1 // SPDX-License-Identifier: GPL-2.0 2 /* 3 * Functions for incremental mean and variance. 4 * 5 * This program is free software; you can redistribute it and/or modify it 6 * under the terms of the GNU General Public License version 2 as published by 7 * the Free Software Foundation. 8 * 9 * This program is distributed in the hope that it will be useful, but WITHOUT 10 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 11 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for 12 * more details. 13 * 14 * Copyright © 2022 Daniel B. Hill 15 * 16 * Author: Daniel B. Hill <daniel@gluo.nz> 17 * 18 * Description: 19 * 20 * This is includes some incremental algorithms for mean and variance calculation 21 * 22 * Derived from the paper: https://fanf2.user.srcf.net/hermes/doc/antiforgery/stats.pdf 23 * 24 * Create a struct and if it's the weighted variant set the w field (weight = 2^k). 25 * 26 * Use mean_and_variance[_weighted]_update() on the struct to update it's state. 27 * 28 * Use the mean_and_variance[_weighted]_get_* functions to calculate the mean and variance, some computation 29 * is deferred to these functions for performance reasons. 30 * 31 * see lib/math/mean_and_variance_test.c for examples of usage. 32 * 33 * DO NOT access the mean and variance fields of the weighted variants directly. 34 * DO NOT change the weight after calling update. 35 */ 36 37 #include <linux/bug.h> 38 #include <linux/compiler.h> 39 #include <linux/export.h> 40 #include <linux/limits.h> 41 #include <linux/math.h> 42 #include <linux/math64.h> 43 #include <linux/module.h> 44 45 #include "mean_and_variance.h" 46 47 u128_u u128_div(u128_u n, u64 d) 48 { 49 u128_u r; 50 u64 rem; 51 u64 hi = u128_hi(n); 52 u64 lo = u128_lo(n); 53 u64 h = hi & ((u64) U32_MAX << 32); 54 u64 l = (hi & (u64) U32_MAX) << 32; 55 56 r = u128_shl(u64_to_u128(div64_u64_rem(h, d, &rem)), 64); 57 r = u128_add(r, u128_shl(u64_to_u128(div64_u64_rem(l + (rem << 32), d, &rem)), 32)); 58 r = u128_add(r, u64_to_u128(div64_u64_rem(lo + (rem << 32), d, &rem))); 59 return r; 60 } 61 EXPORT_SYMBOL_GPL(u128_div); 62 63 /** 64 * mean_and_variance_get_mean() - get mean from @s 65 * @s: mean and variance number of samples and their sums 66 */ 67 s64 mean_and_variance_get_mean(struct mean_and_variance s) 68 { 69 return s.n ? div64_u64(s.sum, s.n) : 0; 70 } 71 EXPORT_SYMBOL_GPL(mean_and_variance_get_mean); 72 73 /** 74 * mean_and_variance_get_variance() - get variance from @s1 75 * @s1: mean and variance number of samples and sums 76 * 77 * see linked pdf equation 12. 78 */ 79 u64 mean_and_variance_get_variance(struct mean_and_variance s1) 80 { 81 if (s1.n) { 82 u128_u s2 = u128_div(s1.sum_squares, s1.n); 83 u64 s3 = abs(mean_and_variance_get_mean(s1)); 84 85 return u128_lo(u128_sub(s2, u128_square(s3))); 86 } else { 87 return 0; 88 } 89 } 90 EXPORT_SYMBOL_GPL(mean_and_variance_get_variance); 91 92 /** 93 * mean_and_variance_get_stddev() - get standard deviation from @s 94 * @s: mean and variance number of samples and their sums 95 */ 96 u32 mean_and_variance_get_stddev(struct mean_and_variance s) 97 { 98 return int_sqrt64(mean_and_variance_get_variance(s)); 99 } 100 EXPORT_SYMBOL_GPL(mean_and_variance_get_stddev); 101 102 /** 103 * mean_and_variance_weighted_update() - exponentially weighted variant of mean_and_variance_update() 104 * @s: mean and variance number of samples and their sums 105 * @x: new value to include in the &mean_and_variance_weighted 106 * 107 * see linked pdf: function derived from equations 140-143 where alpha = 2^w. 108 * values are stored bitshifted for performance and added precision. 109 */ 110 void mean_and_variance_weighted_update(struct mean_and_variance_weighted *s, s64 x) 111 { 112 // previous weighted variance. 113 u8 w = s->weight; 114 u64 var_w0 = s->variance; 115 // new value weighted. 116 s64 x_w = x << w; 117 s64 diff_w = x_w - s->mean; 118 s64 diff = fast_divpow2(diff_w, w); 119 // new mean weighted. 120 s64 u_w1 = s->mean + diff; 121 122 if (!s->init) { 123 s->mean = x_w; 124 s->variance = 0; 125 } else { 126 s->mean = u_w1; 127 s->variance = ((var_w0 << w) - var_w0 + ((diff_w * (x_w - u_w1)) >> w)) >> w; 128 } 129 s->init = true; 130 } 131 EXPORT_SYMBOL_GPL(mean_and_variance_weighted_update); 132 133 /** 134 * mean_and_variance_weighted_get_mean() - get mean from @s 135 * @s: mean and variance number of samples and their sums 136 */ 137 s64 mean_and_variance_weighted_get_mean(struct mean_and_variance_weighted s) 138 { 139 return fast_divpow2(s.mean, s.weight); 140 } 141 EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_mean); 142 143 /** 144 * mean_and_variance_weighted_get_variance() -- get variance from @s 145 * @s: mean and variance number of samples and their sums 146 */ 147 u64 mean_and_variance_weighted_get_variance(struct mean_and_variance_weighted s) 148 { 149 // always positive don't need fast divpow2 150 return s.variance >> s.weight; 151 } 152 EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_variance); 153 154 /** 155 * mean_and_variance_weighted_get_stddev() - get standard deviation from @s 156 * @s: mean and variance number of samples and their sums 157 */ 158 u32 mean_and_variance_weighted_get_stddev(struct mean_and_variance_weighted s) 159 { 160 return int_sqrt64(mean_and_variance_weighted_get_variance(s)); 161 } 162 EXPORT_SYMBOL_GPL(mean_and_variance_weighted_get_stddev); 163 164 MODULE_AUTHOR("Daniel B. Hill"); 165 MODULE_LICENSE("GPL"); 166