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Diffstat (limited to 'libbcachefs/mean_and_variance.c')
-rw-r--r-- | libbcachefs/mean_and_variance.c | 165 |
1 files changed, 0 insertions, 165 deletions
diff --git a/libbcachefs/mean_and_variance.c b/libbcachefs/mean_and_variance.c deleted file mode 100644 index bf0ef668..00000000 --- a/libbcachefs/mean_and_variance.c +++ /dev/null @@ -1,165 +0,0 @@ -// SPDX-License-Identifier: GPL-2.0 -/* - * Functions for incremental mean and variance. - * - * This program is free software; you can redistribute it and/or modify it - * under the terms of the GNU General Public License version 2 as published by - * the Free Software Foundation. - * - * This program is distributed in the hope that it will be useful, but WITHOUT - * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or - * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for - * more details. - * - * Copyright © 2022 Daniel B. Hill - * - * Author: Daniel B. Hill <daniel@gluo.nz> - * - * Description: - * - * This is includes some incremental algorithms for mean and variance calculation - * - * Derived from the paper: https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf - * - * Create a struct and if it's the weighted variant set the w field (weight = 2^k). - * - * Use mean_and_variance[_weighted]_update() on the struct to update it's state. - * - * Use the mean_and_variance[_weighted]_get_* functions to calculate the mean and variance, some computation - * is deferred to these functions for performance reasons. - * - * see lib/math/mean_and_variance_test.c for examples of usage. - * - * DO NOT access the mean and variance fields of the weighted variants directly. - * DO NOT change the weight after calling update. - */ - -#include <linux/bug.h> -#include <linux/compiler.h> -#include <linux/export.h> -#include <linux/limits.h> -#include <linux/math.h> -#include <linux/math64.h> -#include <linux/module.h> - -#include "mean_and_variance.h" - -u128_u u128_div(u128_u n, u64 d) -{ - u128_u r; - u64 rem; - u64 hi = u128_hi(n); - u64 lo = u128_lo(n); - u64 h = hi & ((u64) U32_MAX << 32); - u64 l = (hi & (u64) U32_MAX) << 32; - - r = u128_shl(u64_to_u128(div64_u64_rem(h, d, &rem)), 64); - r = u128_add(r, u128_shl(u64_to_u128(div64_u64_rem(l + (rem << 32), d, &rem)), 32)); - r = u128_add(r, u64_to_u128(div64_u64_rem(lo + (rem << 32), d, &rem))); - return r; -} -EXPORT_SYMBOL_GPL(u128_div); - -/** - * mean_and_variance_get_mean() - get mean from @s - * @s: mean and variance number of samples and their sums - */ -s64 mean_and_variance_get_mean(struct mean_and_variance s) -{ - return s.n ? div64_u64(s.sum, s.n) : 0; -} -EXPORT_SYMBOL_GPL(mean_and_variance_get_mean); - -/** - * mean_and_variance_get_variance() - get variance from @s1 - * @s1: mean and variance number of samples and sums - * - * see linked pdf equation 12. - */ -u64 mean_and_variance_get_variance(struct mean_and_variance s1) -{ - if (s1.n) { - u128_u s2 = u128_div(s1.sum_squares, s1.n); - u64 s3 = abs(mean_and_variance_get_mean(s1)); - - return u128_lo(u128_sub(s2, u128_square(s3))); - } else { - return 0; - } -} -EXPORT_SYMBOL_GPL(mean_and_variance_get_variance); - -/** - * mean_and_variance_get_stddev() - get standard deviation from @s - * @s: mean and variance number of samples and their sums - */ -u32 mean_and_variance_get_stddev(struct mean_and_variance s) -{ - return int_sqrt64(mean_and_variance_get_variance(s)); -} -EXPORT_SYMBOL_GPL(mean_and_variance_get_stddev); - -/** - * mean_and_variance_weighted_update() - exponentially weighted variant of mean_and_variance_update() - * @s: mean and variance number of samples and their sums - * @x: new value to include in the &mean_and_variance_weighted - * - * see linked pdf: function derived from equations 140-143 where alpha = 2^w. - * values are stored bitshifted for performance and added precision. - */ -void mean_and_variance_weighted_update(struct mean_and_variance_weighted *s, s64 x) -{ - // previous weighted variance. - u8 w = s->weight; - u64 var_w0 = s->variance; - // new value weighted. - s64 x_w = x << w; - s64 diff_w = x_w - s->mean; - s64 diff = fast_divpow2(diff_w, w); - // new mean weighted. - s64 u_w1 = s->mean + diff; - - if (!s->init) { - s->mean = x_w; - s->variance = 0; - } else { - s->mean = u_w1; - s->variance = ((var_w0 << w) - var_w0 + ((diff_w * (x_w - u_w1)) >> w)) >> w; - } - s->init = true; -} -EXPORT_SYMBOL_GPL(mean_and_variance_weighted_update); - -/** - * mean_and_variance_weighted_get_mean() - get mean from @s - * @s: mean and variance number of samples and their sums - */ -s64 mean_and_variance_weighted_get_mean(struct mean_and_variance_weighted s) -{ - return fast_divpow2(s.mean, s.weight); -} -EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_mean); - -/** - * mean_and_variance_weighted_get_variance() -- get variance from @s - * @s: mean and variance number of samples and their sums - */ -u64 mean_and_variance_weighted_get_variance(struct mean_and_variance_weighted s) -{ - // always positive don't need fast divpow2 - return s.variance >> s.weight; -} -EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_variance); - -/** - * mean_and_variance_weighted_get_stddev() - get standard deviation from @s - * @s: mean and variance number of samples and their sums - */ -u32 mean_and_variance_weighted_get_stddev(struct mean_and_variance_weighted s) -{ - return int_sqrt64(mean_and_variance_weighted_get_variance(s)); -} -EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_stddev); - -MODULE_AUTHOR("Daniel B. Hill"); -MODULE_LICENSE("GPL"); |