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use super::core::{ af_array, dim_t, AfError, Array, CovarianceComputable, HasAfEnum, MedianComputable, RealFloating, RealNumber, TopkFn, VarianceBias, HANDLE_ERROR, }; use libc::{c_double, c_int, c_uint}; extern "C" { fn af_mean(out: *mut af_array, arr: af_array, dim: dim_t) -> c_int; fn af_median(out: *mut af_array, arr: af_array, dim: dim_t) -> c_int; fn af_mean_weighted(out: *mut af_array, arr: af_array, wts: af_array, dim: dim_t) -> c_int; fn af_var_weighted(out: *mut af_array, arr: af_array, wts: af_array, dim: dim_t) -> c_int; fn af_mean_all(real: *mut c_double, imag: *mut c_double, arr: af_array) -> c_int; fn af_median_all(real: *mut c_double, imag: *mut c_double, arr: af_array) -> c_int; fn af_mean_all_weighted( real: *mut c_double, imag: *mut c_double, arr: af_array, wts: af_array, ) -> c_int; fn af_var_all_weighted( real: *mut c_double, imag: *mut c_double, arr: af_array, wts: af_array, ) -> c_int; fn af_corrcoef(real: *mut c_double, imag: *mut c_double, X: af_array, Y: af_array) -> c_int; fn af_topk( vals: *mut af_array, idxs: *mut af_array, arr: af_array, k: c_int, dim: c_int, order: c_uint, ) -> c_int; fn af_meanvar( mean: *mut af_array, var: *mut af_array, input: af_array, weights: af_array, bias: c_uint, dim: dim_t, ) -> c_int; fn af_var_v2(out: *mut af_array, arr: af_array, bias_kind: c_uint, dim: dim_t) -> c_int; fn af_cov_v2(out: *mut af_array, X: af_array, Y: af_array, bias_kind: c_uint) -> c_int; fn af_stdev_v2(out: *mut af_array, arr: af_array, bias_kind: c_uint, dim: dim_t) -> c_int; fn af_var_all_v2( real: *mut c_double, imag: *mut c_double, arr: af_array, bias_kind: c_uint, ) -> c_int; fn af_stdev_all_v2( real: *mut c_double, imag: *mut c_double, arr: af_array, bias_kind: c_uint, ) -> c_int; } /// Find the median along a given dimension /// ///# Parameters /// /// - `input` is the input Array /// - `dim` is dimension along which median has to be found /// ///# Return Values /// /// An Array whose size is equal to input except along the dimension which /// median needs to be found. Array size along `dim` will be reduced to one. pub fn median<T>(input: &Array<T>, dim: i64) -> Array<T> where T: HasAfEnum + MedianComputable, { unsafe { let mut temp: af_array = std::ptr::null_mut(); let err_val = af_median(&mut temp as *mut af_array, input.get(), dim); HANDLE_ERROR(AfError::from(err_val)); temp.into() } } macro_rules! stat_func_def { ($doc_str: expr, $fn_name: ident, $ffi_fn: ident) => { #[doc=$doc_str] /// ///# Parameters /// /// - `input` is the input Array /// - `dim` is dimension along which the current stat has to be computed /// ///# Return Values /// /// An Array whose size is equal to input except along the dimension which /// the stat operation is performed. Array size along `dim` will be reduced to one. pub fn $fn_name<T>(input: &Array<T>, dim: i64) -> Array<T::MeanOutType> where T: HasAfEnum, T::MeanOutType: HasAfEnum, { unsafe { let mut temp: af_array = std::ptr::null_mut(); let err_val = $ffi_fn(&mut temp as *mut af_array, input.get(), dim); HANDLE_ERROR(AfError::from(err_val)); temp.into() } } }; } stat_func_def!("Mean along specified dimension", mean, af_mean); macro_rules! stat_wtd_func_def { ($doc_str: expr, $fn_name: ident, $ffi_fn: ident) => { #[doc=$doc_str] /// ///# Parameters /// /// - `input` is the input Array /// - `weights` Array has the weights to be used during the stat computation /// - `dim` is dimension along which the current stat has to be computed /// ///# Return Values /// /// An Array whose size is equal to input except along the dimension which /// the stat operation is performed. Array size along `dim` will be reduced to one. pub fn $fn_name<T, W>( input: &Array<T>, weights: &Array<W>, dim: i64, ) -> Array<T::MeanOutType> where T: HasAfEnum, T::MeanOutType: HasAfEnum, W: HasAfEnum + RealFloating, { unsafe { let mut temp: af_array = std::ptr::null_mut(); let err_val = $ffi_fn(&mut temp as *mut af_array,input.get(), weights.get(), dim); HANDLE_ERROR(AfError::from(err_val)); temp.into() } } }; } stat_wtd_func_def!( "Weighted mean along specified dimension", mean_weighted, af_mean_weighted ); stat_wtd_func_def!( "Weight variance along specified dimension", var_weighted, af_var_weighted ); /// Compute Variance along a specific dimension /// /// # Parameters /// /// - `arr` is the input Array /// - `bias_kind` of type [VarianceBias][1] denotes the type of variane to be computed /// - `dim` is the dimension along which the variance is extracted /// /// # Return Values /// /// Array with variance of input Array `arr` along dimension `dim`. /// /// [1]: ./enum.VarianceBias.html pub fn var_v2<T>(arr: &Array<T>, bias_kind: VarianceBias, dim: i64) -> Array<T::MeanOutType> where T: HasAfEnum, T::MeanOutType: HasAfEnum, { unsafe { let mut temp: af_array = std::ptr::null_mut(); let err_val = af_var_v2( &mut temp as *mut af_array, arr.get(), bias_kind as c_uint, dim, ); HANDLE_ERROR(AfError::from(err_val)); temp.into() } } /// Compute Variance along a specific dimension /// /// # Parameters /// /// - `arr` is the input Array /// - `isbiased` is boolean denoting population variance(False) or Sample variance(True) /// - `dim` is the dimension along which the variance is extracted /// /// # Return Values /// /// Array with variance of input Array `arr` along dimension `dim`. #[deprecated(since = "3.8.0", note = "Please use var_v2 API")] pub fn var<T>(arr: &Array<T>, isbiased: bool, dim: i64) -> Array<T::MeanOutType> where T: HasAfEnum, T::MeanOutType: HasAfEnum, { var_v2( arr, if isbiased { VarianceBias::SAMPLE } else { VarianceBias::POPULATION }, dim, ) } /// Compute covariance of two Arrays /// /// # Parameters /// /// - `x` is the first Array /// - `y` is the second Array /// - `bias_kind` of type [VarianceBias][1] denotes the type of variane to be computed /// /// # Return Values /// /// An Array with Covariance values /// /// [1]: ./enum.VarianceBias.html pub fn cov_v2<T>(x: &Array<T>, y: &Array<T>, bias_kind: VarianceBias) -> Array<T::MeanOutType> where T: HasAfEnum + CovarianceComputable, T::MeanOutType: HasAfEnum, { unsafe { let mut temp: af_array = std::ptr::null_mut(); let err_val = af_cov_v2( &mut temp as *mut af_array, x.get(), y.get(), bias_kind as c_uint, ); HANDLE_ERROR(AfError::from(err_val)); temp.into() } } /// Compute covariance of two Arrays /// /// # Parameters /// /// - `x` is the first Array /// - `y` is the second Array /// - `isbiased` is boolean denoting if biased estimate should be taken(default: False) /// /// # Return Values /// /// An Array with Covariance values #[deprecated(since = "3.8.0", note = "Please use cov_v2 API")] pub fn cov<T>(x: &Array<T>, y: &Array<T>, isbiased: bool) -> Array<T::MeanOutType> where T: HasAfEnum + CovarianceComputable, T::MeanOutType: HasAfEnum, { cov_v2( x, y, if isbiased { VarianceBias::SAMPLE } else { VarianceBias::POPULATION }, ) } /// Compute Variance of all elements /// /// # Parameters /// /// - `input` is the input Array /// - `bias_kind` of type [VarianceBias][1] denotes the type of variane to be computed /// /// # Return Values /// /// A tuple of 64-bit floating point values that has the variance of `input` Array. /// /// [1]: ./enum.VarianceBias.html pub fn var_all_v2<T: HasAfEnum>(input: &Array<T>, bias_kind: VarianceBias) -> (f64, f64) { let mut real: f64 = 0.0; let mut imag: f64 = 0.0; unsafe { let err_val = af_var_all_v2( &mut real as *mut c_double, &mut imag as *mut c_double, input.get(), bias_kind as c_uint, ); HANDLE_ERROR(AfError::from(err_val)); } (real, imag) } /// Compute Variance of all elements /// /// # Parameters /// /// - `input` is the input Array /// - `isbiased` is boolean denoting population variance(False) or sample variance(True) /// /// # Return Values /// /// A tuple of 64-bit floating point values that has the variance of `input` Array. #[deprecated(since = "3.8.0", note = "Please use var_all_v2 API")] pub fn var_all<T: HasAfEnum>(input: &Array<T>, isbiased: bool) -> (f64, f64) { var_all_v2( input, if isbiased { VarianceBias::SAMPLE } else { VarianceBias::POPULATION }, ) } macro_rules! stat_all_func_def { ($doc_str: expr, $fn_name: ident, $ffi_fn: ident) => { #[doc=$doc_str] /// ///# Parameters /// /// - `input` is the input Array /// ///# Return Values /// /// A tuple of 64-bit floating point values with the stat values. pub fn $fn_name<T: HasAfEnum>(input: &Array<T>) -> (f64, f64) { let mut real: f64 = 0.0; let mut imag: f64 = 0.0; unsafe { let err_val = $ffi_fn( &mut real as *mut c_double, &mut imag as *mut c_double, input.get(), ); HANDLE_ERROR(AfError::from(err_val)); } (real, imag) } }; } stat_all_func_def!("Compute mean of all data", mean_all, af_mean_all); /// Compute median of all data /// ///# Parameters /// /// - `input` is the input Array /// ///# Return Values /// /// A tuple of 64-bit floating point values with the median pub fn median_all<T>(input: &Array<T>) -> (f64, f64) where T: HasAfEnum + MedianComputable, { let mut real: f64 = 0.0; let mut imag: f64 = 0.0; unsafe { let err_val = af_median_all( &mut real as *mut c_double, &mut imag as *mut c_double, input.get(), ); HANDLE_ERROR(AfError::from(err_val)); } (real, imag) } macro_rules! stat_wtd_all_func_def { ($doc_str: expr, $fn_name: ident, $ffi_fn: ident) => { #[doc=$doc_str] /// ///# Parameters /// /// - `input` is the input Array /// - `weights` Array has the weights /// ///# Return Values /// /// A tuple of 64-bit floating point values with the stat values. pub fn $fn_name<T, W>(input: &Array<T>, weights: &Array<W>) -> (f64, f64) where T: HasAfEnum, W: HasAfEnum + RealFloating, { let mut real: f64 = 0.0; let mut imag: f64 = 0.0; unsafe { let err_val = $ffi_fn( &mut real as *mut c_double, &mut imag as *mut c_double, input.get(), weights.get(), ); HANDLE_ERROR(AfError::from(err_val)); } (real, imag) } }; } stat_wtd_all_func_def!( "Compute weighted mean of all data", mean_all_weighted, af_mean_all_weighted ); stat_wtd_all_func_def!( "Compute weighted variance of all data", var_all_weighted, af_var_all_weighted ); /// Compute correlation coefficient /// /// # Parameters /// /// - `x` is the first Array /// - `y` isthe second Array /// /// # Return Values /// A tuple of 64-bit floating point values with the coefficients. pub fn corrcoef<T>(x: &Array<T>, y: &Array<T>) -> (f64, f64) where T: HasAfEnum + RealNumber, { let mut real: f64 = 0.0; let mut imag: f64 = 0.0; unsafe { let err_val = af_corrcoef( &mut real as *mut c_double, &mut imag as *mut c_double, x.get(), y.get(), ); HANDLE_ERROR(AfError::from(err_val)); } (real, imag) } /// Find top k elements along a given dimension /// /// This function returns the top k values along a given dimension of the input /// array. The indices along with their values are returned. /// /// If the input is a multi-dimensional array, the indices will be the index of /// the value in that dimension. Order of duplicate values are not preserved. /// /// This function is optimized for small values of k. Currently, topk elements /// can be found only along dimension 0. /// /// # Parameters /// /// - `input` is the values from which top k elements are to be retrieved /// - `k` is the number of top elements to be retrieve /// - `dim` is the dimension along which the retrieval operation has to performed /// - `order` is an enum that can take values of type [TopkFn](./enum.TopkFn.html) /// /// # Return Values /// /// A tuple(couple) of Array's with the first Array containing the topk values /// with the second Array containing the indices of the topk values in the input /// data. pub fn topk<T>(input: &Array<T>, k: u32, dim: i32, order: TopkFn) -> (Array<T>, Array<u32>) where T: HasAfEnum, { unsafe { let mut t0: af_array = std::ptr::null_mut(); let mut t1: af_array = std::ptr::null_mut(); let err_val = af_topk( &mut t0 as *mut af_array, &mut t1 as *mut af_array, input.get(), k as c_int, dim as c_int, order as c_uint, ); HANDLE_ERROR(AfError::from(err_val)); (t0.into(), t1.into()) } } /// Calculate mean and variance in single API call /// ///# Parameters /// /// - `input` is the input Array /// - `weights` Array has the weights to be used during the stat computation /// - `bias` is type of bias used for variance calculation /// - `dim` is dimension along which the current stat has to be computed /// ///# Return Values /// /// A tuple of Arrays, whose size is equal to input except along the dimension which /// the stat operation is performed. Array size along `dim` will be reduced to one. /// /// - First Array contains mean values /// - Second Array contains variance values pub fn meanvar<T, W>( input: &Array<T>, weights: &Array<W>, bias: VarianceBias, dim: i64, ) -> (Array<T::MeanOutType>, Array<T::MeanOutType>) where T: HasAfEnum, T::MeanOutType: HasAfEnum, W: HasAfEnum + RealFloating, { unsafe { let mut mean: af_array = std::ptr::null_mut(); let mut var: af_array = std::ptr::null_mut(); let err_val = af_meanvar( &mut mean as *mut af_array, &mut var as *mut af_array, input.get(), weights.get(), bias as c_uint, dim, ); HANDLE_ERROR(AfError::from(err_val)); (mean.into(), var.into()) } } /// Standard deviation along given axis /// ///# Parameters /// /// - `input` is the input Array /// - `bias_kind` of type [VarianceBias][1] denotes the type of variane to be computed /// - `dim` is dimension along which the current stat has to be computed /// ///# Return Values /// /// An Array whose size is equal to input except along the dimension which /// the stat operation is performed. Array size along `dim` will be reduced to one. /// /// [1]: ./enum.VarianceBias.html pub fn stdev_v2<T>(input: &Array<T>, bias_kind: VarianceBias, dim: i64) -> Array<T::MeanOutType> where T: HasAfEnum, T::MeanOutType: HasAfEnum, { unsafe { let mut temp: af_array = std::ptr::null_mut(); let err_val = af_stdev_v2( &mut temp as *mut af_array, input.get(), bias_kind as c_uint, dim, ); HANDLE_ERROR(AfError::from(err_val)); temp.into() } } /// Standard deviation along specified axis /// ///# Parameters /// /// - `input` is the input Array /// - `dim` is dimension along which the current stat has to be computed /// ///# Return Values /// /// An Array whose size is equal to input except along the dimension which /// the stat operation is performed. Array size along `dim` will be reduced to one. #[deprecated(since = "3.8.0", note = "Please use stdev_v2 API")] pub fn stdev<T>(input: &Array<T>, dim: i64) -> Array<T::MeanOutType> where T: HasAfEnum, T::MeanOutType: HasAfEnum, { stdev_v2(input, VarianceBias::POPULATION, dim) } /// Compute standard deviation of all data /// ///# Parameters /// /// - `input` is the input Array /// - `bias_kind` of type [VarianceBias][1] denotes the type of variane to be computed /// ///# Return Values /// /// A tuple of 64-bit floating point values with the stat values. /// /// [1]: ./enum.VarianceBias.html pub fn stdev_all_v2<T: HasAfEnum>(input: &Array<T>, bias_kind: VarianceBias) -> (f64, f64) { let mut real: f64 = 0.0; let mut imag: f64 = 0.0; unsafe { let err_val = af_stdev_all_v2( &mut real as *mut c_double, &mut imag as *mut c_double, input.get(), bias_kind as c_uint, ); HANDLE_ERROR(AfError::from(err_val)); } (real, imag) } /// Compute standard deviation of all data /// ///# Parameters /// /// - `input` is the input Array /// ///# Return Values /// /// A tuple of 64-bit floating point values with the stat values. pub fn stdev_all<T: HasAfEnum>(input: &Array<T>) -> (f64, f64) { stdev_all_v2(input, VarianceBias::POPULATION) }