/usr/include/af/statistics.h is in libarrayfire-dev 3.3.2+dfsg1-4.
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* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#pragma once
#include <af/defines.h>
#ifdef __cplusplus
namespace af
{
class array;
/**
C++ Interface for mean
\param[in] in is the input array
\param[in] dim the dimension along which the mean is extracted
\return the mean of the input array along dimension \p dim
\ingroup stat_func_mean
\note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
*/
AFAPI array mean(const array& in, const dim_t dim=-1);
/**
C++ Interface for mean of weighted inputs
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting mean
\param[in] dim the dimension along which the mean is extracted
\return the mean of the weighted input array along dimension \p dim
\ingroup stat_func_mean
\note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
*/
AFAPI array mean(const array& in, const array& weights, const dim_t dim=-1);
/**
C++ Interface for variance
\param[in] in is the input array
\param[in] isbiased is boolean denoting Population variance (false) or Sample Variance (true)
\param[in] dim the dimension along which the variance is extracted
\return the variance of the input array along dimension \p dim
\ingroup stat_func_var
\note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
*/
AFAPI array var(const array& in, const bool isbiased=false, const dim_t dim=-1);
/**
C++ Interface for variance of weighted inputs
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting variance
\param[in] dim the dimension along which the variance is extracted
\return the variance of the weighted input array along dimension \p dim
\ingroup stat_func_var
\note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
*/
AFAPI array var(const array& in, const array &weights, const dim_t dim=-1);
/**
C++ Interface for standard deviation
\param[in] in is the input array
\param[in] dim the dimension along which the standard deviation is extracted
\return the standard deviation of the input array along dimension \p dim
\ingroup stat_func_stdev
\note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
*/
AFAPI array stdev(const array& in, const dim_t dim=-1);
/**
C++ Interface for covariance
\param[in] X is the first input array
\param[in] Y is the second input array
\param[in] isbiased is boolean specifying if biased estimate should be taken (default: false)
\return the covariance of the input arrays
\ingroup stat_func_cov
*/
AFAPI array cov(const array& X, const array& Y, const bool isbiased=false);
/**
C++ Interface for median
\param[in] in is the input array
\param[in] dim the dimension along which the median is extracted
\return the median of the input array along dimension \p dim
\ingroup stat_func_median
\note \p dim is -1 by default. -1 denotes the first non-singleton dimension.
*/
AFAPI array median(const array& in, const dim_t dim=-1);
/**
C++ Interface for mean of all elements
\param[in] in is the input array
\return mean of the entire input array
\ingroup stat_func_mean
*/
template<typename T>
AFAPI T mean(const array& in);
/**
C++ Interface for mean of all elements in weighted input
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting mean
\return mean of the entire weighted input array
\ingroup stat_func_mean
*/
template<typename T>
AFAPI T mean(const array& in, const array& weights);
/**
C++ Interface for variance of all elements
\param[in] in is the input array
\param[in] isbiased is boolean denoting Population variance (false) or Sample Variance (true)
\return variance of the entire input array
\ingroup stat_func_var
*/
template<typename T>
AFAPI T var(const array& in, const bool isbiased=false);
/**
C++ Interface for variance of all elements in weighted input
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting variance
\return variance of the entire input array
\ingroup stat_func_var
*/
template<typename T>
AFAPI T var(const array& in, const array& weights);
/**
C++ Interface for standard deviation of all elements
\param[in] in is the input array
\return standard deviation of the entire input array
\ingroup stat_func_stdev
*/
template<typename T>
AFAPI T stdev(const array& in);
/**
C++ Interface for median of all elements
\param[in] in is the input array
\return median of the entire input array
\ingroup stat_func_median
*/
template<typename T>
AFAPI T median(const array& in);
/**
C++ Interface for correlation coefficient
\param[in] X is the first input array
\param[in] Y is the second input array
\return correlation coefficient of the input arrays
\note There are many ways correlation coefficient is calculated. This algorithm returns Pearson product-moment correlation coefficient.
\ingroup stat_func_corrcoef
*/
template<typename T>
AFAPI T corrcoef(const array& X, const array& Y);
}
#endif
#ifdef __cplusplus
extern "C" {
#endif
/**
C Interface for mean
\param[out] out will contain the mean of the input array along dimension \p dim
\param[in] in is the input array
\param[in] dim the dimension along which the mean is extracted
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_mean
*/
AFAPI af_err af_mean(af_array *out, const af_array in, const dim_t dim);
/**
C Interface for mean of weighted input array
\param[out] out will contain the mean of the input array along dimension \p dim
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting mean
\param[in] dim the dimension along which the mean is extracted
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_mean
*/
AFAPI af_err af_mean_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim);
/**
C Interface for variance
\param[out] out will contain the variance of the input array along dimension \p dim
\param[in] in is the input array
\param[in] isbiased is boolean denoting Population variance (false) or Sample Variance (true)
\param[in] dim the dimension along which the variance is extracted
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_var
*/
AFAPI af_err af_var(af_array *out, const af_array in, const bool isbiased, const dim_t dim);
/**
C Interface for variance of weighted input array
\param[out] out will contain the variance of the input array along dimension \p dim
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting variance
\param[in] dim the dimension along which the variance is extracted
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_var
*/
AFAPI af_err af_var_weighted(af_array *out, const af_array in, const af_array weights, const dim_t dim);
/**
C Interface for standard deviation
\param[out] out will contain the standard deviation of the input array along dimension \p dim
\param[in] in is the input array
\param[in] dim the dimension along which the standard deviation is extracted
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_stdev
*/
AFAPI af_err af_stdev(af_array *out, const af_array in, const dim_t dim);
/**
C Interface for covariance
\param[out] out will the covariance of the input arrays
\param[in] X is the first input array
\param[in] Y is the second input array
\param[in] isbiased is boolean specifying if biased estimate should be taken (default: false)
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_cov
*/
AFAPI af_err af_cov(af_array* out, const af_array X, const af_array Y, const bool isbiased);
/**
C Interface for median
\param[out] out will contain the median of the input array along dimension \p dim
\param[in] in is the input array
\param[in] dim the dimension along which the median is extracted
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_median
*/
AFAPI af_err af_median(af_array* out, const af_array in, const dim_t dim);
/**
C Interface for mean of all elements
\param[out] real will contain the real part of mean of the entire input array
\param[out] imag will contain the imaginary part of mean of the entire input array
\param[in] in is the input array
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_mean
*/
AFAPI af_err af_mean_all(double *real, double *imag, const af_array in);
/**
C Interface for mean of all elements in weighted input
\param[out] real will contain the real part of mean of the entire weighted input array
\param[out] imag will contain the imaginary part of mean of the entire weighted input array
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting mean
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_mean
*/
AFAPI af_err af_mean_all_weighted(double *real, double *imag, const af_array in, const af_array weights);
/**
C Interface for variance of all elements
\param[out] realVal will contain the real part of variance of the entire input array
\param[out] imagVal will contain the imaginary part of variance of the entire input array
\param[in] in is the input array
\param[in] isbiased is boolean denoting Population variance (false) or Sample Variance (true)
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_var
*/
AFAPI af_err af_var_all(double *realVal, double *imagVal, const af_array in, const bool isbiased);
/**
C Interface for variance of all elements in weighted input
\param[out] realVal will contain the real part of variance of the entire weighted input array
\param[out] imagVal will contain the imaginary part of variance of the entire weighted input array
\param[in] in is the input array
\param[in] weights is used to scale input \p in before getting variance
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_var
*/
AFAPI af_err af_var_all_weighted(double *realVal, double *imagVal, const af_array in, const af_array weights);
/**
C Interface for standard deviation of all elements
\param[out] real will contain the real part of standard deviation of the entire input array
\param[out] imag will contain the imaginary part of standard deviation of the entire input array
\param[in] in is the input array
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_stdev
*/
AFAPI af_err af_stdev_all(double *real, double *imag, const af_array in);
/**
C Interface for median
\param[out] realVal will contain the real part of median of the entire input array
\param[out] imagVal will contain the imaginary part of median of the entire input array
\param[in] in is the input array
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\ingroup stat_func_median
*/
AFAPI af_err af_median_all(double *realVal, double *imagVal, const af_array in);
/**
C Interface for correlation coefficient
\param[out] realVal will contain the real part of correlation coefficient of the inputs
\param[out] imagVal will contain the imaginary part of correlation coefficient of the inputs
\param[in] X is the first input array
\param[in] Y is the second input array
\return \ref AF_SUCCESS if the operation is successful,
otherwise an appropriate error code is returned.
\note There are many ways correlation coefficient is calculated. This algorithm returns Pearson product-moment correlation coefficient.
\ingroup stat_func_corrcoef
*/
AFAPI af_err af_corrcoef(double *realVal, double *imagVal, const af_array X, const af_array Y);
#ifdef __cplusplus
}
#endif
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