time-to-botec/squiggle/node_modules/@stdlib/stats/base/dnanmeanpw/src/dnanmeanpw.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/dnanmeanpw.h"
#include <stdint.h>

/**
* Computes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using pairwise summation.
*
* ## Method
*
* -   This implementation uses pairwise summation, which accrues rounding error `O(log2 N)` instead of `O(N)`. The recursion depth is also `O(log2 N)`.
*
* ## References
*
* -   Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." _SIAM Journal on Scientific Computing_ 14 (4): 783–99. doi:[10.1137/0914050](https://doi.org/10.1137/0914050).
*
* @private
* @param N       number of indexed elements
* @param W       two-element output array
* @param X       input array
* @param stride  stride length
* @return        output value
*/
static void dnansumpw( const int64_t N, double *W, const double *X, const int64_t stride ) {
	double *xp1;
	double *xp2;
	double sum;
	int64_t ix;
	int64_t M;
	int64_t n;
	int64_t i;
	double s0;
	double s1;
	double s2;
	double s3;
	double s4;
	double s5;
	double s6;
	double s7;
	double v;

	if ( N <= 0 ) {
		return;
	}
	if ( N == 1 || stride == 0 ) {
		if ( X[ 0 ] == X[ 0 ] ) {
			W[ 0 ] += X[ 0 ];
			W[ 1 ] += 1;
			return;
		}
		return;
	}
	if ( stride < 0 ) {
		ix = (1-N) * stride;
	} else {
		ix = 0;
	}
	if ( N < 8 ) {
		// Use simple summation...
		sum = 0.0;
		n = 0;
		for ( i = 0; i < N; i++ ) {
			v = X[ ix ];
			if ( v == v ) {
				sum += X[ ix ];
				n += 1;
			}
			ix += stride;
		}
		W[ 0 ] += sum;
		W[ 1 ] += n;
		return;
	}
	// Blocksize for pairwise summation: 128 (NOTE: decreasing the blocksize decreases rounding error as more pairs are summed, but also decreases performance. Because the inner loop is unrolled eight times, the blocksize is effectively `16`.)
	if ( N <= 128 ) {
		// Sum a block with 8 accumulators (by loop unrolling, we lower the effective blocksize to 16)...
		s0 = 0.0;
		s1 = 0.0;
		s2 = 0.0;
		s3 = 0.0;
		s4 = 0.0;
		s5 = 0.0;
		s6 = 0.0;
		s7 = 0.0;
		n = 0;

		M = N % 8;
		for ( i = 0; i < N-M; i += 8 ) {
			v = X[ ix ];
			if ( v == v ) {
				s0 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s1 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s2 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s3 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s4 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s5 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s6 += v;
				n += 1;
			}
			ix += stride;
			v = X[ ix ];
			if ( v == v ) {
				s7 += v;
				n += 1;
			}
			ix += stride;
		}
		// Pairwise sum the accumulators:
		sum = ((s0+s1) + (s2+s3)) + ((s4+s5) + (s6+s7));

		// Clean-up loop...
		for (; i < N; i++ ) {
			v = X[ ix ];
			if ( v == v ) {
				sum += X[ ix ];
				n += 1;
			}
			ix += stride;
		}
		W[ 0 ] += sum;
		W[ 1 ] += n;
		return;
	}
	// Recurse by dividing by two, but avoiding non-multiples of unroll factor...
	n = N / 2;
	n -= n % 8;
	if ( stride < 0 ) {
		xp1 = (double *)X + ( (n-N)*stride );
		xp2 = (double *)X;
	} else {
		xp1 = (double *)X;
		xp2 = (double *)X + ( n*stride );
	}
	dnansumpw( n, W, xp1, stride );
	dnansumpw( N-n, W, xp2, stride );
}

/**
* Computes the arithmetic mean of a double-precision floating-point strided array, ignoring `NaN` values and using pairwise summation.
*
* @param N       number of indexed elements
* @param X       input array
* @param stride  stride length
* @return        output value
*/
double stdlib_strided_dnanmeanpw( const int64_t N, const double *X, const int64_t stride ) {
	double W[] = { 0.0, 0.0 };
	dnansumpw( N, W, X, stride );
	return W[ 0 ] / W[ 1 ];
}