/** * @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/svarianceyc.h" #include /** * Computes the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer. * * ## Method * * - This implementation uses a one-pass algorithm, as proposed by Youngs and Cramer (1971). * * ## References * * - Youngs, Edward A., and Elliot M. Cramer. 1971. "Some Results Relevant to Choice of Sum and Sum-of-Product Algorithms." _Technometrics_ 13 (3): 657–65. doi:[10.1080/00401706.1971.10488826](https://doi.org/10.1080/00401706.1971.10488826). * * @param N number of indexed elements * @param correction degrees of freedom adjustment * @param X input array * @param stride stride length * @return output value */ float stdlib_strided_svarianceyc( const int64_t N, const float correction, const float *X, const int64_t stride ) { int64_t ix; int64_t i; double di; float sum; double n; float S; float v; float d; n = (double)N - (double)correction; if ( N <= 0 || n <= 0.0f ) { return 0.0f / 0.0f; // NaN } if ( N == 1 || stride == 0 ) { return 0.0f; } if ( stride < 0 ) { ix = (1-N) * stride; } else { ix = 0; } sum = X[ ix ]; ix += stride; S = 0.0f; for ( i = 2; i <= N; i++ ) { di = (double)i; v = X[ ix ]; sum += v; d = (float)(di*(double)v) - sum; S += (float)(1.0/(di*(di-1.0))) * d * d; ix += stride; } return (double)S / n; }