11 Colt Aggregate Functions
This chapter provides a reference to Colt aggregate functions provided in Oracle Continuous Query Language (Oracle CQL). Colt aggregate functions are based on the Colt open source libraries for high performance scientific and technical computing.
For more information, see Section 1.1.11, "Functions".
This chapter includes the following section:
11.1 Introduction to Oracle CQL Built-In Aggregate Colt Functions
Table 11-1 lists the built-in aggregate Colt functions that Oracle CQL provides.
Table 11-1 Oracle CQL Built-in Aggregate Colt-Based Functions
| Colt Package | Function |
|---|---|
|
A set of basic descriptive statistics functions. |
|
Note:
Built-in function names are case sensitive and you must use them in the case shown (in lower case).
Note:
In stream input examples, lines beginning with h (such as h 3800) are heartbeat input tuples. These inform Oracle Event Processing that no further input will have a timestamp lesser than the heartbeat value.
In relation output examples, the first tuple output is:
-9223372036854775808:+
This value is -Long.MIN_VALUE() and represents the largest negative timestamp possible.
For more information, see:
-
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
-
Section 11.1.2, "Colt Aggregate Functions and the Where, Group By, and Having Clauses"
11.1.1 Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments
Note that the signatures of the Oracle CQL Colt aggregate functions do not match the signatures of the corresponding Colt aggregate functions.
Consider the following Colt aggregate function:
double autoCorrelation(DoubleArrayList data, int lag, double mean, double variance)
In this signature, data is the Collection over which aggregates will be calculated and mean and variance are the other two parameter aggregates which are required to calculate autoCorrelation (where mean and variance aggregates are calculated on data).
In Oracle Event Processing, data will never come in the form of a Collection. The Oracle CQL function receives input data in a stream of tuples.
So suppose our stream is defined as S:(double val, integer lag). On each input tuple, the Oracle CQL autoCorrelation function will compute two intermediate aggregates, mean and variance, and one final aggregate, autoCorrelation.
Since the function expects a stream of tuples having a double data value and an integer lag value only, the signature of the Oracle CQL autoCorrelation function is:
double autoCorrelation (double data, int lag)
11.1.2 Colt Aggregate Functions and the Where, Group By, and Having Clauses
In Oracle CQL, the where clause is applied before the group by and having clauses. This means the Oracle CQL statement in Example 11-1 is invalid:
Example 11-1 Invalid Use of count
<query id="q1"><![CDATA[
select * from InputChannel[rows 4 slide 4] as ic where geometricMean(c3) > 4
]]></query>
Instead, you must use the Oracle CQL statement that Example 11-2 shows:
Example 11-2 Valid Use of count
<query id="q1"><![CDATA[
select * from InputChannel[rows 4 slide 4] as ic, myGeoMean = geometricMean(c3) where myGeoMean > 4
]]></query>
For more information, see:
autoCorrelation
autoCorrelation is based on cern.jet.stat.Descriptive.autoCorrelation(DoubleArrayList data, int lag, double mean, double variance). It returns the auto-correlation of a data sequence of the input arguments as a double.
Note:
This function has semantics different from "lag1"
This function takes the following tuple arguments:
-
double1: data value. -
int1: lag.
For more information, see
Consider the query qColtAggr1 in Example 11-3. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-4, the query returns the relation in Example 11-5.
Example 11-3 autoCorrelation Function Query
<query id="qColtAggr1"><![CDATA[
select autoCorrelation(c3, 0) from SColtAggrFunc
]]></query>
Example 11-4 autoCorrelation Function Stream Input
Timestamp Tuple 10 5.441341838866902 1000 6.1593756700951054 1200 3.7269733222923676 1400 4.625160266213489 1600 3.490061774090248 1800 3.6354484064421917 2000 5.635401664977703 2200 5.006087562207967 2400 3.632574304861612 2600 7.618087248962962 h 8000 h 200000000
Example 11-5 autoCorrelation Function Relation Output
Timestamp Tuple Kind Tuple -9223372036854775808:+ 10: - 10: + NaN 1000: - NaN 1000: + 1.0 1200: - 1.0 1200: + 1.0 1400: - 1.0 1400: + 1.0 1600: - 1.0 1600: + 1.000000000000002 1800: - 1.000000000000002 1800: + 1.0 2000: - 1.0 2000: + 0.9999999999999989 2200: - 0.9999999999999989 2200: + 0.999999999999999 2400: - 0.999999999999999 2400: + 0.9999999999999991 2600: - 0.9999999999999991 2600: + 1.0000000000000013
correlation
correlation is based on cern.jet.stat.Descriptive.correlation(DoubleArrayList data1, double standardDev1, DoubleArrayList data2, double standardDev2) . It returns the correlation of two data sequences of the input arguments as a double.
This function takes the following tuple arguments:
-
double1: data value 1. -
double2: data value 2.
For more information, see
Consider the query qColtAggr2 in Example 11-6. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-7, the query returns the relation in Example 11-8.
Example 11-6 correlation Function Query
<query id="qColtAggr2"><![CDATA[
select correlation(c3, c3) from SColtAggrFunc
]]></query>
covariance
covariance is based on cern.jet.stat.Descriptive.covariance(DoubleArrayList data1, DoubleArrayList data2). It returns the correlation of two data sequences (see Figure 11-1) of the input arguments as a double.
This function takes the following tuple arguments:
-
double1: data value 1. -
double2: data value 2.
For more information, see:
Consider the query qColtAggr3 in Example 11-9. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-10, the query returns the relation in Example 11-11.
Example 11-9 covariance Function Query
<query id="qColtAggr3"><![CDATA[
select covariance(c3, c3) from SColtAggrFunc
]]></query>
geometricMean
geometricMean is based on cern.jet.stat.Descriptive.geometricMean(DoubleArrayList data). It returns the geometric mean of a data sequence (see Figure 11-2) of the input argument as a double.
This function takes the following tuple arguments:
-
double1: data value.
Note that for a geometric mean to be meaningful, the minimum of the data values must not be less than or equal to zero.
For more information, see:
Consider the query qColtAggr6 in Example 11-12. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-13, the query returns the relation in Example 11-14.
Example 11-12 geometricMean Function Query
<query id="qColtAggr6"><![CDATA[
select geometricMean(c3) from SColtAggrFunc
]]></query>
geometricMean1
geometricMean1 is based on cern.jet.stat.Descriptive.geometricMean(double sumOfLogarithms). It returns the geometric mean of a data sequence (see Figure 11-3) of the input arguments as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr7 in Example 11-15. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-16, the query returns the relation in Example 11-17.
Example 11-15 geometricMean1 Function Query
<query id="qColtAggr7"><![CDATA[
select geometricMean1(c3) from SColtAggrFunc
]]></query>
harmonicMean
harmonicMean is based on cern.jet.stat.Descriptive.harmonicMean(int size, double sumOfInversions). It returns the harmonic mean of a data sequence as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
-
http://acs.lbl.gov/~hoschek/colt/api/cern/jet/stat/Descriptive.html#harmonicMean(int,%20double) -
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
Consider the query qColtAggr8 in Example 11-18. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-19, the query returns the relation in Example 11-20.
Example 11-18 harmonicMean Function Query
<query id="qColtAggr8"><![CDATA[
select harmonicMean(c3) from SColtAggrFunc
]]></query>
Example 11-19 harmonicMean Function Stream Input
Timestamp Tuple 10 5.441341838866902 1000 6.1593756700951054 1200 3.7269733222923676 1400 4.625160266213489 1600 3.490061774090248 1800 3.6354484064421917 2000 5.635401664977703 2200 5.006087562207967 2400 3.632574304861612 2600 7.618087248962962 h 8000 h 200000000
Example 11-20 harmonicMean Function Relation Output
Timestamp Tuple Kind Tuple -9223372036854775808:+ 10: - 10: + 5.441341876983643 1000: - 5.441341876983643 1000: + 5.778137193205395 1200: - 5.778137193205395 1200: + 4.882442561720335 1400: - 4.882442561720335 1400: + 4.815475325819701 1600: - 4.815475325819701 1600: + 4.475541862878903 1800: - 4.475541862878903 1800: + 4.309563447664887 2000: - 4.309563447664887 2000: + 4.45944509362759 2200: - 4.45944509362759 2200: + 4.5211563834502515 2400: - 4.5211563834502515 2400: + 4.401525382790638 2600: - 4.401525382790638 2600: + 4.595562422157167
kurtosis
kurtosis is based on cern.jet.stat.Descriptive.kurtosis(DoubleArrayList data, double mean, double standardDeviation). It returns the kurtosis or excess (see Figure 11-4) of a data sequence as a double.
Figure 11-4 cern.jet.stat.Descriptive.kurtosis(DoubleArrayList data, double mean, double standardDeviation)
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
Consider the query qColtAggr12 in Example 11-21. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-22, the query returns the relation in Example 11-23.
Example 11-21 kurtosis Function Query
<query id="qColtAggr12"><![CDATA[
select kurtosis(c3) from SColtAggrFunc
]]></query>
lag1
lag1 is based on cern.jet.stat.Descriptive.lag1(DoubleArrayList data, double mean). It returns the lag - 1 auto-correlation of a dataset as a double.
Note:
This function has semantics different from "autoCorrelation".
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
Consider the query qColtAggr14 in Example 11-24. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-25, the query returns the relation in Example 11-26.
Example 11-24 lag1 Function Query
<query id="qColtAggr14"><![CDATA[
select lag1(c3) from SColtAggrFunc
]]></query>
mean
mean is based on cern.jet.stat.Descriptive.mean(DoubleArrayList data). It returns the arithmetic mean of a data sequence (see Figure 11-5) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr16 in Example 11-27. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-28, the query returns the relation in Example 11-29.
Example 11-27 mean Function Query
<query id="qColtAggr16"><![CDATA[
select mean(c3) from SColtAggrFunc
]]></query>
meanDeviation
meanDeviation is based on cern.jet.stat.Descriptive.meanDeviation(DoubleArrayList data, double mean). It returns the mean deviation of a dataset (see Figure 11-6) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
Consider the query qColtAggr17 in Example 11-30. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-31, the query returns the relation in Example 11-32.
Example 11-30 meanDeviation Function Query
<query id="qColtAggr17"><![CDATA[
select meanDeviation(c3) from SColtAggrFunc
]]></query>
median
median is based on cern.jet.stat.Descriptive.median(DoubleArrayList sortedData). It returns the median of a sorted data sequence as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr18 in Example 11-33. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-34, the query returns the relation in Example 11-35.
Example 11-33 median Function Query
<query id="qColtAggr18"><![CDATA[
select median(c3) from SColtAggrFunc
]]></query>
moment
moment is based on cern.jet.stat.Descriptive.moment(DoubleArrayList data, int k, double c). It returns the moment of the k-th order with constant c of a data sequence (see Figure 11-7) as a double.
This function takes the following tuple arguments:
-
double1: data value. -
int1:k. -
double2:c.
For more information, see:
Consider the query qColtAggr21 in Example 11-36. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-37, the query returns the relation in Example 11-38.
Example 11-36 moment Function Query
<query id="qColtAggr21"><![CDATA[
select moment(c3, c1, c3) from SColtAggrFunc
]]></query>
pooledMean
pooledMean is based on cern.jet.stat.Descriptive.pooledMean(int size1, double mean1, int size2, double mean2). It returns the pooled mean of two data sequences (see Figure 11-8) as a double.
This function takes the following tuple arguments:
-
double1: mean 1. -
double2: mean 2.
For more information, see
Consider the query qColtAggr22 in Example 11-39. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-40, the query returns the relation in Example 11-41.
Example 11-39 pooledMean Function Query
<query id="qColtAggr22"><![CDATA[
select pooledMean(c3, c3) from SColtAggrFunc
]]></query>
pooledVariance
pooledVariance is based on cern.jet.stat.Descriptive.pooledVariance(int size1, double variance1, int size2, double variance2). It returns the pooled variance of two data sequences (see Figure 11-9) as a double.
Figure 11-9 cern.jet.stat.Descriptive.pooledVariance(int size1, double variance1, int size2, double variance2)
This function takes the following tuple arguments:
-
double1: variance 1. -
double2: variance 2.
For more information, see
Consider the query qColtAggr23 in Example 11-42. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-43, the query returns the relation in Example 11-44.
Example 11-42 pooledVariance Function Query
<query id="qColtAggr23"><![CDATA[
select pooledVariance(c3, c3) from SColtAggrFunc
]]></query>
product
product is based on cern.jet.stat.Descriptive.product(DoubleArrayList data). It returns the product of a data sequence (see Figure 11-10) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr24 in Example 11-45. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-46, the query returns the relation in Example 11-47.
Example 11-45 product Function Query
<query id="qColtAggr24"><![CDATA[
select product(c3) from SColtAggrFunc
]]></query>
quantile
quantile is based on cern.jet.stat.Descriptive.quantile(DoubleArrayList sortedData, double phi). It returns the phi-quantile as a double; that is, an element elem for which holds that phi percent of data elements are less than elem.
This function takes the following tuple arguments:
-
double1: data value. -
double2: phi; the percentage; must satisfy0 <= phi <= 1.
For more information, see:
Consider the query qColtAggr26 in Example 11-48. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-49, the query returns the relation in Example 11-50.
Example 11-48 quantile Function Query
<query id="qColtAggr26"><![CDATA[
select quantile(c3, c2) from SColtAggrFunc
]]></query>
quantileInverse
quantileInverse is based on cern.jet.stat.Descriptive.quantileInverse(DoubleArrayList sortedList, double element). It returns the percentage phi of elements <= element (0.0 <= phi <= 1.0) as a double. This function does linear interpolation if the element is not contained but lies in between two contained elements.
This function takes the following tuple arguments:
-
double1: data. -
double2:element.
For more information, see:
Consider the query qColtAggr27 in Example 11-51. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-52, the query returns the relation in Example 11-53.
Example 11-51 quantileInverse Function Query
<query id="qColtAggr27"><![CDATA[
select quantileInverse(c3, c3) from SColtAggrFunc
]]></query>
rankInterpolated
rankInterpolated is based on cern.jet.stat.Descriptive.rankInterpolated(DoubleArrayList sortedList, double element). It returns the linearly interpolated number of elements in a list less or equal to a given element as a double.
The rank is the number of elements <= element. Ranks are of the form{0, 1, 2,..., sortedList.size()}. If no element is <= element, then the rank is zero. If the element lies in between two contained elements, then linear interpolation is used and a non-integer value is returned.
This function takes the following tuple arguments:
-
double1: data value. -
double2:element.
For more information, see:
Consider the query qColtAggr29 in Example 11-54. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-55, the query returns the relation in Example 11-56.
Example 11-54 rankInterpolated Function Query
<query id="qColtAggr29"><![CDATA[
select rankInterpolated(c3, c3) from SColtAggrFunc
]]></query>
rms
rms is based on cern.jet.stat.Descriptive.rms(int size, double sumOfSquares). It returns the Root-Mean-Square (RMS) of a data sequence (see Figure 11-11) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
-
http://acs.lbl.gov/~hoschek/colt/api/cern/jet/stat/Descriptive.html#rms(int,%20double) -
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
Consider the query qColtAggr30 in Example 11-57. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-58, the query returns the relation in Example 11-59.
Example 11-57 rms Function Query
<query id="qColtAggr30"><![CDATA[
select rms(c3) from SColtAggrFunc
]]></query>
sampleKurtosis
sampleKurtosis is based on cern.jet.stat.Descriptive.sampleKurtosis(DoubleArrayList data, double mean, double sampleVariance). It returns the sample kurtosis (excess) of a data sequence as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr31 in Example 11-60. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-61, the query returns the relation in Example 11-62.
Example 11-60 sampleKurtosis Function Query
<query id="qColtAggr31"><![CDATA[
select sampleKurtosis(c3) from SColtAggrFunc
]]></query>
sampleKurtosisStandardError
sampleKurtosisStandardError is based on cern.jet.stat.Descriptive.sampleKurtosisStandardError(int size). It returns the standard error of the sample Kurtosis as a double.
This function takes the following tuple arguments:
-
int1: data value.
For more information, see:
-
http://acs.lbl.gov/~hoschek/colt/api/cern/jet/stat/Descriptive.html#sampleKurtosisStandardError(int) -
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
Consider the query qColtAggr33 in Example 11-63. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-64, the query returns the relation in Example 11-65.
Example 11-63 sampleKurtosisStandardError Function Query
<query id="qColtAggr33"><![CDATA[
select sampleKurtosisStandardError(c1) from SColtAggrFunc
]]></query>
sampleSkew
sampleSkew is based on cern.jet.stat.Descriptive.sampleSkew(DoubleArrayList data, double mean, double sampleVariance). It returns the sample skew of a data sequence as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr34 in Example 11-66. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-67, the query returns the relation in Example 11-68.
Example 11-66 sampleSkew Function Query
<query id="qColtAggr34"><![CDATA[
select sampleSkew(c3) from SColtAggrFunc
]]></query>
sampleSkewStandardError
sampleSkewStandardError is based on cern.jet.stat.Descriptive.sampleSkewStandardError(int size). It returns the standard error of the sample skew as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
-
http://acs.lbl.gov/~hoschek/colt/api/cern/jet/stat/Descriptive.html#sampleSkewStandardError(int) -
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
Consider the query qColtAggr36 in Example 11-69. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-70, the query returns the relation in Example 11-71.
Example 11-69 sampleSkewStandardError Function Query
<query id="qColtAggr36"><![CDATA[
select sampleSkewStandardError(c1) from SColtAggrFunc
]]></query>
sampleVariance
sampleVariance is based on cern.jet.stat.Descriptive.sampleVariance(DoubleArrayList data, double mean). It returns the sample variance of a data sequence (see Figure 11-12) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr38 in Example 11-72. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-73, the query returns the relation in Example 11-74.
Example 11-72 sampleVariance Function Query
<query id="qColtAggr38"><![CDATA[
select sampleVariance(c3) from SColtAggrFunc
]]></query>
skew
skew is based on cern.jet.stat.Descriptive.skew(DoubleArrayList data, double mean, double standardDeviation). It returns the skew of a data sequence of a data sequence (see Figure 11-13) as a double.
Figure 11-13 cern.jet.stat.Descriptive.skew(DoubleArrayList data, double mean, double standardDeviation)
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr41 in Example 11-75. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-76, the query returns the relation in Example 11-77.
Example 11-75 skew Function Query
<query id="qColtAggr41"><![CDATA[
select skew(c3) from SColtAggrFunc
]]></query>
standardDeviation
standardDeviation is based on cern.jet.stat.Descriptive.standardDeviation(double variance). It returns the standard deviation from a variance as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
-
http://acs.lbl.gov/~hoschek/colt/api/cern/jet/stat/Descriptive.html#standardDeviation(double) -
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
Consider the query qColtAggr44 in Example 11-78. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-79, the query returns the relation in Example 11-80.
Example 11-78 standardDeviation Function Query
<query id="qColtAggr44"><![CDATA[
select standardDeviation(c3) from SColtAggrFunc
]]></query>
standardError
standardError is based on cern.jet.stat.Descriptive.standardError(int size, double variance). It returns the standard error of a data sequence (see Figure 11-14) as a double.
Figure 11-14 cern.jet.stat.Descriptive.cern.jet.stat.Descriptive.standardError(int size, double variance)
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
-
http://acs.lbl.gov/~hoschek/colt/api/cern/jet/stat/Descriptive.html#standardError(int,%20double) -
Section 11.1.1, "Oracle CQL Colt Aggregate Function Signatures and Tuple Arguments"
Consider the query qColtAggr45 in Example 11-81. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-82, the query returns the relation in Example 11-83.
Example 11-81 standardError Function Query
<query id="qColtAggr45"><![CDATA[
select standardError(c3) from SColtAggrFunc
]]></query>
sumOfInversions
sumOfInversions is based on cern.jet.stat.Descriptive.sumOfInversions(DoubleArrayList data, int from, int to). It returns the sum of inversions of a data sequence (see Figure 11-15) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr48 in Example 11-84. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-85, the query returns the relation in Example 11-86.
Example 11-84 sumOfInversions Function Query
<query id="qColtAggr48"><![CDATA[
select sumOfInversions(c3) from SColtAggrFunc
]]></query>
sumOfLogarithms
sumOfLogarithms is based on cern.jet.stat.Descriptive.sumOfLogarithms(DoubleArrayList data, int from, int to). It returns the sum of logarithms of a data sequence (see Figure 11-16) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr49 in Example 11-87. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-88, the query returns the relation in Example 11-89.
Example 11-87 sumOfLogarithms Function Query
<query id="qColtAggr49"><![CDATA[
select sumOfLogarithms(c3) from SColtAggrFunc
]]></query>
sumOfPowerDeviations
sumOfPowerDeviations is based on cern.jet.stat.Descriptive.sumOfPowerDeviations(DoubleArrayList data, int k, double c). It returns sum of power deviations of a data sequence (see Figure 11-17) as a double.
This function is optimized for common parameters like c == 0.0, k == -2 .. 4, or both.
This function takes the following tuple arguments:
-
double1: data value. -
int1:k. -
double2:c.
For more information, see:
Consider the query qColtAggr50 in Example 11-90. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-91, the query returns the relation in Example 11-92.
Example 11-90 sumOfPowerDeviations Function Query
<query id="qColtAggr50"><![CDATA[
select sumOfPowerDeviations(c3, c1, c3) from SColtAggrFunc
]]></query>
sumOfPowers
sumOfPowers is based on cern.jet.stat.Descriptive.sumOfPowers(DoubleArrayList data, int k). It returns the sum of powers of a data sequence (see Figure 11-18) as a double.
This function takes the following tuple arguments:
-
double1: data value. -
int1:k.
For more information, see:
Consider the query qColtAggr52 in Example 11-93. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-94, the query returns the relation in Example 11-95.
Example 11-93 sumOfPowers Function Query
<query id="qColtAggr52"><![CDATA[
select sumOfPowers(c3, c1) from SColtAggrFunc
]]></query>
sumOfSquaredDeviations
sumOfSquaredDeviations is based on cern.jet.stat.Descriptive.sumOfSquaredDeviations(int size, double variance). It returns the sum of squared mean deviation of a data sequence (see Figure 11-19) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see
Consider the query qColtAggr53 in Example 11-96. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-97, the query returns the relation in Example 11-98.
Example 11-96 sumOfSquaredDeviations Function Query
<query id="qColtAggr53"><![CDATA[
select sumOfSquaredDeviations(c3) from SColtAggrFunc
]]></query>
sumOfSquares
sumOfSquares is based on cern.jet.stat.Descriptive.sumOfSquares(DoubleArrayList data). It returns the sum of squares of a data sequence (see Figure 11-20) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr54 in Example 11-99. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-100, the query returns the relation in Example 11-101.
Example 11-99 sumOfSquares Function Query
<query id="qColtAggr54"><![CDATA[
select sumOfSquares(c3) from SColtAggrFunc
]]></query>
trimmedMean
trimmedMean is based on cern.jet.stat.Descriptive.trimmedMean(DoubleArrayList sortedData, double mean, int left, int right). It returns the trimmed mean of an ascending sorted data sequence as a double.
This function takes the following tuple arguments:
-
double1: data value. -
int1:left. -
int2:right.
For more information, see:
Consider the query qColtAggr55 in Example 11-102. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-103, the query returns the relation in Example 11-104.
Example 11-102 trimmedMean Function Query
<query id="qColtAggr55"><![CDATA[
select trimmedMean(c3, c1, c1) from SColtAggrFunc
]]></query>
variance
variance is based on cern.jet.stat.Descriptive.variance(int size, double sum, double sumOfSquares). It returns the variance of a data sequence (see Figure 11-21) as a double.
This function takes the following tuple arguments:
-
double1: data value.
For more information, see:
Consider the query qColtAggr57 in Example 11-105. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-106, the query returns the relation in Example 11-107.
Example 11-105 variance Function Query
<query id="qColtAggr57"><![CDATA[
select variance(c3) from SColtAggrFunc
]]></query>
weightedMean
weightedMean is based on cern.jet.stat.Descriptive.weightedMean(DoubleArrayList data, DoubleArrayList weights). It returns the weighted mean of a data sequence (see Figure 11-22) as a double.
This function takes the following tuple arguments:
-
double1: data value. -
double2: weight value.
For more information, see:
Consider the query qColtAggr58 in Example 11-108. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-109, the query returns the relation in Example 11-110.
Example 11-108 weightedMean Function Query
<query id="qColtAggr58"><![CDATA[
select weightedMean(c3, c3) from SColtAggrFunc
]]></query>
winsorizedMean
winsorizedMean is based on cern.jet.stat.Descriptive.winsorizedMean(DoubleArrayList sortedData, double mean, int left, int right). It returns the winsorized mean of a sorted data sequence as a double.
This function takes the following tuple arguments:
-
double1: data value. -
int1:left. -
int2:right.
For more information, see:
Consider the query qColtAggr60 in Example 11-111. Given the data stream SColtAggrFunc with schema (c1 integer, c2 float, c3 double, c4 bigint) in Example 11-112, the query returns the relation in Example 11-113.
Example 11-111 winsorizedMean Function Query
<query id="qColtAggr60"><![CDATA[
select winsorizedMean(c3, c1, c1) from SColtAggrFunc
]]></query>