Move c_src dirs back to toplevel

We just wanted c sourcefiles out of the top level, not c source
directories.

Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>

1 files changed, 0 insertions, 165 deletions

diff --git a/c_src/libbcachefs/mean_and_variance.c b/c_src/libbcachefs/mean_and_variance.c
deleted file mode 100644
index bf0ef668..00000000
--- a/c_src/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");