libcore: add N(0,1) and Exp(1) distributions to core::rand.

src/etc/ziggurat_tables.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# xfail-license
+
+# This creates the tables used for distributions implemented using the
+# ziggurat algorithm in `core::rand::distributions;`. They are
+# (basically) the tables as used in the ZIGNOR variant (Doornik 2005).
+# They are changed rarely, so the generated file should be checked in
+# to git.
+#
+# It creates 3 tables: X as in the paper, F which is f(x_i), and
+# F_DIFF which is f(x_i) - f(x_{i-1}). The latter two are just cached
+# values which is not done in that paper (but is done in other
+# variants). Note that the adZigR table is unnecessary because of
+# algebra.
+#
+# It is designed to be compatible with Python 2 and 3.
+
+from math import exp, sqrt, log, floor
+import random
+
+# The order should match the return value of `tables`
+TABLE_NAMES = ['X', 'F', 'F_DIFF']
+
+# The actual length of the table is 1 more, to stop
+# index-out-of-bounds errors. This should match the bitwise operation
+# to find `i` in `zigurrat` in `libstd/rand/mod.rs`. Also the *_R and
+# *_V constants below depend on this value.
+TABLE_LEN = 256
+
+# equivalent to `zigNorInit` in Doornik2005, but generalised to any
+# distribution. r = dR, v = dV, f = probability density function,
+# f_inv = inverse of f
+def tables(r, v, f, f_inv):
+    # compute the x_i
+    xvec = [0]*(TABLE_LEN+1)
+
+    xvec[0] = v / f(r)
+    xvec[1] = r
+
+    for i in range(2, TABLE_LEN):
+        last = xvec[i-1]
+        xvec[i] = f_inv(v / last + f(last))
+
+    # cache the f's
+    fvec = [0]*(TABLE_LEN+1)
+    fdiff = [0]*(TABLE_LEN+1)
+    for i in range(TABLE_LEN+1):
+        fvec[i] = f(xvec[i])
+        if i > 0:
+            fdiff[i] = fvec[i] - fvec[i-1]
+
+    return xvec, fvec, fdiff
+
+# Distributions
+# N(0, 1)
+def norm_f(x):
+    return exp(-x*x/2.0)
+def norm_f_inv(y):
+    return sqrt(-2.0*log(y))
+
+NORM_R = 3.6541528853610088
+NORM_V = 0.00492867323399
+
+NORM = tables(NORM_R, NORM_V,
+              norm_f, norm_f_inv)
+
+# Exp(1)
+def exp_f(x):
+    return exp(-x)
+def exp_f_inv(y):
+    return -log(y)
+
+EXP_R = 7.69711747013104972
+EXP_V = 0.0039496598225815571993
+
+EXP = tables(EXP_R, EXP_V,
+             exp_f, exp_f_inv)
+
+
+# Output the tables/constants/types
+
+def render_static(name, type, value):
+    # no space or
+    return 'pub static %s: %s =%s;\n' % (name, type, value)
+
+# static `name`: [`type`, .. `len(values)`] =
+#     [values[0], ..., values[3],
+#      values[4], ..., values[7],
+#      ... ];
+def render_table(name, values):
+    rows = []
+    # 4 values on each row
+    for i in range(0, len(values), 4):
+        row = values[i:i+4]
+        rows.append(', '.join('%.18f' % f for f in row))
+
+    rendered = '\n    [%s]' % ',\n     '.join(rows)
+    return render_static(name, '[f64, .. %d]' % len(values), rendered)
+
+
+with open('ziggurat_tables.rs', 'w') as f:
+    f.write('''// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// http://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+// Tables for distributions which are sampled using the ziggurat
+// algorithm. Autogenerated by `ziggurat_tables.py`.
+
+pub type ZigTable = &\'static [f64, .. %d];
+'''  % (TABLE_LEN + 1))
+    for name, tables, r in [('NORM', NORM, NORM_R),
+                            ('EXP', EXP, EXP_R)]:
+        f.write(render_static('ZIG_%s_R' % name, 'f64', ' %.18f' % r))
+        for (tabname, table) in zip(TABLE_NAMES, tables):
+            f.write(render_table('ZIG_%s_%s' % (name, tabname), table))

src/libcore/rand.rs

@@ -16,6 +16,9 @@ and so can be used to generate any type that implements `Rand`. Type inference
 means that often a simple call to `rand::random()` or `rng.gen()` will
 suffice, but sometimes an annotation is required, e.g. `rand::random::<float>()`.
 
+See the `distributions` submodule for sampling random numbers from
+distributions like normal and exponential.
+
 # Examples
 ~~~
 use core::rand::RngUtil;
@@ -47,6 +50,9 @@ use util;
 use vec;
 use libc::size_t;
 
+#[path="rand/distributions.rs"]
+pub mod distributions;
+
 /// A type that can be randomly generated using an Rng
 pub trait Rand {
     fn rand<R: Rng>(rng: &R) -> Self;

src/libcore/rand/distributions.rs

@@ -0,0 +1,148 @@
+// Copyright 2013 The Rust Project Developers. See the COPYRIGHT
+// file at the top-level directory of this distribution and at
+// http://rust-lang.org/COPYRIGHT.
+//
+// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
+// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
+// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
+// option. This file may not be copied, modified, or distributed
+// except according to those terms.
+
+//! Sampling from random distributions
+
+// Some implementations use the Ziggurat method
+// https://en.wikipedia.org/wiki/Ziggurat_algorithm
+//
+// The version used here is ZIGNOR [Doornik 2005, "An Improved
+// Ziggurat Method to Generate Normal Random Samples"] which is slower
+// (about double, it generates an extra random number) than the
+// canonical version [Marsaglia & Tsang 2000, "The Ziggurat Method for
+// Generating Random Variables"], but more robust. If one wanted, one
+// could implement VIZIGNOR the ZIGNOR paper for more speed.
+
+use prelude::*;
+use rand::{Rng,Rand};
+
+mod ziggurat_tables;
+
+// inlining should mean there is no performance penalty for this
+#[inline(always)]
+fn ziggurat<R:Rng>(rng: &R,
+                   center_u: bool,
+                   X: ziggurat_tables::ZigTable,
+                   F: ziggurat_tables::ZigTable,
+                   F_DIFF: ziggurat_tables::ZigTable,
+                   pdf: &'static fn(f64) -> f64, // probability density function
+                   zero_case: &'static fn(&R, f64) -> f64) -> f64 {
+    loop {
+        let u = if center_u {2.0 * rng.gen() - 1.0} else {rng.gen()};
+        let i: uint = rng.gen::<uint>() & 0xff;
+        let x = u * X[i];
+
+        let test_x = if center_u {f64::abs(x)} else {x};
+
+        // algebraically equivalent to |u| < X[i+1]/X[i] (or u < X[i+1]/X[i])
+        if test_x < X[i + 1] {
+            return x;
+        }
+        if i == 0 {
+            return zero_case(rng, u);
+        }
+        // algebraically equivalent to f1 + DRanU()*(f0 - f1) < 1
+        if F[i+1] + F_DIFF[i+1] * rng.gen() < pdf(x) {
+            return x;
+        }
+    }
+}
+
+/// A wrapper around an `f64` to generate N(0, 1) random numbers (a.k.a.  a
+/// standard normal, or Gaussian). Multiplying the generated values by the
+/// desired standard deviation `sigma` then adding the desired mean `mu` will
+/// give N(mu, sigma^2) distributed random numbers.
+///
+/// Note that this has to be unwrapped before use as an `f64` (using either
+/// `*` or `cast::transmute` is safe).
+///
+/// # Example
+///
+/// ~~~
+/// use core::rand::distributions::StandardNormal;
+///
+/// fn main() {
+///     let normal = 2.0 + (*rand::random::<StandardNormal>()) * 3.0;
+///     println(fmt!("%f is from a N(2, 9) distribution", normal))
+/// }
+/// ~~~
+pub struct StandardNormal(f64);
+
+impl Rand for StandardNormal {
+    fn rand<R:Rng>(rng: &R) -> StandardNormal {
+        #[inline(always)]
+        fn pdf(x: f64) -> f64 {
+            f64::exp((-x*x/2.0) as f64) as f64
+        }
+        #[inline(always)]
+        fn zero_case<R:Rng>(rng: &R, u: f64) -> f64 {
+            // compute a random number in the tail by hand
+
+            // strange initial conditions, because the loop is not
+            // do-while, so the condition should be true on the first
+            // run, they get overwritten anyway (0 < 1, so these are
+            // good).
+            let mut x = 1.0, y = 0.0;
+
+            // XXX infinities?
+            while -2.0*y < x * x {
+                x = f64::ln(rng.gen()) / ziggurat_tables::ZIG_NORM_R;
+                y = f64::ln(rng.gen());
+            }
+            if u < 0.0 {x-ziggurat_tables::ZIG_NORM_R} else {ziggurat_tables::ZIG_NORM_R-x}
+        }
+
+        StandardNormal(ziggurat(
+            rng,
+            true, // this is symmetric
+            &ziggurat_tables::ZIG_NORM_X,
+            &ziggurat_tables::ZIG_NORM_F, &ziggurat_tables::ZIG_NORM_F_DIFF,
+            pdf, zero_case))
+    }
+}
+
+/// A wrapper around an `f64` to generate Exp(1) random numbers. Dividing by
+/// the desired rate `lambda` will give Exp(lambda) distributed random
+/// numbers.
+///
+/// Note that this has to be unwrapped before use as an `f64` (using either
+/// `*` or `cast::transmute` is safe).
+///
+/// # Example
+///
+/// ~~~
+/// use core::rand::distributions::Exp1;
+///
+/// fn main() {
+///     let exp2 = (*rand::random::<Exp1>()) * 0.5;
+///     println(fmt!("%f is from a Exp(2) distribution", exp2));
+/// }
+/// ~~~
+pub struct Exp1(f64);
+
+// This could be done via `-f64::ln(rng.gen::<f64>())` but that is slower.
+impl Rand for Exp1 {
+    #[inline]
+    fn rand<R:Rng>(rng: &R) -> Exp1 {
+        #[inline(always)]
+        fn pdf(x: f64) -> f64 {
+            f64::exp(-x)
+        }
+        #[inline(always)]
+        fn zero_case<R:Rng>(rng: &R, _u: f64) -> f64 {
+            ziggurat_tables::ZIG_EXP_R - f64::ln(rng.gen())
+        }
+
+        Exp1(ziggurat(rng, false,
+                      &ziggurat_tables::ZIG_EXP_X,
+                      &ziggurat_tables::ZIG_EXP_F, &ziggurat_tables::ZIG_EXP_F_DIFF,
+                      pdf, zero_case))
+    }
+}