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- Algorithms</title><script type="text/javascript" src="dlib.js"></script><link rel="stylesheet" type="text/css" href="dlib.css"></head><body><a name="top"></a><div id="page_header"><a href="http://dlib.net"><img src="dlib-logo.png"></a></div><div id="top_content"><div id="main_menu" class="menu"><div class="menu_top"><b>The Library</b><ul class="tree"><li><a href="algorithms.html" class="menu">Algorithms</a></li><li><a href="api.html" class="menu">API Wrappers</a></li><li><a href="bayes.html" class="menu">Bayesian Nets</a></li><li><a href="compression.html" class="menu">Compression</a></li><li><a href="containers.html" class="menu">Containers</a></li><li><a href="graph_tools.html" class="menu">Graph Tools</a></li><li><a href="imaging.html" class="menu">Image Processing</a></li><li><a href="linear_algebra.html" class="menu">Linear Algebra</a></li><li><a href="ml.html" class="menu">Machine Learning</a></li><li><a href="metaprogramming.html" class="menu">Metaprogramming</a></li><li><a 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Last Modified:<br>Mar 28, 2021</div></div><div id="main_text"><div id="main_text_title">Algorithms</div><div id="main_text_body"><p> |
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This page documents library components that are all basically just implementations of |
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mathematical functions or algorithms that don't fit in any of the other pages |
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of the dlib documentation. So this includes things like checksums, cryptographic hashes, |
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sorting, etc. |
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</p></div></div><div id="right_menu" class="menu"><div class="menu_top"><b>Tools</b><ul class="tree"><li><a href="#bigint" class="menu">bigint</a></li><li><a href="#disjoint_subsets" class="menu">disjoint_subsets</a></li><li><a href="#disjoint_subsets_sized" class="menu">disjoint_subsets_sized</a></li><li><a href="#hsort_array" class="menu">hsort_array</a></li><li><a href="#integrate_function_adapt_simp" class="menu">integrate_function_adapt_simp</a></li><li><a href="#isort_array" class="menu">isort_array</a></li><li><a href="#numeric_constants" class="menu">numeric_constants</a></li><li><a href="#put_in_range" class="menu">put_in_range</a></li><li><a href="#qsort_array" class="menu">qsort_array</a></li><li><a onclick="Toggle(this)" class="sub menu"><img src="plus.gif">Quantum Computing</a><ul style="display:none;"><li><a href="#gate" class="menu">gate</a></li><li><a href="#quantum_register" class="menu">quantum_register</a></li></ul></li><li><a onclick="Toggle(this)" class="sub menu"><img src="plus.gif">Set Utilities</a><ul style="display:none;"><li><a href="#set_difference" class="menu">set_difference</a></li><li><a href="#set_intersection" class="menu">set_intersection</a></li><li><a href="#set_intersection_size" class="menu">set_intersection_size</a></li><li><a href="#set_union" class="menu">set_union</a></li></ul></li><li><a href="#split_array" class="menu">split_array</a></li><li><a href="#square_root" class="menu">square_root</a></li></ul><br><b>Statistics</b><ul class="tree"><li><a href="#binomial_random_vars_are_different" class="menu">binomial_random_vars_are_different</a></li><li><a href="#correlation" class="menu">correlation</a></li><li><a href="#count_steps_without_decrease" class="menu">count_steps_without_decrease</a></li><li><a href="#count_steps_without_decrease_robust" class="menu">count_steps_without_decrease_robust</a></li><li><a href="#count_steps_without_increase" class="menu">count_steps_without_increase</a></li><li><a href="#covariance" class="menu">covariance</a></li><li><a href="#event_correlation" class="menu">event_correlation</a></li><li><a href="#find_upper_quantile" class="menu">find_upper_quantile</a></li><li><a href="#max_scoring_element" class="menu">max_scoring_element</a></li><li><a href="#mean_sign_agreement" class="menu">mean_sign_agreement</a></li><li><a href="#mean_squared_error" class="menu">mean_squared_error</a></li><li><a href="#median" class="menu">median</a></li><li><a href="#min_scoring_element" class="menu">min_scoring_element</a></li><li><a href="#probability_values_are_increasing" class="menu">probability_values_are_increasing</a></li><li><a href="#probability_values_are_increasing_robust" class="menu">probability_values_are_increasing_robust</a></li><li><a href="#rand" class="menu">rand</a></li><li><a href="#randomly_subsample" class="menu">randomly_subsample</a></li><li><a href="#random_subset_selector" class="menu">random_subset_selector</a></li><li><a href="#running_covariance" class="menu">running_covariance</a></li><li><a href="#running_cross_covariance" class="menu">running_cross_covariance</a></li><li><a href="#running_gradient" class="menu">running_gradient</a></li><li><a href="#running_scalar_covariance" class="menu">running_scalar_covariance</a></li><li><a href="#running_scalar_covariance_decayed" class="menu">running_scalar_covariance_decayed</a></li><li><a href="#running_stats" class="menu">running_stats</a></li><li><a href="#running_stats_decayed" class="menu">running_stats_decayed</a></li><li><a href="#r_squared" class="menu">r_squared</a></li></ul><br><b>Hashing</b><ul class="tree"><li><a href="#count_bits" class="menu">count_bits</a></li><li><a href="#crc32" class="menu">crc32</a></li><li><a href="#create_max_margin_projection_hash" class="menu">create_max_margin_projection_hash</a></li><li><a href="#create_random_projection_hash" class="menu">create_random_projection_hash</a></li><li><a href="#gaussian_random_hash" class="menu">gaussian_random_hash</a></li><li><a href="#hamming_distance" class="menu">hamming_distance</a></li><li><a href="#hash" class="menu">hash</a></li><li><a href="#hash_samples" class="menu">hash_samples</a></li><li><a href="#hash_similar_angles_128" class="menu">hash_similar_angles_128</a></li><li><a href="#hash_similar_angles_256" class="menu">hash_similar_angles_256</a></li><li><a href="#hash_similar_angles_512" class="menu">hash_similar_angles_512</a></li><li><a href="#hash_similar_angles_64" class="menu">hash_similar_angles_64</a></li><li><a href="#md5" class="menu">md5</a></li><li><a href="#murmur_hash3" class="menu">murmur_hash3</a></li><li><a href="#murmur_hash3_128bit" class="menu">murmur_hash3_128bit</a></li><li><a href="#projection_hash" class="menu">projection_hash</a></li><li><a href="#uniform_random_hash" class="menu">uniform_random_hash</a></li></ul><br><b>Filtering</b><ul class="tree"><li><a href="#find_optimal_momentum_filter" class="menu">find_optimal_momentum_filter</a></li><li><a href="#find_optimal_rect_filter" class="menu">find_optimal_rect_filter</a></li><li><a href="#kalman_filter" class="menu">kalman_filter</a></li><li><a href="#momentum_filter" class="menu">momentum_filter</a></li><li><a href="#rect_filter" class="menu">rect_filter</a></li><li><a href="#rls_filter" class="menu">rls_filter</a></li></ul><br></div><div class="menu_footer"></div></div></div><div id="bottom_content"><a name="bigint"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">bigint</h1><BR><BR> |
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This object represents an arbitrary precision unsigned integer. It's pretty simple. |
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It's interface is just like a normal int, you don't have to tell it how much memory |
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to use or anything unusual. It just goes :) |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/bigint/bigint_kernel_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/bigint.h></div></div><BR><BR><B>Implementations:</B><blockquote><a href="dlib/bigint/bigint_kernel_1.h.html">bigint_kernel_1</a>: |
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<br> |
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This implementation is done using an array of unsigned shorts. It is also reference counted. |
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For further details see the above link. Also note that kernel_2 should be |
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faster in almost every case so you should really just use that version of the bigint object. |
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<div class="typedefs"><table CELLSPACING="0" CELLPADDING="0" bgcolor="white"><tr><td bgcolor="#E3E3E3" valign="top"><div class="tdn">kernel_1a</div></td><td width="100%" bgcolor="#E3E3E3">is a typedef for bigint_kernel_1</td></tr><tr><td valign="top"><div class="tdn">kernel_1a_c</div></td><td width="100%"> |
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is a typedef for kernel_1a that checks its preconditions. |
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</td></tr></table></div></blockquote><blockquote><a href="dlib/bigint/bigint_kernel_2.h.html">bigint_kernel_2</a>: |
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<br> |
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This implementation is basically the same as kernel_1 except it uses the |
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Fast Fourier Transform to perform multiplications much faster. |
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<div class="typedefs"><table CELLSPACING="0" CELLPADDING="0" bgcolor="white"><tr><td bgcolor="#E3E3E3" valign="top"><div class="tdn">kernel_2a</div></td><td width="100%" bgcolor="#E3E3E3">is a typedef for bigint_kernel_2</td></tr><tr><td valign="top"><div class="tdn">kernel_2a_c</div></td><td width="100%"> |
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is a typedef for kernel_2a that checks its preconditions. |
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</td></tr></table></div></blockquote></div><a name="binomial_random_vars_are_different"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">binomial_random_vars_are_different</h1><BR><BR> |
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This function performs a simple statistical test to check if two binomially |
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distributed random variables have the same parameter (i.e. the chance of |
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"success"). It uses the simple likelihood ratio test discussed in |
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the following paper: |
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<blockquote> |
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Dunning, Ted. "Accurate methods for the statistics of surprise and |
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coincidence." Computational linguistics 19.1 (1993): 61-74. |
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</blockquote> |
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So for an extended discussion of the method see the above paper. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#binomial_random_vars_are_different">More Details...</a><div class="include_file">#include <dlib/statistics/statistic.h></div></div></div><a name="correlation"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">correlation</h1><BR><BR> |
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This is a function for computing the correlation between |
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matching elements of two std::vectors. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#correlation">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="count_bits"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">count_bits</h1><BR><BR> |
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This function counts the number of bits in an unsigned integer which are |
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set to 1. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/count_bits_abstract.h.html#count_bits">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div><a name="count_steps_without_decrease"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">count_steps_without_decrease</h1><BR><BR> |
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Given a potentially noisy time series, this function returns a count of how |
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long the time series has gone without noticeably decreasing in value. It does |
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this by adding the elements of the time series into a <a href="#running_gradient">running_gradient</a> object and counting how many |
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elements, starting with the most recent, you need to examine before you |
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are confident that the series has been decreasing in value. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#count_steps_without_decrease">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="count_steps_without_decrease_robust"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">count_steps_without_decrease_robust</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#count_steps_without_decrease_robust">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="count_steps_without_increase"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">count_steps_without_increase</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#count_steps_without_increase">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="covariance"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">covariance</h1><BR><BR> |
|
This is a function for computing the covariance between |
|
matching elements of two std::vectors. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#covariance">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="crc32"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">crc32</h1><BR><BR> |
|
This object represents the CRC-32 algorithm for calculating checksums. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/crc32/crc32_kernel_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/crc32.h></div></div></div><a name="create_max_margin_projection_hash"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">create_max_margin_projection_hash</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/create_random_projection_hash_abstract.h.html#create_max_margin_projection_hash">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="create_random_projection_hash"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">create_random_projection_hash</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/create_random_projection_hash_abstract.h.html#create_random_projection_hash">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="disjoint_subsets"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">disjoint_subsets</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/disjoint_subsets/disjoint_subsets_abstract.h.html#disjoint_subsets">More Details...</a><div class="include_file">#include <dlib/disjoint_subsets.h></div></div></div><a name="disjoint_subsets_sized"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">disjoint_subsets_sized</h1><BR><BR> |
|
This object is just like <a href="#disjoint_subsets">disjoint_subsets</a> except that it |
|
also keeps track of the size of each set. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/disjoint_subsets/disjoint_subsets_sized_abstract.h.html#disjoint_subsets_sized">More Details...</a><div class="include_file">#include <dlib/disjoint_subsets.h></div></div></div><a name="event_correlation"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">event_correlation</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#event_correlation">More Details...</a><div class="include_file">#include <dlib/statistics/statistic.h></div></div></div><a name="find_optimal_momentum_filter"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">find_optimal_momentum_filter</h1><BR><BR> |
|
This function finds the "optimal" settings of a <a href="#momentum_filter">momentum_filter</a> |
|
based on unfiltered measurement data. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/filtering/kalman_filter_abstract.h.html#find_optimal_momentum_filter">More Details...</a><div class="include_file">#include <dlib/filtering.h></div></div></div><a name="find_optimal_rect_filter"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">find_optimal_rect_filter</h1><BR><BR> |
|
This function finds the "optimal" settings of a <a href="#rect_filter">rect_filter</a> |
|
based on unfiltered measurement data. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/filtering/kalman_filter_abstract.h.html#find_optimal_rect_filter">More Details...</a><div class="include_file">#include <dlib/filtering.h></div></div></div><a name="find_upper_quantile"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">find_upper_quantile</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#find_upper_quantile">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="gate"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">gate</h1><BR><BR> |
|
This object represents a quantum gate that operates on a |
|
<a href="#quantum_register">quantum_register</a>. |
|
<BR><BR>C++ Example Programs: <a href="quantum_computing_ex.cpp.html">quantum_computing_ex.cpp</a><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/quantum_computing/quantum_computing_abstract.h.html#gate">More Details...</a><div class="include_file">#include <dlib/quantum_computing.h></div></div></div><a name="gaussian_random_hash"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">gaussian_random_hash</h1><BR><BR> |
|
This function uses hashing to generate Gaussian distributed random values |
|
with mean 0 and variance 1. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/random_hashing_abstract.h.html#gaussian_random_hash">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div><a name="hamming_distance"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hamming_distance</h1><BR><BR> |
|
This function returns the hamming distance between two unsigned integers. |
|
That is, it returns the number of bits which differer in the two integers. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/count_bits_abstract.h.html#hamming_distance">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div><a name="hash"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hash</h1><BR><BR> |
|
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><BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/hash_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div><a name="hash_samples"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hash_samples</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/graph_utils/find_k_nearest_neighbors_lsh_abstract.h.html#hash_samples">More Details...</a><div class="include_file">#include <dlib/graph_utils_threaded.h></div></div></div><a name="hash_similar_angles_128"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hash_similar_angles_128</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/hashes_abstract.h.html#hash_similar_angles_128">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="hash_similar_angles_256"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hash_similar_angles_256</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/hashes_abstract.h.html#hash_similar_angles_256">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="hash_similar_angles_512"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hash_similar_angles_512</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/hashes_abstract.h.html#hash_similar_angles_512">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="hash_similar_angles_64"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hash_similar_angles_64</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/hashes_abstract.h.html#hash_similar_angles_64">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="hsort_array"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">hsort_array</h1><BR><BR> |
|
hsort_array is an implementation of the heapsort algorithm. It will sort anything that has an |
|
array like operator[] interface. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/sort.h.html#hsort_array">More Details...</a><div class="include_file">#include <dlib/sort.h></div></div></div><a name="integrate_function_adapt_simp"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">integrate_function_adapt_simp</h1><BR><BR> |
|
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><BR><BR>C++ Example Programs: <a href="integrate_function_adapt_simp_ex.cpp.html">integrate_function_adapt_simp_ex.cpp</a><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/numerical_integration/integrate_function_adapt_simpson_abstract.h.html#integrate_function_adapt_simp">More Details...</a><div class="include_file">#include <dlib/numerical_integration.h></div></div></div><a name="isort_array"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">isort_array</h1><BR><BR> |
|
isort_array is an implementation of the insertion sort algorithm. It will sort anything that has an |
|
array like operator[] interface. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/sort.h.html#isort_array">More Details...</a><div class="include_file">#include <dlib/sort.h></div></div></div><a name="kalman_filter"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">kalman_filter</h1><BR><BR> |
|
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><BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/filtering/kalman_filter_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/filtering.h></div></div></div><a name="max_scoring_element"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">max_scoring_element</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/algs.h.html#max_scoring_element">More Details...</a><div class="include_file">#include <dlib/algs.h></div></div></div><a name="md5"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">md5</h1><BR><BR> |
|
This is an implementation of The MD5 Message-Digest Algorithm as described in rfc1321. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/md5/md5_kernel_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/md5.h></div></div></div><a name="mean_sign_agreement"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">mean_sign_agreement</h1><BR><BR> |
|
This is a function for computing the probability that |
|
matching elements of two std::vectors have the same sign. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#mean_sign_agreement">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="mean_squared_error"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">mean_squared_error</h1><BR><BR> |
|
This is a function for computing the mean squared error between |
|
matching elements of two std::vectors. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#mean_squared_error">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="median"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">median</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/algs.h.html#median">More Details...</a><div class="include_file">#include <dlib/algs.h></div></div></div><a name="min_scoring_element"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">min_scoring_element</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/algs.h.html#min_scoring_element">More Details...</a><div class="include_file">#include <dlib/algs.h></div></div></div><a name="momentum_filter"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">momentum_filter</h1><BR><BR> |
|
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. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/filtering/kalman_filter_abstract.h.html#momentum_filter">More Details...</a><div class="include_file">#include <dlib/filtering.h></div></div></div><a name="murmur_hash3"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">murmur_hash3</h1><BR><BR> |
|
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>. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/murmur_hash3_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div><a name="murmur_hash3_128bit"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">murmur_hash3_128bit</h1><BR><BR> |
|
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>. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/murmur_hash3_abstract.h.html#murmur_hash3_128bit">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div><a name="numeric_constants"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">numeric_constants</h1><BR><BR> |
|
This is just a header file containing definitions of common numeric constants such as pi and e. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/numeric_constants.h.html">More Details...</a><div class="include_file">#include <dlib/numeric_constants.h></div></div></div><a name="probability_values_are_increasing"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">probability_values_are_increasing</h1><BR><BR> |
|
Given a potentially noisy time series, this function returns the probability that those |
|
values are increasing in magnitude. |
|
<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#probability_values_are_increasing">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="probability_values_are_increasing_robust"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">probability_values_are_increasing_robust</h1><BR><BR> |
|
This function behaves just like <a href="#probability_values_are_increasing">probability_values_are_increasing</a> except |
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that it ignores times series values that are anomalously large. This makes it |
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robust to sudden noisy but transient spikes in the time series values. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#probability_values_are_increasing_robust">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="projection_hash"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">projection_hash</h1><BR><BR> |
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This is a tool for hashing elements of a vector space into the integers. |
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It is intended to represent locality sensitive hashing functions such as |
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the popular <a href="#create_random_projection_hash">random projection hashing</a> method. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/lsh/projection_hash_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/lsh.h></div></div></div><a name="put_in_range"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">put_in_range</h1><BR><BR> |
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This is a simple function that takes a range and a value and returns the given |
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value if it is within the range. If it isn't in the range then it returns the |
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end of range value that is closest. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/algs.h.html#put_in_range">More Details...</a><div class="include_file">#include <dlib/algs.h></div></div></div><a name="qsort_array"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">qsort_array</h1><BR><BR> |
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qsort_array is an implementation of the QuickSort algorithm. It will sort anything that has an array like |
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operator[] interface. If the quick sort becomes unstable then it switches to a heap sort. This |
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way sorting is guaranteed to take at most N*log(N) time. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/sort.h.html#qsort_array">More Details...</a><div class="include_file">#include <dlib/sort.h></div></div></div><a name="quantum_register"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">quantum_register</h1><BR><BR> |
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This object represents a set of quantum bits. It can be used |
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with the quantum <a href="#gate">gate</a> object to simulate |
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quantum algorithms. |
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<BR><BR>C++ Example Programs: <a href="quantum_computing_ex.cpp.html">quantum_computing_ex.cpp</a><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/quantum_computing/quantum_computing_abstract.h.html#quantum_register">More Details...</a><div class="include_file">#include <dlib/quantum_computing.h></div></div></div><a name="rand"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">rand</h1><BR><BR> |
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This object represents a pseudorandom number generator. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/rand/rand_kernel_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/rand.h></div></div></div><a name="randomly_subsample"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">randomly_subsample</h1><BR><BR> |
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This is a set of convenience functions for |
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creating <a href="#random_subset_selector">random subsets</a> of data. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/random_subset_selector_abstract.h.html#randomly_subsample">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="random_subset_selector"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">random_subset_selector</h1><BR><BR> |
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This object is a tool to help you select a random subset of a large body of data. |
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In particular, it is useful when the body of data is too large to fit into memory. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/random_subset_selector_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="rect_filter"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">rect_filter</h1><BR><BR> |
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This object is just a <a href="#momentum_filter">momentum_filter</a> applied to the |
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four corners of a <a href="linear_algebra.html#rectangle">rectangle</a>. It allows |
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you to filter a stream of rectangles, for instance, bounding boxes from an object detector |
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applied to a video stream. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/filtering/kalman_filter_abstract.h.html#rect_filter">More Details...</a><div class="include_file">#include <dlib/filtering.h></div></div></div><a name="rls_filter"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">rls_filter</h1><BR><BR> |
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This object is a tool for doing time series prediction using |
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linear <a href="ml.html#rls">recursive least squares</a>. In particular, |
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this object takes a sequence of points from the user and, at each |
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step, attempts to predict the value of the next point. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/filtering/rls_filter_abstract.h.html">More Details...</a><div class="include_file">#include <dlib/filtering.h></div></div></div><a name="running_covariance"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_covariance</h1><BR><BR> |
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This object is a simple tool for computing the mean and |
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covariance of a sequence of vectors. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#running_covariance">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="running_cross_covariance"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_cross_covariance</h1><BR><BR> |
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This object is a simple tool for computing the mean and |
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cross-covariance matrices of a sequence of pairs of vectors. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#running_cross_covariance">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="running_gradient"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_gradient</h1><BR><BR> |
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This object is a tool for estimating if a noisy sequence of numbers is |
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trending up or down and by how much. It does this by finding the least |
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squares fit of a line to the data and then allows you to perform a |
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statistical test on the slope of that line. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/running_gradient_abstract.h.html#running_gradient">More Details...</a><div class="include_file">#include <dlib/statistics/running_gradient.h></div></div></div><a name="running_scalar_covariance"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_scalar_covariance</h1><BR><BR> |
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This object is a simple tool for computing the covariance of a |
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sequence of scalar values. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#running_scalar_covariance">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="running_scalar_covariance_decayed"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_scalar_covariance_decayed</h1><BR><BR> |
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This object represents something that can compute the running covariance of |
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a stream of real number pairs. It is essentially the same as |
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<a href="#running_scalar_covariance">running_scalar_covariance</a> except that it forgets about data it has seen |
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after a certain period of time. It does this by exponentially decaying old |
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statistics. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#running_scalar_covariance_decayed">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="running_stats"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_stats</h1><BR><BR> |
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This object represents something that can compute the running mean, |
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variance, skewness, and kurtosis statistics of a stream of real numbers. |
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<BR><BR>C++ Example Programs: <a href="running_stats_ex.cpp.html">running_stats_ex.cpp</a>, |
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<a href="kcentroid_ex.cpp.html">kcentroid_ex.cpp</a><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#running_stats">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="running_stats_decayed"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">running_stats_decayed</h1><BR><BR> |
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This object represents something that can compute the running mean and |
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variance of a stream of real numbers. It is similar to <a href="#running_stats">running_stats</a> |
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except that it forgets about data it has seen after a certain period of |
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time. It does this by exponentially decaying old statistics. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#running_stats_decayed">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="r_squared"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">r_squared</h1><BR><BR> |
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This is a function for computing the R squared coefficient between |
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matching elements of two std::vectors. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/statistics/statistics_abstract.h.html#r_squared">More Details...</a><div class="include_file">#include <dlib/statistics.h></div></div></div><a name="set_difference"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">set_difference</h1><BR><BR> |
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This function takes two <a href="containers.html#set">set</a> objects and |
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gives you their difference. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/set_utils/set_utils_abstract.h.html#set_difference">More Details...</a><div class="include_file">#include <dlib/set_utils.h></div></div></div><a name="set_intersection"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">set_intersection</h1><BR><BR> |
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This function takes two <a href="containers.html#set">set</a> objects and |
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gives you their intersection. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/set_utils/set_utils_abstract.h.html#set_intersection">More Details...</a><div class="include_file">#include <dlib/set_utils.h></div></div></div><a name="set_intersection_size"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">set_intersection_size</h1><BR><BR> |
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This function takes two <a href="containers.html#set">set</a> objects and tells you |
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how many items they have in common. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/set_utils/set_utils_abstract.h.html#set_intersection_size">More Details...</a><div class="include_file">#include <dlib/set_utils.h></div></div></div><a name="set_union"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">set_union</h1><BR><BR> |
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This function takes two <a href="containers.html#set">set</a> objects and |
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gives you their union. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/set_utils/set_utils_abstract.h.html#set_union">More Details...</a><div class="include_file">#include <dlib/set_utils.h></div></div></div><a name="split_array"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">split_array</h1><BR><BR> |
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This function is used to efficiently split <a href="containers.html#array">array</a> |
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like objects into two parts. It uses the global swap() function instead |
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of copying to move elements around, so it works on arrays of non-copyable |
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types. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/array/array_tools_abstract.h.html#split_array">More Details...</a><div class="include_file">#include <dlib/array.h></div></div></div><a name="square_root"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">square_root</h1><BR><BR> |
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square_root is a function which takes an unsigned long and returns the square root of it or |
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if the root is not an integer then it is rounded up to the next integer. |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/algs.h.html#square_root">More Details...</a><div class="include_file">#include <dlib/algs.h></div></div></div><a name="uniform_random_hash"></a><div class="component"><a href="#top"><font size="2"><center>[top]</center></font></a><h1 style="margin:0px;">uniform_random_hash</h1><BR><BR> |
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This function uses hashing to generate uniform random values in the range [0,1). |
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<BR><div class="include_file_more_details_wrapper"><a class="more_details" href="dlib/general_hash/random_hashing_abstract.h.html#uniform_random_hash">More Details...</a><div class="include_file">#include <dlib/hash.h></div></div></div></div></body></html> |
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