Friday, February 01, 2013 11:10 AM
I am Developing a Neural Network Model to predict the placement of certain objects on a diagram , by predicting the X-Coordinate and Y-Coordinate of these objects.
Some predictions for the (X,Y) topple are being returned same for different objects.
Is there a way to make sure list of predicted results returned are unique ?
by saying something like previous predictions are no longer possible results for a prediction ?
or if there's a way to return the next most probable prediction ?
If not maybe i can return all possible predictions for an object then by code, or SQL script return a unique result for each object.
Which i dont know how to do in DMX.
Here is the DMX i am currently using:
SELECT t.[FileName], t.[SheetName], t.[ID], t.[Pointer2DX], t.[Pointer2DY], t.[Pointer3DX], t.[Pointer3DY], t.[Pointer3DZ], t.[Type], Predict([Symbol].[Symbol2DX]) AS PredictedSymbol2DX, predictprobability([Symbol].[Symbol2DX]) AS ProbSymbol2DX, Predict([Symbol].[Symbol2DY]) AS PredictedSymbol2DY, predictprobability([Symbol].[Symbol2DY]) AS ProbSymbol2DY From [Symbol] PREDICTION JOIN OpenQuery ([7lpt-aghali], 'SELECT [FileName] ,[SheetName] ,[Type] ,[ID] ,[Pointer2DX] ,[Pointer2DY] ,[Pointer3DX] ,[Pointer3DY] ,[Pointer3DZ] ,[Symbol2DX] ,[Symbol2DY] ,[SymbolWidth] ,[SymbolHeight] From [Symbol] order by [FileName],[SheetName], [ID]' ) AS t ON [Symbol].[ID] = t.[ID] AND [Symbol].[Pointer2DX] = t.[Pointer2DX] AND [Symbol].[Pointer2DY] = t.[Pointer2DY] AND [Symbol].[Pointer3DX] = t.[Pointer3DX] AND [Symbol].[Pointer3DY] = t.[Pointer3DY] AND [Symbol].[Pointer3DZ] = t.[Pointer3DZ] AND [Symbol].[Type] = t.[Type]
Any help is appreciated thnx
Monday, February 11, 2013 7:56 AMModerator
The Microsoft Neural Network algorithm supports both classification and regression. You can use prediction functions with these models to provide new data and create either singleton or batch predictions. When you perform a prediction join, you map the model to a new data source, and Analysis Services then makes predictions for each row in the new data based on patterns in the model. A prediction join is useful when you have lots of information in a table or other external data source and want to make predictions by using the trained model. Details please see: http://msdn.microsoft.com/en-us/library/bb522459(v=sql.105).aspx#bkmk_BatchQuery
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- Marked As Answer by Eileen ZhaoMicrosoft Contingent Staff, Moderator Monday, February 25, 2013 1:43 AM