Effect of using Predict Function

hi All,
we have a data mining model using decision trees that we use to predict one output thats a continuous value, its simply the predicted amount of sales per customer.
now our model works and give results, however we were not using the predict() function rather simply using the predicted field directly from the model
for example:
SELECT
t.[customerId],
[MyModel].[Revenue]
From
[MyModel]
PREDICTION JOIN
OPENQUERY([MYData],'Select ....') AS t
VS
SELECT
t.[customerId],
Predict([MyModel].[Revenue])
From
[MyModel]
PREDICTION JOIN
OPENQUERY([MYData],'Select ....') AS t
any help will be appreciated?
regards,
Ramez

There is no difference in calling Predict(Column_Reference) or using Column_Reference directly in the prediction join query. Predict function in useful when you want to specify addition options (e.g. number of items to return from forecasting or association
models).
Tatyana Yakushev [PredixionSoftware.com]

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Oracle Data Mining - How to use PREDICTION function with a regression model

I've been searching this site for Data Mining Q&A specifically related to prediction function and I wasn't able to find something useful on this topic. So I hope that posting it as a new thread will get useful answers for a beginner in oracle data mining.
So here is my issue with prediction function:
Given a table with 17 weeks of sales for a given product, I would like to do a forecast to predict the sales for the week 18th.
For that let's start preparing the necessary objects and data:
CREATE TABLE T_SALES
PURCHASE_WEEK DATE,
WEEK NUMBER,
SALES NUMBER
SET DEFINE OFF;
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('11/27/2010 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 1, 55488);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('12/04/2010 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 2, 78336);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('12/11/2010 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 3, 77248);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('12/18/2010 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 4, 106624);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('12/25/2010 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 5, 104448);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('01/01/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 6, 90304);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('01/08/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 7, 44608);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('01/15/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 8, 95744);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('01/22/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 9, 129472);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('01/29/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 10, 110976);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('02/05/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 11, 139264);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('02/12/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 12, 87040);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('02/19/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 13, 47872);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('02/26/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 14, 120768);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('03/05/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 15, 98463.65);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('03/12/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 16, 67455.84);
Insert into T_SALES
(PURCHASE_WEEK, WEEK, SALES)
Values
(TO_DATE('3/19/2011 23:59:59', 'MM/DD/YYYY HH24:MI:SS'), 17, 100095.66);
COMMIT;
There are a lot of linear regression models and approaches for sales forecast out on the market, however I will focus on what oracle 11g offers i.e. package SYS.DBMS_DATA_MINING to create a model using regression as mining function and then, once the model is created, to apply prediction function on the model.
Therefore I'll have to go through few steps:
i) normalization of data
CREATE OR REPLACE VIEW t_sales_norm AS
SELECT week,
sales,
(sales - 91423.95)/27238.3693126778 sales_norm
FROM t_sales;
whereas the numerical values are the mean and the standard deviation:
select avg(sales) from t_sales;
91423.95
select stddev(sales) from t_sales;
27238.3693126778
ii) auto-correlation. For the sake of simplicity, I will safely assume that there is no auto-correlation (no repetitive pattern in sales among the weeks). Therefore to define the lag data I will consider the whole set:
CREATE OR REPLACE VIEW t_sales_lag AS
SELECT a.*
FROM (SELECT week,
sales,
LAG(sales_norm, 1) OVER (ORDER BY week) L1,
LAG(sales_norm, 2) OVER (ORDER BY week) L2,
LAG(sales_norm, 3) OVER (ORDER BY week) L3,
LAG(sales_norm, 4) OVER (ORDER BY week) L4,
LAG(sales_norm, 5) OVER (ORDER BY week) L5,
LAG(sales_norm, 6) OVER (ORDER BY week) L6,
LAG(sales_norm, 7) OVER (ORDER BY week) L7,
LAG(sales_norm, 8) OVER (ORDER BY week) L8,
LAG(sales_norm, 9) OVER (ORDER BY week) L9,
LAG(sales_norm, 10) OVER (ORDER BY week) L10,
LAG(sales_norm, 11) OVER (ORDER BY week) L11,
LAG(sales_norm, 12) OVER (ORDER BY week) L12,
LAG(sales_norm, 13) OVER (ORDER BY week) L13,
LAG(sales_norm, 14) OVER (ORDER BY week) L14,
LAG(sales_norm, 15) OVER (ORDER BY week) L15,
LAG(sales_norm, 16) OVER (ORDER BY week) L16,
LAG(sales_norm, 17) OVER (ORDER BY week) L17
FROM t_sales_norm) a;
iii) choosing the training data. Again, I will choose the whole set of 17 weeks, as for this discussion in not relevant how big should be the set of training data.
CREATE OR REPLACE VIEW t_sales_train AS
SELECT week, sales,
L1, L2, L3, L4, L5, L6, L7, L8, L9, L10,
L11, L12, L13, L14, L15, L16, L17
FROM t_sales_lag a
WHERE week >= 1 AND week <= 17;
iv) build the model
-- exec SYS.DBMS_DATA_MINING.DROP_MODEL('t_SVM');
BEGIN
sys.DBMS_DATA_MINING.CREATE_MODEL( model_name => 't_SVM',
mining_function => dbms_data_mining.regression,
data_table_name => 't_sales_train',
case_id_column_name => 'week',
target_column_name => 'sales');
END;
v) finally, where I am confused is applying the prediction function against this model and making sense of the results.
On a search on Google I found 2 ways of applying this function to my case.
One way is the following:
SELECT week, sales,
PREDICTION(t_SVM USING
LAG(sales,1) OVER (ORDER BY week) as l1,
LAG(sales,2) OVER (ORDER BY week) as l2,
LAG(sales,3) OVER (ORDER BY week) as l3,
LAG(sales,4) OVER (ORDER BY week) as l4,
LAG(sales,5) OVER (ORDER BY week) as l5,
LAG(sales,6) OVER (ORDER BY week) as l6,
LAG(sales,7) OVER (ORDER BY week) as l7,
LAG(sales,8) OVER (ORDER BY week) as l8,
LAG(sales,9) OVER (ORDER BY week) as l9,
LAG(sales,10) OVER (ORDER BY week) as l10,
LAG(sales,11) OVER (ORDER BY week) as l11,
LAG(sales,12) OVER (ORDER BY week) as l12,
LAG(sales,13) OVER (ORDER BY week) as l13,
LAG(sales,14) OVER (ORDER BY week) as l14,
LAG(sales,15) OVER (ORDER BY week) as l15,
LAG(sales,16) OVER (ORDER BY week) as l16,
LAG(sales,17) OVER (ORDER BY week) as l17
) pred
FROM t_sales a;
WEEK, SALES, PREDICTION
1, 55488, 68861.084076412
2, 78336, 104816.995823913
3, 77248, 104816.995823913
4, 106624, 104816.995823913
As you can see for the first row there is a value of 68861.084 and for the rest of 16 values is always one and the same 104816.995.
Question: where is my week 18 prediction ? or maybe I should say which one is it ?
Another way of using prediction even more confusing is against the lag table:
SELECT week, sales,
PREDICTION(t_svm USING a.*) pred
FROM t_sales_lag a;
WEEK, SALES, PREDICTION
1, 55488, 68861.084076412
2, 78336, 75512.3642096908
3, 77248, 85711.5003385927
4, 106624, 98160.5009687461
Each row out of 17, its own 'prediction' result.
Same question: which one is my week 18th prediction ?
Thank you very much for all help that you can provide on this matter.
It is as always highly appreciated.
Serge F.

Kindly let me know how to give input to predict the values for example script to create model is as follows
drop table data_4svm
drop table svm_settings
begin
dbms_data_mining.drop_model('MODEL_SVMR1');
CREATE TABLE data_4svm (
id NUMBER,
a NUMBER,
b NUMBER
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settings_table_name => 'svm_settings');
END;
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select class, attribute_name, attribute_value, coefficient
from table(dbms_data_mining.get_model_details_svm('MODEL_SVMR1')) a, table(a.attribute_set) b
order by abs(coefficient) desc
-- to get predicted values (Q1)
SELECT PREDICTION(MODEL_SVMR1 USING *
) pred
FROM data_4svm a;
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Denis Konovalov wrote:
> if those reports were saved with security - you're not goingt o be able to open them with Xi3.1 Deski.
Forgive me for my mundane question but what does saving with security mean?
Thanks

I want to use the Function Generator VI to send command signals through the NI 7344 motion controller. This will be a closed loop servo valve system. I want to be able to change from say a square wave to a sine wave on the fly. Idea's?

I am going to run tests that require an actuator to move using various types of arbitrary waveforms such as sine or square. The NI 7344 is hooked to the UMI that is going through a driver for a servo valve. The loop is analog and it is closed. I have played with some of the examples but can't get it to work. I have used the function generator VI to generate a signal but I think I am using the wrong input VI to the motion control board. When I use what I have it moves the servo and then stops. It doesn't continually generate the signal.
I would love to use the controls on the function generator vi to control frequency and amplitude ect. Any help or pointer would be helpful. Thank you in advance.

Hello,
I'm not clear on exactly how you want to use the generated data but I'm assuming they will be used as your target points.
There's a built-in example for motion called 'One-Axis Contour Move.vi'. This example demonstrates how to provide your target points as 1-D array. All you need to do is to replace the input array with the output of the function generator. In order to have it run continuously, use a while loop. You can further program your application so that it'll change the waveform on the fly by monitoring the user interface but this might be little little tricky as you will need to reset the move and load the new generated points while keeping track of your current position.
I hope this helps. Let me know if you have further questions regarding this
application.
Best regards,
Yusuf C.
Applications Engineering
National Instruments

How to use read_text function module

Hi how to use read_text function module to read purchase order header text .what are all tht things to pass in ID,Name and Object
thanks,
Mahe

Dear,
Use below code.
DATA:IT_LINE LIKE TLINE OCCURS 0 WITH HEADER LINE,
V_TDNAME LIKE THEAD-TDNAME.
V_TDNAME = PO_NUMBER.
CALL FUNCTION 'READ_TEXT'
EXPORTING
*   CLIENT                        = SY-MANDT
    ID                            = 'F01'
    LANGUAGE                      = 'EN'
    NAME                          = V_TDNAME
    OBJECT                        = 'EKKO'
TABLES
    LINES                         = IT_LINE.
Thanks and Regards,

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