SAP HANA Predictive Analysis Library (PAL)

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Thus, the Euclidean distance between T1 and T2 is: Where γ is the weight to be given to the transposed categorical attributes to lessen the impact on the clustering from the 0/1 attributes. Then you can use the traditional method to update the mean of every cluster. Assuming one cluster only has T1 and T2, the mean is: Table 36: Customer ID Age Income Gender_1 Gender_2 Center1 29.0 9000.0 0.5 0.5 The means of categorical attributes will not be outputted. Instead, the means will be replaced by the modes similar to the K-Modes algorithm. Take the below center for example: Table 37: Age Income Gender_1 Gender_2 Center 29.0 9000.0 0.25 0.75 Because "Gender_2" is the maximum value, the output will be: Table 38: Age Income Gender Center 29.0 9000.0 Female Prerequisites ● ● The input data contains an ID column and the other columns are of integer or double data type. The input data does not contain null value. The algorithm will issue errors when encountering null values. KMEANS This is a clustering function using the k-means algorithm. Procedure Generation CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘KMEANS’, ‘’, '', ); 64 P U B L I C SAP HANA Predictive Analysis Library (PAL) PAL Functions
The signature table should contain the following records: Table 39: Position Schema Name Table Type Name Parameter Type 1 IN 2 IN 3 OUT 4 OUT 5 OUT (optional) 6 OUT (optional) 7 OUT (optional) 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 40: Table Column Column Data Type Description Constraint Data 1st column Integer, bigint, varchar, or nvarchar ID This must be the first column. Other columns Integer, double, varchar, or nvarchar Attribute data Parameter Table Mandatory Parameters None. Optional Parameters SAP HANA Predictive Analysis Library (PAL) PAL Functions P U B L I C 65
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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
Page 13 and 14: CREATE COLUMN TABLE PAL_ARIMAX_DATA
Page 15 and 16: For more information on how to use
Page 17 and 18: Category PAL Algorithm Built-in Fun
Page 19 and 20: Category PAL Algorithm Built-in Fun
Page 21 and 22: The procedure name is the same as s
Page 23 and 24: ● USER1 has been assigned the AFL
Page 25 and 26: 3.1.2 Agglomerate Hierarchical Clus
Page 27 and 28: Table 13: Name Data Type Default Va
Page 29 and 30: Name Data Type Default Value Descri
Page 31 and 32: INSERT INTO DATA_TBL VALUES ('17',
Page 33 and 34: Prerequisites ● ● The input dat
Page 37 and 38: INSERT INTO PAL_AD_DATA_TBL VALUES
Page 39 and 40: The signature table should contain
Page 41 and 42: CREATE TYPE PAL_KMEANS_CENTERS_T AS
Page 43 and 44: PAL_CLUSTER_ASSIGNED_TBL: For DBSCA
Page 45 and 46: For SOM: SET SCHEMA DM_PAL; DROP TY
Page 47 and 48: PAL_CLUSTER_ASSIGNED_TBL: Related I
Page 49 and 50: Table 24: Name Data Type Descriptio
Page 51 and 52: DROP TYPE PAL_CONTROL_T; CREATE TYP
Page 53 and 54: 3.1.6 Gaussian Mixture Model (GMM)
Page 55 and 56: Table 29: Name Data Type Descriptio
Page 57 and 58: Output Tables Output format 0: Tabl
Page 59 and 60: INSERT INTO PAL_GMM_PDATA_TBL VALUE
Page 61 and 62: Example 2 SET SCHEMA DM_PAL; DROP T
Page 63: PAL_GMM_RESULTSMODEL_TBL: 3.1.7 K-M
Page 69 and 70: Table 43: Table Column Column Data
Page 71 and 72: INSERT INTO #PAL_CONTROL_TBL VALUES
Page 73 and 74: PAL_KMEANS_MODEL_TBL: VALIDATEKMEAN
Page 75 and 76: "V000" DOUBLE, "A0" INTEGER, "A1" I
Page 77 and 78: Expected Result PAL_SILHOUETTE_RESU
Page 79 and 80: Table Column Column Data Type Descr
Page 81 and 82: Example Assume that: ● ● ● DM
Page 83 and 84: PAL_KMEDIANS_ASSIGN_TBL: 3.1.9 K-Me
Page 85 and 86: Position Schema Name Table Type Nam
Page 91 and 92: The input, parameter, and output ta
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Page 95 and 96: INSERT INTO PARAMETERS_TBL VALUES (
Page 97 and 98: DOCTOPICDIST_TBL: GENERALINFO_TBL:
Page 99 and 100: Procedure Calling CALL .(, , ) with
Page 103 and 104: Expected Result DOCTOPICDIST_TBL: 3
Page 105 and 106: The following parameters are option
Page 107 and 108: Table Column Column Data Type Descr
Page 111 and 112: CREATE TYPE PAL_SOM_MODEL_T AS TABL
Page 113 and 114: PAL_SOM_RESASSIGN_TBL: PAL_SOM_MODE
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Table 78: Table Column Column Data
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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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Name Data Type Default Value Descri
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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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The signature table should contain
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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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Table Column Column Data Type Descr
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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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INSERT INTO #PAL_CONTROL_TBL VALUES
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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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3.6.6 Posterior Scaling Posterior s
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Expected Result PAL_NEW_SCALING_TBL
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The following parameters are option
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INSERT INTO PAL_PCA_DATA_TBL VALUES
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INSERT INTO PAL_PCAPROJ_PDATA_TBL V
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INSERT INTO PAL_DISTRRANDOM_PDATA_T
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INSERT INTO PAL_SAMPLING_DATA_TBL V
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If method is 4 and SAMPLING_SIZE is
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DROP TABLE PAL_SAMPLING_PDATA_TBL;
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Expected Result 3.6.10 Scaling Rang
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Prerequisite Each column must conta
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PAL_MISSING_VALUES_RESULT_TBL: PAL_
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The procedure name is the same as s
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INSERT INTO PAL_VT_DATA_TBL VALUES
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The procedure name is the same as s
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3.7.2 Chi-Squared Test for Independ
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DISTRPROB This function calculates
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"VALUE" DOUBLE ); DROP TABLE PDATA_
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"DOUBLEARGS" DOUBLE, "STRINGARGS" V
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"TYPE_NAME" NVARCHAR(256), "PARAMET
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2. Calculate PAL also supports the
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The Kaplan-Meier estimator can be r
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PAL_KMSURV_LOGRANK_STAT2_TBL: 3.7.7
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CREATE TYPE PAL_DISTRQUANTILE_RESUL
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where x' = x - Table 404: Definitio
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Let the sample mean of x and y be:
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CREATE TYPE PAL_VAREQUALTEST_DATA1_
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The quantity is computed as a weigh
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Prerequisites ● ● Input data ca
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"PARAMETER_TYPE" VARCHAR(7) ); INSE
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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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