SAP HANA Predictive Analysis Library (PAL)

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CALL DM_PAL.PAL_CHISQTESTIND_PROC(PAL_CHISQTESTIND_DATA_TBL, #PAL_CONTROL_TBL, PAL_CHISQTESTIND_EXPECTEDRESULT_TBL, PAL_CHISQTESTIND_STATVALUE_TBL) WITH OVERVIEW; SELECT * FROM PAL_CHISQTESTIND_EXPECTEDRESULT_TBL; SELECT * FROM PAL_CHISQTESTIND_STATVALUE_TBL; Expected Result PAL_CHISQTESTIND_EXPECTEDRESULT_TBL: PAL_CHISQTESTIND_STATVALUE_TBL: 3.7.3 Cumulative Distribution Function This algorithm evaluates the probability of a variable x from the cumulative distribution function (CDF) or complementary cumulative distribution function (CCDF) for a given probability distribution. CDF CDF describes the lower tail probability of a probability distribution. It is the probability that a random variable X with a given probability distribution takes a value less than or equal to x. The CDF F(x) of a real-valued random variable X is given by: F(x)=P[X≤x] CCDF CCDF describes the upper tail probability of a probability distribution. It is the probability that a random variable X with a given probability distribution takes a value greater than x. The CCDF random variable X is given by: =P[X>x]=1-F(x) of a real-valued Prerequisites ● ● The input data is numeric, not categorical. The input data and distribution parameters do not contain null data. The algorithm will issue errors when encountering null values. 500 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
DISTRPROB This function calculates the value of CDF or CCDF (depending on the parameter given by user) for a given distribution. Procedure Generation CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘DISTRPROB’, ‘’, '', ); The signature table should contain the following records: Table 372: Position Schema Name Table Type Name Parameter Type 1 2 IN IN 3 IN 4 OUT Procedure Calling CALL .(, , , ) with overview; The procedure name is the same as specified in the procedure generation. The input, parameter, and output tables must be of the types specified in the signature table. Signature Input Tables Table 373: Table Column Column Data Type Description Data 1st column Double Input data Distribution Parameter 1st column Varchar or nvarchar Names of the distribution parameters. See Distribution Parameters Definition table for details. 2nd column Varchar or nvarchar Values of distribution parameters. See Distribution Parameters Definition table for details. Distribution Parameters Definition Table The following parameters are mandatory and must be given a value. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 501
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SAP HANA Platform SPS 12 Document V
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Naive Bayes. ......................
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1 What is PAL? SAP HANA’s SQLScri
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2 Getting Started with PAL This sec
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The result will tell you whether th
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● : The name of the schema where
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CREATE COLUMN TABLE PAL_ARIMAX_DATA
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For more information on how to use
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Category PAL Algorithm Built-in Fun
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Category PAL Algorithm Built-in Fun
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The procedure name is the same as s
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● USER1 has been assigned the AFL
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3.1.2 Agglomerate Hierarchical Clus
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Table 13: Name Data Type Default Va
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Name Data Type Default Value Descri
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INSERT INTO DATA_TBL VALUES ('17',
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Prerequisites ● ● The input dat
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INSERT INTO PAL_AD_DATA_TBL VALUES
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The signature table should contain
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CREATE TYPE PAL_KMEANS_CENTERS_T AS
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PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
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For SOM: SET SCHEMA DM_PAL; DROP TY
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PAL_CLUSTER_ASSIGNED_TBL: Related I
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Table 24: Name Data Type Descriptio
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DROP TYPE PAL_CONTROL_T; CREATE TYP
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3.1.6 Gaussian Mixture Model (GMM)
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Table 29: Name Data Type Descriptio
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Output Tables Output format 0: Tabl
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INSERT INTO PAL_GMM_PDATA_TBL VALUE
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Example 2 SET SCHEMA DM_PAL; DROP T
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PAL_GMM_RESULTSMODEL_TBL: 3.1.7 K-M
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Table 43: Table Column Column Data
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INSERT INTO #PAL_CONTROL_TBL VALUES
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PAL_KMEANS_MODEL_TBL: VALIDATEKMEAN
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"V000" DOUBLE, "A0" INTEGER, "A1" I
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Expected Result PAL_SILHOUETTE_RESU
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Table Column Column Data Type Descr
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Example Assume that: ● ● ● DM
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PAL_KMEDIANS_ASSIGN_TBL: 3.1.9 K-Me
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Position Schema Name Table Type Nam
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The input, parameter, and output ta
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INSERT INTO PARAMETERS_TBL VALUES (
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DOCTOPICDIST_TBL: GENERALINFO_TBL:
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Procedure Calling CALL .(, , ) with
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Expected Result DOCTOPICDIST_TBL: 3
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The following parameters are option
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CREATE TYPE PAL_SOM_MODEL_T AS TABL
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PAL_SOM_RESASSIGN_TBL: PAL_SOM_MODE
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INSERT INTO PAL_SLIGHT_SIL_PDATA_TB
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AUC Procedure Generation CALL SYS.A
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Table 85: Name Data Type Default Va
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Example 2 SET SCHEMA DM_PAL; DROP T
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3.2.2 Back Propagation Neural Netwo
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CREATEBPNN This function trains a B
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INSERT INTO PAL_TRAIN_NN_DATA_TBL V
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PAL_TRAIN_NN_RESULT_TBL: PAL_REGRES
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Classification example: SET SCHEMA
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"NAME" VARCHAR(100), "MODEL" CLOB )
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CREATE TYPE PAL_C45_DATA_T AS TABLE
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CART This function is used for clas
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Prerequisites ● ● The target co
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PAL_CHAID_TREEMODEL_TBL: PAL_CHAID_
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INSERT INTO PAL_CM_DATA_TBL VALUES(
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The following parameters are option
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PAL_KNN_RESULTS_TBL: 3.2.8 Logistic
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Procedure Calling CALL .(, , , , )
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PAL_LOGISTICR_STAT_TBL: PAL_LOGISTI
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Procedure Generation CALL SYS.AFLLA
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Assume that: ● ● ● DM_PAL is
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PAL_FLOGISTICR_FITTED_TBL: Related
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Expected Result PAL_LRMC_MODEL_TBL:
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Table 137: Name Data Type Default V
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SAP HANA Predictive Analysis Librar
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CALL SYS.AFLLANG_WRAPPER_PROCEDURE_
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3.2.10 Naive Bayes Naive Bayes is a
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● ● USER1 has been assigned the
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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Position Schema Name Table Type Nam
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DROP TABLE CV_LR_FUNC_TBL; CREATE C
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CREATE COLUMN TABLE FUNC_TBL LIKE F
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DROP TABLE EVALUATION_RESULT_TBL; C
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INSERT INTO PAL_PARA_TBL VALUES('TR
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3.2.13 Random Forest The ransom for
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INSERT INTO PAL_RF_DATA_TBL VALUES
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3.2.14 Support Vector Machine Suppo
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Table 168: Name Data Type Descripti
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CREATE TABLE PAL_SVM_PDATA_TBL("POS
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STRINGARGS VARCHAR(100) ); DROP TYP
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Example 3: Support vector classific
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Related Information SAP HANA Smart
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INSERT INTO PAL_GR_PDATA_TBL values
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Procedure Calling CALL .(, , , ) wi
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3.3.2 Bi-Variate Natural Logarithmi
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CREATE LOCAL TEMPORARY COLUMN TABLE
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Where β0…βn are parameters that
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Assume we have m observation pairs
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Example 1: Fitting multiple linear
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CREATE COLUMN TABLE PAL_ENET_MLR_PD
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3.3.5 Polynomial Regression Polynom
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insert into PAL_PR_PDATA_TBL values
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The input, parameter, and output ta
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3.4 Association Algorithms This sec
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"STRINGARGS" VARCHAR (100) ); INSER
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Optional Parameters The following p
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INSERT INTO PAL_APRIORI_DATA_TBL VA
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PAL_APRIORI_CONS_ITEMS_TBL: LITEAPR
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INSERT INTO PAL_LITEAPRIORI_DATA_TB
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FPGROWTH (Relational Output) FP-Gro
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); DROP TYPE PAL_FPGROWTH_POSTRULE_
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PAL_FPGROWTH_VALUES_TBL: 3.4.3 K-Op
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INSERT INTO PAL_KORD_DATA_TBL VALUE
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Prerequisite No missing or null dat
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. Initial P 0 can be calculated by
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Output Table (Model Table) Table 24
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DROP TYPE PAL_ARIMAX_DATA_T; CREATE
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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We get And variance of the forecast
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CREATE COLUMN TABLE PAL_ARIMA_PDATA
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INSERT INTO PAL_ARIMAX_DATA_TBL VAL
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3.5.2 Auto ARIMA This function auto
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● USER1 has been assigned the AFL
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Example 3: Auto Seasonal ARIMA SET
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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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INSERT INTO PAL_ARIMA_DATA_TBL VALU
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F 1 = a 0 + b 0 2. Calculation: S t
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INSERT INTO PAL_BROWNSMOOTH_DATA_TB
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3.5.4 Croston's Method The Croston
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SELECT * FROM PAL_CROSTON_STATISTIC
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PAL_FORECASTACCURACYMEASURES_RESULT
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PAL_OUTPARAMETER_TBL: PAL_FORECASTS
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PAL_OUTPARAMETER_TBL: SAP HANA Pred
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CREATE TYPE PAL_CONTROL_T AS TABLE
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PAL_FORECASTTRIPLESMOOTHING_RESULT_
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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PAL_OUTPARAMETER_TBL: PAL_FORECASTM
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Procedure Calling CALL .(, , , ) wi
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PAL_FORECASTSLR_RESULT_TBL: PAL_FOR
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Note The algorithm is backward comp
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Note F 0 is not defined because the
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CREATE COLUMN TABLE PAL_DOUBLESMOOT
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3.5.10 Triple Exponential Smoothing
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PAL calculates the prediction inter
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Output Table Table 294: Table Colum
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PAL_TRIPLESMOOTH_RESULT_TBL: PAL_TR
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Once the trend and seasonality are
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CREATE COLUMN TABLE PAL_TSSEASONALI
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The difference-sign test counts the
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Optional Parameters The following p
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PAL_TSTREND_TREND_TBL: PAL_TSTREND_
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Parameter Table Mandatory Parameter
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3.6 Preprocessing Algorithms The re
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● ● Smoothing by bin medians: e
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3.6.2 Binning Assignment Binning as
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3.6.3 Convert Category Type to Bina
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"NAME" VARCHAR(100), "INTARGS" INTE
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Procedure Generation CALL SYS.AFLLA
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INSERT INTO PAL_IQR_TESTDT_TBL VALU
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Page 453 and 454: 3.6.6 Posterior Scaling Posterior s
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Page 457 and 458: Expected Result PAL_NEW_SCALING_TBL
Page 459 and 460: The following parameters are option
Page 461 and 462: INSERT INTO PAL_PCA_DATA_TBL VALUES
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Page 465 and 466: INSERT INTO PAL_PCAPROJ_PDATA_TBL V
Page 469 and 470: INSERT INTO PAL_DISTRRANDOM_PDATA_T
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Page 473 and 474: INSERT INTO PAL_SAMPLING_DATA_TBL V
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Page 477 and 478: DROP TABLE PAL_SAMPLING_PDATA_TBL;
Page 479 and 480: Expected Result 3.6.10 Scaling Rang
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Page 485 and 486: Prerequisite Each column must conta
Page 487 and 488: Assume that: ● ● ● DM_PAL is
Page 489 and 490: PAL_MISSING_VALUES_RESULT_TBL: PAL_
Page 491 and 492: The procedure name is the same as s
Page 493 and 494: INSERT INTO PAL_VT_DATA_TBL VALUES
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Page 497 and 498: 3.7.2 Chi-Squared Test for Independ
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Page 515 and 516: 2. Calculate PAL also supports the
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Page 519 and 520: The Kaplan-Meier estimator can be r
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Page 531 and 532: CREATE TYPE PAL_DISTRQUANTILE_RESUL
Page 533 and 534: where x' = x - Table 404: Definitio
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Page 537 and 538: Let the sample mean of x and y be:
Page 539 and 540: CREATE TYPE PAL_VAREQUALTEST_DATA1_
Page 541 and 542: The quantity is computed as a weigh
Page 545 and 546: Prerequisites ● ● Input data ca
Page 547 and 548: "PARAMETER_TYPE" VARCHAR(7) ); INSE
Page 549 and 550: Signature Input Tables Table 425: T
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CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
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"DISTANCE" DOUBLE ); DROP TYPE PAL_
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"STRINGARGS" VARCHAR(100) ); --crea
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The projected estimates are single
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INSERT INTO PDATA VALUES (2, 'DM_PA
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----product revenue---- DELETE FROM
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---- calculate net present value of
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UPDATE PAL_DISTRPROB_DISTRPARAM_TBL
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Then we get the survival estimates
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INSERT INTO PAL_TRIAL_DATA_TBL VALU
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INSERT INTO PAL_DISTRFITCENSORED_DA
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The expected results are as follows
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); INSERT INTO PAL_DISTRPROB_PDATA_
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Important Disclaimers and Legal Inf
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SAP HANA Predictive Analysis Library (PAL)

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