Subject: [Boost-commit] svn:boost r66719 - trunk/libs/math/example
From: pbristow_at_[hidden]
Date: 2010-11-24 11:31:13
Author: pbristow
Date: 2010-11-24 11:31:10 EST (Wed, 24 Nov 2010)
New Revision: 66719
URL: http://svn.boost.org/trac/boost/changeset/66719
Log:
Comments etc
Text files modified:
trunk/libs/math/example/neg_binom_confidence_limits.cpp | 2
trunk/libs/math/example/neg_binomial_sample_sizes.cpp | 216 ++++++++++++++++++++-------------------
trunk/libs/math/example/normal_misc_examples.cpp | 4
trunk/libs/math/example/policy_eg_1.cpp | 15 ++
trunk/libs/math/example/policy_ref_snip8.cpp | 6
5 files changed, 132 insertions(+), 111 deletions(-)
Modified: trunk/libs/math/example/neg_binom_confidence_limits.cpp
==============================================================================
--- trunk/libs/math/example/neg_binom_confidence_limits.cpp (original)
+++ trunk/libs/math/example/neg_binom_confidence_limits.cpp 2010-11-24 11:31:10 EST (Wed, 24 Nov 2010)
@@ -37,7 +37,6 @@
double alpha[] = { 0.5, 0.25, 0.1, 0.05, 0.01, 0.001, 0.0001, 0.00001 };
/*`
-
Confidence value as % is (1 - alpha) * 100, so alpha 0.05 == 95% confidence
that the true occurence frequency lies *inside* the calculated interval.
@@ -81,7 +80,6 @@
single-sided interval, for example: ['"Calculate a lower bound so that we are P%
sure that the true occurrence frequency is greater than some value"]
then we would *not* have divided by two.
-
*/
// Now print out the upper and lower limits for the alpha table values.
Modified: trunk/libs/math/example/neg_binomial_sample_sizes.cpp
==============================================================================
--- trunk/libs/math/example/neg_binomial_sample_sizes.cpp (original)
+++ trunk/libs/math/example/neg_binomial_sample_sizes.cpp 2010-11-24 11:31:10 EST (Wed, 24 Nov 2010)
@@ -29,8 +29,8 @@
//[neg_binomial_sample_sizes
/*`
-It centres around a routine that prints out
-a table of minimum sample sizes for various probability thresholds:
+It centres around a routine that prints out a table of
+minimum sample sizes (number of trials) for various probability thresholds:
*/
void find_number_of_trials(double failures, double p);
/*`
@@ -41,6 +41,8 @@
/*`
Confidence value as % is (1 - alpha) * 100, so alpha 0.05 == 95% confidence
that the desired number of failures will be observed.
+The values range from a very low 0.5 or 50% confidence up to an extremely high
+confidence of 99.999.
Much of the rest of the program is pretty-printing, the important part
is in the calculation of minimum number of trials required for each
@@ -48,13 +50,8 @@
(int)ceil(negative_binomial::find_minimum_number_of_trials(failures, p, alpha[i]);
-*/
-
-/*`
-
find_minimum_number_of_trials returns a double,
-so ceil rounds this up to ensure we have an integral minimum number of trials.
-
+so `ceil` rounds this up to ensure we have an integral minimum number of trials.
*/
void find_number_of_trials(double failures, double p)
@@ -66,11 +63,10 @@
// Calculate how many trials we need to ensure the
// required number of failures DOES exceed "failures".
- cout << "\n""Target number of failures = " << failures;
- cout << ", Success fraction = " << 100 * p << "%" << endl;
+ cout << "\n""Target number of failures = " << (int)failures;
+ cout << ", Success fraction = " << fixed << setprecision(1) << 100 * p << "%" << endl;
// Print table header:
- cout << "\n\n"
- "____________________________\n"
+ cout << "____________________________\n"
"Confidence Min Number\n"
" Value (%) Of Trials \n"
"____________________________\n";
@@ -106,98 +102,108 @@
/*
Output is:
-
-Autorun "i:\boost-06-05-03-1300\libs\math\test\Math_test\debug\neg_binomial_sample_sizes.exe"
-Target number of failures = 5, Success fraction = 50%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 11
- 75.000 14
- 90.000 17
- 95.000 18
- 99.000 22
- 99.900 27
- 99.990 31
- 99.999 36
-Target number of failures = 50.000, Success fraction = 50.000%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 101
- 75.000 109
- 90.000 115
- 95.000 119
- 99.000 128
- 99.900 137
- 99.990 146
- 99.999 154
-Target number of failures = 500.000, Success fraction = 50.000%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 1001
- 75.000 1023
- 90.000 1043
- 95.000 1055
- 99.000 1078
- 99.900 1104
- 99.990 1126
- 99.999 1146
-Target number of failures = 50.000, Success fraction = 10.000%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 56
- 75.000 58
- 90.000 60
- 95.000 61
- 99.000 63
- 99.900 66
- 99.990 68
- 99.999 71
-Target number of failures = 500.000, Success fraction = 10.000%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 556
- 75.000 562
- 90.000 567
- 95.000 570
- 99.000 576
- 99.900 583
- 99.990 588
- 99.999 594
-Target number of failures = 5.000, Success fraction = 90.000%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 57
- 75.000 73
- 90.000 91
- 95.000 103
- 99.000 127
- 99.900 159
- 99.990 189
- 99.999 217
-Target number of failures = 5.000, Success fraction = 40.000%
-____________________________
-Confidence Min Number
- Value (%) Of Trials
-____________________________
- 50.000 10
- 75.000 11
- 90.000 13
- 95.000 15
- 99.000 18
- 99.900 21
- 99.990 25
- 99.999 28
+Target number of failures = 5, Success fraction = 50.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 11
+ 75.000 14
+ 90.000 17
+ 95.000 18
+ 99.000 22
+ 99.900 27
+ 99.990 31
+ 99.999 36
+
+
+ Target number of failures = 50, Success fraction = 50.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 101
+ 75.000 109
+ 90.000 115
+ 95.000 119
+ 99.000 128
+ 99.900 137
+ 99.990 146
+ 99.999 154
+
+
+ Target number of failures = 500, Success fraction = 50.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 1001
+ 75.000 1023
+ 90.000 1043
+ 95.000 1055
+ 99.000 1078
+ 99.900 1104
+ 99.990 1126
+ 99.999 1146
+
+
+ Target number of failures = 50, Success fraction = 10.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 56
+ 75.000 58
+ 90.000 60
+ 95.000 61
+ 99.000 63
+ 99.900 66
+ 99.990 68
+ 99.999 71
+
+
+ Target number of failures = 500, Success fraction = 10.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 556
+ 75.000 562
+ 90.000 567
+ 95.000 570
+ 99.000 576
+ 99.900 583
+ 99.990 588
+ 99.999 594
+
+
+ Target number of failures = 5, Success fraction = 90.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 57
+ 75.000 73
+ 90.000 91
+ 95.000 103
+ 99.000 127
+ 99.900 159
+ 99.990 189
+ 99.999 217
+
+
+ Target number of failures = 5, Success fraction = 95.0%
+ ____________________________
+ Confidence Min Number
+ Value (%) Of Trials
+ ____________________________
+ 50.000 114
+ 75.000 148
+ 90.000 184
+ 95.000 208
+ 99.000 259
+ 99.900 324
+ 99.990 384
+ 99.999 442
*/
Modified: trunk/libs/math/example/normal_misc_examples.cpp
==============================================================================
--- trunk/libs/math/example/normal_misc_examples.cpp (original)
+++ trunk/libs/math/example/normal_misc_examples.cpp 2010-11-24 11:31:10 EST (Wed, 24 Nov 2010)
@@ -40,6 +40,10 @@
double step = 1.; // in z
double range = 4; // min and max z = -range to +range.
int precision = 17; // traditional tables are only computed to much lower precision.
+ // but std::numeric_limits<double>::max_digits10; on new Standard Libraries gives
+ // 17, the maximum number of digits that can possibly be significant.
+ // std::numeric_limits<double>::digits10; == 15 is number of guaranteed digits,
+ // the other two digits being 'noisy'.
// Construct a standard normal distribution s
normal s; // (default mean = zero, and standard deviation = unity)
Modified: trunk/libs/math/example/policy_eg_1.cpp
==============================================================================
--- trunk/libs/math/example/policy_eg_1.cpp (original)
+++ trunk/libs/math/example/policy_eg_1.cpp 2010-11-24 11:31:10 EST (Wed, 24 Nov 2010)
@@ -14,7 +14,20 @@
using boost::math::tgamma;
// Define the policy to use:
-using namespace boost::math::policies;
+using namespace boost::math::policies; // may be convenient, or
+
+using boost::math::policies::policy;
+// Types of error whose action can be altered by policies:.
+using boost::math::policies::evaluation_error;
+using boost::math::policies::domain_error;
+using boost::math::policies::overflow_error;
+using boost::math::policies::domain_error;
+using boost::math::policies::pole_error;
+// Actions on error (in enum error_policy_type):
+using boost::math::policies::errno_on_error;
+using boost::math::policies::ignore_error;
+using boost::math::policies::throw_on_error;
+using boost::math::policies::user_error;
typedef policy<
domain_error<errno_on_error>,
Modified: trunk/libs/math/example/policy_ref_snip8.cpp
==============================================================================
--- trunk/libs/math/example/policy_ref_snip8.cpp (original)
+++ trunk/libs/math/example/policy_ref_snip8.cpp 2010-11-24 11:31:10 EST (Wed, 24 Nov 2010)
@@ -19,9 +19,9 @@
policy<discrete_quantile<integer_round_nearest> >
> dist_type;
-// Lower quantile rounded up:
+// Lower quantile rounded (down) to nearest:
double x = quantile(dist_type(20, 0.3), 0.05); // 27
-// Upper quantile rounded down:
+// Upper quantile rounded (down) to nearest:
double y = quantile(complement(dist_type(20, 0.3), 0.05)); // 68
//] //[/policy_ref_snip8]
@@ -40,8 +40,8 @@
Output:
+ using policy<discrete_quantile<integer_round_nearest>
quantile(dist_type(20, 0.3), 0.05) = 27
quantile(complement(dist_type(20, 0.3), 0.05)) 68
-
*/