зеркало из https://github.com/mozilla/pjs.git
Bug #244357 --> Fix several underflow situations in the chi2 algorithm used for junk mail detection. Simplify the math to make it less computensive.
Patch origianlly by tenthumbs@cybernex.net sr=mscott
This commit is contained in:
scott%scott-macgregor.org
Родитель
2a560e1ea5
Коммит
1a4937b94c
|
|
@ -79,6 +79,7 @@
|
|||
#include "nsIHTMLToTextSink.h"
|
||||
#include "nsIDocumentEncoder.h"
|
||||
|
||||
#include "nsIncompleteGamma.h"
|
||||
#include <math.h>
|
||||
|
||||
static PRLogModuleInfo *BayesianFilterLogModule = nsnull;
|
||||
|
|
@ -923,21 +924,28 @@ PR_STATIC_CALLBACK(int) compareTokens(const void* p1, const void* p2, void* /* d
|
|||
inline double dmax(double x, double y) { return (x > y ? x : y); }
|
||||
inline double dmin(double x, double y) { return (x < y ? x : y); }
|
||||
|
||||
double chi2Q (double x2, PRUint32 v)
|
||||
// Chi square functions are implemented by an incomplete gamma function.
|
||||
// Note that chi2P's callers multiply the arguments by 2 but chi2P
|
||||
// divides them by 2 again. Inlining chi2P gives the compiler a
|
||||
// chance to notice this.
|
||||
|
||||
// Both chi2P and nsIncompleteGammaP set *error negative on domain
|
||||
// errors and nsIncompleteGammaP sets it posivive on internal errors.
|
||||
// This may be useful but the chi2P callers treat any error as fatal.
|
||||
|
||||
// Note that converting unsigned ints to floating point can be slow on
|
||||
// some platforms (like Intel) so use signed quantities for the numeric
|
||||
// routines.
|
||||
static inline double chi2P (double chi2, double nu, PRInt32 *error)
|
||||
{
|
||||
PRUint32 i;
|
||||
double m = x2 / 2.0;
|
||||
double sum = exp(-m);
|
||||
double term = sum;
|
||||
|
||||
NS_ASSERTION(!(v & 1), "chi2Q called with odd value");
|
||||
for (i=1; i < v/2; ++i)
|
||||
{
|
||||
term *= m / i;
|
||||
sum += term;
|
||||
}
|
||||
|
||||
return dmin(sum,1.0);
|
||||
// domain checks; set error and return a dummy value
|
||||
if (chi2 < 0.0 || nu <= 0.0)
|
||||
{
|
||||
*error = -1;
|
||||
return 0.0;
|
||||
}
|
||||
// reversing the arguments is intentional
|
||||
return nsIncompleteGammaP (nu/2.0, chi2/2.0, error);
|
||||
}
|
||||
|
||||
void nsBayesianFilter::classifyMessage(Tokenizer& tokenizer, const char* messageURI,
|
||||
|
|
@ -1037,12 +1045,18 @@ void nsBayesianFilter::classifyMessage(Tokenizer& tokenizer, const char* message
|
|||
|
||||
if (goodclues > 0)
|
||||
{
|
||||
S = 1.0 - chi2Q(-2.0 * S, 2 * goodclues);
|
||||
H = 1.0 - chi2Q(-2.0 * H, 2 * goodclues);
|
||||
prob = (S-H +1.0) / 2.0;
|
||||
PRInt32 chi_error;
|
||||
S = chi2P(-2.0 * S, 2.0 * goodclues, &chi_error);
|
||||
if (!chi_error)
|
||||
H = chi2P(-2.0 * H, 2.0 * goodclues, &chi_error);
|
||||
// if any error toss the entire calculation
|
||||
if (!chi_error)
|
||||
prob = (S-H +1.0) / 2.0;
|
||||
else
|
||||
prob = 0.5;
|
||||
}
|
||||
else
|
||||
prob = 0.5;
|
||||
prob = 0.5;
|
||||
|
||||
PRBool isJunk = (prob >= mJunkProbabilityThreshold);
|
||||
PR_LOG(BayesianFilterLogModule, PR_LOG_ALWAYS, ("%s is junk probability = (%f) HAM SCORE:%f SPAM SCORE:%f", messageURI, prob,H,S));
|
||||
|
|
|
|||
|
|
@ -0,0 +1,291 @@
|
|||
/* -*- Mode: C++; tab-width: 2; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
|
||||
/***** BEGIN LICENSE BLOCK *****
|
||||
Version: MPL 1.1/GPL 2.0/LGPL 2.1
|
||||
|
||||
The contents of this file are subject to the Mozilla Public License Version
|
||||
1.1 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
http://www.mozilla.org/MPL/
|
||||
|
||||
Software distributed under the License is distributed on an "AS IS" basis,
|
||||
WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
|
||||
for the specific language governing rights and limitations under the
|
||||
License.
|
||||
|
||||
The Original Code is bayesian spam filter
|
||||
|
||||
Portions created by the Initial Developer are Copyright (C) 2004
|
||||
the Initial Developer. All Rights Reserved.
|
||||
|
||||
Contributor(s):
|
||||
Scott MacGregor <mscott@mozilla.org>
|
||||
tenthumbs@cybernex.net
|
||||
|
||||
Alternatively, the contents of this file may be used under the terms of
|
||||
either of the GNU General Public License Version 2 or later (the "GPL"),
|
||||
or the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
|
||||
in which case the provisions of the GPL or the LGPL are applicable instead
|
||||
of those above. If you wish to allow use of your version of this file only
|
||||
under the terms of either the GPL or the LGPL, and not to allow others to
|
||||
use your version of this file under the terms of the MPL, indicate your
|
||||
decision by deleting the provisions above and replace them with the notice
|
||||
and other provisions required by the GPL or the LGPL. If you do not delete
|
||||
the provisions above, a recipient may use your version of this file under
|
||||
the terms of any one of the MPL, the GPL or the LGPL.
|
||||
|
||||
***** END LICENSE BLOCK *****/
|
||||
|
||||
#ifndef nsIncompleteGamma_h__
|
||||
#define nsIncompleteGamma_h__
|
||||
|
||||
/* An implementation of the incomplete gamma functions for real
|
||||
arguments. P is defined as
|
||||
|
||||
x
|
||||
/
|
||||
1 [ a - 1 - t
|
||||
P(a, x) = -------- I t e dt
|
||||
Gamma(a) ]
|
||||
/
|
||||
0
|
||||
|
||||
and
|
||||
|
||||
infinity
|
||||
/
|
||||
1 [ a - 1 - t
|
||||
Q(a, x) = -------- I t e dt
|
||||
Gamma(a) ]
|
||||
/
|
||||
x
|
||||
|
||||
so that P(a,x) + Q(a,x) = 1.
|
||||
|
||||
Both a series expansion and a continued fraction exist. This
|
||||
implementation uses the more efficient method based on the arguments.
|
||||
|
||||
Either case involves calculating a multiplicative term:
|
||||
e^(-x)*x^a/Gamma(a).
|
||||
Here we calculate the log of this term. Most math libraries have a
|
||||
"lgamma" function but it is not re-entrant. Some libraries have a
|
||||
"lgamma_r" which is re-entrant. Use it if possible. I have included a
|
||||
simple replacement but it is certainly not as accurate.
|
||||
|
||||
Relative errors are almost always < 1e-10 and usually < 1e-14. Very
|
||||
small and very large arguments cause trouble.
|
||||
|
||||
The region where a < 0.5 and x < 0.5 has poor error properties and is
|
||||
not too stable. Get a better routine if you need results in this
|
||||
region.
|
||||
|
||||
The error argument will be set negative if there is a domain error or
|
||||
positive for an internal calculation error, currently lack of
|
||||
convergence. A value is always returned, though.
|
||||
|
||||
*/
|
||||
|
||||
#include <math.h>
|
||||
#include <float.h>
|
||||
|
||||
// the main routine
|
||||
static double nsIncompleteGammaP (double a, double x, int *error);
|
||||
|
||||
// nsLnGamma(z): either a wrapper around lgamma_r or the internal function.
|
||||
// C_m = B[2*m]/(2*m*(2*m-1)) where B is a Bernoulli number
|
||||
static const double C_1 = 1.0 / 12.0;
|
||||
static const double C_2 = -1.0 / 360.0;
|
||||
static const double C_3 = 1.0 / 1260.0;
|
||||
static const double C_4 = -1.0 / 1680.0;
|
||||
static const double C_5 = 1.0 / 1188.0;
|
||||
static const double C_6 = -691.0 / 360360.0;
|
||||
static const double C_7 = 1.0 / 156.0;
|
||||
static const double C_8 = -3617.0 / 122400.0;
|
||||
static const double C_9 = 43867.0 / 244188.0;
|
||||
static const double C_10 = -174611.0 / 125400.0;
|
||||
static const double C_11 = 77683.0 / 5796.0;
|
||||
|
||||
// truncated asymptotic series in 1/z
|
||||
static inline double lngamma_asymp (double z)
|
||||
{
|
||||
double w, w2, sum;
|
||||
w = 1.0 / z;
|
||||
w2 = w * w;
|
||||
sum = w * (w2 * (w2 * (w2 * (w2 * (w2 * (w2 * (w2 * (w2 * (w2
|
||||
* (C_11 * w2 + C_10) + C_9) + C_8) + C_7) + C_6)
|
||||
+ C_5) + C_4) + C_3) + C_2) + C_1);
|
||||
|
||||
return sum;
|
||||
}
|
||||
|
||||
struct fact_table_s
|
||||
{
|
||||
double fact;
|
||||
double lnfact;
|
||||
};
|
||||
|
||||
// for speed and accuracy
|
||||
static const struct fact_table_s FactTable[] = {
|
||||
{1.000000000000000, 0.0000000000000000000000e+00},
|
||||
{1.000000000000000, 0.0000000000000000000000e+00},
|
||||
{2.000000000000000, 6.9314718055994530942869e-01},
|
||||
{6.000000000000000, 1.7917594692280550007892e+00},
|
||||
{24.00000000000000, 3.1780538303479456197550e+00},
|
||||
{120.0000000000000, 4.7874917427820459941458e+00},
|
||||
{720.0000000000000, 6.5792512120101009952602e+00},
|
||||
{5040.000000000000, 8.5251613610654142999881e+00},
|
||||
{40320.00000000000, 1.0604602902745250228925e+01},
|
||||
{362880.0000000000, 1.2801827480081469610995e+01},
|
||||
{3628800.000000000, 1.5104412573075515295248e+01},
|
||||
{39916800.00000000, 1.7502307845873885839769e+01},
|
||||
{479001600.0000000, 1.9987214495661886149228e+01},
|
||||
{6227020800.000000, 2.2552163853123422886104e+01},
|
||||
{87178291200.00000, 2.5191221182738681499610e+01},
|
||||
{1307674368000.000, 2.7899271383840891566988e+01},
|
||||
{20922789888000.00, 3.0671860106080672803835e+01},
|
||||
{355687428096000.0, 3.3505073450136888885825e+01},
|
||||
{6402373705728000., 3.6395445208033053576674e+01}
|
||||
};
|
||||
#define FactTableLength (int)(sizeof(FactTable)/sizeof(FactTable[0]))
|
||||
|
||||
// for speed
|
||||
static const double ln_2pi_2 = 0.918938533204672741803; // log(2*PI)/2
|
||||
|
||||
/* A simple lgamma function, not very robust.
|
||||
|
||||
Valid for z_in > 0 ONLY.
|
||||
|
||||
For z_in > 8 precision is quite good, relative errors < 1e-14 and
|
||||
usually better. For z_in < 8 relative errors increase but are usually
|
||||
< 1e-10. In two small regions, 1 +/- .001 and 2 +/- .001 errors
|
||||
increase quickly.
|
||||
*/
|
||||
static double nsLnGamma (double z_in, int *gsign)
|
||||
{
|
||||
double scale, z, sum, result;
|
||||
*gsign = 1;
|
||||
|
||||
int zi = (int) z_in;
|
||||
if (z_in == (double) zi)
|
||||
{
|
||||
if (0 < zi && zi <= FactTableLength)
|
||||
return FactTable[zi - 1].lnfact; // gamma(z) = (z-1)!
|
||||
}
|
||||
|
||||
for (scale = 1.0, z = z_in; z < 8.0; ++z)
|
||||
scale *= z;
|
||||
|
||||
sum = lngamma_asymp (z);
|
||||
result = (z - 0.5) * log (z) - z + ln_2pi_2 - log (scale);
|
||||
result += sum;
|
||||
return result;
|
||||
}
|
||||
|
||||
// log( e^(-x)*x^a/Gamma(a) )
|
||||
static inline double lnPQfactor (double a, double x)
|
||||
{
|
||||
int gsign; // ignored because a > 0
|
||||
return a * log (x) - x - nsLnGamma (a, &gsign);
|
||||
}
|
||||
|
||||
static double Pseries (double a, double x, int *error)
|
||||
{
|
||||
double sum, term;
|
||||
const double eps = 2.0 * DBL_EPSILON;
|
||||
const int imax = 5000;
|
||||
int i;
|
||||
|
||||
sum = term = 1.0 / a;
|
||||
for (i = 1; i < imax; ++i)
|
||||
{
|
||||
term *= x / (a + i);
|
||||
sum += term;
|
||||
if (fabs (term) < eps * fabs (sum))
|
||||
break;
|
||||
}
|
||||
|
||||
if (i >= imax)
|
||||
*error = 1;
|
||||
|
||||
return sum;
|
||||
}
|
||||
|
||||
static double Qcontfrac (double a, double x, int *error)
|
||||
{
|
||||
double result, D, C, e, f, term;
|
||||
const double eps = 2.0 * DBL_EPSILON;
|
||||
const double small =
|
||||
DBL_EPSILON * DBL_EPSILON * DBL_EPSILON * DBL_EPSILON;
|
||||
const int imax = 5000;
|
||||
int i;
|
||||
|
||||
// modified Lentz method
|
||||
f = x - a + 1.0;
|
||||
if (fabs (f) < small)
|
||||
f = small;
|
||||
C = f + 1.0 / small;
|
||||
D = 1.0 / f;
|
||||
result = D;
|
||||
for (i = 1; i < imax; ++i)
|
||||
{
|
||||
e = i * (a - i);
|
||||
f += 2.0;
|
||||
D = f + e * D;
|
||||
if (fabs (D) < small)
|
||||
D = small;
|
||||
D = 1.0 / D;
|
||||
C = f + e / C;
|
||||
if (fabs (C) < small)
|
||||
C = small;
|
||||
term = C * D;
|
||||
result *= term;
|
||||
if (fabs (term - 1.0) < eps)
|
||||
break;
|
||||
}
|
||||
|
||||
if (i >= imax)
|
||||
*error = 1;
|
||||
return result;
|
||||
}
|
||||
|
||||
static double nsIncompleteGammaP (double a, double x, int *error)
|
||||
{
|
||||
double result, dom, ldom;
|
||||
// domain errors. the return values are meaningless but have
|
||||
// to return something.
|
||||
*error = -1;
|
||||
if (a <= 0.0)
|
||||
return 1.0;
|
||||
if (x < 0.0)
|
||||
return 0.0;
|
||||
*error = 0;
|
||||
if (x == 0.0)
|
||||
return 0.0;
|
||||
|
||||
ldom = lnPQfactor (a, x);
|
||||
dom = exp (ldom);
|
||||
// might need to adjust the crossover point
|
||||
if (a <= 0.5)
|
||||
{
|
||||
if (x < a + 1.0)
|
||||
result = dom * Pseries (a, x, error);
|
||||
else
|
||||
result = 1.0 - dom * Qcontfrac (a, x, error);
|
||||
}
|
||||
else
|
||||
{
|
||||
if (x < a)
|
||||
result = dom * Pseries (a, x, error);
|
||||
else
|
||||
result = 1.0 - dom * Qcontfrac (a, x, error);
|
||||
}
|
||||
|
||||
// not clear if this can ever happen
|
||||
if (result > 1.0)
|
||||
result = 1.0;
|
||||
if (result < 0.0)
|
||||
result = 0.0;
|
||||
return result;
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
Загрузка…
Ссылка в новой задаче