// @(#)Sample.cc 6.2 (Berkeley) 2/25/91
// Modified for Berkeley Unix by Donn Seeley, donn@okeeffe.berkeley.edu
// This may look like C code, but it is really -*- C++ -*-
Copyright (C) 1988 Free Software Foundation
written by Dirk Grunwald (grunwald@cs.uiuc.edu)
This file is part of GNU CC.
GNU CC is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY. No author or distributor
accepts responsibility to anyone for the consequences of using it
or for whether it serves any particular purpose or works at all,
unless he says so in writing. Refer to the GNU CC General Public
License for full details.
Everyone is granted permission to copy, modify and redistribute
GNU CC, but only under the conditions described in the
GNU CC General Public License. A copy of this license is
supposed to have been given to you along with GNU CC so you
can know your rights and responsibilities. It should be in a
file named COPYING. Among other things, the copyright notice
and this notice must be preserved on all copies.
void default_SampleStatistic_error_handler(const char* msg
)
cerr
<< "Fatal SampleStatistic error. " << msg
<< "\n";
one_arg_error_handler_t SampleStatistic_error_handler
= default_SampleStatistic_error_handler
;
one_arg_error_handler_t
set_SampleStatistic_error_handler(one_arg_error_handler_t f
)
one_arg_error_handler_t old
= SampleStatistic_error_handler
;
SampleStatistic_error_handler
= f
;
void SampleStatistic::error(const char* msg
)
(*SampleStatistic_error_handler
)(msg
);
// t-distribution: given p-value and degrees of freedom, return t-value
// adapted from Peizer & Pratt JASA, vol63, p1416
double tval(double p
, int df
)
p
= (positive
)? 1.0 - p
: p
;
t
= 1.0 / tan((p
+ p
) * 1.57079633);
t
= sqrt(1.0 / ((p
+ p
) * (1.0 - p
)) - 2.0);
double a
= sqrt(log(1.0 / (p
* p
)));
a
= a
- ((2.515517 + (0.802853 * a
) + (0.010328 * aa
)) /
(1.0 + (1.432788 * a
) + (0.189269 * aa
) +
t
= ddf
- 0.666666667 + 1.0 / (10.0 * ddf
);
t
= sqrt(ddf
* (exp(a
* a
* (ddf
- 0.833333333) / (t
* t
)) - 1.0));
return (positive
)? t
: -t
;
SampleStatistic::operator+=(double value
)
if ( minValue
> value
) minValue
= value
;
if ( maxValue
< value
) maxValue
= value
;
return(( x2
- ((x
* x
) / n
)) / ( n
- 1));
SampleStatistic::stdDev()
if ( n
<= 0 || this -> var() <= 0) {
return( (double) sqrt( var() ) );
SampleStatistic::confidence(int interval
)
if (df
<= 0) return HUGE
;
double t
= tval(double(100 + interval
) * 0.005, df
);
return (t
* stdDev()) / sqrt(double(n
));
SampleStatistic::confidence(double p_value
)
if (df
<= 0) return HUGE
;
double t
= tval((1.0 + p_value
) * 0.5, df
);
return (t
* stdDev()) / sqrt(double(n
));
const int SampleHistogramMinimum
= -2;
const int SampleHistogramMaximum
= -1;
SampleHistogram::SampleHistogram(double low
, double high
, double width
)
width
= (high
- low
) / 10;
howManyBuckets
= int((high
- low
) / width
) + 2;
bucketCount
= new int[howManyBuckets
];
bucketLimit
= new double[howManyBuckets
];
for (int i
= 0; i
< howManyBuckets
; i
++) {
bucketLimit
[howManyBuckets
-1] = HUGE
; /* from math.h */
SampleHistogram::~SampleHistogram()
if (howManyBuckets
> 0) {
SampleHistogram::operator+=(double value
)
for (i
= 0; i
< howManyBuckets
; i
++) {
if (value
< bucketLimit
[i
]) break;
this->SampleStatistic::operator+=(value
);
SampleHistogram::similarSamples(double d
)
for (i
= 0; i
< howManyBuckets
; i
++) {
if (d
< bucketLimit
[i
]) return(bucketCount
[i
]);
SampleHistogram::printBuckets(ostream
& s
)
for(int i
= 0; i
< howManyBuckets
; i
++) {
if (bucketLimit
[i
] >= HUGE
) {
s
<< "< max : " << bucketCount
[i
] << "\n";
s
<< "< " << bucketLimit
[i
] << " : " << bucketCount
[i
] << "\n";
this->SampleStatistic::reset();
if (howManyBuckets
> 0) {
for (register int i
= 0; i
< howManyBuckets
; i
++) {