mirror of https://github.com/davisking/dlib.git
updated docs
This commit is contained in:
Davis King
parent
82c7e5f66e
commit
66d6d68fbd
|
|
@ -77,6 +77,11 @@
|
|||
<item>count_steps_without_decrease</item>
|
||||
<item>count_steps_without_increase</item>
|
||||
|
||||
<item>binomial_random_vars_are_different</item>
|
||||
<item>event_correlation</item>
|
||||
<item>max_scoring_element</item>
|
||||
<item>min_scoring_element</item>
|
||||
|
||||
</section>
|
||||
|
||||
<section>
|
||||
|
|
@ -104,6 +109,10 @@
|
|||
<name>Filtering</name>
|
||||
<item>kalman_filter</item>
|
||||
<item>rls_filter</item>
|
||||
<item>momentum_filter</item>
|
||||
<item>rect_filter</item>
|
||||
<item>find_optimal_rect_filter</item>
|
||||
<item>find_optimal_momentum_filter</item>
|
||||
</section>
|
||||
|
||||
</top>
|
||||
|
|
@ -326,7 +335,85 @@
|
|||
by Greg Welch and Gary Bishop
|
||||
</blockquote>
|
||||
</description>
|
||||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>momentum_filter</name>
|
||||
<file>dlib/filtering.h</file>
|
||||
<spec_file link="true">dlib/filtering/kalman_filter_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a simple tool for filtering a single scalar value that
|
||||
measures the location of a moving object that has some non-trivial
|
||||
momentum. Importantly, the measurements are noisy and the object can
|
||||
experience sudden unpredictable accelerations. To accomplish this
|
||||
filtering we use a simple <a href="#kalman_filter">Kalman filter</a> with a
|
||||
state transition model of:
|
||||
<pre>
|
||||
|
||||
position_{i+1} = position_{i} + velocity_{i}
|
||||
velocity_{i+1} = velocity_{i} + some_unpredictable_acceleration
|
||||
|
||||
</pre>
|
||||
|
||||
and a measurement model of:
|
||||
<pre>
|
||||
|
||||
measured_position_{i} = position_{i} + measurement_noise
|
||||
|
||||
</pre>
|
||||
|
||||
Where <tt>some_unpredictable_acceleration</tt> and <tt>measurement_noise</tt> are 0 mean Gaussian
|
||||
noise sources.
|
||||
|
||||
To allow for really sudden and large but infrequent accelerations, at each
|
||||
step we check if the current measured position deviates from the predicted
|
||||
filtered position by more than a user specified amount,
|
||||
and if so we adjust the filter's state to keep it within these bounds.
|
||||
This allows the moving object to undergo large unmodeled accelerations, far
|
||||
in excess of what would be suggested by the basic Kalman filter's noise model, without
|
||||
then experiencing a long lag time where the Kalman filter has to "catch
|
||||
up" to the new position.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>rect_filter</name>
|
||||
<file>dlib/filtering.h</file>
|
||||
<spec_file link="true">dlib/filtering/kalman_filter_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is just a <a href="#momentum_filter">momentum_filter</a> applied to the
|
||||
four corners of a <a href="linear_algebra.html#rectangle">rectangle</a>. It allows
|
||||
you to filter a stream of rectangles, for instance, bounding boxes from an object detector
|
||||
applied to a video stream.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_optimal_momentum_filter</name>
|
||||
<file>dlib/filtering.h</file>
|
||||
<spec_file link="true">dlib/filtering/kalman_filter_abstract.h</spec_file>
|
||||
<description>
|
||||
This function finds the "optimal" settings of a <a href="#momentum_filter">momentum_filter</a>
|
||||
based on unfiltered measurement data.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>find_optimal_rect_filter</name>
|
||||
<file>dlib/filtering.h</file>
|
||||
<spec_file link="true">dlib/filtering/kalman_filter_abstract.h</spec_file>
|
||||
<description>
|
||||
This function finds the "optimal" settings of a <a href="#rect_filter">rect_filter</a>
|
||||
based on unfiltered measurement data.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
|
@ -572,6 +659,69 @@
|
|||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>binomial_random_vars_are_different</name>
|
||||
<file>dlib/statistics/statistic.h</file>
|
||||
<spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
|
||||
<description>
|
||||
This function performs a simple statistical test to check if two binomially
|
||||
distributed random variables have the same parameter (i.e. the chance of
|
||||
"success"). It uses the simple likelihood ratio test discussed in
|
||||
the following paper:
|
||||
<blockquote>
|
||||
Dunning, Ted. "Accurate methods for the statistics of surprise and
|
||||
coincidence." Computational linguistics 19.1 (1993): 61-74.
|
||||
</blockquote>
|
||||
So for an extended discussion of the method see the above paper.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>event_correlation</name>
|
||||
<file>dlib/statistics/statistic.h</file>
|
||||
<spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file>
|
||||
<description>
|
||||
This function does a statistical test to determine if two events co-occur in a
|
||||
statistically significant way. It uses the simple likelihood ratio
|
||||
test discussed in the following paper:
|
||||
<blockquote>
|
||||
Dunning, Ted. "Accurate methods for the statistics of surprise and
|
||||
coincidence." Computational linguistics 19.1 (1993): 61-74.
|
||||
</blockquote>
|
||||
So for an extended discussion of the method see the above paper.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>max_scoring_element</name>
|
||||
<file>dlib/algs.h</file>
|
||||
<spec_file link="true">dlib/algs.h</spec_file>
|
||||
<description>
|
||||
This function finds the element of container that has the largest score,
|
||||
according to a user supplied score function, and returns a std::pair containing
|
||||
that maximal element along with the score.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>min_scoring_element</name>
|
||||
<file>dlib/algs.h</file>
|
||||
<spec_file link="true">dlib/algs.h</spec_file>
|
||||
<description>
|
||||
This function finds the element of container that has the smallest score,
|
||||
according to a user supplied score function, and returns a std::pair containing
|
||||
that minimal element along with the score.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
|
|||
|
|
@ -457,6 +457,10 @@
|
|||
<name>conv_valid</name>
|
||||
<link>dlib/matrix/matrix_conv_abstract.h.html#conv_valid</link>
|
||||
</item>
|
||||
<item>
|
||||
<name>xcorr_fft</name>
|
||||
<link>dlib/matrix/matrix_conv_abstract.h.html#xcorr_fft</link>
|
||||
</item>
|
||||
<item>
|
||||
<name>xcorr</name>
|
||||
<link>dlib/matrix/matrix_conv_abstract.h.html#xcorr</link>
|
||||
|
|
|
|||
|
|
@ -69,6 +69,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
<item>svr_linear_trainer</item>
|
||||
<item>rvm_regression_trainer</item>
|
||||
<item>rbf_network_trainer</item>
|
||||
<item>random_forest_regression_trainer</item>
|
||||
</section>
|
||||
<section>
|
||||
<name>Structured Prediction</name>
|
||||
|
|
@ -149,6 +150,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
<name>cont</name>
|
||||
<link>dlib/dnn/layers_abstract.h.html#cont_</link>
|
||||
</item>
|
||||
<item>
|
||||
<name>scale</name>
|
||||
<link>dlib/dnn/layers_abstract.h.html#scale_</link>
|
||||
</item>
|
||||
<item>
|
||||
<name>extract</name>
|
||||
<link>dlib/dnn/layers_abstract.h.html#extract_</link>
|
||||
|
|
@ -254,6 +259,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
<name>loss_binary_log</name>
|
||||
<link>dlib/dnn/loss_abstract.h.html#loss_binary_log_</link>
|
||||
</item>
|
||||
<item>
|
||||
<name>loss_multimulticlass_log</name>
|
||||
<link>dlib/dnn/loss_abstract.h.html#loss_multimulticlass_log_</link>
|
||||
</item>
|
||||
<item>
|
||||
<name>loss_multiclass_log</name>
|
||||
<link>dlib/dnn/loss_abstract.h.html#loss_multiclass_log_</link>
|
||||
|
|
@ -413,6 +422,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
|
||||
<section>
|
||||
<name>Function Objects</name>
|
||||
<item>random_forest_regression_function</item>
|
||||
<item>decision_function</item>
|
||||
<item>projection_function</item>
|
||||
<item>distance_function</item>
|
||||
|
|
@ -438,6 +448,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
<item>load_libsvm_formatted_data</item>
|
||||
<item>save_libsvm_formatted_data</item>
|
||||
<item>fix_nonzero_indexing</item>
|
||||
<item>make_bounding_box_regression_training_data</item>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
|
|
@ -1704,6 +1715,30 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>random_forest_regression_trainer</name>
|
||||
<file>dlib/random_forest.h</file>
|
||||
<spec_file link="true">dlib/random_forest/random_forest_regression_abstract.h</spec_file>
|
||||
<description>
|
||||
This object implements Breiman's classic random forest regression
|
||||
algorithm.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>random_forest_regression_function</name>
|
||||
<file>dlib/random_forest.h</file>
|
||||
<spec_file link="true">dlib/random_forest/random_forest_regression_abstract.h</spec_file>
|
||||
<description>
|
||||
This object represents a random forest that maps objects to real numbers. You
|
||||
can learn its parameters using the <a href="#random_forest_regression_trainer">random_forest_regression_trainer</a>.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
@ -2837,6 +2872,28 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
|
|||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>make_bounding_box_regression_training_data</name>
|
||||
<file>dlib/image_processing.h</file>
|
||||
<spec_file link="true">dlib/image_processing/shape_predictor_trainer_abstract.h</spec_file>
|
||||
<description>
|
||||
Suppose you have an object detector that can roughly locate objects in an
|
||||
image. This means your detector draws boxes around objects, but these are
|
||||
<i>rough</i> boxes in the sense that they aren't positioned super accurately. For
|
||||
instance, HOG based detectors usually have a stride of 8 pixels. So the
|
||||
positional accuracy is going to be, at best, +/-8 pixels.
|
||||
|
||||
<p>
|
||||
If you want to get better positional accuracy one easy thing to do is train a
|
||||
<a href="#shape_predictor_trainer">shape_predictor</a> to give you the location
|
||||
of the object's box. The make_bounding_box_regression_training_data() routine
|
||||
helps you do this by creating an appropriate training dataset.
|
||||
</p>
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
|
|||
|
|
@ -65,6 +65,7 @@
|
|||
<item>find_max_parse_cky</item>
|
||||
<item>min_cut</item>
|
||||
<item>elastic_net</item>
|
||||
<item>isotonic_regression</item>
|
||||
</section>
|
||||
|
||||
<section>
|
||||
|
|
@ -950,6 +951,23 @@ Or it can use the elastic net regularizer:
|
|||
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>isotonic_regression</name>
|
||||
<file>dlib/optimization.h</file>
|
||||
<spec_file link="true">dlib/optimization/isotonic_regression_abstract.h</spec_file>
|
||||
<description>
|
||||
This object is a tool for performing 1-D isotonic regression. That is, it
|
||||
finds the least squares fit of a non-parametric curve to some user supplied
|
||||
data, subject to the constraint that the fitted curve is non-decreasing.
|
||||
|
||||
<p>
|
||||
This is done using the fast O(n) pool adjacent violators algorithm.
|
||||
</p>
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
|
|
|
|||
|
|
@ -58,6 +58,8 @@
|
|||
|
||||
<section>
|
||||
<name>Global Functions</name>
|
||||
<item>ramdump</item>
|
||||
<item>check_serialized_version</item>
|
||||
<item>deserialize</item>
|
||||
<item>serialize</item>
|
||||
<item>zero_extend_cast</item>
|
||||
|
|
@ -1003,6 +1005,51 @@
|
|||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>ramdump</name>
|
||||
<file>dlib/serialize.h</file>
|
||||
<spec_file link="true">dlib/serialize.h</spec_file>
|
||||
<description>
|
||||
This is a type decoration used to indicate that serialization should be
|
||||
done by simply dumping the memory of some object to disk as fast as
|
||||
possible without any sort of conversions. This means that the data written
|
||||
will be "non-portable" in the sense that the format output by a RAM dump
|
||||
may depend on things like the endianness of your CPU or settings of certain
|
||||
compiler switches.
|
||||
|
||||
<p>
|
||||
You use this object like this:
|
||||
<code_box>
|
||||
serialize("yourfile.dat") << ramdump(yourobject);
|
||||
deserialize("yourfile.dat") >> ramdump(yourobject); </code_box>
|
||||
or
|
||||
<code_box>
|
||||
serialize(ramdump(yourobject), out);
|
||||
deserialize(ramdump(yourobject), in); </code_box>
|
||||
|
||||
Also, not all objects have a ramdump mode. If you try to use ramdump on an
|
||||
object that does not define a serialization dump for ramdump you will get a
|
||||
compiler error.
|
||||
</p>
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>check_serialized_version</name>
|
||||
<file>dlib/serialize.h</file>
|
||||
<spec_file link="true">dlib/serialize.h</spec_file>
|
||||
<description>
|
||||
This function deserializes a string and checks if it matches a user supplied
|
||||
string (the version). If they don't match then dlib::serialization_error is
|
||||
thrown. The point of this function is to make checking version strings in
|
||||
serialized files a little more convenient.
|
||||
</description>
|
||||
</component>
|
||||
|
||||
<!-- ************************************************************************* -->
|
||||
|
||||
<component>
|
||||
<name>dlib_testing_suite</name>
|
||||
<description>
|
||||
|
|
|
|||
|
|
@ -11,6 +11,53 @@
|
|||
<!-- ************************************************************************************** -->
|
||||
|
||||
<current>
|
||||
New Features and Improvements:
|
||||
- Made orthogonalize() faster.
|
||||
- Added binomial_random_vars_are_different() and event_correlation().
|
||||
- Added scale_ layer, allowing implementation of squeeze-and-excitation networks.
|
||||
- Added xcorr_fft()
|
||||
- Added loss_multimulticlass_log: used for learning a collection of multi-class classifiers.
|
||||
- Added the ramdump type decorator for invoking faster serialization routines.
|
||||
- Added check_serialized_version()
|
||||
- Added max_scoring_element() and min_scoring_element()
|
||||
- Added make_bounding_box_regression_training_data()
|
||||
- Added isotonic_regression.
|
||||
- Added momentum_filter and rect_filter as well as find_optimal_momentum_filter() and find_optimal_rect_filter()
|
||||
- Added a random forest regression tool. see random_forest_regression_trainer.
|
||||
- Python API:
|
||||
- Add Python rvm_trainer
|
||||
- Added probability_that_sequence_is_increasing() to python API
|
||||
- Made dlib.point() have writable x and y properties.
|
||||
- Added a __time_compiled__ field to the python API.
|
||||
- Exposed the image_dataset_metadata routines for parsing XML datasets to Python.
|
||||
- Added num_threads to shape_predictor_training_options.
|
||||
- Added set_dnn_prefer_smallest_algorithms()
|
||||
- Added support for variadic Python functions in find_max_global.
|
||||
- Added python interface to cuda::set_device() and other relevant functions.
|
||||
- Added python interface to the more general global_function_search object.
|
||||
|
||||
Non-Backwards Compatible Changes:
|
||||
- Changed cmake so that there is only the dlib target and it isn't forced to
|
||||
be static or shared, instead, the build type will toggle based on the state
|
||||
of CMake's BUILD_SHARED_LIBS variable. So there is no longer a dlib_shared target.
|
||||
- Change types of tensor's size-related members to prevent integer overflow.
|
||||
|
||||
Bug fixes:
|
||||
- Fixed memory leak in java swig array binding tool.
|
||||
- Fixed windows include order problem in all/source.cpp file.
|
||||
- Fixed cont_ layers not printing the correct num_filters parameter when they are printed to cout or xml.
|
||||
- Fixed code not handling OBJECT_PART_NOT_PRESENT for full_object_detection objects.
|
||||
- Fixed fft_inplace() not compiling for compile time sized matrices.
|
||||
- The shape_predictor_trainer could have very bad runtime for some really
|
||||
bad parameter settings. This has been fixed and also warning messages about
|
||||
really bad training data or parameters have been added.
|
||||
- Fixed the decayed running stats objects so they use unbiased estimators.
|
||||
|
||||
</current>
|
||||
|
||||
<!-- ************************************************************************************** -->
|
||||
|
||||
<old name="19.9" date="Jan 22, 2018">
|
||||
New Features and Improvements:
|
||||
- Switched the Python API from Boost.Python to pybind11. This means Python
|
||||
users don't need to install Boost anymore, making building dlib's Python API
|
||||
|
|
@ -29,7 +76,7 @@ Non-Backwards Compatible Changes:
|
|||
Bug fixes:
|
||||
- Fixed global_optimization.py not working in Python 3.
|
||||
|
||||
</current>
|
||||
</old>
|
||||
|
||||
<!-- ************************************************************************************** -->
|
||||
|
||||
|
|
|
|||
|
|
@ -126,6 +126,7 @@
|
|||
<term file="dlib/dnn/loss_abstract.h.html" name="EXAMPLE_LOSS_LAYER_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_binary_hinge_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_binary_log_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_multimulticlass_log_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_multiclass_log_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_multiclass_log_per_pixel_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/loss_abstract.h.html" name="loss_multiclass_log_per_pixel_weighted_" include="dlib/dnn.h"/>
|
||||
|
|
@ -152,6 +153,7 @@
|
|||
<term file="dlib/dnn/layers_abstract.h.html" name="extract_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="upsample_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="cont_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="scale_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="l2normalize_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="dropout_" include="dlib/dnn.h"/>
|
||||
<term file="dlib/dnn/layers_abstract.h.html" name="multiply_" include="dlib/dnn.h"/>
|
||||
|
|
@ -197,6 +199,11 @@
|
|||
<term file="algorithms.html" name="kalman_filter" include="dlib/filtering.h"/>
|
||||
<term file="algorithms.html" name="rls_filter" include="dlib/filtering.h"/>
|
||||
|
||||
<term file="algorithms.html" name="momentum_filter" include="dlib/filtering.h"/>
|
||||
<term file="algorithms.html" name="rect_filter" include="dlib/filtering.h"/>
|
||||
<term file="algorithms.html" name="find_optimal_rect_filter" include="dlib/filtering.h"/>
|
||||
<term file="algorithms.html" name="find_optimal_momentum_filter" include="dlib/filtering.h"/>
|
||||
|
||||
<term file="dlib/error.h.html" name="error_type" include="dlib/error.h"/>
|
||||
<term file="dlib/error.h.html" name="error" include="dlib/error.h"/>
|
||||
<term file="dlib/error.h.html" name="fatal_error" include="dlib/error.h"/>
|
||||
|
|
@ -264,6 +271,7 @@
|
|||
<term file="optimization.html" name="find_max_box_constrained" include="dlib/optimization.h"/>
|
||||
<term file="optimization.html" name="find_max_global" include="dlib/global_optimization.h"/>
|
||||
<term file="optimization.html" name="find_min_global" include="dlib/global_optimization.h"/>
|
||||
<term file="optimization.html" name="isotonic_regression" include="dlib/optimization.h"/>
|
||||
<term file="optimization.html" name="global_function_search" include="dlib/global_optimization.h"/>
|
||||
<term file="optimization.html" name="upper_bound_function" include="dlib/global_optimization.h"/>
|
||||
<term file="optimization.html" name="call_function_and_expand_args" include="dlib/global_optimization.h"/>
|
||||
|
|
@ -436,6 +444,8 @@
|
|||
<term file="algorithms.html" name="running_cross_covariance" include="dlib/statistics.h"/>
|
||||
<term file="algorithms.html" name="random_subset_selector" include="dlib/statistics.h"/>
|
||||
<term file="algorithms.html" name="randomly_subsample" include="dlib/statistics.h"/>
|
||||
<term file="algorithms.html" name="event_correlation" include="dlib/statistics.h"/>
|
||||
<term file="algorithms.html" name="binomial_random_vars_are_different" include="dlib/statistics.h"/>
|
||||
|
||||
<term file="ml.html" name="lspi" include="dlib/control.h"/>
|
||||
<term file="ml.html" name="policy" include="dlib/control.h"/>
|
||||
|
|
@ -530,6 +540,7 @@
|
|||
<term file="ml.html" name="svm_c_linear_dcd_trainer" include="dlib/svm.h"/>
|
||||
<term file="ml.html" name="svm_rank_trainer" include="dlib/svm.h"/>
|
||||
<term file="ml.html" name="shape_predictor_trainer" include="dlib/image_processing.h"/>
|
||||
<term file="ml.html" name="make_bounding_box_regression_training_data" include="dlib/image_processing.h"/>
|
||||
<term file="ml.html" name="test_shape_predictor" include="dlib/image_processing.h"/>
|
||||
<term file="imaging.html" name="shape_predictor" include="dlib/image_processing.h"/>
|
||||
<term file="imaging.html" name="render_face_detections" include="dlib/image_processing/render_face_detections.h"/>
|
||||
|
|
@ -545,6 +556,12 @@
|
|||
<term file="ml.html" name="svm_c_ekm_trainer" include="dlib/svm.h"/>
|
||||
<term file="ml.html" name="rvm_trainer" include="dlib/svm.h"/>
|
||||
<term file="ml.html" name="krr_trainer" include="dlib/svm.h"/>
|
||||
|
||||
<term file="ml.html" name="random_forest_regression_trainer" include="dlib/random_forest.h"/>
|
||||
<term file="ml.html" name="random_forest_regression_function" include="dlib/random_forest.h"/>
|
||||
<term file="dlib/random_forest/random_forest_regression_abstract.h.html" name="dense_feature_extractor" include="dlib/random_forest.h"/>
|
||||
<term file="dlib/random_forest/random_forest_regression_abstract.h.html" name="internal_tree_node" include="dlib/random_forest.h"/>
|
||||
|
||||
<term file="ml.html" name="rr_trainer" include="dlib/svm.h"/>
|
||||
<term file="ml.html" name="svr_trainer" include="dlib/svm.h"/>
|
||||
<term file="ml.html" name="svr_linear_trainer" include="dlib/svm.h"/>
|
||||
|
|
@ -713,6 +730,7 @@
|
|||
<term file="dlib/matrix/matrix_conv_abstract.h.html" name="conv_same" include="dlib/matrix.h"/>
|
||||
<term file="dlib/matrix/matrix_conv_abstract.h.html" name="conv_valid" include="dlib/matrix.h"/>
|
||||
<term file="dlib/matrix/matrix_conv_abstract.h.html" name="xcorr" include="dlib/matrix.h"/>
|
||||
<term file="dlib/matrix/matrix_conv_abstract.h.html" name="xcorr_fft" include="dlib/matrix.h"/>
|
||||
<term file="dlib/matrix/matrix_conv_abstract.h.html" name="xcorr_same" include="dlib/matrix.h"/>
|
||||
<term file="dlib/matrix/matrix_conv_abstract.h.html" name="xcorr_valid" include="dlib/matrix.h"/>
|
||||
|
||||
|
|
@ -928,6 +946,8 @@
|
|||
<term link="dlib/float_details.h.html" name="float_details" include="dlib/float_details.h"/>
|
||||
<term file="other.html" name="copy_functor" include="dlib/algs.h"/>
|
||||
<term file="other.html" name="deserialize" include="dlib/serialize.h"/>
|
||||
<term file="other.html" name="ramdump" include="dlib/serialize.h"/>
|
||||
<term file="other.html" name="check_serialized_version" include="dlib/serialize.h"/>
|
||||
<term file="other.html" name="error" include="dlib/error.h"/>
|
||||
<term file="other.html" name="memory_manager" include="dlib/memory_manager.h"/>
|
||||
<term file="other.html" name="default_memory_manager" include="dlib/algs.h"/>
|
||||
|
|
@ -1330,6 +1350,8 @@
|
|||
|
||||
|
||||
|
||||
<term file="algorithms.html" name="max_scoring_element" include="dlib/algs.h"/>
|
||||
<term file="algorithms.html" name="min_scoring_element" include="dlib/algs.h"/>
|
||||
|
||||
<term file="algorithms.html" name="bigint" include="dlib/bigint.h"/>
|
||||
<term file="algorithms.html" name="crc32" include="dlib/crc32.h"/>
|
||||
|
|
|
|||
Loading…
Reference in New Issue