Stream Evaluator Reference

The abs function will return the absolute value of the provided single parameter. The abs function will fail to execute if the value is non-numeric. If a null value is found then null will be returned as the result.

The acos function returns the trigonometric arccosine of a number.

The add function will take 2 or more numeric values and add them together. The add function will fail to execute if any of the values are non-numeric. If a null value is found then null will be returned as the result.

The analyze function analyzes text using a Lucene/Solr analyzer and returns a list of tokens emitted by the analyzer. The analyze function can be called on its own or within the select and cartesianProduct streaming expressions.

The and function will return the logical AND of at least 2 boolean parameters. The function will fail to execute if any parameters are non-boolean or null. Returns a boolean value.

The anova function calculates the analysis of variance for two or more numeric arrays.

The array function returns an array of numerics or other objects including other arrays.

The asin function returns the trigonometric arcsine of a number.

The atan function returns the trigonometric arctangent of a number.

The betaDistribution function returns a beta probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, kolmogorovSmirnov and cumulativeProbability functions.

The binomialCoefficient function returns a Binomial Coefficient, the number of k-element subsets that can be selected from an n-element set.

The binomialDistribution function returns a binomial probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.

The cbrt function returns the trigonometric cube root of a number.

The ceil function rounds a decimal value to the next highest whole number.

The col function returns a numeric array from a list of Tuples. The col function is used to create numeric arrays from stream sources.

The colAt function returns the column of a matrix at a specific index as a numeric array.

The columnCount function returns the number of columns in a matrix.

The constantDistribution function returns a constant probability distribution based on its parameter. This function is part of the probability distribution framework and is designed to work with the sample and cumulativeProbability functions.

When sampled the constant distribution always returns its constant value.

The conv function returns the convolution of two numeric arrays.

The copyOf function creates a copy of a numeric array.

The copyOfRange function creates a copy of a range of a numeric array.

The corr function returns the correlation of two numeric arrays or the correlation matrix for a matrix.

The corr function support Pearson’s, Kendall’s and Spearman’s correlations.

The cos function returns the trigonometric cosine of a number.

The cosineSimilarity function returns the cosine similarity of two numeric arrays.

The cov function returns the covariance of two numeric array or the covariance matrix for matrix.

The cumulativeProbability function returns the cumulative probability of a random variable within a probability distribution. The cumulative probability is the total probability of all random variables less then or equal to a random variable.

The derivative function returns the derivative of a function. The derivative function can compute the derivative of the spline function and the loess function. The derivative can also take the derivative of a derivative.

The describe function returns a tuple containing the descriptive statistics for an array.

The diff functions performs time series differencing.

Time series differencing is often used to make a time series stationary before further analysis.

The distance function computes the distance of two numeric arrays or the distance matrix for a matrix.

The div function will take two numeric values and divide them. The function will fail to execute if any of the values are non-numeric or null, or the 2nd value is 0. Returns a numeric value.

The dotProduct function returns the dotproduct of a numeric array.

The ebeAdd function performs an element-by-element addition of two numeric arrays.

The ebeDivide function performs an element-by-element division of two numeric arrays.

The ebeMultiply function performs an element-by-element multiplication of two numeric arrays.

The ebeSubtract function performs an element-by-element subtraction of two numeric arrays.

The empiricalDistribution function returns empirical distribution function, a continuous probability distribution function based on an actual data set. This function is part of the probability distribution framework and is designed to work with the sample, kolmogorovSmirnov and cumulativeProbability functions.

This function is designed to work with continuous data. To build a distribution from a discrete data set use the enumeratedDistribution.

The enumeratedDistribution function returns a discrete probability distribution function based on an actual data set or a pre-defined set of data and probabilities. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.

The enumeratedDistribution can be called in two different scenarios:

1) Single array of discrete values. This works like an empirical distribution for discrete data.

2) An array of singleton discrete values and an array of double values representing the probabilities of the discrete values.

This function is designed to work with discrete data. To build a distribution from a continuous data set use the empiricalDistribution.

The eor function will return the logical exclusive or of at least two boolean parameters. The function will fail to execute if any parameters are non-boolean or null. Returns a boolean value.

The eq function will return whether all the parameters are equal, as per Java’s standard equals(…​) function. The function accepts parameters of any type, but will fail to execute if all the parameters are not of the same type. That is, all are Boolean, all are String, or all are Numeric. If any any parameters are null and there is at least one parameter that is not null then false will be returned. Returns a boolean value.

The expMovingAverage function computes an exponential moving average for a numeric array.

The factorial function returns the factorial of its parameter.

The finddelay function performs a cross-correlation between two numeric arrays and returns the delay.

The floor function rounds a decimal value to the next lowest whole number.

The freqTable function returns a frequency distribution from an array of discrete values.

This function is designed to work with discrete values. To work with continuous data use the hist function.

The gammaDistribution function returns a gamma probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, kolmogorovSmirnov and cumulativeProbability functions.

The geometricDistribution function returns a geometric probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.

The getAttribute function returns an attribute from a matrix by its key. Any function that returns a matrix can also set attributes on the matrix with additional information. The setAttribute function can also be used to set attributes on a matrix. The key to an attribute is always a string. The value of attribute can be any object including numerics, arrays, maps, matrixes etc…​

The getAttributes function returns the attribute map from matrix. See the getAttribute function for more details on attributes.

The getColumnLabels function returns the columns labels of a matrix. The column labels can be optionally set by any function that returns a matrix. The column labels can also be set via the setColumnLabels function.

The getRowLabels function returns the row labels of a matrix. The row labels can be optionally set by any function that returns a matrix. The row labels can also be set via the setRowLabels function.

The getValue function returns the value of a single Tuple entry by key.

The grandSum function sums all the values in a matrix.

The gt function will return whether the first parameter is greater than the second parameter. The function accepts numeric or string parameters, but will fail to execute if all the parameters are not of the same type. That is, all are String or all are Numeric. If any any parameters are null then an error will be raised. Returns a boolean value.

The gteq function will return whether the first parameter is greater than or equal to the second parameter. The function accepts numeric and string parameters, but will fail to execute if all the parameters are not of the same type. That is, all are String or all are Numeric. If any any parameters are null then an error will be raised. Returns a boolean value.

The hist function creates a histogram from a numeric array. The hist function is designed to work with continuous variables.

The hsin function returns the trigonometric hyperbolic sine of a number.

The if function works like a standard conditional if/then statement. If the first parameter is true, then the second parameter will be returned, else the third parameter will be returned. The function accepts a boolean as the first parameter and anything as the second and third parameters. An error will occur if the first parameter is not a boolean or is null.

The indexOf function returns the index of a string in an array of strings.

The integrate function computes the integral of an interpolation function for a specific range of the curve.

The length function returns the length of a numeric array.

The leoss function is a smoothing curve fitter which uses a local regression algorithm. Unlike the spline function which touches each control point, the loess function puts a smooth curve through the control points without having to touch the control points. The loess result can be used by the derivative function to produce smooth derivatives from data that is not smooth.

The log function will return the natural log of the provided single parameter. The log function will fail to execute if the value is non-numeric. If a null value is found, then null will be returned as the result.

The logNormalDistribution function returns a log normal probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, kolmogorovSmirnov and cumulativeProbability functions.

The kolmogorovSmirnov function performs a Kolmogorov Smirnov test, between a reference continuous probability distribution and a sample set.

The supported distribution functions are: empiricalDistribution, normalDistribution, logNormalDistribution, weibullDistribution, gammaDistribution, and betaDistribution.

The lt function will return whether the first parameter is less than the second parameter. The function accepts numeric or string parameters, but will fail to execute if all the parameters are not of the same type. That is, all are String or all are Numeric. If any any parameters are null then an error will be raised. Returns a boolean value.

The lteq function will return whether the first parameter is less than or equal to the second parameter. The function accepts numeric and string parameters, but will fail to execute if all the parameters are not of the same type. That is, all are String or all are Numeric. If any any parameters are null then an error will be raised. Returns a boolean value.

The markovChain function can be used to perform Markov Chain simulations. The markovChain function takes as its parameter a transition matrix and returns a mathematical model that can be sampled using the sample function. Each sample taken from the Markov Chain represents the current state of system.

The matrix function returns a matrix which can be operated on by functions that support matrix operations.

The meanDifference function calculates the mean of the differences following the element-by-element subtraction between two numeric arrays.

The minMaxScale function scales numeric arrays within a minimum and maximum value. By default minMaxScale scales between 0 and 1. The minMaxScale function can operate on both numeric arrays and matrices.

When operating on a matrix the minMaxScale function operates on each row of the matrix.

The mod function returns the remainder (modulo) of the first parameter divided by the second parameter.

The monteCarlo function performs a Monte Carlo simulation (https://en.wikipedia.org/wiki/Monte_Carlo_method) based on its parameters. The monteCarlo function runs another function a specified number of times and returns the results.

The function being run typically has one or more variables that are drawn from probability distributions on each run. The sample function is used in the function to draw the samples.

The simulation’s result array can then be treated as an empirical distribution to understand the probabilities of the simulation results.

The movingAvg function calculates a moving average over an array of numbers.

The movingMedian function calculates a moving median over an array of numbers.

The mult function will take two or more numeric values and multiply them together. The mult function will fail to execute if any of the values are non-numeric. If a null value is found then null will be returned as the result.

The normalDistribution function returns a normal probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, kolmogorovSmirnov and cumulativeProbability functions.

The normalizeSum function scales numeric arrays so that they sum to 1. The normalizeSum function can operate on both numeric arrays and matrices.

When operating on a matrix the normalizeSum function operates on each row of the matrix.

The not function will return the logical NOT of a single boolean parameter. The function will fail to execute if the parameter is non-boolean or null. Returns a boolean value.

The olsRegress function performs ordinary least squares, multivariate, linear regression.

The olsRegress function returns a single Tuple containing the regression model with estimated regression parameters, RSquared and regression diagnostics.

The output of olsRegress can be used with the predict function to predict values based on the regression model.

The or function will return the logical OR of at least 2 boolean parameters. The function will fail to execute if any parameters are non-boolean or null. Returns a boolean value.

The poissonDistribution function returns a poisson probability distribution based on its parameter. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.

The polyFit function performs polynomial curve fitting.

The pow function returns the value of its first parameter raised to the power of its second parameter.

The predict function predicts the value of dependent variables based on regression models or functions.

The predict function can predict values based on the output of the following functions: spline, loess, regress, olsRegress.

The primes function returns an array of prime numbers starting from a specified number.

The probability function returns the probability of a random variable within a probability distribution.

The probability function computes the probability between random variable ranges for both continuous and discrete probability distributions.

The probability function can compute probabilities for a specific random variable for discrete probability distributions only.

The supported continuous distribution functions are: normalDistribution, logNormalDistribution, betaDistribution, gammaDistribution, empiricalDistribution, triangularDistribution, weibullDistribution, uniformDistribution, constantDistribution

The supported discreet distributions are: poissonDistribution, binomialDistribution, enumeratedDistribution, zipFDistribution, geometricDistribution, uniformIntegerDistribution

The rank performs a rank transformation on a numeric array.

The raw function will return whatever raw value is the parameter. This is useful for cases where you want to use a string as part of another evaluator.

The regress function performs a simple regression of two numeric arrays.

The result of this expression is also used by the predict function.

The rev function reverses the order of a numeric array.

The round function returns the closest whole number to the argument.

The rowAt function returns the row of a matrix at a specific index as a numeric array.

The rowCount function returns the number of rows in a matrix.

The sample function can be used to draw random samples from a probability distribution or Markov Chain.

The scalarAdd function adds a scalar value to every value in a numeric array or matrix. When working with numeric arrays, scalarAdd returns a new array with the new values. When working with a matrix, scalarAdd returns a new matrix with new values.

The scalarDivide function divides each number in numeric array or matrix by a scalar value. When working with numeric arrays, scalarDivide returns a new array with the new values. When working with a matrix, scalarDivide returns a new matrix with new values.

The scalarMultiply function multiplies each element in a numeric array or matrix by a scalar value. When working with numeric arrays, scalarMultiply returns a new array with the new values. When working with a matrix, scalarMultiply returns a new matrix with new values.

The scalarSubtract function subtracts a scalar value from every value in a numeric array or matrix. When working with numeric arrays, scalarSubtract returns a new array with the new values. When working with a matrix, scalarSubtract returns a new matrix with new values.

The scale function multiplies all the elements of an array by a number.

The sequence function returns an array of numbers based on its parameters.

The setAttributes function sets an attributes map of a matrix.

The setColumnLabels function sets the columns labels of a matrix.

The setRowLabels function sets the row labels of a matrix.

The setValue function sets a new value for a Tuple entry.

The sin function returns the trigonometric sine of a number.

The spline function performs a cubic spline interpolation (https://en.wikiversity.org/wiki/Cubic_Spline_Interpolation) of a curve given a set of x,y coordinates. The return value of the spline function is an interpolation function which can be used to predict values along the curve and generate a derivative of the curve.

The sqrt function returns the trigonometric square root of a number.

The standardize function standardizes a numeric array so that values within the array have a mean of 0 and standard deviation of 1.

The sub function will take 2 or more numeric values and subtract them, from left to right. The sub function will fail to execute if any of the values are non-numeric. If a null value is found then null will be returned as the result.

The sumDifference function calculates the sum of the differences following an element-by-element subtraction between two numeric arrays.

The sumColumns function sums the columns in a matrix and returns a numeric array with the result.

The sumRows function sums the rows in a matrix and returns a numeric array with the result.

The sumSq function returns the sum-of-squares of the values in a numeric array.

The transpose function transposes a matrix .

The triangularDistribution function returns a triangular probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.

The uniformDistribution function returns a continuous uniform probability distribution based on its parameters. See the uniformIntegerDistribution to work with discrete uniform distributions. This function is part of the probability distribution framework and is designed to work with the sample and cumulativeProbability functions.

The uniformIntegerDistribution function returns a discrete uniform probability distribution based on its parameters. See the uniformDistribution to work with continuous uniform distributions. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.

The unitize function scales numeric arrays to a magnitude of 1, often called unit vectors. The unitize function can operate on both numeric arrays and matrices.

When operating on a matrix the unitize function unitizes each row of the matrix.

The weibullDistribution function returns a Weibull probability distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, kolmogorovSmirnov and cumulativeProbability functions.

The zipFDistribution function returns a ZipF distribution based on its parameters. This function is part of the probability distribution framework and is designed to work with the sample, probability and cumulativeProbability functions.