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SOLR-11753: minor typos

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Cassandra Targett 2017-12-18 11:54:51 -06:00
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solr/solr-ref-guide/src/stream-evaluator-reference.adoc
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@ -377,7 +377,7 @@ copyOfRange(numericArray, startIndex, endIndex)
The `corr` function returns the correlation of two numeric arrays or the correlation matrix for a matrix. The `corr` function returns the correlation of two numeric arrays or the correlation matrix for a matrix.
The `corr` function support Pearsons, Kendals and Spearmans correlation. The `corr` function support Pearson's, Kendall's and Spearman's correlations.
=== corr Positional Parameters === corr Positional Parameters
@ -390,7 +390,7 @@ OR
=== corr Named Parameters === corr Named Parameters
* `type`: (Optional) pearsons | kendalls | spearmans, Defaults to pearsons. * `type`: (Optional) The type of correlation. Possible values are `pearsons`, `kendalls`, or `spearmans`. The default is `pearsons`.
=== corr Syntax === corr Syntax
@ -402,7 +402,7 @@ corr(matrix, type=spearmans) // Compute the Spearmans correlation matrix for a m
=== corr Returns === corr Returns
number | matrix : Either the correlation or correlation matrix. number | matrix: Either the correlation or correlation matrix.
== cos == cos
The `cos` function returns the trigonometric cosine of a number. The `cos` function returns the trigonometric cosine of a number.
@ -461,7 +461,7 @@ cov(matrix) // Computes the covariance matrix for the matrix.
=== cov Returns === cov Returns
number | matrix : Either the covariance or covariance matrix. number | matrix: Either the covariance or covariance matrix.
== cumulativeProbability == cumulativeProbability
@ -525,8 +525,8 @@ Time series differencing is often used to make a time series stationary before f
=== diff Parameters === diff Parameters
* `numeric array`: The time series data * `numeric array`: The time series data.
* `integer`: (Optional)lag. Defaults to 1. * `integer`: (Optional) The lag. Defaults to 1.
=== diff Syntax === diff Syntax
@ -544,16 +544,16 @@ The `distance` function computes the distance of two numeric arrays or the dista
=== distance Positional Parameters === distance Positional Parameters
* `numeric array` : The first numeric array * `numeric array`: The first numeric array
* `numeric array` : The second numeric array * `numeric array`: The second numeric array
OR OR
* `matrix` : The matrix to compute the distance matrix for. Note that distance is computed between the `columns` in the matrix. * `matrix`: The matrix to compute the distance matrix for. Note that distance is computed between the `columns` in the matrix.
=== distance Named Parameters === distance Named Parameters
* `type` : (Optional) euclidean | manhattan | canberra | earthMovers. Defaults to euclidean. * `type`: (Optional) The distance type. Possible values are `euclidean`, `manhattan`, `canberra`, or `earthMovers`. The default is `euclidean`.
=== distance Syntax === distance Syntax
@ -565,7 +565,7 @@ distance(matrix, type=canberra) // Computes the canberra distance matrix for a m
=== distance Returns === distance Returns
number | matrix : Either the distance or distance matrix. number | matrix: Either the distance or distance matrix.
== div == div
@ -695,6 +695,7 @@ A probability distribution function.
=== empiricalDistribution Syntax === empiricalDistribution Syntax
[source,text]
empiricalDistribution(numericArray) empiricalDistribution(numericArray)
== enumeratedDistribution == enumeratedDistribution
@ -912,7 +913,7 @@ The `grandSum` function sums all the values in a matrix.
=== grandSum Parameters === grandSum Parameters
* `matrix`: The matrix to operate on * `matrix`: The matrix to operate on.
=== grandSum Syntax === grandSum Syntax
@ -1036,8 +1037,8 @@ length(numericArray)
== loess == loess
The `leoss` function is a smoothing curve fitter which uses a https://en.wikipedia.org/wiki/Local_regression[local regression] algorithm. The `leoss` function is a smoothing curve fitter which uses a https://en.wikipedia.org/wiki/Local_regression[local regression] algorithm.
Unlike the <<spline>> function which touches each control point, the loess function puts a smooth curve through 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 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. data that is not smooth.
=== loess Positional Parameters === loess Positional Parameters
@ -1047,8 +1048,8 @@ data that is not smooth.
=== loess Named Parameters === loess Named Parameters
* `bandwidth` : (Optional) The percent of the data points to use when drawing the local regression line, defaults to .25. Decreasing the bandwidth increases the number of curves that loess can fit. * `bandwidth`: (Optional) The percent of the data points to use when drawing the local regression line, defaults to .25. Decreasing the bandwidth increases the number of curves that loess can fit.
* `robustIterations` : (Optional)The number of iterations used to smooth outliers, defaults to 2. * `robustIterations`: (Optional) The number of iterations used to smooth outliers, defaults to 2.
=== loess Syntax === loess Syntax
@ -1059,7 +1060,7 @@ loess(xValues, yValues, bandwidth=.15) // This will fit a smooth curve through t
=== loess Returns === loess Returns
function : The function can be treated as both a `numeric array` of the smoothed data points and `function`. function: The function can be treated as both a `numeric array` of the smoothed data points and `function`.
== log == log
@ -1165,7 +1166,7 @@ lteq(add(fieldA,fieldB),6) // fieldA + fieldB <= 6
== markovChain == markovChain
The `markovChain` function can be used to perform https://en.wikipedia.org/wiki/Markov_chain[Markov Chain] simulations. The `markovChain` function can be used to perform https://en.wikipedia.org/wiki/Markov_chain[Markov Chain] simulations.
The markovChain function takes as its parameter a https://en.wikipedia.org/wiki/Stochastic_matrix[transition matrix] and The `markovChain` function takes as its parameter a https://en.wikipedia.org/wiki/Stochastic_matrix[transition matrix] and
returns a mathematical model that can be sampled using the <<sample>> function. Each sample taken 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. from the Markov Chain represents the current state of system.
@ -1221,17 +1222,17 @@ meanDifference(numericArray, numericArray)
== minMaxScale == minMaxScale
The `minMaxScale` function scales numeric arrays within a min and max value. 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 By default `minMaxScale` scales between 0 and 1. The `minMaxScale` function can operate on
both numeric arrays and matrices. both numeric arrays and matrices.
When operating on a matrix the minMaxScale function operates on each row of the matrix. When operating on a matrix the `minMaxScale` function operates on each row of the matrix.
=== minMaxScale Parameters === minMaxScale Parameters
* `numeric array` | `matrix` : The array or matrix to scale * `numeric array` | `matrix`: The array or matrix to scale
* `double` : (Optional) The min value. Defaults to 0. * `double`: (Optional) The min value. Defaults to 0.
* `double` : (Optional) The max value. Defaults to 1. * `double`: (Optional) The max value. Defaults to 1.
=== minMaxScale Syntax === minMaxScale Syntax
@ -1243,7 +1244,7 @@ minMaxScale(matrix, 0, 100) // Scale each row in a matrix between 0 and 100
=== minMaxScale Returns === minMaxScale Returns
numeric array or matrix A numeric array or matrix
== mod == mod
The `mod` function returns the remainder (modulo) of the first parameter divided by the second parameter. The `mod` function returns the remainder (modulo) of the first parameter divided by the second parameter.
@ -1379,13 +1380,13 @@ normalDistribution(mean, stddev)
== normalizeSum == normalizeSum
The `normalizeSum` function scales numeric arrays so that they sum to 1. The `normalizeSum` function scales numeric arrays so that they sum to 1.
The normalizeSum function can operate on both numeric arrays and matrices. 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. When operating on a matrix the `normalizeSum` function operates on each row of the matrix.
=== normalizeSum Parameters === normalizeSum Parameters
* `numeric array` | matrix * `numeric array` | `matrix`
=== normalizeSum Syntax === normalizeSum Syntax
@ -1423,7 +1424,7 @@ The `olsRegress` function performs https://en.wikipedia.org/wiki/Ordinary_least_
The `olsRegress` function returns a single Tuple containing the regression model with estimated regression parameters, RSquared and regression diagnostics. 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 output of `olsRegress` can be used with the <<predict>> function to predict values based on the regression model.
=== olsRegress Parameters === olsRegress Parameters
@ -1432,6 +1433,7 @@ The output of olsRegress can be used with the <<predict>> function to predict va
=== olsRegress Syntax === olsRegress Syntax
[source,text]
olsRegress(matrix, numericArray) // This performs the olsRegression analysis on given regressor matrix and outcome array. olsRegress(matrix, numericArray) // This performs the olsRegression analysis on given regressor matrix and outcome array.
=== olsRegress Returns === olsRegress Returns
@ -1506,8 +1508,8 @@ The `pow` function returns the value of its first parameter raised to the power
=== pow Parameters === pow Parameters
* `Field Name | Raw Number | Number Evaluator`: Parameter 1 * `Field Name` | `Raw Number` | `Number Evaluator`: Parameter 1
* `Field Name | Raw Number | Number Evaluator`: Parameter 2 * `Field Name` | `Raw Number` | `Number Evaluator`: Parameter 2
=== pow Syntax === pow Syntax
@ -1523,12 +1525,9 @@ if(gt(fieldA,fieldB),pow(fieldA,fieldB),pow(fieldB,fieldA)) // if fieldA > field
== predict == predict
The `predict` function predicts the value of dependant variables based on regression models or functions. 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 `predict` function can predict values based on the output of the following functions: <<spline>>, <<loess>>, <<regress>>, <<olsRegress>>.
=== predict Parameters === predict Parameters
@ -1538,12 +1537,19 @@ The `predict` function can predict values based on the output of the following f
=== predict Syntax === predict Syntax
[source,text] [source,text]
----
predict(regressModel, number) // predict using the output of the <<regress>> function and single numeric predictor. This will return a single numeric prediction. 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(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, 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(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, 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. 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 == primes
The `primes` function returns an array of prime numbers starting from a specified number. The `primes` function returns an array of prime numbers starting from a specified number.
@ -1592,9 +1598,11 @@ The supported discreet distributions are:
=== probability Syntax === probability Syntax
[source,text] [source,text]
----
probability(poissonDistribution(10), 7) // Returns the probability of a random sample of 7 in a poisson distribution with a mean of 10. 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.
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.
----
=== probability Returns === probability Returns
@ -1672,7 +1680,7 @@ The `round` function returns the closest whole number to the argument.
=== round Parameters === round Parameters
* `Field Name | Raw Number | Number Evaluator`: The value to return the square root of. * `Field Name` | `Raw Number` | `Number Evaluator`: The value to return the square root of.
=== round Syntax === round Syntax
@ -1689,7 +1697,7 @@ The `sample` function can be used to draw random samples from a probability dist
=== sample Parameters === sample Parameters
* `probability distribution` | `Markov Chain` : The distribution or Markov Chain to sample. * `probability distribution` | `Markov Chain`: The distribution or Markov Chain to sample.
* `integer`: (Optional) Sample size. Defaults to 1. * `integer`: (Optional) Sample size. Defaults to 1.
=== sample Returns === sample Returns
@ -1711,11 +1719,12 @@ with a matrix, `scalarAdd` returns a new matrix with new values.
=== scalarAdd Parameters === scalarAdd Parameters
number: value to add `number`: value to add
numeric array | matrix: the numeric array or matrix to add the value to. `numeric array` | `matrix`: the numeric array or matrix to add the value to.
=== scalarAdd Syntax === scalarAdd Syntax
[source,text]
scalarAdd(number, numericArray) // Adds the number to each element in the number in the array. 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 scalarAdd(number, matrix) // Adds the number to each value in a matrix
@ -1731,11 +1740,12 @@ with a matrix, `scalarDivide` returns a new matrix with new values.
=== scalarDivide Parameters === scalarDivide Parameters
number : value to divide by `number`: value to divide by
numeric array | matrix : the numeric array or matrix to divide by the value to. `numeric array` | `matrix`: the numeric array or matrix to divide by the value to.
=== scalarDivide Syntax === scalarDivide Syntax
[source,text]
scalarDivide(number, numericArray) // Divides each element in the numeric array by the number. 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. scalarDivide(number, matrix) // Divides each element in the matrix by the number.
@ -1751,11 +1761,12 @@ with a matrix, `scalarMultiply` returns a new matrix with new values.
=== scalarMultiply Parameters === scalarMultiply Parameters
number: value to divide by `number`: value to divide by
numeric array | matrix: the numeric array or matrix to divide by the value to. `numeric array` | `matrix`: the numeric array or matrix to divide by the value to.
=== scalarMultiply Syntax === scalarMultiply Syntax
[source,text]
scalarMultiply(number, numericArray) // Multiplies each element in the numeric array 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. scalarMultiply(number, matrix) // Multiplies each element in the matrix by the number.
@ -1771,11 +1782,12 @@ with a matrix, `scalarSubtract` returns a new matrix with new values.
=== scalarSubtract Parameters === scalarSubtract Parameters
number : value to add `number`: value to add
numeric array | matrix : the numeric array or matrix to subtract the value from. `numeric array` | `matrix`: the numeric array or matrix to subtract the value from.
=== scalarSubtract Syntax === scalarSubtract Syntax
[source,text]
scalarSubtract(number, numericArray) // Subtracts the number from each element in the number in the array. 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 scalarSubtract(number, matrix) // Subtracts the number from each value in a matrix
@ -1992,9 +2004,9 @@ probability distribution framework and is designed to work with the `<<sample>>`
=== triangularDistribution Parameters === triangularDistribution Parameters
* `double` : low value * `double`: low value
* `double` : most likely value * `double`: most likely value
* `double` : high value * `double`: high value
=== triangularDistribution Syntax === triangularDistribution Syntax
@ -2054,10 +2066,11 @@ When operating on a matrix the unitize function unitizes each row of the matrix.
=== unitize Parameters === unitize Parameters
* numeric array | matrix: The array or matrix to unitize * `numeric array` | `matrix`: The array or matrix to unitize
=== unitize Syntax === unitize Syntax
[source,text]
unitize(numericArray) // Unitize a numeric array unitize(numericArray) // Unitize a numeric array
unitize(matrix) // Unitize each row in a matrix unitize(matrix) // Unitize each row in a matrix