| // 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 |
| */ |
| 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 |
| * |
| * 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 |
| */ |
| 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() |
| * @s1: .. |
| * @s2: .. |
| * |
| * 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 |
| */ |
| 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 |
| */ |
| 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 |
| */ |
| 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"); |