS([SKU], 'B') OR CONTAINS([SKU], 'C')
THEN [Backup storage] END))
Step-3 get your desired view
Good Luck
Variable name is PRC. This is what I have so far. First block to delete negative values. Second block is to delete missing values.
data work.crspselected;
set work.crspraw;
where crspyear=2016;
if (PRC < 0)
then delete;
where ticker = 'SKYW';
run;
data work.crspselected;
set work.crspraw;
where ticker = 'SKYW';
where crspyear=2016;
where=(PRC ne .) ;
run;
Instead of using a function to remove negative and missing values, it can be done more simply when inputting or outputting the data. It can also be done with only one data step:
data work.crspselected;
set work.crspraw(where = (PRC >= 0 & PRC ^= .)); * delete values that are negative and missing;
where crspyear = 2016;
where ticker = 'SKYW';
run;
The section that does it is:
(where = (PRC >= 0 & PRC ^= .))
Which can be done for either the input dataset (work.crspraw) or the output dataset (work.crspselected).
If you must use a function, then the function missing() includes only missing values as per this answer. Hence ^missing() would do the opposite and include only non-missing values. There is not a function for non-negative values. But I think it's easier and quicker to do both together simultaneously without a function.
You don't need more than your first test to remove negative and missing values. SAS treats all 28 missing values (., ._, .A ... .Z) as less than any actual number.
I have a query that returns a dynamic number of columns. I need to dynamically add the same amount of custom columns. I have successfully gotten this far. I'm stuck creating the formulas for the custom columns. This is what I have so far. (This is not the actual query, this is simplified)
Here is the Code:
Test = List.Accumulate(MyList, Source,
(state, current) => Table.AddColumn(
state, "A Temp" & Number.ToText(current), each [A1])
)
For now, I just added [A1] as a place holder for the formula. I need the formula to accumulate as follows:
A Temp1 = [A1] / [TOTAL]
A Temp2 = [A2] / [TOTAL]
A Temp3 = [A3] / [TOTAL]
Above is not actual code. Just what I need the formulas to do for each custom column.
Is this possible? I have tried everything I could think of. I'm using power query in excel BTW.
This isn't exactly what you asked for, but I think it will help.
Test = List.Accumulate(
List.Select(Table.ColumnNames(Source), each _ <> "TOTAL"),
Source,
(state, current) => Table.AddColumn(state,
"Temp " & current,
each Record.Field(_, current) / [TOTAL]))
It's not exactly what you asked for as it gives column names like Temp A1 instead of A Temp1.
I have an issue with a Crystal Report that I'm creating. I am using fields from a database and am pulling in the result value where the analysis field is equal to certain values.
In the condition the first check looks at the analysis field and checks if its equal to "Conf". The result for this is "<10"
The second check looks at the analysis field and checks if its equal to "Original". The result for this is "20".
I want the results to display in the order above however with the following basic logic it returns the result of 20.
if analysis = "conf" then result
else if analysis = "Original" then result
I was having this issue with multiple records however solved it by converting both results to numbers (toNumber(Result)). However this record has the less than symbol contained within the field value which causes the conf result to "be skipped" and will display the original result instead. I've tried a few things without success. Here is the code for the condition of where I'm at below. I fell this is way to complex logic but I've just added to it as I've had ideas and it shows what I've tried.
if {UNITS} = "CFU_G" then
if {ANALYSIS} = "CONF" and
{RESULT}="" or
{RESULT} = "0" then 0
else if {ANALYSIS} = "CONF"
then if isNumeric({RESULT}) then
tonumber({RESULT}) else
tonumber(Replace ({RESULT}, "<", ""))
else
if {UNITS} = "CFU_G" then
if {ANALYSIS} = "Original" and
{RESULT}="" or
{RESULT} = "0" then 0
else if {ANALYSIS} = "Original"
then if isNumeric({RESULT}) then
tonumber({RESULT}) else
tonumber(Replace ({RESULT}, "<", ""))
Thanks,
Tom
This was the solution I came up with.
Field 1
whileprintingrecords;
stringvar vResult := "";
Field 2
whileprintingrecords;
stringvar vResult;
vResult := if {RESULT.UNITS} = "CFU_G"
and {RESULT.ANALYSIS} = "CRA_LIS_ENU_CONF_MPCRAM29"
then {RESULT.FORMATTED_ENTRY}
else if {RESULT.ANALYSIS} = "CRA_LIST_ENU_MPCRAM29"
and {RESULT.UNITS} = "CFU_G"
and vResult = ""
then {RESULT.FORMATTED_ENTRY}
Field 3
whileprintingrecords;
stringvar vResult;
vResult;
I'm trying to classify an unlabelled string using Weka, I'm not an expert in data mining so i have been struggling with the different terms. What I'm doing is I am providing the training data and setting the unlabeled string after running the M5Rules classifier, I'm actually getting an output but i have no idea what it mean:
run:
{17 1,35 1,64 1,135 1,205 1,214 1,215 1,284 1,288 1,309 1,343 1,461 1,493 1,500 1,552 1,806 -0.038168} | -0.03816793850062397
-0.03816793850062397 ->
Results
======
Correlation coefficient 0
Mean absolute error 0
Root mean squared error 0
Relative absolute error 0 %
Root relative squared error 0 %
Total Number of Instances 1
BUILD SUCCESSFUL (total time: 1 second)
The source code is as follows:
public Categorizer(){
try{
//*** READ ARRF FILES *///////////////////////////////////////////////////////
//BufferedReader trainReader = new BufferedReader(new FileReader("c:/Users/Yehia A.Salam/Desktop/dd/training-data.arff"));//File with text examples
//BufferedReader classifyReader = new BufferedReader(new FileReader("c:/Users/Yehia A.Salam/Desktop/dd/test-data.arff"));//File with text to classify
// Create trainning data instance
TextDirectoryLoader loader = new TextDirectoryLoader();
loader.setDirectory(new File("c:/Users/Yehia A.Salam/Desktop/dd/training-data"));
Instances dataRaw = loader.getDataSet();
StringToWordVector filter = new StringToWordVector();
filter.setInputFormat(dataRaw);
Instances dataTraining = Filter.useFilter(dataRaw, filter);
dataTraining.setClassIndex(dataRaw.numAttributes() - 1);
// Create test data instances
loader.setDirectory(new File("c:/Users/Yehia A.Salam/Desktop/dd/test-data"));
dataRaw = loader.getDataSet();
Instances dataTest = Filter.useFilter(dataRaw, filter);
dataTest.setClassIndex(dataTest.numAttributes() - 1);
// Classify
FilteredClassifier model = new FilteredClassifier();
model.setFilter(new StringToWordVector());
model.setClassifier(new M5Rules());
model.buildClassifier(dataTraining);
for (int i = 0; i < dataTest.numInstances(); i++) {
dataTest.instance(i).setClassMissing();
double cls = model.classifyInstance(dataTest.instance(i));
dataTest.instance(i).setClassValue(cls);
System.out.println(dataTest.instance(i).toString() + " | " + cls);
System.out.println(cls + " -> " + dataTest.instance(i).classAttribute().value((int) cls));
// evaluate classifier and print some statistics
Evaluation eval = new Evaluation(dataTraining);
eval.evaluateModelOnce(cls, dataTest.instance(i));
System.out.println(eval.toSummaryString("\nResults\n======\n", false));
}
}
catch(FileNotFoundException e){
System.err.println(e.getMessage());
}
catch(IOException i){
System.err.println(i.getMessage());
}
catch(Exception o){
System.err.println(o.getMessage());
}
}
And finally a couple of screenshots in case i made anything wrong in the folder hierarchy:
tl;dr:
You set the class index to a random feature
You have to use a classifier, not a regression algorithm
The problem is how you initialize your data sets. Although weka usually puts the class in the last column, the TextDirectoryLoader doesn't. In fact, you don't need to set the class index manually, it is already set, so remove the lines
dataTraining.setClassIndex(dataRaw.numAttributes() - 1);
dataTest.setClassIndex(dataTest.numAttributes() - 1);
(The first line is wrong anyway, because you use the number of attributes from the raw data set, but choose the column of the already filtered data set.)
If you then run your code, you will get this:
weka.classifiers.functions.LinearRegression: Cannot handle binary class!
As I already guessed, M5Rules is not a classifier, but for regression. If you use a classifier like J48 or RandomForest, you will get a more sensible output. Just change the line
model.setClassifier(new M5Rules());
to
model.setClassifier(new RandomForest());
As for your output, here is what I make of it:
{17 1,35 1,64 1,135 1,205 1,214 1,215 1,284 1,288 1,309 1,343 1,461 1,493 1,500 1,552 1,806 -0.038168} | -0.03816793850062397
-0.03816793850062397 ->
is the result of the lines
System.out.println(dataTest.instance(i).toString() + " | " + cls);
System.out.println(cls + " -> " + dataTest.instance(i).classAttribute().value((int) cls));
So you see the features of your instance serialized as sparse ARFF followed by | and the class.
Usually, the class should be an integer, but from the documentation of M5Rules I get that it is a classifier for regression problems, so you won't get discrete classes, but continuous values, in your case -0.03816793850062397
Since you (incorrectly) set a numerical feature as class label, M5Rules didn't complain and gave you an output. If you use an actual classifier, you will get your labels "health" or "travel".
The rest are standard statistics about the classifiers performance, but they are pretty useless for only one classifier instance. It looks like the one sample was classified correctly, so all errors are zero.
Correlation coefficient 0
Mean absolute error 0
Root mean squared error 0
Relative absolute error 0 %
Root relative squared error 0 %
Total Number of Instances 1
Just in case someone else got the same error with M5P, try to see if the Arff is just a header or empty.
Otherwise try
model.buildClassifier(....)
instead of
model.setClassifier(....);
That solved it for me.