91 lines
2.5 KiB
C
91 lines
2.5 KiB
C
|
/**
|
|||
|
|
* @license Apache-2.0
|
|||
|
|
*
|
|||
|
|
* Copyright (c) 2020 The Stdlib Authors.
|
|||
|
|
*
|
|||
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|||
|
|
* you may not use this file except in compliance with the License.
|
|||
|
|
* You may obtain a copy of the License at
|
|||
|
|
*
|
|||
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|||
|
|
*
|
|||
|
|
* Unless required by applicable law or agreed to in writing, software
|
|||
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|||
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|||
|
|
* See the License for the specific language governing permissions and
|
|||
|
|
* limitations under the License.
|
|||
|
|
*/
|
|||
|
|
|
|||
|
|
#include "stdlib/stats/base/snanmeanpn.h"
|
|||
|
|
#include <stdint.h>
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* Computes the arithmetic mean of a single-precision floating-point strided array, ignoring `NaN` values and using a two-pass error correction algorithm.
|
|||
|
|
*
|
|||
|
|
* ## Method
|
|||
|
|
*
|
|||
|
|
* - This implementation uses a two-pass approach, as suggested by Neely (1966).
|
|||
|
|
*
|
|||
|
|
* ## References
|
|||
|
|
*
|
|||
|
|
* - Neely, Peter M. 1966. "Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients." _Communications of the ACM_ 9 (7). Association for Computing Machinery: 496–99. doi:[10.1145/365719.365958](https://doi.org/10.1145/365719.365958).
|
|||
|
|
* - Schubert, Erich, and Michael Gertz. 2018. "Numerically Stable Parallel Computation of (Co-)Variance." In _Proceedings of the 30th International Conference on Scientific and Statistical Database Management_. New York, NY, USA: Association for Computing Machinery. doi:[10.1145/3221269.3223036](https://doi.org/10.1145/3221269.3223036).
|
|||
|
|
*
|
|||
|
|
* @param N number of indexed elements
|
|||
|
|
* @param X input array
|
|||
|
|
* @param stride stride length
|
|||
|
|
* @return output value
|
|||
|
|
*/
|
|||
|
|
float stdlib_strided_snanmeanpn( const int64_t N, const float *X, const int64_t stride ) {
|
|||
|
|
int64_t ix;
|
|||
|
|
int64_t i;
|
|||
|
|
int64_t n;
|
|||
|
|
int64_t o;
|
|||
|
|
double dn;
|
|||
|
|
float s;
|
|||
|
|
float t;
|
|||
|
|
float v;
|
|||
|
|
|
|||
|
|
if ( N <= 0 ) {
|
|||
|
|
return 0.0f / 0.0f; // NaN
|
|||
|
|
}
|
|||
|
|
if ( N == 1 || stride == 0 ) {
|
|||
|
|
return X[ 0 ];
|
|||
|
|
}
|
|||
|
|
if ( stride < 0 ) {
|
|||
|
|
ix = (1-N) * stride;
|
|||
|
|
} else {
|
|||
|
|
ix = 0;
|
|||
|
|
}
|
|||
|
|
o = ix;
|
|||
|
|
|
|||
|
|
// Compute an estimate for the mean...
|
|||
|
|
s = 0.0f;
|
|||
|
|
n = 0;
|
|||
|
|
for ( i = 0; i < N; i++ ) {
|
|||
|
|
v = X[ ix ];
|
|||
|
|
if ( v == v ) {
|
|||
|
|
s += v;
|
|||
|
|
n += 1;
|
|||
|
|
}
|
|||
|
|
ix += stride;
|
|||
|
|
}
|
|||
|
|
if ( n == 0 ) {
|
|||
|
|
return 0.0f / 0.0f; // NaN
|
|||
|
|
}
|
|||
|
|
dn = (double)n;
|
|||
|
|
s = (double)s / dn;
|
|||
|
|
|
|||
|
|
// Compute an error term...
|
|||
|
|
t = 0.0f;
|
|||
|
|
ix = o;
|
|||
|
|
for ( i = 0; i < N; i++ ) {
|
|||
|
|
v = X[ ix ];
|
|||
|
|
if ( v == v ) {
|
|||
|
|
t += v - s;
|
|||
|
|
}
|
|||
|
|
ix += stride;
|
|||
|
|
}
|
|||
|
|
return s + (float)((double)t/dn);
|
|||
|
|
}
|