mirror of https://github.com/apache/lucene.git
Solr Ref Guide: 7.1 Statistical functions.
This commit is contained in:
Joel Bernstein
parent
603db0f690
commit
60d5e229ef
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@ -199,6 +199,83 @@ atan(fieldA) // returns the arctangent for fieldA.
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if(gt(fieldA,fieldB),atan(fieldA),atan(fieldB)) // if fieldA > fieldB then return the arctanget of fieldA, else return the arctangent of fieldB
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----
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== betaDistribution
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The `betaDistribution` function returns a beta probability distribution (https://en.wikipedia.org/wiki/Beta_distribution)
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based on its parameters. This function is part of the
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probability distribution framework and is designed to work with the `sample`, `kolmogorovSmirnov` and `cumulativeProbability` functions.
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=== betaDistribution Parameters
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* `double` : shape1
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* `double` : shape2
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=== betaDistribution Returns
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probability distribution function
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=== betaDistribution Syntax
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[source,text]
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betaDistribution(1, 5)
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== binomialCoefficient
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The `binomialCoefficient` function returns the number of k-element subsets that can
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be selected from an n-element set (https://en.wikipedia.org/wiki/Binomial_coefficient).
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=== binomialCoefficient Parameters
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* `integer` : [n] set
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* `integer` : [k] subset
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=== binomialCoefficient Returns
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long value : The number of k-element subsets that can be selected from an n-element set.
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=== binomialCoefficient Syntax
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[source,text]
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binomialCoefficient(8, 3) // Returns the number of 3 element subsets from an 8 element set.
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== binomialDistribution
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The `binomialDistribution` function returns a binomial probability distribution (https://en.wikipedia.org/wiki/Binomial_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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work with the `sample`, `probability` and `cumulativeProbability` functions.
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=== binomialDistribution Parameters
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* `integer` : number of trials
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* `double` : probability of success
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=== binomialDistribution Returns
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probability distribution function
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=== binomialDistribution Syntax
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[source,text]
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binomialDistribution(1000, .5)
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== canberraDistance
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The `canberraDistance` function calculates the Canberra distance (https://en.wikipedia.org/wiki/Canberra_distance) of two numeric arrays.
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=== canberraDistance Parameters
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* `numeric array`
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* `numeric array`
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=== canberraDistance Syntax
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[source,text]
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canberraDistance(numericArray1, numuericArray2))
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=== canberraDistance Returns
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numeric
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== cbrt
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The `cbrt` function returns the trigonometric cube root of a number.
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@ -234,6 +311,24 @@ ceil(fieldA) // returns the next highest whole number for fieldA.
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if(gt(fieldA,fieldB),ceil(fieldA),ceil(fieldB)) // if fieldA > fieldB then return the ceil of fieldA, else return the ceil of fieldB.
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----
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== chebyshevDistance
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The `chebyshevDistance` function calculates the Chebyshev distance (https://en.wikipedia.org/wiki/Chebyshev_distance) of two numeric arrays.
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=== chebyshevDistance Parameters
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* `numeric array`
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* `numeric array`
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=== chebyshevDistance Syntax
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[source,text]
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chebyshevDistance(numericArray1, numuericArray2))
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=== chebyshevDistance Returns
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numeric
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== col
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The `col` function returns a numeric array from a list of Tuples. The `col`
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@ -251,6 +346,27 @@ function is used to create numeric arrays from stream sources.
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[source,text]
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col(tupleList, fieldName)
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== constantDistribution
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The `constantDistribution` function returns a constant probability distribution based on its parameter.
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This function is part of the probability distribution framework and is designed to
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work with the `sample` and `cumulativeProbability` functions.
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When sampled the constant distribution always returns its constant value.
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=== constantDistribution Parameters
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* `double` : constant value
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=== constantDistribution Returns
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probability distribution function
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=== constantDistribution Syntax
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[source,text]
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constantDistribution(constantValue)
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== conv
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The `conv` function returns the https://en.wikipedia.org/wiki/Convolution[convolution] of two numeric arrays.
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@ -331,6 +447,26 @@ cos(fieldA) // returns the arccosine for fieldA.
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if(gt(fieldA,fieldB),cos(fieldA),cos(fieldB)) // if fieldA > fieldB then return the arccosine of fieldA, else return the cosine of fieldB
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----
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== cosineSimilarity
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The `cosineSimilarity` function returns the cosine similarity (https://en.wikipedia.org/wiki/Cosine_similarity) of two numeric arrays.
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=== cosineSimilarity Parameters
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* `numeric array`
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* `numeric array`
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=== cosineSimilarity Syntax
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[source,text]
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----
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cosineSimilarity(numericArray, numericArray)
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----
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=== cosineSimilarity Returns
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numeric
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== cov
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The `cov` function returns the covariance of two numeric arrays.
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@ -346,6 +482,26 @@ The `cov` function returns the covariance of two numeric arrays.
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[source,text]
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cov(numericArray, numericArray)
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== cumulativeProbability
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The `cumulativeProbability` function returns the cumulative probability of a random variable within a
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probability distribution. The cumulative probability is the total probability of
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all random variables less then or equal to a random variable.
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=== cumulativeProbability Parameters
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* `probability distribution`
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* `number` : Value to compute the probability for.
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=== cumulativeProbability Returns
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double : the cumulative probability
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=== cumulativeProbability Syntax
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[source,text]
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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.
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== describe
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The `describe` function returns a tuple containing the descriptive statistics for an array.
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@ -394,6 +550,167 @@ div(fieldA,1.4) // fieldA / 1.4
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div(fieldA,add(fieldA,fieldB)) // fieldA / (fieldA + fieldB)
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----
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== dotProduct
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The `dotProduct` function returns the dotproduct (https://en.wikipedia.org/wiki/Dot_product) of a numeric array.
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=== dotProduct Parameters
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* `numeric array`
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=== dotProduct Syntax
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[source,text]
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dotProduct(numericArray)
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=== dotProduct Returns
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number
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== earthMoversDistance
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The `earthMoversDistance` function calculates the Earth Movers distance (https://en.wikipedia.org/wiki/Earth_mover%27s_distance) of two numeric arrays.
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=== earthMoversDistance Parameters
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* `numeric array`
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* `numeric array`
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=== earthMoversDistance Syntax
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[source,text]
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earthMoversDistance(numericArray1, numuericArray2))
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=== earthMoversDistance Returns
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numeric
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== ebeAdd
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The `ebeAdd` function performs an element-by-element addition of two numeric arrays.
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=== ebeAdd Parameters
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* `numeric array`
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* `numeric array`
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=== ebeAdd Syntax
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[source,text]
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ebeAdd(numericArray, numericArray)
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=== ebeAdd Returns
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numeric array
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== ebeDivide
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The `ebeDivide` function performs an element-by-element division of two numeric arrays.
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=== ebeDivide Parameters
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* `numeric array`
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* `numeric array`
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=== ebeDivide Syntax
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[source,text]
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ebeDivide(numericArray, numericArray)
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=== ebeDivide Returns
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numeric array
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== ebeMultiple
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The `ebeMultiply` function performs an element-by-element multiplication of two numeric arrays.
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=== ebeMultiply Parameters
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* `numeric array`
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* `numeric array`
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=== ebeMultiply Syntax
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[source,text]
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ebeMultiply(numericArray, numericArray)
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=== ebeMultiply Returns
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numeric array
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== ebeSubtract
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The `ebeSubtract` function performs an element-by-element subtraction of two numeric arrays.
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=== ebeSubtract Parameters
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* `numeric array`
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* `numeric array`
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=== ebeSubtract Syntax
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[source,text]
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ebeSubtract(numericArray, numericArray)
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=== ebeSubtract Returns
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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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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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=== empiricalDistribution Parameters
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* `numeric array` : empirical observations
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=== empiricalDistribution Returns
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probability distribution function
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=== empiricalDistribution Syntax
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empiricalDistribution(numericArray)
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== enumeratedDistribution
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The `enumeratedDistribution` function returns a discrete probability distribution function based
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on an actual data set or a pre-defined set of data and probabilities.
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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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The enumeratedDistribution can be called in two different scenarios:
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1) Single array of discrete values. This works like an empirical distribution for
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discrete data.
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2) An array of singleton discrete values and an array of double values representing
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the probabilities of the discrete values.
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This function is designed to work with discrete data. To build a distribution from
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a continuous data set use the `empiricalDistribution`.
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=== enumeratedDistribution Parameters
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* `integer array` : discrete observations or singleton discrete values.
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* `double array` : (Optional) values representing the probabilities of the singleton discrete values.
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=== enumeratedDistribution Returns
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probability distribution function
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=== enumeratedDistribution Syntax
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[source,text]
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enumeratedDistribution(integerArray) // This creates an enumerated distribution from the observations in the numeric array.
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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.
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== eor
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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.
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@ -439,6 +756,45 @@ eq(fieldA,val(foo)) fieldA == "foo"
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eq(add(fieldA,fieldB),6) // fieldA + fieldB == 6
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----
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== expMovingAge
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The `expMovingAverage` function computes an exponential moving average (https://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average) for a numeric array.
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=== expMovingAge Parameters
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* `numeric array` : The array to compute the exponential moving average from.
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* `integer`: window size
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=== expMovingAvg Returns
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numeric array : (The first element of the returned array will start from the windowSize-1 index of the original array)
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=== expMovingAvg Syntax
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[source,text]
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----
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expMovingAvg(numericArray, 5) //Computes an exponential moving average with a window size of 5.
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----
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== factorial
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The `factorial` function returns the factorial (https://en.wikipedia.org/wiki/Factorial) of its parameter.
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=== factorial Parameters
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* `integer` : The value to compute the factorial for. The largest supported value of this parameter is 170.
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=== factorial Returns
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double
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=== factorial Syntax
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[source,text]
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----
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factorial(100) //Computes the factorial of 100
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----
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== finddelay
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The `finddelay` function performs a cross-correlation between two numeric arrays and returns the delay.
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@ -471,6 +827,49 @@ ceil(fieldA) // returns the next lowestt whole number for fieldA.
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if(gt(fieldA,fieldB),floor(fieldA),floor(fieldB)) // if fieldA > fieldB then return the floor of fieldA, else return the floor of fieldB.
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----
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== freqTable
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The `freqTable` function returns a frequency distribution (https://en.wikipedia.org/wiki/Frequency_distribution) from
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an array of discrete values.
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This function is designed to work with discrete values. To work with continuous data
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use the `hist` function.
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=== freqTable Parameters
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* `integer array` : The values to build the frequency distribution from.
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=== freqTable Returns
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A list of tuples containing the frequency information for each discrete value.
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=== freqTable Syntax
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[source,text]
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----
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freqTable(integerArray)
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----
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== gammaDistribution
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The `gammaDistribution` function returns a gamma probability distribution (https://en.wikipedia.org/wiki/Gamma_distribution)
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based on its parameters. This function is part of the
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probability distribution framework and is designed to work with the `sample`, `kolmogorovSmirnov` and `cumulativeProbability` functions.
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=== gammaDistribution Parameters
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* `double` : shape
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* `double` : scale
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=== gammaDistribution Returns
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probability distribution function
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=== gammaDistribution Syntax
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[source,text]
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gammaDistribution(1, 10)
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== gt
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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.
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if(eq(fieldB,null), null, div(fieldA,fieldB)) // if fieldB is null then null else fieldA / fieldB
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----
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== kendallsCorr
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The `kendallsCorr` function returns the Kendall's Tau-b Rank Correlation (https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient) of two numeric arrays.
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=== kendallsCorr Parameters
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* `numeric array`
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* `numeric array`
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=== kendalsCorr Returns
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double between -1 and 1
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=== kendalsCorr Synax
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[source,text]
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kendalsCorr(numericArray1, numericArray2)
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== length
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The `length` function returns the length of a numeric array.
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@ -600,6 +1018,48 @@ log(add(fieldA,fieldB))
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log(fieldA)
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----
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== logNormalDistribution
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The `logNormalDistribution` function returns a log normal probability distribution (https://en.wikipedia.org/wiki/Log-normal_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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work with the `sample`, `kolmogorovSmirnov` and `cumulativeProbability` functions.
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=== logNormalDistribution Parameters
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* `double` : shape
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* `double` : scale
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=== logNormalDistribution Returns
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probability distribution function
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=== logNormalDistribution Syntax
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[source,text]
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logNormalDistribution(.3, .0)
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== kolmogorovSmirnov
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The `kolmogorovSmirnov` function performs a Kolmogorov Smirnov test (https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test),
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between a reference continuous probability distribution and a sample set.
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The supported distribution functions are:
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(empiricalDistribution, normalDistribution, logNormalDistribution, weibullDistribution, gammaDistribution, betaDistribution)
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=== kolmogorovSmirnov Parameters
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* `continuous probability distribution` : Reference distribution
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* `numeric array` : sample set
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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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=== kolmogorovSmirnov Syntax
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[source,text]
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kolmogorovSmirnov(normalDistribution(10, 2), sampleSet)
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== lt
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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.
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@ -642,6 +1102,44 @@ lteq(fieldA,val(foo)) fieldA <= "foo"
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lteq(add(fieldA,fieldB),6) // fieldA + fieldB <= 6
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----
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== manhattanDistance
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The `manhattanDistance` function calculates the Manhattan distance (https://en.wiktionary.org/wiki/Manhattan_distance) of two numeric arrays.
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=== manhattanDistance Parameters
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* `numeric array`
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* `numeric array`
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=== manhattanDistance Syntax
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[source,text]
|
||||
manhattanDistance(numericArray1, numuericArray2))
|
||||
|
||||
=== manhattanDistance Returns
|
||||
|
||||
numeric
|
||||
|
||||
== meanDifference
|
||||
|
||||
The `meanDifference` function calculates the mean of the differences following the element-by-element subtraction between two numeric arrays.
|
||||
|
||||
=== meanDifference Parameters
|
||||
|
||||
* `numeric array`
|
||||
* `numeric array`
|
||||
|
||||
=== meanDifference Returns
|
||||
|
||||
numeric
|
||||
|
||||
=== meanDifference Syntax
|
||||
|
||||
[source,text]
|
||||
----
|
||||
meanDifference(numericArray, numericArray)
|
||||
----
|
||||
|
||||
== mod
|
||||
The `mod` function returns the remainder (modulo) of the first parameter divided by the second parameter.
|
||||
|
||||
|
|
@ -662,21 +1160,72 @@ 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.
|
||||
----
|
||||
|
||||
== monteCarlo
|
||||
|
||||
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 set 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.
|
||||
|
||||
=== monteCarlo Parameters
|
||||
|
||||
* `numeric function` : The function being run by the simulation, which must return a numeric value.
|
||||
* `integer` : The number of times to run the function.
|
||||
|
||||
=== monteCarlo Returns
|
||||
|
||||
numeric array: The results of simulation runs.
|
||||
|
||||
=== monteCarlo Syntax
|
||||
|
||||
[source,text]
|
||||
let(a=uniformIntegerDistribution(1, 6),
|
||||
b=uniformIntegerDistribution(1, 6),
|
||||
c=monteCarlo(add(sample(a), sample(b)), 1000))
|
||||
|
||||
In the expression above the monteCarlo function is running the function `add(sample(a), sample(b))`
|
||||
1000 times and returning the result. Each time the function is run samples are drawn from the
|
||||
probability distributions stored in variables `a` and `b`.
|
||||
|
||||
== movingAvg
|
||||
|
||||
The `movingAvg` function calculates a https://en.wikipedia.org/wiki/Moving_average[moving average] over an array of numbers.
|
||||
|
||||
=== movingAvg Parameters
|
||||
|
||||
//TODO 7.1 - fill in details of Parameters
|
||||
* `numeric array`
|
||||
* `window size`: The array returned will be smaller than this value.
|
||||
* `window size`
|
||||
|
||||
=== movingAvg Returns
|
||||
|
||||
numeric array (The first element of the returned array will start from the windowSize-1 index of the original array)
|
||||
|
||||
=== movingAvg Syntax
|
||||
|
||||
[source,text]
|
||||
movingAverage(numericArray, 30)
|
||||
|
||||
== movingMedian
|
||||
|
||||
The `movingMedian` function calculates a moving median over an array of numbers.
|
||||
|
||||
=== movingMedian Parameters
|
||||
|
||||
* `numeric array`
|
||||
* `window size`
|
||||
|
||||
=== movingMedian Syntax
|
||||
|
||||
[source,text]
|
||||
movingMedian(numericArray, 30)
|
||||
|
||||
=== movingMedian Returns
|
||||
|
||||
numeric array (The first element of the returned array will start from the windowSize-1 index of the original array)
|
||||
|
||||
== mult
|
||||
|
||||
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.
|
||||
|
|
@ -702,6 +1251,26 @@ mult(fieldA,div(fieldA,fieldB)) // value of fieldA * (value of fieldA / value of
|
|||
mult(fieldA,if(gt(fieldA,fieldB),fieldA,fieldB)) // if fieldA > fieldB then fieldA * fieldA, else fieldA * fieldB
|
||||
----
|
||||
|
||||
== normalDistribution
|
||||
|
||||
The `normalDistribution` function returns a normal probability distribution (https://en.wikipedia.org/wiki/Normal_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.
|
||||
|
||||
=== normalDistribution Parameters
|
||||
|
||||
* `double` : mean
|
||||
* `double` : standard deviation
|
||||
|
||||
=== normalDistribution Returns
|
||||
|
||||
probability distribution function
|
||||
|
||||
=== normalDistribution Syntax
|
||||
|
||||
[source,text]
|
||||
normalDistribution(mean, stddev)
|
||||
|
||||
== normalize
|
||||
|
||||
The `normalize` function normalizes a numeric array so that values within the array
|
||||
|
|
@ -758,21 +1327,64 @@ or(and(fieldA,fieldB),fieldC) // (fieldA && fieldB) || fieldC
|
|||
or(fieldA,fieldB,fieldC,and(fieldD,fieldE),fieldF)
|
||||
----
|
||||
|
||||
== predict
|
||||
== poissonDistribution
|
||||
|
||||
The `predict` function predicts the value of an dependent variable based on
|
||||
the output of the regress function.
|
||||
The `poissonDistribution` function returns a poisson probability distribution (https://en.wikipedia.org/wiki/Poisson_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.
|
||||
|
||||
=== predict Parameters
|
||||
=== poissonDistribution Parameters
|
||||
|
||||
//TODO 7.1 - fill in details of Parameters
|
||||
* `regress output`
|
||||
* `numeric predictor`
|
||||
* `double` : mean
|
||||
|
||||
=== predict Syntax
|
||||
=== poissonDistribution Returns
|
||||
|
||||
probability distribution function
|
||||
|
||||
=== poissonDistribution Syntax
|
||||
|
||||
[source,text]
|
||||
predict(regressOutput, predictor)
|
||||
poissonDistribution(mean)
|
||||
|
||||
== polyFit
|
||||
|
||||
The `polyFit` function performs polynomial curve fitting (https://en.wikipedia.org/wiki/Curve_fitting#Fitting_lines_and_polynomial_functions_to_data_points).
|
||||
|
||||
=== polyFit Parameters
|
||||
|
||||
* `numeric array` : (Optional) x values. If omitted an sequence will be created for the x values.
|
||||
* `numeric array` : y values
|
||||
* `integer` : (Optional) polynomial degree. Defaults to 3.
|
||||
|
||||
=== polyFit Returns
|
||||
|
||||
numeric array : curve that was fit to the data points.
|
||||
|
||||
=== polyFit Syntax
|
||||
|
||||
[source,text]
|
||||
polyFit(yValues) // This creates the xValues automatically and fits a curve through the data points using a the default 3 degree polynomial.
|
||||
polyFit(xValues, yValues, 5) // This will fit a curve through the data points using a 5 degree polynomial.
|
||||
|
||||
== polyfitDerivative
|
||||
|
||||
The `polyfitDerivative` function returns the derivative of the curve created by the polynomial curve fitter.
|
||||
|
||||
=== polyfitDerivative Parameters
|
||||
|
||||
* `numeric array` : (Optional) x values. If omitted an sequence will be created for the x values.
|
||||
* `numeric array` : y values
|
||||
* `integer` : (Optional) polynomial degree. Defaults to 3.
|
||||
|
||||
=== polyfitDerivative Returns
|
||||
|
||||
numeric array : The curve for the derivative created by the polynomial curve fitter.
|
||||
|
||||
=== polyfitDerivative Syntax
|
||||
|
||||
[source,text]
|
||||
polyfitDerivative(yValues) // This creates the xValues automatically and returns the polyfit derivative
|
||||
polyfitDerivative(xValues, yValues, 5) // This will fit a curve through the data points using a 5 degree polynomial and returns the polyfit derivative.
|
||||
|
||||
== pow
|
||||
The `pow` function returns the value of its first parameter raised to the power of its second parameter.
|
||||
|
|
@ -794,6 +1406,60 @@ 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
|
||||
|
||||
The `predict` function predicts the value of an dependent variable based on
|
||||
the output of the regress function.
|
||||
|
||||
=== predict Parameters
|
||||
|
||||
//TODO 7.1 - fill in details of Parameters
|
||||
* `regress output`
|
||||
* `numeric predictor`
|
||||
|
||||
=== predict Syntax
|
||||
|
||||
[source,text]
|
||||
predict(regressOutput, predictor)
|
||||
|
||||
== primes
|
||||
The `primes` function returns an array of prime numbers starting from a specified number.
|
||||
|
||||
=== primes Parameters
|
||||
|
||||
* `integer`: The number of primes to return in the list
|
||||
* `integer`: The starting point for returning the primes
|
||||
|
||||
=== primes Syntax
|
||||
|
||||
[source,text]
|
||||
----
|
||||
primes(100, 2000) // returns 100 primes starting from 2000
|
||||
----
|
||||
|
||||
=== primes Returns
|
||||
|
||||
numeric array
|
||||
|
||||
== probability
|
||||
|
||||
The `probability` function returns the probability of encountering a random variable within a discrete
|
||||
probability distribution.
|
||||
|
||||
=== probability Parameters
|
||||
|
||||
* `discrete probability distribution` : poissonDistribution | binomialDistribution | uniformDistribution | enumeratedDistribution
|
||||
* `integer` : Value to compute the probability for.
|
||||
|
||||
=== probability Returns
|
||||
|
||||
double : the probability
|
||||
|
||||
=== probability Syntax
|
||||
|
||||
[source,text]
|
||||
probability(poissonDistribution(10), 7) // Returns the probability of encountering a random sample if 7 in a poisson distribution with a mean of 10.
|
||||
|
||||
== rank
|
||||
|
||||
The `rank` performs a rank transformation on a numeric array.
|
||||
|
|
@ -833,7 +1499,7 @@ eq(raw(fieldA), fieldA) // true if the value of fieldA equals the string "fieldA
|
|||
|
||||
The `regress` function performs a simple regression on two numeric arrays.
|
||||
|
||||
The result of this expression is also used by the `predict` function.
|
||||
The result of this expression is also used by the `predict` and `residuals` functions.
|
||||
|
||||
=== regress Parameters
|
||||
|
||||
|
|
@ -846,6 +1512,28 @@ The result of this expression is also used by the `predict` function.
|
|||
[source,text]
|
||||
regress(numericArray1, numericArray2)
|
||||
|
||||
== residuals
|
||||
|
||||
The `residuals` function takes three parameters: a simple regression model, an array of predictor values
|
||||
and an array of actual values. The residuals function applies the simple regression model to the
|
||||
array of predictor values and computes a predictions array. The actual values array is then
|
||||
subtracted from the predictions array to compute the residuals array.
|
||||
|
||||
=== residuals Parameters
|
||||
|
||||
* `regress output`
|
||||
* `numeric array`: The array of predictor values
|
||||
* `numeric array`: The array of actual values
|
||||
|
||||
=== residuals Syntax
|
||||
|
||||
[source,text]
|
||||
residuals(regressOutput, numericArray, numericArray)
|
||||
|
||||
=== residuals Returns
|
||||
|
||||
numeric array of residuals
|
||||
|
||||
== rev
|
||||
|
||||
The `rev` function reverses the order of a numeric array.
|
||||
|
|
@ -876,6 +1564,25 @@ 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
|
||||
----
|
||||
|
||||
== sample
|
||||
|
||||
The `sample` function can be used to draw random samples from a probability distribution.
|
||||
|
||||
=== sample Parameters
|
||||
|
||||
* `probability distribution`: The distribution to sample.
|
||||
* `integer`: (Optional) Sample size. Defaults to 1.
|
||||
|
||||
=== sample Returns
|
||||
|
||||
Either a single numeric random sample, or a numeric array depending on the sample size parameter.
|
||||
|
||||
=== sample Syntax
|
||||
|
||||
[source,text]
|
||||
sample(normalDistribution(50, 5)) // Return a single random sample from a normalDistribution with mean of 50 and standard deviation of 5.
|
||||
sample(poissonDistribution(5), 1000) // Return 1000 random samples from poissonDistribution with a mean of 5.
|
||||
|
||||
== scale
|
||||
|
||||
The `scale` function multiplies all the elements of an array by a number.
|
||||
|
|
@ -923,6 +1630,24 @@ 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
|
||||
----
|
||||
|
||||
== spearmansCorr
|
||||
|
||||
The `spearmansCorr` function returns the Spearmans Rank Correlation (https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) of two numeric arrays.
|
||||
|
||||
=== spearmansCorr Parameters
|
||||
|
||||
* `numeric array`
|
||||
* `numeric array`
|
||||
|
||||
=== spearmansCorr Returns
|
||||
|
||||
double between -1 and 1
|
||||
|
||||
=== spearmansCorr Synax
|
||||
|
||||
[source,text]
|
||||
spearmansCorr(numericArray1, numericArray2)
|
||||
|
||||
== sqrt
|
||||
|
||||
The `sqrt` function returns the trigonometric square root of a number.
|
||||
|
|
@ -964,3 +1689,104 @@ 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
|
||||
|
||||
The `sumDifference` function calculates the sum of the differences following an element-by-element subtraction between two numeric arrays.
|
||||
|
||||
=== sumDifference Parameters
|
||||
|
||||
* `numeric array`
|
||||
* `numeric array`
|
||||
|
||||
=== sumDifference Returns
|
||||
|
||||
numeric
|
||||
|
||||
=== sumDifference Syntax
|
||||
|
||||
[source,text]
|
||||
----
|
||||
sumDifference(numericArray, numericArray)
|
||||
----
|
||||
|
||||
== uniformDistribution
|
||||
|
||||
The `uniformDistribution` function returns a continuous uniform probability distribution (https://en.wikipedia.org/wiki/Uniform_distribution_(continuous))
|
||||
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.
|
||||
|
||||
=== uniforDistribution Parameters
|
||||
|
||||
* `double` : start
|
||||
* `double` : end
|
||||
|
||||
=== uniformDistribution Returns
|
||||
|
||||
probability distribution function
|
||||
|
||||
=== uniformDistribution Syntax
|
||||
|
||||
[source,text]
|
||||
uniformDistribution(0.0, 100.0)
|
||||
|
||||
== uniformIntegerDistribution
|
||||
|
||||
The `uniformIntegerDistribution` function returns a discrete uniform probability distribution (https://en.wikipedia.org/wiki/Discrete_uniform_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.
|
||||
|
||||
=== uniformIntegerDistribution Parameters
|
||||
|
||||
* `integer` : start
|
||||
* `integer` : end
|
||||
|
||||
=== uniformIntegerDistribution Returns
|
||||
|
||||
probability distribution function
|
||||
|
||||
=== uniformIntegerDistribution Syntax
|
||||
|
||||
[source,text]
|
||||
uniformDistribution(1, 6)
|
||||
|
||||
== weibullDistribution
|
||||
|
||||
The `weibullDistribution` function returns a Weibull probability distribution (https://en.wikipedia.org/wiki/Weibull_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.
|
||||
|
||||
=== weibullDistribution Parameters
|
||||
|
||||
* `double` : shape
|
||||
* `double` : scale
|
||||
|
||||
=== weibullDistribution Returns
|
||||
|
||||
probability distribution function
|
||||
|
||||
=== weibullDistribution Syntax
|
||||
|
||||
[source,text]
|
||||
weibullDistribution(.5, 10)
|
||||
|
||||
== zipFDistribution
|
||||
|
||||
The `zipFDistribution` function returns a ZipF distribution (https://en.wikipedia.org/wiki/Zeta_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.
|
||||
|
||||
=== zipFDistribution Parameters
|
||||
|
||||
* `integer` : size
|
||||
* `double` : exponent
|
||||
|
||||
=== zipFDistribution Returns
|
||||
|
||||
probability distribution function
|
||||
|
||||
=== zipFDistribution Syntax
|
||||
|
||||
[source,text]
|
||||
zipFDistribution(5000, 1.0)
|
||||
Loading…
Reference in New Issue