流评估器参考

abs(1) // 1, not really a good use case for it
abs(-1) // 1, not really a good use case for it
abs(add(fieldA,fieldB)) // absolute value of fieldA + fieldB
abs(fieldA) // absolute value of fieldA

acos(100.4)  // returns the arccosine of 100.4
acos(fieldA) // returns the arccosine for fieldA.
if(gt(fieldA,fieldB),sin(fieldA),sin(fieldB)) // if fieldA > fieldB then return the arccosine of fieldA, else return the arccosine of fieldB

add(1,2,3,4) // 1 + 2 + 3 + 4 == 10
add(1,fieldA) // 1 + value of fieldA
add(fieldA,1.4) // value of fieldA + 1.4
add(fieldA,fieldB,fieldC) // value of fieldA + value of fieldB + value of fieldC
add(fieldA,div(fieldA,fieldB)) // value of fieldA + (value of fieldA / value of fieldB)
add(fieldA,if(gt(fieldA,fieldB),fieldA,fieldB)) // if fieldA > fieldB then fieldA + fieldA, else fieldA + fieldB

and(true,fieldA) // true && fieldA
and(fieldA,fieldB) // fieldA && fieldB
and(or(fieldA,fieldB),fieldC) // (fieldA || fieldB) && fieldC
and(fieldA,fieldB,fieldC,or(fieldD,fieldE),fieldF)

anova(numericArray1, numericArray2) // calculates ANOVA for two numeric arrays
anova(numericArray1, numericArray2, numericArray2) // calculates ANOVA for three numeric arrays

array(1, 2, 3)  // Array of numerics
array(array(1,2,3), array(4,5,6)) // Array of arrays

asin(100.4)  // returns the sine of 100.4
asine(fieldA) // returns the sine for fieldA.
if(gt(fieldA,fieldB),asin(fieldA),asin(fieldB)) // if fieldA > fieldB then return the asine of fieldA, else return the asine of fieldB

atan(100.4)  // returns the arctangent of 100.4
atan(fieldA) // returns the arctangent for fieldA.
if(gt(fieldA,fieldB),atan(fieldA),atan(fieldB)) // if fieldA > fieldB then return the arctanget of fieldA, else return the arctangent of fieldB

betaDistribution(1, 5)

binomialCoefficient(8, 3) // Returns the number of 3 element subsets from an 8 element set.

binomialDistribution(1000, .5)

cbrt(100.4)  // returns the square root of 100.4
cbrt(fieldA) // returns the square root for fieldA.
if(gt(fieldA,fieldB),cbrt(fieldA),cbrt(fieldB)) // if fieldA > fieldB then return the cbrt of fieldA, else return the cbrt of fieldB

ceil(100.4) // returns 101.
ceil(fieldA) // returns the next highest whole number for fieldA.
if(gt(fieldA,fieldB),ceil(fieldA),ceil(fieldB)) // if fieldA > fieldB then return the ceil of fieldA, else return the ceil of fieldB.

col(tupleList, fieldName)

colAt(matrix, 10)

columnCount(matrix)

constantDistribution(constantValue)

conv(numericArray1, numericArray2)

copyOf(numericArray, length)

copyOfRange(numericArray, startIndex, endIndex)

corr(numericArray1, numericArray2) // Compute the Pearsons correlation for two numeric arrays
corr(numericArray1, numericArray2, type=kendalls) // Compute the Kendalls correlation for two numeric arrays
corr(matrix) // Compute the Pearsons correlation matrix for a matrix
corr(matrix, type=spearmans) // Compute the Spearmans correlation matrix for a matrix

cos(100.4)  // returns the arccosine of 100.4
cos(fieldA) // returns the arccosine for fieldA.
if(gt(fieldA,fieldB),cos(fieldA),cos(fieldB)) // if fieldA > fieldB then return the arccosine of fieldA, else return the cosine of fieldB

cosineSimilarity(numericArray, numericArray)

cov(numericArray, numericArray) // Computes the covariance of a two numeric arrays
cov(matrix) // Computes the covariance matrix for the matrix.

cumulativeProbability(normalDistribution(500, 25), 502) // Returns the cumulative probability of the random sample 502 in a normal distribution with a mean of 500 and standard deviation of 25.

derivative(spline(...))
derivative(loess(...))
derivative(derivative(...))

describe(numericArray)

diff(numericArray1) // Perform time series differencing with a default lag of 1.
diff(numericArray1, 30) // Perform time series differencing with a lag of 30.

distance(numericArray1, numericArray2) // Computes the euclidean distance for two numeric arrays.
distance(numericArray1, numericArray2, type=manhattan) // Computes the manhattan distance for two numeric arrays.
distance(matrix) // Computes the euclidean distance matrix for a matrix.
distance(matrix, type=canberra) // Computes the canberra distance matrix for a matrix.

div(1,2) // 1 / 2
div(1,fieldA) // 1 / fieldA
div(fieldA,1.4) // fieldA / 1.4
div(fieldA,add(fieldA,fieldB)) // fieldA / (fieldA + fieldB)

dotProduct(numericArray, numericArray)

ebeAdd(numericArray, numericArray)

ebeDivide(numericArray, numericArray)

ebeMultiply(numericArray, numericArray)

ebeSubtract(numericArray, numericArray)

empiricalDistribution(numericArray)

enumeratedDistribution(integerArray) // This creates an enumerated distribution from the observations in the numeric array.
enumeratedDistribution(array(1,2,3,4), array(.25,.25,.25,.25)) // This creates an enumerated distribution with four discrete values (1,2,3,4) each with a probability of .25.

eor(true,fieldA) // true iff fieldA is false
eor(fieldA,fieldB) // true iff either fieldA or fieldB is true but not both
eor(eq(fieldA,fieldB),eq(fieldC,fieldD)) // true iff either fieldA == fieldB or fieldC == fieldD but not both

eq(1,2) // 1 == 2
eq(1,fieldA) // 1 == fieldA
eq(fieldA,val(foo)) fieldA == "foo"
eq(add(fieldA,fieldB),6) // fieldA + fieldB == 6

expMovingAvg(numericArray, 5) //Computes an exponential moving average with a window size of 5.

factorial(100) //Computes the factorial of 100

finddelay(numericArray1, numericArray2)

floor(100.4) // returns 100.
ceil(fieldA) // returns the next lowestt whole number for fieldA.
if(gt(fieldA,fieldB),floor(fieldA),floor(fieldB)) // if fieldA > fieldB then return the floor of fieldA, else return the floor of fieldB.

freqTable(integerArray)

gammaDistribution(1, 10)

geometricDistribution(.5) // Creates a geometric distribution with probability of .5

getAttribute(matrix, key)

getAttributes(matrix)

grandSum(matrix)

gt(1,2) // 1 > 2
gt(1,fieldA) // 1 > fieldA
gt(fieldA,val(foo)) // fieldA > "foo"
gt(add(fieldA,fieldB),6) // fieldA + fieldB > 6

gteq(1,2) // 1 >= 2
gteq(1,fieldA) // 1 >= fieldA
gteq(fieldA,val(foo)) fieldA >= "foo"
gteq(add(fieldA,fieldB),6) // fieldA + fieldB >= 6

hist(numericArray, bins)

hsin(100.4)  // returns the hsine of 100.4
hsin(fieldA) // returns the hsine for fieldA.
if(gt(fieldA,fieldB),sin(fieldA),sin(fieldB)) // if fieldA > fieldB then return the hsine of fieldA, else return the hsine of fieldB

if(fieldA,fieldB,fieldC) // if fieldA is true then fieldB else fieldC
if(gt(fieldA,5), fieldA, 5) // if fieldA > 5 then fieldA else 5
if(eq(fieldB,null), null, div(fieldA,fieldB)) // if fieldB is null then null else fieldA / fieldB

indexOf(stringArray, string)

integrate(function, start, end)

length(numericArray)

loess(yValues) // This creates the xValues automatically and fits a smooth curve through the data points.
loess(xValues, yValues) // This will fit a smooth curve through the data points.
loess(xValues, yValues, bandwidth=.15) // This will fit a smooth curve through the data points using 15 percent of the data points for each local regression line.

log(100)
log(add(fieldA,fieldB))
log(fieldA)

logNormalDistribution(.3, .0)

kolmogorovSmirnov(normalDistribution(10, 2), sampleSet)

lt(1,2) // 1 < 2
lt(1,fieldA) // 1 < fieldA
lt(fieldA,val(foo)) fieldA < "foo"
lt(add(fieldA,fieldB),6) // fieldA + fieldB < 6

lteq(1,2) // 1 <= 2
lteq(1,fieldA) // 1 <= fieldA
lteq(fieldA,val(foo)) fieldA <= "foo"
lteq(add(fieldA,fieldB),6) // fieldA + fieldB <= 6

sample(markovChain(transitionMatrix), 5)  // This creates a Markov Chain given a specific transition matrix.
The sample function takes 5 samples from the Markov Chain, representing the next five states of the system.

matrix(numericArray1, numericArray2, numericArray3) // Returns a matrix with three rows of data: numericaArray1, numericArray2, numericArray3

meanDifference(numericArray, numericArray)

minMaxScale(numericArray) // scale a numeric array between 0 and 1
minMaxScale(numericArray, 0, 100) // scale a numeric array between 1 and 100
minMaxScale(matrix) // Scale each row in a matrix between 0 and 1
minMaxScale(matrix, 0, 100) // Scale each row in a matrix between 0 and 100

mod(100,3) // returns the remainder of 100 / 3 .
mod(100,fieldA) // returns the remainder of 100 divided by the value of fieldA.
mod(fieldA,1.4) // returns the remainder of fieldA divided by 1.4.
if(gt(fieldA,fieldB),mod(fieldA,fieldB),mod(fieldB,fieldA)) // if fieldA > fieldB then return the remainder of fieldA/fieldB, else return the remainder of fieldB/fieldA.

let(a=uniformIntegerDistribution(1, 6),
    b=uniformIntegerDistribution(1, 6),
    c=monteCarlo(add(sample(a), sample(b)), 1000))

movingAverage(numericArray, 30)

movingMedian(numericArray, 30)

mult(1,2,3,4) // 1 * 2 * 3 * 4
mult(1,fieldA) // 1 * value of fieldA
mult(fieldA,1.4) // value of fieldA * 1.4
mult(fieldA,fieldB,fieldC) // value of fieldA * value of fieldB * value of fieldC
mult(fieldA,div(fieldA,fieldB)) // value of fieldA * (value of fieldA / value of fieldB)
mult(fieldA,if(gt(fieldA,fieldB),fieldA,fieldB)) // if fieldA > fieldB then fieldA * fieldA, else fieldA * fieldB

normalDistribution(mean, stddev)

normalizeSum(numericArray)
normalizeSum(matrix)

not(true) // false
not(fieldA) // true if fieldA is false else false
not(eq(fieldA,fieldB)) // true if fieldA != fieldB

olsRegress(matrix, numericArray) // This performs the olsRegression analysis on given regressor matrix and outcome array.

or(true,fieldA) // true || fieldA
or(fieldA,fieldB) // fieldA || fieldB
or(and(fieldA,fieldB),fieldC) // (fieldA && fieldB) || fieldC
or(fieldA,fieldB,fieldC,and(fieldD,fieldE),fieldF)

poissonDistribution(mean)

polyFit(yValues) // This creates the xValues automatically and fits a curve through the data points using the default 3 degree polynomial.
polyFit(yValues, 5) // This creates the xValues automatically and fits a curve through the data points using a 5 degree polynomial.
polyFit(xValues, yValues, 5) // This will fit a curve through the data points using a 5 degree polynomial.

pow(2,3) // returns 2 raised to the 3rd power.
pow(4,fieldA) // returns 4 raised by the value of fieldA.
pow(fieldA,1.4) // returns the value of fieldA raised by 1.4.
if(gt(fieldA,fieldB),pow(fieldA,fieldB),pow(fieldB,fieldA)) // if fieldA > fieldB then raise fieldA by fieldB, else raise fieldB by fieldA.

predict(regressModel, number) // predict using the output of the <<regress>> function and single numeric predictor. This will return a single numeric prediction.

predict(regressModel, numericArray) // predict using the output of the <<regress>> function and a numeric array of predictors. This will return a numeric array of predictions.

predict(splineFunc, number) // predict using the output of the <<spline>> function and single numeric predictor. This will return a single numeric prediction.

predict(splineFunc, numericArray) // predict using the output of the <<spline>> function and a numeric array of predictors. This will return a numeric array of predictions.

predict(olsRegressModel, numericArray) // predict using the output of the <<olsRegress>> function and a numeric array containing one multi-variate predictor. This will return a single numeric prediction.

predict(olsRegressModel, matrix) // predict using the output of the <<olsRegress>> function and a matrix containing rows of multi-variate predictor arrays. This will return a numeric array of predictions.

primes(100, 2000) // returns 100 primes starting from 2000

probability(poissonDistribution(10), 7) // Returns the probability of a random sample of 7 in a poisson distribution with a mean of 10.

probability(normalDistribution(10, 2), 7.5, 8.5) // Returns the probability between the range of 7.5 to 8.5 for a normal distribution with a mean of 10 and standard deviation of 2.

rank(numericArray)

raw(foo) // "foo"
raw(count(*)) // "count(*)"
raw(45) // 45
raw(true) // "true" (note: this returns the string "true" and not the boolean true)
eq(raw(fieldA), fieldA) // true if the value of fieldA equals the string "fieldA"

regress(numericArray1, numericArray2)

rev(numericArray)

round(100.4)
round(fieldA)
if(gt(fieldA,fieldB),sqrt(fieldA),sqrt(fieldB)) // if fieldA > fieldB then return the round of fieldA, else return the round of fieldB

rowAt(matrix, 10)

rowCount(matrix)

sample(poissonDistribution(5)) // Returns a single random sample from a poissonDistribution with mean of 5.
sample(poissonDistribution(5), 1000) // Returns 1000 random samples from poissonDistribution with a mean of 5.
sample(markovChain(transitionMatrix), 1000) // Returns 1000 random samples from a Markov Chain.

scalarAdd(number, numericArray) // Adds the number to each element in the number in the array.
scalarAdd(number, matrix) // Adds the number to each value in a matrix

scalarDivide(number, numericArray) // Divides each element in the numeric array by the number.
scalarDivide(number, matrix) // Divides each element in the matrix by the number.

scalarMultiply(number, numericArray) // Multiplies each element in the numeric array by the number.
scalarMultiply(number, matrix) // Multiplies each element in the matrix by the number.

scalarSubtract(number, numericArray) // Subtracts the number from each element in the number in the array.
scalarSubtract(number, matrix) // Subtracts the number from each value in a matrix

scale(number, numericArray)

sequence(100, 0, 1) // Returns a sequence of length 100, starting from 0 with a stride of 1.

setAttributes(matrix, map)

sin(100.4)  // returns the sine of 100.4
sine(fieldA) // returns the sine for fieldA.
if(gt(fieldA,fieldB),sin(fieldA),sin(fieldB)) // if fieldA > fieldB then return the sine of fieldA, else return the sine of fieldB

spline(yValues) // This creates the xValues automatically and fits a spline through the data points.
spline(xValues, yValues) // This will fit a spline through the data points.

sqrt(100.4)  // returns the square root of 100.4
sqrt(fieldA) // returns the square root for fieldA.
if(gt(fieldA,fieldB),sqrt(fieldA),sqrt(fieldB)) // if fieldA > fieldB then return the sqrt of fieldA, else return the sqrt of fieldB

standardize(numericArray)

sub(1,2,3,4) // 1 - 2 - 3 - 4
sub(1,fieldA) // 1 - value of fieldA
sub(fieldA,1.4) // value of fieldA - 1.4
sub(fieldA,fieldB,fieldC) // value of fieldA - value of fieldB - value of fieldC
sub(fieldA,div(fieldA,fieldB)) // value of fieldA - (value of fieldA / value of fieldB)
if(gt(fieldA,fieldB),sub(fieldA,fieldB),sub(fieldB,fieldA)) // if fieldA > fieldB then fieldA - fieldB, else fieldB - field

sumDifference(numericArray, numericArray)

sumColumns(matrix)

sumRows(matrix)

sumSq(numericArray)

transpose(matrix)

triangularDistribution(10, 15, 20) // A triangular distribution with a low value of 10, most likely value of 15 and high value of 20.

uniformDistribution(0.0, 100.0)

uniformDistribution(1, 6)

unitize(numericArray) // Unitize a numeric array
unitize(matrix) // Unitize each row in a matrix

weibullDistribution(.5, 10)

zipFDistribution(5000, 1.0)