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Solr Ref Guide: update 7.1 statistical function docs
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Joel Bernstein
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@ -455,10 +455,81 @@ Returns the following response:
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== Setting Variables with let
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The `let` function sets variables and runs a streaming expression that references the variables. The `let` function can be used to
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write small statistical programs.
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The `let` function sets variables and returns the last variable. The output of any statistical function can be set to a variable.
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A variable can be set to the output of any streaming expression. Here is a very simple example:
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Below is a simple example setting three variables `a`, `b` and `correlation`.
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[source,text]
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----
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let(a=array(1,2,3),
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b=array(10, 20, 30),
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correlation=corr(a, b))
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----
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Here is the output:
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[source,json]
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----
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{
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"result-set": {
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"docs": [
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{
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"correlation": 1
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},
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{
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"EOF": true,
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"RESPONSE_TIME": 0
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}
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]
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}
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}
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----
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All variables can be output by setting the `echo` variable to `true`.
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[source,text]
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----
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let(echo=true,
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a=array(1,2,3),
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b=array(10, 20, 30),
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correlation=corr(a, b))
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----
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Here is the output:
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[source,json]
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----
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{
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"result-set": {
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"docs": [
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{
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"a": [
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1,
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2,
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3
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],
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"b": [
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10,
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20,
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30
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],
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"correlation": 1
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},
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{
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"EOF": true,
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"RESPONSE_TIME": 0
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}
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]
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}
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}
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----
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Streaming expressions can also be used inside of a `let` expression in the following ways:
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* A variable can be set to the output of any streaming expression.
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* A streaming expression can be executed after all variables have been set. The variables can then be referenced by the streaming expression that is executed. The `let` expression will stream the tuples that are emitted by the final streaming expression.
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Here is a very simple example:
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[source,text]
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----
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@ -660,8 +660,8 @@ numeric array
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== empiricalDistribution
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The `empiricalDistribution` function returns a continuous probability distribution function based
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on an actual data set (https://en.wikipedia.org/wiki/Empirical_distribution_function). This function is part of the probability distribution framework and is designed to
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work with the `sample`, `kolmogorovSmirnov` and `cumulativeProbability` functions.
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on an actual data set (https://en.wikipedia.org/wiki/Empirical_distribution_function). This function is part of the probability distribution framework and is
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designed to work with the `sample`, `kolmogorovSmirnov` and `cumulativeProbability` functions.
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This function is designed to work with continuous data. To build a distribution from
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a discrete data set use the `enumeratedDistribution`.
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@ -1053,7 +1053,7 @@ The supported distribution functions are:
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=== kolmogorovSmirnov Returns
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result tuple : A tuple containing the p-value and d-statistic for test result.
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result tuple : A tuple containing the p-value and d-statistic for the test result.
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=== kolmogorovSmirnov Syntax
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@ -1163,7 +1163,7 @@ if(gt(fieldA,fieldB),mod(fieldA,fieldB),mod(fieldB,fieldA)) // if fieldA > field
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== monteCarlo
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The `monteCarlo` function performs a Monte Carlo simulation (https://en.wikipedia.org/wiki/Monte_Carlo_method)
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based on its parameters. The monteCarlo function runs another function a set number of times and returns the results.
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based on its parameters. The monteCarlo function runs another function a specified number of times and returns the results.
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The function being run typically has one or more variables that are drawn from probability
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distributions on each run. The `sample` function is used in the function to draw the samples.
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@ -1330,7 +1330,7 @@ or(fieldA,fieldB,fieldC,and(fieldD,fieldE),fieldF)
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== poissonDistribution
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The `poissonDistribution` function returns a poisson probability distribution (https://en.wikipedia.org/wiki/Poisson_distribution)
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based on its parameters. This function is part of the probability distribution framework and is designed to
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based on its parameter. This function is part of the probability distribution framework and is designed to
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work with the `sample`, `probability` and `cumulativeProbability` functions.
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=== poissonDistribution Parameters
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@ -1352,7 +1352,7 @@ The `polyFit` function performs polynomial curve fitting (https://en.wikipedia.o
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=== polyFit Parameters
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* `numeric array` : (Optional) x values. If omitted an sequence will be created for the x values.
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* `numeric array` : (Optional) x values. If omitted a sequence will be created for the x values.
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* `numeric array` : y values
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* `integer` : (Optional) polynomial degree. Defaults to 3.
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@ -1363,7 +1363,8 @@ numeric array : curve that was fit to the data points.
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=== polyFit Syntax
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[source,text]
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polyFit(yValues) // This creates the xValues automatically and fits a curve through the data points using a the default 3 degree polynomial.
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polyFit(yValues) // This creates the xValues automatically and fits a curve through the data points using the default 3 degree polynomial.
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polyFit(yValues, 5) // This creates the xValues automatically and fits a curve through the data points using a 5 degree polynomial.
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polyFit(xValues, yValues, 5) // This will fit a curve through the data points using a 5 degree polynomial.
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== polyfitDerivative
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@ -1372,7 +1373,7 @@ The `polyfitDerivative` function returns the derivative of the curve created by
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=== polyfitDerivative Parameters
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* `numeric array` : (Optional) x values. If omitted an sequence will be created for the x values.
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* `numeric array` : (Optional) x values. If omitted a sequence will be created for the x values.
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* `numeric array` : y values
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* `integer` : (Optional) polynomial degree. Defaults to 3.
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@ -1384,6 +1385,7 @@ numeric array : The curve for the derivative created by the polynomial curve fit
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[source,text]
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polyfitDerivative(yValues) // This creates the xValues automatically and returns the polyfit derivative
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polyfitDerivative(yValues, 5) // This creates the xValues automatically and fits a curve through the data points using a 5 degree polynomial and returns the polyfit derivative.
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polyfitDerivative(xValues, yValues, 5) // This will fit a curve through the data points using a 5 degree polynomial and returns the polyfit derivative.
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== pow
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@ -1443,13 +1445,12 @@ numeric array
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== probability
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The `probability` function returns the probability of encountering a random variable within a discrete
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probability distribution.
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The `probability` function returns the probability of a random variable within a discrete probability distribution.
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=== probability Parameters
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* `discrete probability distribution` : poissonDistribution | binomialDistribution | uniformDistribution | enumeratedDistribution
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* `integer` : Value to compute the probability for.
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* `integer` : Value of the random variable to compute the probability for.
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=== probability Returns
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@ -1458,7 +1459,7 @@ double : the probability
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=== probability Syntax
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[source,text]
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probability(poissonDistribution(10), 7) // Returns the probability of encountering a random sample if 7 in a poisson distribution with a mean of 10.
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probability(poissonDistribution(10), 7) // Returns the probability of a random sample of 7 in a poisson distribution with a mean of 10.
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== rank
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@ -1497,7 +1498,7 @@ eq(raw(fieldA), fieldA) // true if the value of fieldA equals the string "fieldA
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== regress
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The `regress` function performs a simple regression on two numeric arrays.
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The `regress` function performs a simple regression of two numeric arrays.
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The result of this expression is also used by the `predict` and `residuals` functions.
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@ -1516,8 +1517,8 @@ regress(numericArray1, numericArray2)
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The `residuals` function takes three parameters: a simple regression model, an array of predictor values
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and an array of actual values. The residuals function applies the simple regression model to the
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array of predictor values and computes a predictions array. The actual values array is then
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subtracted from the predictions array to compute the residuals array.
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array of predictor values and computes a predictions array. The predicted values array is then
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subtracted from the actual value array to compute the residuals array.
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=== residuals Parameters
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@ -1580,8 +1581,8 @@ Either a single numeric random sample, or a numeric array depending on the sampl
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=== sample Syntax
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[source,text]
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sample(normalDistribution(50, 5)) // Return a single random sample from a normalDistribution with mean of 50 and standard deviation of 5.
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sample(poissonDistribution(5), 1000) // Return 1000 random samples from poissonDistribution with a mean of 5.
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sample(poissonDistribution(5)) // Returns a single random sample from a poissonDistribution with mean of 5.
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sample(poissonDistribution(5), 1000) // Returns 1000 random samples from poissonDistribution with a mean of 5.
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== scale
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