SAP HANA Predictive Analysis Library (PAL)

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3.7.4 Distribution Fitting This algorithm aims to fit a probability distribution for a variable according to a series of measurements to the variable. There are many probability distributions of which some can be fitted more closely to the observed variable than others. In PAL, you can choose one probability distribution type from a supporting list (Normal, Gamma, Weibull, and Uniform) and then PAL will calculate the optimized parameters of this distribution which fit the observed variable best. PAL supports two distribution fitting interfaces: DISTRFIT and DISTRFITCENSORED. DISTRFIT fits uncensored data while DISTRFITCENSORED fits censored data. Maximum likelihood and median rank are two estimation methods for finding the optimized parameters. In PAL, the maximum likelihood method supports all distribution types in the supporting list for un-censored data and supports Weibull distribution fitting for a mixture of left, right, and interval censored data. The median rank method supports the Weibull distribution fitting for both right-censored and un-censored data but it does not support other distribution types. Prerequisites ● ● The input data is numeric, not categorical. For DISTRFIT, the input data does not contain null data. The algorithm will issue errors when encountering null values. DISTRFIT This is a distribution fitting function with un-censored data. Procedure Generation CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘DISTRFIT’, ‘’, '', ); 504 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
The signature table should contain the following records: Table 377: Position Schema Name Table Type Name Parameter Type 1 IN 2 IN 3 OUT 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 Table Table 378: Table Column Column Data Type Description Data 1st column Double Input data Parameter Table Mandatory Parameter The following parameter is mandatory and must be given a value. Table 379: Name Data Type Description DISTRIBUTIONNAME Varchar Distribution name: ● ● ● ● Normal Gamma Weibull Uniform Optional Parameter The following parameter is optional. If it is not specified, PAL will use its default value. SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 505
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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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PARTITION This function reads the i
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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
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Page 465 and 466: INSERT INTO PAL_PCAPROJ_PDATA_TBL V
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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
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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 501 and 502: DISTRPROB This function calculates
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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 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
Page 551 and 552: CALL "SYS".AFLLANG_WRAPPER_PROCEDUR
Page 553 and 554: "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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