changelog:
0.3.3.5
* Fixed bug when converting the Scientific:
`scientific 0 someBigExponent` to a bounded Integral using toBoundedInteger
or to a bounded RealFloat using toBoundedRealFloat.
If someBigExponent was big enough to trigger the big-exponent protection
the beforementioned functions didn't return 0.
This is fixed by explicitly handling a coefficient of 0.
changelog:
0.3.3.4
* Relax upper version bounds of base and deepseq
for the test suite and benchmarks.
0.3.3.3
* Add support for `deepseq-1.4`.
0.3.3.2
* Fix parsing of empty digit string (#21).
0.07 Fri Jan 2 10:35:47 2015
- Perl 5.22 exports POSIX::round, so "use POSIX" had to be
changed to "use POSIX ()". Thanks to Jarkko Hietaniemi and
Slaven Rezic for the tip.
Own detection in configure script with compiler name basisis is not good enough,
and required header file may not be installed.
resolve PR pkg/49496 by Sevan Janiyan.
Requested by Kai-Uwe Eckhardt.
Remove an incorrect comment.
Changes:
One of the mpfr_exp implementations uses a left shift on an integer
that can be negative: exps <<= 1; and this has an undefined behavior
according to the ISO C standard. In most cases, this will correspond
to a multiplication by 2, and the code will behave as expected
(this is why the bug hadn't been detected until now). But problems
may occur if a sanitizer is used (this is how this bug was detected)
or possibly in case of advanced optimizations, such as LTO. This
is fixed by the exp_2 patch.
The mpfr_fits_u*_p functions return 0 ("doesn't fit") instead of
non-zero ("fits") on negative arguments for which the rounding to
an integer in the given rounding mode is 0. This bug is fixed by
the fits-smallneg patch, which also updates the testcases.
Some tget_flt tests fail in environments where native C floating-point
division by zero is not supported, e.g. regarded as an error, such
as with Clang's sanitizer; some similar tests were already disabled
in such a case, but not all. The tset_ld test triggers a useless
overflow on a double. These problems are fixed by the clang-divby0
patch, which also disables constant division by zero on the native
C type double with Clang in order to avoid incorrect code.
The formatted output functions (mpfr_*printf) are incorrect on the
value 0 when using the alternative form (# flag), a positive
precision, and the g or G conversion specifier: there is one
additional trailing 0. The corresponding test is also incorrect
(explaining why the bug was not detected). These problems are fixed
by the printf-alt0 patch, which also provides some additional
related tests.
Only for applications using the custom interface: The mpfr_custom_init_set
macro has a typo in a variable name, which can yield incorrect
behavior if the second argument is not a simple expression. This
bug is fixed by the custom_init_set patch.
The build fails on li2.c with the GCC -Werror=return-type option
when logging has been enabled. This problem is fixed by the li2-return
patch.
The rounding of mpfr_exp can be incorrect for output precisions
larger than or equal to MPFR_EXP_THRESHOLD (several thousands of
bits; its value depends on the architecture). This bug is fixed by
the exp3 patch, which also provides a testcase.
This MPFR release fails to build with GMP 6 when the --with-gmp-build
configure option is used. The gmp6-compat patch fixes this
compatibility problem.
When dividing a very large number (near the maximum finite one, in
absolute value) by a very small number (near the minimum one, in
absolute value), an integer overflow occurs in the computation of
the exponent of the result, yielding undefined behavior, such as
the result 0 instead of infinity. This bug is fixed by the div-overflow
patch, which also provides a testcase.
The vasprintf.c source file contains incorrect assertions, which
may fail while the computation is valid; this can occur only when
outputting tiny numbers (very small exponents). These assertions
are fixed by the vasprintf patch, which also provides a testcase.
A buffer overflow may occur in mpfr_strtofr. This is due to incorrect
GMP documentation for mpn_set_str about the size of a buffer
(discussion; first fix in the GMP documentation). This bug is
present in the MPFR versions from 2.1.0 (adding mpfr_strtofr) to
this one, and can be detected by running "make check" in a 32-bit
ABI under GNU/Linux with alloca disabled (this is currently possible
by using the --with-gmp-build configure option where alloca has
been disabled in the GMP build). It is fixed by the strtofr patch.
Upstream changes:
Version 0.9
2013-12-10 Change LICENCE following advince from Kurt Hornik
Version 0.8
2013-12-10 date stamp for CRAN submssion
2013-09-30 mv inst/doc vignettes
rmdir inst
Version 0.7
2013-09-27 Add MIT to LICENSE
Version 0.6
2012-04-16 Change "integer gmax/50/" into "integer gmax, data gmax/50/" in lmm.f
as suggested by Brook Milligan [mailto:brook@nmsu.edu]
2012-04-11 Revise example.Rd
2012-04-06 Include incomplete data in marijuana.rda
Accommodate the change in example.Rd
2012-04-05 Fix typos and add title to Table 1 in lmm-tr.Rnw
labeling sequential data. The first priority of this software is to
train and use CRF models as fast as possible even at the expense of
its memory space and code generality. CRFsuite runs 5.4 - 61.8 times
faster than C++ implementations for training. CRFsuite supports
parameter estimation with L1 regularization (Laplacian prior) using
Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2
regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS)
method.
analysis. It can handle the classification of, for example, titles,
questions, sentences, and short messages. Main features of
LibShortText include
* It is more efficient than general text-mining packages. On a
typical computer, processing and training 10 million short texts
takes only around half an hour.
* The fast training and testing is built upon the linear classifier
* LIBLINEAR
* Default options often work well without tedious tuning.
* An interactive tool for error analysis is included. Based on the
property that each short text contains few words, LibShortText
provides details in predicting each text.
LIBLINEAR is a linear classifier for data with millions of instances
and features. It supports
L2-regularized classifiers
L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
L1-regularized classifiers (after version 1.4)
L2-loss linear SVM and logistic regression (LR)
L2-regularized support vector regression (after version 1.9)
L2-loss linear SVR and L1-loss linear SVR.
Main features of LIBLINEAR include
Same data format as LIBSVM, our general-purpose SVM solver,
and also similar usage
Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
Cross validation for model selection
Probability estimates (logistic regression only)
Weights for unbalanced data
MATLAB/Octave, Java, Python, Ruby interfaces
is finally consistent.
This is a graphical UI for the computer algebra system maxima.
Changes compared to math/wxMaxima:
14.09.0:
Bugfixes and compatibility with wxGTK-3.x.
