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SOLR-11753: minor typos
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Cassandra Targett
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33a64afee7
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@ -377,7 +377,7 @@ copyOfRange(numericArray, startIndex, endIndex)
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The `corr` function returns the correlation of two numeric arrays or the correlation matrix for a matrix.
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The `corr` function support Pearsons, Kendals and Spearmans correlation.
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The `corr` function support Pearson's, Kendall's and Spearman's correlations.
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=== corr Positional Parameters
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@ -390,7 +390,7 @@ OR
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=== corr Named Parameters
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* `type`: (Optional) pearsons | kendalls | spearmans, Defaults to pearsons.
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* `type`: (Optional) The type of correlation. Possible values are `pearsons`, `kendalls`, or `spearmans`. The default is `pearsons`.
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=== corr Syntax
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@ -402,7 +402,7 @@ corr(matrix, type=spearmans) // Compute the Spearmans correlation matrix for a m
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=== corr Returns
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number | matrix : Either the correlation or correlation matrix.
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number | matrix: Either the correlation or correlation matrix.
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== cos
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The `cos` function returns the trigonometric cosine of a number.
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@ -461,7 +461,7 @@ cov(matrix) // Computes the covariance matrix for the matrix.
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=== cov Returns
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number | matrix : Either the covariance or covariance matrix.
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number | matrix: Either the covariance or covariance matrix.
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== cumulativeProbability
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@ -525,8 +525,8 @@ Time series differencing is often used to make a time series stationary before f
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=== diff Parameters
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* `numeric array`: The time series data
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* `integer`: (Optional)lag. Defaults to 1.
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* `numeric array`: The time series data.
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* `integer`: (Optional) The lag. Defaults to 1.
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=== diff Syntax
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@ -544,16 +544,16 @@ The `distance` function computes the distance of two numeric arrays or the dista
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=== distance Positional Parameters
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* `numeric array` : The first numeric array
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* `numeric array` : The second numeric array
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* `numeric array`: The first numeric array
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* `numeric array`: The second numeric array
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OR
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* `matrix` : The matrix to compute the distance matrix for. Note that distance is computed between the `columns` in the matrix.
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* `matrix`: The matrix to compute the distance matrix for. Note that distance is computed between the `columns` in the matrix.
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=== distance Named Parameters
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* `type` : (Optional) euclidean | manhattan | canberra | earthMovers. Defaults to euclidean.
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* `type`: (Optional) The distance type. Possible values are `euclidean`, `manhattan`, `canberra`, or `earthMovers`. The default is `euclidean`.
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=== distance Syntax
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@ -565,7 +565,7 @@ distance(matrix, type=canberra) // Computes the canberra distance matrix for a m
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=== distance Returns
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number | matrix : Either the distance or distance matrix.
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number | matrix: Either the distance or distance matrix.
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== div
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@ -695,6 +695,7 @@ A probability distribution function.
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=== empiricalDistribution Syntax
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[source,text]
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empiricalDistribution(numericArray)
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== enumeratedDistribution
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@ -912,7 +913,7 @@ The `grandSum` function sums all the values in a matrix.
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=== grandSum Parameters
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* `matrix`: The matrix to operate on
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* `matrix`: The matrix to operate on.
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=== grandSum Syntax
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@ -1036,8 +1037,8 @@ length(numericArray)
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== loess
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The `leoss` function is a smoothing curve fitter which uses a https://en.wikipedia.org/wiki/Local_regression[local regression] algorithm.
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Unlike the <<spline>> function which touches each control point, the loess function puts a smooth curve through
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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
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Unlike the <<spline>> function which touches each control point, the `loess` function puts a smooth curve through
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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
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data that is not smooth.
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=== loess Positional Parameters
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@ -1047,8 +1048,8 @@ data that is not smooth.
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=== loess Named Parameters
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* `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.
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* `robustIterations` : (Optional)The number of iterations used to smooth outliers, defaults to 2.
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* `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.
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* `robustIterations`: (Optional) The number of iterations used to smooth outliers, defaults to 2.
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=== loess Syntax
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@ -1059,7 +1060,7 @@ loess(xValues, yValues, bandwidth=.15) // This will fit a smooth curve through t
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=== loess Returns
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function : The function can be treated as both a `numeric array` of the smoothed data points and `function`.
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function: The function can be treated as both a `numeric array` of the smoothed data points and `function`.
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== log
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@ -1165,7 +1166,7 @@ lteq(add(fieldA,fieldB),6) // fieldA + fieldB <= 6
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== markovChain
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The `markovChain` function can be used to perform https://en.wikipedia.org/wiki/Markov_chain[Markov Chain] simulations.
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The markovChain function takes as its parameter a https://en.wikipedia.org/wiki/Stochastic_matrix[transition matrix] and
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The `markovChain` function takes as its parameter a https://en.wikipedia.org/wiki/Stochastic_matrix[transition matrix] and
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returns a mathematical model that can be sampled using the <<sample>> function. Each sample taken
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from the Markov Chain represents the current state of system.
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@ -1221,17 +1222,17 @@ meanDifference(numericArray, numericArray)
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== minMaxScale
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The `minMaxScale` function scales numeric arrays within a min and max value.
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By default minMaxScale scales between 0 and 1. The minMaxScale function can operate on
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The `minMaxScale` function scales numeric arrays within a minimum and maximum value.
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By default `minMaxScale` scales between 0 and 1. The `minMaxScale` function can operate on
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both numeric arrays and matrices.
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When operating on a matrix the minMaxScale function operates on each row of the matrix.
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When operating on a matrix the `minMaxScale` function operates on each row of the matrix.
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=== minMaxScale Parameters
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* `numeric array` | `matrix` : The array or matrix to scale
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* `double` : (Optional) The min value. Defaults to 0.
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* `double` : (Optional) The max value. Defaults to 1.
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* `numeric array` | `matrix`: The array or matrix to scale
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* `double`: (Optional) The min value. Defaults to 0.
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* `double`: (Optional) The max value. Defaults to 1.
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=== minMaxScale Syntax
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@ -1243,7 +1244,7 @@ minMaxScale(matrix, 0, 100) // Scale each row in a matrix between 0 and 100
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=== minMaxScale Returns
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numeric array or matrix
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A numeric array or matrix
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== mod
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The `mod` function returns the remainder (modulo) of the first parameter divided by the second parameter.
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@ -1379,13 +1380,13 @@ normalDistribution(mean, stddev)
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== normalizeSum
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The `normalizeSum` function scales numeric arrays so that they sum to 1.
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The normalizeSum function can operate on both numeric arrays and matrices.
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The `normalizeSum` function can operate on both numeric arrays and matrices.
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When operating on a matrix the normalizeSum function operates on each row of the matrix.
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When operating on a matrix the `normalizeSum` function operates on each row of the matrix.
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=== normalizeSum Parameters
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* `numeric array` | matrix
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* `numeric array` | `matrix`
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=== normalizeSum Syntax
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@ -1423,7 +1424,7 @@ The `olsRegress` function performs https://en.wikipedia.org/wiki/Ordinary_least_
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The `olsRegress` function returns a single Tuple containing the regression model with estimated regression parameters, RSquared and regression diagnostics.
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The output of olsRegress can be used with the <<predict>> function to predict values based on the regression model.
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The output of `olsRegress` can be used with the <<predict>> function to predict values based on the regression model.
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=== olsRegress Parameters
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@ -1432,6 +1433,7 @@ The output of olsRegress can be used with the <<predict>> function to predict va
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=== olsRegress Syntax
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[source,text]
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olsRegress(matrix, numericArray) // This performs the olsRegression analysis on given regressor matrix and outcome array.
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=== olsRegress Returns
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@ -1506,8 +1508,8 @@ The `pow` function returns the value of its first parameter raised to the power
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=== pow Parameters
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* `Field Name | Raw Number | Number Evaluator`: Parameter 1
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* `Field Name | Raw Number | Number Evaluator`: Parameter 2
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* `Field Name` | `Raw Number` | `Number Evaluator`: Parameter 1
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* `Field Name` | `Raw Number` | `Number Evaluator`: Parameter 2
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=== pow Syntax
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@ -1523,12 +1525,9 @@ if(gt(fieldA,fieldB),pow(fieldA,fieldB),pow(fieldB,fieldA)) // if fieldA > field
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== predict
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The `predict` function predicts the value of dependant variables based on regression models or functions.
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The `predict` function can predict values based on the output of the following functions:
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<<spline>>, <<loess>>, <<regress>>, <<olsRegress>>
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The `predict` function predicts the value of dependent variables based on regression models or functions.
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The `predict` function can predict values based on the output of the following functions: <<spline>>, <<loess>>, <<regress>>, <<olsRegress>>.
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=== predict Parameters
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@ -1538,12 +1537,19 @@ The `predict` function can predict values based on the output of the following f
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=== predict Syntax
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[source,text]
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----
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predict(regressModel, number) // predict using the output of the <<regress>> function and single numeric predictor. This will return a single numeric prediction.
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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.
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predict(splineFunc, number) // predict using the output of the <<spline>> function and single numeric predictor. This will return a single numeric prediction.
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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.
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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.
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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.
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----
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== primes
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The `primes` function returns an array of prime numbers starting from a specified number.
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@ -1592,9 +1598,11 @@ The supported discreet distributions are:
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=== probability Syntax
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[source,text]
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----
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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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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.
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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.
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----
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=== probability Returns
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@ -1672,7 +1680,7 @@ The `round` function returns the closest whole number to the argument.
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=== round Parameters
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* `Field Name | Raw Number | Number Evaluator`: The value to return the square root of.
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* `Field Name` | `Raw Number` | `Number Evaluator`: The value to return the square root of.
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=== round Syntax
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@ -1689,7 +1697,7 @@ The `sample` function can be used to draw random samples from a probability dist
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=== sample Parameters
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* `probability distribution` | `Markov Chain` : The distribution or Markov Chain to sample.
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* `probability distribution` | `Markov Chain`: The distribution or Markov Chain to sample.
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* `integer`: (Optional) Sample size. Defaults to 1.
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=== sample Returns
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@ -1711,11 +1719,12 @@ with a matrix, `scalarAdd` returns a new matrix with new values.
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=== scalarAdd Parameters
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number: value to add
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numeric array | matrix: the numeric array or matrix to add the value to.
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`number`: value to add
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`numeric array` | `matrix`: the numeric array or matrix to add the value to.
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=== scalarAdd Syntax
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[source,text]
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scalarAdd(number, numericArray) // Adds the number to each element in the number in the array.
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scalarAdd(number, matrix) // Adds the number to each value in a matrix
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@ -1731,11 +1740,12 @@ with a matrix, `scalarDivide` returns a new matrix with new values.
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=== scalarDivide Parameters
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number : value to divide by
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numeric array | matrix : the numeric array or matrix to divide by the value to.
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`number`: value to divide by
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`numeric array` | `matrix`: the numeric array or matrix to divide by the value to.
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=== scalarDivide Syntax
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[source,text]
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scalarDivide(number, numericArray) // Divides each element in the numeric array by the number.
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scalarDivide(number, matrix) // Divides each element in the matrix by the number.
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@ -1751,11 +1761,12 @@ with a matrix, `scalarMultiply` returns a new matrix with new values.
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=== scalarMultiply Parameters
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number: value to divide by
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numeric array | matrix: the numeric array or matrix to divide by the value to.
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`number`: value to divide by
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`numeric array` | `matrix`: the numeric array or matrix to divide by the value to.
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=== scalarMultiply Syntax
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[source,text]
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scalarMultiply(number, numericArray) // Multiplies each element in the numeric array by the number.
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scalarMultiply(number, matrix) // Multiplies each element in the matrix by the number.
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@ -1771,11 +1782,12 @@ with a matrix, `scalarSubtract` returns a new matrix with new values.
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=== scalarSubtract Parameters
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number : value to add
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numeric array | matrix : the numeric array or matrix to subtract the value from.
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`number`: value to add
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`numeric array` | `matrix`: the numeric array or matrix to subtract the value from.
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=== scalarSubtract Syntax
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[source,text]
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scalarSubtract(number, numericArray) // Subtracts the number from each element in the number in the array.
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scalarSubtract(number, matrix) // Subtracts the number from each value in a matrix
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@ -1992,9 +2004,9 @@ probability distribution framework and is designed to work with the `<<sample>>`
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=== triangularDistribution Parameters
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* `double` : low value
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* `double` : most likely value
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* `double` : high value
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* `double`: low value
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* `double`: most likely value
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* `double`: high value
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=== triangularDistribution Syntax
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@ -2054,10 +2066,11 @@ When operating on a matrix the unitize function unitizes each row of the matrix.
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=== unitize Parameters
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* numeric array | matrix: The array or matrix to unitize
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* `numeric array` | `matrix`: The array or matrix to unitize
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=== unitize Syntax
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[source,text]
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unitize(numericArray) // Unitize a numeric array
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unitize(matrix) // Unitize each row in a matrix
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