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diff --git a/ml/dlib/docs/docs/algorithms.xml b/ml/dlib/docs/docs/algorithms.xml deleted file mode 100644 index 82c58f94a..000000000 --- a/ml/dlib/docs/docs/algorithms.xml +++ /dev/null @@ -1,1118 +0,0 @@ -<?xml version="1.0" encoding="ISO-8859-1"?> -<?xml-stylesheet type="text/xsl" href="stylesheet.xsl"?> - -<doc> - <title>Algorithms</title> - - <!-- ************************************************************************* --> - - <body> - - <p> - This page documents library components that are all basically just implementations of - mathematical functions or algorithms that don't fit in any of the other pages - of the dlib documentation. So this includes things like checksums, cryptographic hashes, - sorting, etc. - </p> - - </body> - - <!-- ************************************************************************* --> - - <menu width="150"> - <top> - <section> - <name>Tools</name> - <item>bigint</item> - <item>disjoint_subsets</item> - <item>disjoint_subsets_sized</item> - <item nolink="true"> - <name>Quantum Computing</name> - <sub> - <item>quantum_register</item> - <item>gate</item> - </sub> - </item> - <item>hsort_array</item> - <item>isort_array</item> - <item>numeric_constants</item> - <item>put_in_range</item> - <item>qsort_array</item> - <item>split_array</item> - <item>integrate_function_adapt_simp</item> - <item>square_root</item> - <item nolink="true"> - <name>Set Utilities</name> - <sub> - <item>set_intersection_size</item> - <item>set_intersection</item> - <item>set_union</item> - <item>set_difference</item> - </sub> - </item> - </section> - - - - <section> - <name>Statistics</name> - <item>rand</item> - <item>median</item> - <item>running_stats</item> - <item>running_stats_decayed</item> - <item>running_scalar_covariance_decayed</item> - <item>running_gradient</item> - <item>running_scalar_covariance</item> - <item>mean_sign_agreement</item> - <item>correlation</item> - <item>covariance</item> - <item>r_squared</item> - <item>mean_squared_error</item> - <item>running_covariance</item> - <item>running_cross_covariance</item> - <item>random_subset_selector</item> - <item>randomly_subsample</item> - <item>find_upper_quantile</item> - <item>count_steps_without_decrease_robust</item> - <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> - <name>Hashing</name> - <item>md5</item> - <item>crc32</item> - <item>hash</item> - <item>count_bits</item> - <item>hamming_distance</item> - <item>murmur_hash3</item> - <item>murmur_hash3_128bit</item> - <item>gaussian_random_hash</item> - <item>uniform_random_hash</item> - <item>projection_hash</item> - <item>create_random_projection_hash</item> - <item>create_max_margin_projection_hash</item> - <item>hash_samples</item> - <item>hash_similar_angles_64</item> - <item>hash_similar_angles_128</item> - <item>hash_similar_angles_256</item> - <item>hash_similar_angles_512</item> - </section> - - <section> - <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> - </menu> - - <!-- ************************************************************************* --> - <!-- ************************************************************************* --> - <!-- ************************************************************************* --> - - <components> - - <!-- ************************************************************************* --> - - <component> - <name>hash_similar_angles_64</name> - <file>dlib/lsh.h</file> - <spec_file link="true">dlib/lsh/hashes_abstract.h</spec_file> - <description> - This object is a tool for computing locality sensitive hashes that give - vectors with small angles between each other similar hash values. In - particular, this object creates 64 random planes which pass though the - origin and uses them to create a 64bit hash. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>hash_similar_angles_128</name> - <file>dlib/lsh.h</file> - <spec_file link="true">dlib/lsh/hashes_abstract.h</spec_file> - <description> - This object is a tool for computing locality sensitive hashes that give - vectors with small angles between each other similar hash values. In - particular, this object creates 128 random planes which pass though the - origin and uses them to create a 128bit hash. - </description> - - </component> - - <!-- ************************************************************************* --> - - - <component> - <name>hash_similar_angles_256</name> - <file>dlib/lsh.h</file> - <spec_file link="true">dlib/lsh/hashes_abstract.h</spec_file> - <description> - This object is a tool for computing locality sensitive hashes that give - vectors with small angles between each other similar hash values. In - particular, this object creates 256 random planes which pass though the - origin and uses them to create a 256bit hash. - </description> - - </component> - - <!-- ************************************************************************* --> - - - <component> - <name>hash_similar_angles_512</name> - <file>dlib/lsh.h</file> - <spec_file link="true">dlib/lsh/hashes_abstract.h</spec_file> - <description> - This object is a tool for computing locality sensitive hashes that give - vectors with small angles between each other similar hash values. In - particular, this object creates 512 random planes which pass though the - origin and uses them to create a 512bit hash. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>hash_samples</name> - <file>dlib/graph_utils_threaded.h</file> - <spec_file link="true">dlib/graph_utils/find_k_nearest_neighbors_lsh_abstract.h</spec_file> - <description> - This is a simple function for hashing a bunch of vectors using a - locality sensitive hashing object such as <a href="#hash_similar_angles_128">hash_similar_angles_128</a>. - It is also capable of running in parallel on a multi-core CPU. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component checked="true"> - <name>bigint</name> - <file>dlib/bigint.h</file> - <spec_file>dlib/bigint/bigint_kernel_abstract.h</spec_file> - <description> - This object represents an arbitrary precision unsigned integer. It's pretty simple. - It's interface is just like a normal int, you don't have to tell it how much memory - to use or anything unusual. It just goes :) - </description> - - <implementations> - <implementation> - <name>bigint_kernel_1</name> - <file>dlib/bigint/bigint_kernel_1.h</file> - <description> - This implementation is done using an array of unsigned shorts. It is also reference counted. - For further details see the above link. Also note that kernel_2 should be - faster in almost every case so you should really just use that version of the bigint object. - </description> - - <typedefs> - <typedef> - <name>kernel_1a</name> - <description>is a typedef for bigint_kernel_1</description> - </typedef> - </typedefs> - - </implementation> - - <implementation> - <name>bigint_kernel_2</name> - <file>dlib/bigint/bigint_kernel_2.h</file> - <description> - This implementation is basically the same as kernel_1 except it uses the - Fast Fourier Transform to perform multiplications much faster. - </description> - - <typedefs> - <typedef> - <name>kernel_2a</name> - <description>is a typedef for bigint_kernel_2</description> - </typedef> - </typedefs> - - </implementation> - - </implementations> - - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>crc32</name> - <file>dlib/crc32.h</file> - <spec_file>dlib/crc32/crc32_kernel_abstract.h</spec_file> - <description> - This object represents the CRC-32 algorithm for calculating checksums. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>gaussian_random_hash</name> - <file>dlib/hash.h</file> - <spec_file link="true">dlib/general_hash/random_hashing_abstract.h</spec_file> - <description> - This function uses hashing to generate Gaussian distributed random values - with mean 0 and variance 1. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>uniform_random_hash</name> - <file>dlib/hash.h</file> - <spec_file link="true">dlib/general_hash/random_hashing_abstract.h</spec_file> - <description> - This function uses hashing to generate uniform random values in the range [0,1). - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>murmur_hash3</name> - <file>dlib/hash.h</file> - <spec_file>dlib/general_hash/murmur_hash3_abstract.h</spec_file> - <description> - This function takes a block of memory and returns a 32bit hash. The - hashing algorithm used is Austin Appleby's excellent - <a href="http://code.google.com/p/smhasher/">MurmurHash3</a>. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>murmur_hash3_128bit</name> - <file>dlib/hash.h</file> - <spec_file link="true">dlib/general_hash/murmur_hash3_abstract.h</spec_file> - <description> - This function takes a block of memory and returns a 128bit hash. The - hashing algorithm used is Austin Appleby's excellent - <a href="http://code.google.com/p/smhasher/">MurmurHash3</a>. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>kalman_filter</name> - <file>dlib/filtering.h</file> - <spec_file>dlib/filtering/kalman_filter_abstract.h</spec_file> - <description> - This object implements the Kalman filter, which is a tool for - recursively estimating the state of a process given measurements - related to that process. To use this tool you will have to - be familiar with the workings of the Kalman filter. An excellent - introduction can be found in the paper: - <blockquote> - An Introduction to the Kalman Filter - 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> - - <!-- ************************************************************************* --> - - <component> - <name>rls_filter</name> - <file>dlib/filtering.h</file> - <spec_file>dlib/filtering/rls_filter_abstract.h</spec_file> - <description> - This object is a tool for doing time series prediction using - linear <a href="ml.html#rls">recursive least squares</a>. In particular, - this object takes a sequence of points from the user and, at each - step, attempts to predict the value of the next point. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>projection_hash</name> - <file>dlib/lsh.h</file> - <spec_file>dlib/lsh/projection_hash_abstract.h</spec_file> - <description> - This is a tool for hashing elements of a vector space into the integers. - It is intended to represent locality sensitive hashing functions such as - the popular <a href="#create_random_projection_hash">random projection hashing</a> method. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>create_random_projection_hash</name> - <file>dlib/lsh.h</file> - <spec_file link="true">dlib/lsh/create_random_projection_hash_abstract.h</spec_file> - <description> - Creates a random projection based locality sensitive - <a href="#projection_hash">hashing function</a>. The projection matrix - is generated by sampling its elements from a Gaussian random number generator. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>create_max_margin_projection_hash</name> - <file>dlib/lsh.h</file> - <spec_file link="true">dlib/lsh/create_random_projection_hash_abstract.h</spec_file> - <description> - Creates a random projection based locality sensitive - <a href="#projection_hash">hashing function</a>. - This is accomplished using a variation on the random hyperplane generation - technique from the paper: - <blockquote> - Random Maximum Margin Hashing by Alexis Joly and Olivier Buisson - </blockquote> - In particular, we use a linear support vector machine to generate planes. - We train it on randomly selected and randomly labeled points from - the data to be hashed. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>hash</name> - <file>dlib/hash.h</file> - <spec_file>dlib/general_hash/hash_abstract.h</spec_file> - <description> - This is a set of convenience functions for invoking <a href="#murmur_hash3">murmur_hash3</a> - on std::strings, std::vectors, std::maps, or <a href="linear_algebra.html#matrix">dlib::matrix</a> objects. - <p> - As an aside, the hash() for matrix objects is defined <a href="dlib/matrix/matrix_utilities_abstract.h.html#hash">here</a>. - It has the same interface as all the others. - </p> - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>count_bits</name> - <file>dlib/hash.h</file> - <spec_file link="true">dlib/general_hash/count_bits_abstract.h</spec_file> - <description> - This function counts the number of bits in an unsigned integer which are - set to 1. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>hamming_distance</name> - <file>dlib/hash.h</file> - <spec_file link="true">dlib/general_hash/count_bits_abstract.h</spec_file> - <description> - This function returns the hamming distance between two unsigned integers. - That is, it returns the number of bits which differer in the two integers. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>rand</name> - <file>dlib/rand.h</file> - <spec_file>dlib/rand/rand_kernel_abstract.h</spec_file> - <description> - This object represents a pseudorandom number generator. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>disjoint_subsets</name> - <file>dlib/disjoint_subsets.h</file> - <spec_file link="true">dlib/disjoint_subsets/disjoint_subsets_abstract.h</spec_file> - <description> - This object represents a set of integers which is partitioned into - a number of disjoint subsets. It supports the two fundamental operations - of finding which subset a particular integer belongs to as well as - merging subsets. - </description> - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>disjoint_subsets_sized</name> - <file>dlib/disjoint_subsets.h</file> - <spec_file link="true">dlib/disjoint_subsets/disjoint_subsets_sized_abstract.h</spec_file> - <description> - This object is just like <a href="#disjoint_subsets">disjoint_subsets</a> except that it - also keeps track of the size of each set. - </description> - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>running_stats</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This object represents something that can compute the running mean, - variance, skewness, and kurtosis statistics of a stream of real numbers. - </description> - - <examples> - <example>running_stats_ex.cpp.html</example> - <example>kcentroid_ex.cpp.html</example> - </examples> - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>running_stats_decayed</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This object represents something that can compute the running mean and - variance of a stream of real numbers. It is similar to <a href="#running_stats">running_stats</a> - except that it forgets about data it has seen after a certain period of - time. It does this by exponentially decaying old statistics. - </description> - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>running_scalar_covariance_decayed</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This object represents something that can compute the running covariance of - a stream of real number pairs. It is essentially the same as - <a href="#running_scalar_covariance">running_scalar_covariance</a> except that it forgets about data it has seen - after a certain period of time. It does this by exponentially decaying old - statistics. - </description> - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>running_gradient</name> - <file>dlib/statistics/running_gradient.h</file> - <spec_file link="true">dlib/statistics/running_gradient_abstract.h</spec_file> - <description> - This object is a tool for estimating if a noisy sequence of numbers is - trending up or down and by how much. It does this by finding the least - squares fit of a line to the data and then allows you to perform a - statistical test on the slope of that line. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>find_upper_quantile</name> - <file>dlib/statistics/running_gradient.h</file> - <spec_file link="true">dlib/statistics/running_gradient_abstract.h</spec_file> - <description> - Finds and returns the scalar value such that a user specified percentage of - the values in a container are greater than said value. For example, 0.5 - would find the median value in a container while 0.1 would find the value - that lower bounded the 10% largest values in a container. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>count_steps_without_increase</name> - <file>dlib/statistics/running_gradient.h</file> - <spec_file link="true">dlib/statistics/running_gradient_abstract.h</spec_file> - <description> - Given a potentially noisy time series, this function returns a count of how - long the time series has gone without noticeably increasing in value. It does - this by adding the elements of the time series into a <a - href="#running_gradient">running_gradient</a> object and counting how many - elements, starting with the most recent, you need to examine before you - are confident that the series has been increasing in value. - </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> - <name>count_steps_without_decrease</name> - <file>dlib/statistics/running_gradient.h</file> - <spec_file link="true">dlib/statistics/running_gradient_abstract.h</spec_file> - <description> - Given a potentially noisy time series, this function returns a count of how - long the time series has gone without noticeably decreasing in value. It does - this by adding the elements of the time series into a <a - href="#running_gradient">running_gradient</a> object and counting how many - elements, starting with the most recent, you need to examine before you - are confident that the series has been decreasing in value. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>count_steps_without_decrease_robust</name> - <file>dlib/statistics/running_gradient.h</file> - <spec_file link="true">dlib/statistics/running_gradient_abstract.h</spec_file> - <description> - This function behaves just like <a - href="#count_steps_without_decrease">count_steps_without_decrease</a> except - that it ignores times series values that are anomalously large. This makes it - robust to sudden noisy but transient spikes in the time series values. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>running_covariance</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This object is a simple tool for computing the mean and - covariance of a sequence of vectors. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>running_cross_covariance</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This object is a simple tool for computing the mean and - cross-covariance matrices of a sequence of pairs of vectors. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>running_scalar_covariance</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This object is a simple tool for computing the covariance of a - sequence of scalar values. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>mean_sign_agreement</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This is a function for computing the probability that - matching elements of two std::vectors have the same sign. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>correlation</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This is a function for computing the correlation between - matching elements of two std::vectors. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>covariance</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This is a function for computing the covariance between - matching elements of two std::vectors. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>r_squared</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This is a function for computing the R squared coefficient between - matching elements of two std::vectors. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>mean_squared_error</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/statistics_abstract.h</spec_file> - <description> - This is a function for computing the mean squared error between - matching elements of two std::vectors. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>random_subset_selector</name> - <file>dlib/statistics.h</file> - <spec_file>dlib/statistics/random_subset_selector_abstract.h</spec_file> - <description> - This object is a tool to help you select a random subset of a large body of data. - In particular, it is useful when the body of data is too large to fit into memory. - </description> - - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>randomly_subsample</name> - <file>dlib/statistics.h</file> - <spec_file link="true">dlib/statistics/random_subset_selector_abstract.h</spec_file> - <description> - This is a set of convenience functions for - creating <a href="#random_subset_selector">random subsets</a> of data. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>hsort_array</name> - <file>dlib/sort.h</file> - <spec_file link="true">dlib/sort.h</spec_file> - <description> - hsort_array is an implementation of the heapsort algorithm. It will sort anything that has an - array like operator[] interface. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>put_in_range</name> - <file>dlib/algs.h</file> - <spec_file link="true">dlib/algs.h</spec_file> - <description> - This is a simple function that takes a range and a value and returns the given - value if it is within the range. If it isn't in the range then it returns the - end of range value that is closest. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>isort_array</name> - <file>dlib/sort.h</file> - <spec_file link="true">dlib/sort.h</spec_file> - <description> - isort_array is an implementation of the insertion sort algorithm. It will sort anything that has an - array like operator[] interface. - </description> - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>numeric_constants</name> - <file>dlib/numeric_constants.h</file> - <spec_file>dlib/numeric_constants.h</spec_file> - <description> - This is just a header file containing definitions of common numeric constants such as pi and e. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>qsort_array</name> - <file>dlib/sort.h</file> - <spec_file link="true">dlib/sort.h</spec_file> - <description> - qsort_array is an implementation of the QuickSort algorithm. It will sort anything that has an array like - operator[] interface. If the quick sort becomes unstable then it switches to a heap sort. This - way sorting is guaranteed to take at most N*log(N) time. - </description> - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>split_array</name> - <file>dlib/array.h</file> - <spec_file link="true">dlib/array/array_tools_abstract.h</spec_file> - <description> - This function is used to efficiently split <a href="containers.html#array">array</a> - like objects into two parts. It uses the global swap() function instead - of copying to move elements around, so it works on arrays of non-copyable - types. - </description> - </component> - - <!-- ************************************************************************* --> - - <component> - <name>integrate_function_adapt_simp</name> - <file>dlib/numerical_integration.h</file> - <spec_file link="true">dlib/numerical_integration/integrate_function_adapt_simpson_abstract.h</spec_file> - <description> - Computes an approximation of the integral of a real valued function using the - adaptive Simpson method outlined in - <blockquote> - Gander, W. and W. Gautshi, "Adaptive - Quadrature -- Revisited" BIT, Vol. 40, (2000), pp.84-101 - </blockquote> - </description> - <examples> - <example>integrate_function_adapt_simp_ex.cpp.html</example> - </examples> - - </component> - - - <!-- ************************************************************************* --> - - <component> - <name>md5</name> - <file>dlib/md5.h</file> - <spec_file>dlib/md5/md5_kernel_abstract.h</spec_file> - <description> - This is an implementation of The MD5 Message-Digest Algorithm as described in rfc1321. - </description> - - </component> - - - - <!-- ************************************************************************* --> - - <component> - <name>median</name> - <file>dlib/algs.h</file> - <spec_file link="true">dlib/algs.h</spec_file> - <description> - This function takes three parameters and finds the median of the three. The median is swapped into - the first parameter and the first parameter ends up in one of the other two, unless the first parameter was - the median to begin with of course. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>square_root</name> - <file>dlib/algs.h</file> - <spec_file link="true">dlib/algs.h</spec_file> - <description> - square_root is a function which takes an unsigned long and returns the square root of it or - if the root is not an integer then it is rounded up to the next integer. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>set_intersection</name> - <file>dlib/set_utils.h</file> - <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file> - <description> - This function takes two <a href="containers.html#set">set</a> objects and - gives you their intersection. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>set_union</name> - <file>dlib/set_utils.h</file> - <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file> - <description> - This function takes two <a href="containers.html#set">set</a> objects and - gives you their union. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>set_difference</name> - <file>dlib/set_utils.h</file> - <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file> - <description> - This function takes two <a href="containers.html#set">set</a> objects and - gives you their difference. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>set_intersection_size</name> - <file>dlib/set_utils.h</file> - <spec_file link="true">dlib/set_utils/set_utils_abstract.h</spec_file> - <description> - This function takes two <a href="containers.html#set">set</a> objects and tells you - how many items they have in common. - </description> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>quantum_register</name> - <file>dlib/quantum_computing.h</file> - <spec_file link="true">dlib/quantum_computing/quantum_computing_abstract.h</spec_file> - <description> - This object represents a set of quantum bits. It can be used - with the quantum <a href="#gate">gate</a> object to simulate - quantum algorithms. - </description> - - <examples> - <example>quantum_computing_ex.cpp.html</example> - </examples> - - </component> - - <!-- ************************************************************************* --> - - <component> - <name>gate</name> - <file>dlib/quantum_computing.h</file> - <spec_file link="true">dlib/quantum_computing/quantum_computing_abstract.h</spec_file> - <description> - This object represents a quantum gate that operates on a - <a href="#quantum_register">quantum_register</a>. - </description> - <examples> - <example>quantum_computing_ex.cpp.html</example> - </examples> - - </component> - - <!-- ************************************************************************* --> - - </components> - - <!-- ************************************************************************* --> - - -</doc> - |