4.1 Reductions
The following operations take arrays as input and produce single output values. The boundary values for size 0 arrays are the unit with respect to the combination operation (min, max, sum, or product).
4.1.1 Minimum and maximum
real min(real[] x)
The minimum value in x, or \(+\infty\) if x is size 0.
int min(int[] x)
The minimum value in x, or error if x is size 0.
real max(real[] x)
The maximum value in x, or \(-\infty\) if x is size 0.
int max(int[] x)
The maximum value in x, or error if x is size 0.
4.1.2 Sum, product, and log sum of exp
int sum(int[] x)
The sum of the elements in x, defined for \(x\) of size \(N\) by \[
\text{sum}(x) = \begin{cases} \sum_{n=1}^N x_n & \text{if} N > 0
\\[4pt] 0 & \text{if} N = 0 \end{cases} \]
real sum(real[] x)
The sum of the elements in x; see definition above.
real prod(real[] x)
The product of the elements in x, or 1 if x is size 0.
real prod(int[] x)
The product of the elements in x, \[ \text{product}(x) = \begin{cases}
\prod_{n=1}^N x_n & \text{if} N > 0 \\[4pt] 1 & \text{if} N = 0
\end{cases} \]
real log_sum_exp(real[] x)
The natural logarithm of the sum of the exponentials of the elements
in x, or \(-\infty\) if the array is empty.
4.1.3 Sample mean, variance, and standard deviation
The sample mean, variance, and standard deviation are calculated in the usual way. For i.i.d. draws from a distribution of finite mean, the sample mean is an unbiased estimate of the mean of the distribution. Similarly, for i.i.d. draws from a distribution of finite variance, the sample variance is an unbiased estimate of the variance.2 The sample deviation is defined as the square root of the sample deviation, but is not unbiased.
real mean(real[] x)
The sample mean of the elements in x. For an array \(x\) of size \(N > 0\), \[ \text{mean}(x) \ = \ \bar{x} \ = \ \frac{1}{N} \sum_{n=1}^N
x_n. \] It is an error to the call the mean function with an array of
size \(0\).
real variance(real[] x)
The sample variance of the elements in x. For \(N > 0\), \[
\text{variance}(x) \ = \ \begin{cases} \frac{1}{N-1} \sum_{n=1}^N (x_n
- \bar{x})^2 & \text{if } N > 1 \\[4pt] 0 & \text{if } N = 1
\end{cases} \] It is an error to call the variance function with an
array of size 0.
real sd(real[] x)
The sample standard deviation of elements in x. \[ \text{sd}(x) =
\begin{cases} \sqrt{\, \text{variance}(x)} & \text{if } N > 1 \\[4pt]
0 & \text{if } N = 0 \end{cases} \] It is an error to call the sd
function with an array of size 0.
4.1.4 Euclidean distance and squared distance
real distance(vector x, vector y)
The Euclidean distance between x and y, defined by \[
\text{distance}(x,y) \ = \ \sqrt{\textstyle \sum_{n=1}^N (x_n -
y_n)^2} \] where N is the size of x and y. It is an error to call
distance with arguments of unequal size.
real distance(vector x, row_vector y)
The Euclidean distance between x and y
real distance(row_vector x, vector y)
The Euclidean distance between x and y
real distance(row_vector x, row_vector y)
The Euclidean distance between x and y
real squared_distance(vector x, vector y)
The squared Euclidean distance between x and y, defined by \[
\mathrm{squared\_distance}(x,y) \ = \ \text{distance}(x,y)^2 \ = \
\textstyle \sum_{n=1}^N (x_n - y_n)^2, \] where N is the size of x
and y. It is an error to call squared_distance with arguments of
unequal size.
real squared_distance(vector x, row_vector y)
The squared Euclidean distance between x and y
real squared_distance(row_vector x, vector y)
The squared Euclidean distance between x and y
real squared_distance(row_vector x, row_vector y)
The Euclidean distance between x and y
4.1.5 Quantile
Produces sample quantiles corresponding to the given probabilities. The smallest observation corresponds to a probability of 0 and the largest to a probability of 1.
Implements algorithm 7 from Hyndman, R. J. and Fan, Y., Sample quantiles in Statistical Packages (R’s default quantile function).
real quantile(data real[] x, data real p)
The p-th quantile of x
real[] quantile(data real[] x, data real p[])
An array containing the quantiles of x given by the array of probabilities p
Dividing by \(N\) rather than \((N-1)\) produces a maximum likelihood estimate of variance, which is biased to underestimate variance.↩︎