Statistics: average, median, variance, histogram, etc.

array	avg (const array &input, int dim=-1)
	average along a dimension
template<typename ty >
ty	avg (const array &input)
	average of all elements in array
array	var (const array &input, bool isbiased=false, int dim=-1)
	variance along a dimension
array	var (const array &input, const array weights, int dim=-1)
	weighted variance along a dimension
template<typename ty >
ty	var (const array &input, bool isbiased=false)
	variance of all elements in array
template<typename ty >
ty	var (const array &input, const array weights)
	weighted variance of all elements in array
array	cov (const array &input, bool isbiased=false)
	covariance of row observations
array	cov (const array &X, const array &Y, bool isbiased=false)
	covariance between two jointly distributed random variables
array	std (const array &in, bool isbiased=false, int dim=-1)
	standard deviation along a dimension.
template<typename ty >
ty	std (const array &input, bool isbiased=false)
	standard deviation of all elements in a vector.
array	median (const array &input, int dim=-1)
	median along a dimension
template<typename ty >
ty	median (const array &input)
	median of all elements in array
template<typename ty >
ty	corrcoef (const array &x, const array &y)
	Correlation coefficient between vectors x and y.

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Function Documentation

array af::avg	(	const array &	input,
		int	dim = -1
	)

average along a dimension

Parameters:

[in]	input
[in]	dim	dimension along which to operate (-1 indicates first nonsingleton dimension)

Returns:

array with mean values along the dimension dim in the input

ty af::avg

(

const array &

input

)

average of all elements in array

Parameters:

[in]

input

Returns:

average of all elements in array

array af::var	(	const array &	input,
		bool	isbiased = false,
		int	dim = -1
	)

variance along a dimension

Parameters:

[in]	input
[in]	isbiased	if true then use sample variance (N); otherwise if false (default) use population variance (N - 1) where N=input.elements()
[in]	dim	dimension along which to operate (-1 indicates first nonsingleton dimension)

array af::var	(	const array &	input,
		const array	weights,
		int	dim = -1
	)

weighted variance along a dimension

Parameters:

[in]	input
[in]	weights	placed on each element (same size as input)
[in]	dim	dimension along which to operate (-1 indicates first nonsingleton dimension)

Returns:

variance using nonnegative weights instead of unit weight

ty af::var	(	const array &	input,
		bool	isbiased = false
	)

variance of all elements in array

Parameters:

[in]	input
[in]	isbiased	if true then use sample variance (N); otherwise if false (default) use population variance (N - 1) where N=input.elements()

ty af::var	(	const array &	input,
		const array	weights
	)

weighted variance of all elements in array

Parameters:

[in]	input
[in]	weights	placed on each element (same size as input)

array af::cov	(	const array &	input,
		bool	isbiased = false
	)

covariance of row observations

Parameters:

[in]	input	rows are observations, columns are variables
[in]	isbiased	if true then use sample variance (N); otherwise if false (default) use population variance (N - 1) where N=input.elements()

Returns:

covariance

array af::cov	(	const array &	X,
		const array &	Y,
		bool	isbiased = false
	)

covariance between two jointly distributed random variables

Parameters:

[in]	X
[in]	Y
[in]	isbiased	if true then use sample variance (N); otherwise if false (default) use population variance (N - 1) where N=input.elements()

array af::std	(	const array &	in,
		bool	isbiased = false,
		int	dim = -1
	)

standard deviation along a dimension.

Parameters:

[in]	in	The input matrix
[in]	isbiased	if true then use sample variance (N); otherwise if false (default) use population variance (N - 1) where N=input.elements()
[in]	dim	dimension along which to operate (-1 indicates first nonsingleton dimension)

Returns:

The standard deviation of values along the dimension dim in the input

ty af::std	(	const array &	input,
		bool	isbiased = false
	)

standard deviation of all elements in a vector.

Parameters:

[in]	input
[in]	isbiased	if true then use sample variance (N); otherwise if false (default) use population variance (N - 1) where N=input.elements()

Returns:

The standard deviation of all values in the input

array af::median	(	const array &	input,
		int	dim = -1
	)

median along a dimension

Parameters:

[in]	input
[in]	dim	dimension along which to operate (-1 indicates first nonsingleton dimension)

Returns:

The median of values along the dimension dim in the input

ty af::median

(

const array &

input

)

median of all elements in array

Parameters:

[in]

input

Returns:

median of all elements

ty af::corrcoef	(	const array &	x,
		const array &	y
	)

Correlation coefficient between vectors x and y.