mirror of
https://github.com/TheAlgorithms/C-Plus-Plus.git
synced 2026-03-21 04:18:34 +08:00
Documentation for 4a34bec125
This commit is contained in:
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@@ -95,7 +95,7 @@ $(document).ready(function(){initNavTree('annotated.html',''); initResizable();
|
||||
</div><!--header-->
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||||
<div class="contents">
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||||
<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
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||||
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span>]</div><table class="directory">
|
||||
<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span>]</div><table class="directory">
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||||
<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="d6/d4e/namespaceciphers.html" target="_self">ciphers</a></td><td class="desc">Algorithms for encryption and decryption </td></tr>
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||||
<tr id="row_0_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d6/d26/classciphers_1_1_hill_cipher.html" target="_self">HillCipher</a></td><td class="desc">Implementation of <a href="https://en.wikipedia.org/wiki/Hill_cipher">Hill Cipher</a> algorithm </td></tr>
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||||
<tr id="row_1_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_1_" class="arrow" onclick="toggleFolder('1_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="d1/db4/namespacedata__structure.html" target="_self">data_structure</a></td><td class="desc">Data-structure algorithms </td></tr>
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@@ -116,7 +116,11 @@ $(document).ready(function(){initNavTree('annotated.html',''); initResizable();
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<tr id="row_5_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_5_" class="arrow" onclick="toggleFolder('5_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="d8/d89/namespacelinear__probing.html" target="_self">linear_probing</a></td><td class="desc">An implementation of hash table using <a href="https://en.wikipedia.org/wiki/Linear_probing">linear probing</a> algorithm </td></tr>
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<tr id="row_5_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="db/d19/structlinear__probing_1_1_entry.html" target="_self">Entry</a></td><td class="desc"></td></tr>
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<tr id="row_6_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_6_" class="arrow" onclick="toggleFolder('6_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="d8/d77/namespacemachine__learning.html" target="_self">machine_learning</a></td><td class="desc">Machine learning algorithms </td></tr>
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<tr id="row_6_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d6/d30/classmachine__learning_1_1adaline.html" target="_self">adaline</a></td><td class="desc"></td></tr>
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<tr id="row_6_0_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span id="arr_6_0_" class="arrow" onclick="toggleFolder('6_0_')">▼</span><span class="icona"><span class="icon">N</span></span><b>neural_network</b></td><td class="desc"></td></tr>
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<tr id="row_6_0_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span id="arr_6_0_0_" class="arrow" onclick="toggleFolder('6_0_0_')">▼</span><span class="icona"><span class="icon">N</span></span><b>layers</b></td><td class="desc"></td></tr>
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<tr id="row_6_0_0_0_" class="even"><td class="entry"><span style="width:64px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html" target="_self">DenseLayer</a></td><td class="desc"></td></tr>
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<tr id="row_6_0_1_"><td class="entry"><span style="width:48px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html" target="_self">NeuralNetwork</a></td><td class="desc"></td></tr>
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||||
<tr id="row_6_1_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d6/d30/classmachine__learning_1_1adaline.html" target="_self">adaline</a></td><td class="desc"></td></tr>
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<tr id="row_7_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_7_" class="arrow" onclick="toggleFolder('7_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="d4/dd2/namespacequadratic__probing.html" target="_self">quadratic_probing</a></td><td class="desc">An implementation of hash table using <a href="https://en.wikipedia.org/wiki/Quadratic_probing">quadratic probing</a> algorithm </td></tr>
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<tr id="row_7_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="da/dd1/structquadratic__probing_1_1_entry.html" target="_self">Entry</a></td><td class="desc"></td></tr>
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<tr id="row_8_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_8_" class="arrow" onclick="toggleFolder('8_')">▼</span><span class="icona"><span class="icon">N</span></span><a class="el" href="d2/dcf/namespacestatistics.html" target="_self">statistics</a></td><td class="desc">Statistical algorithms </td></tr>
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@@ -151,9 +155,9 @@ $(document).ready(function(){initNavTree('annotated.html',''); initResizable();
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<tr id="row_35_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d1/dc2/classstack.html" target="_self">stack</a></td><td class="desc"></td></tr>
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<tr id="row_36_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d2/dc4/classstack__linked_list.html" target="_self">stack_linkedList</a></td><td class="desc"></td></tr>
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<tr id="row_37_" class="even"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d2/d2c/structtower.html" target="_self">tower</a></td><td class="desc"></td></tr>
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<tr id="row_38_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d4/dd9/structtrie.html" target="_self">trie</a></td><td class="desc"></td></tr>
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<tr id="row_39_" class="even"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_39_" class="arrow" onclick="toggleFolder('39_')">▼</span><span class="icona"><span class="icon">C</span></span><a class="el" href="dd/d2f/class_trie.html" target="_self">Trie</a></td><td class="desc"></td></tr>
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<tr id="row_39_0_"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="de/d48/struct_trie_1_1_trie_node.html" target="_self">TrieNode</a></td><td class="desc"></td></tr>
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<tr id="row_38_"><td class="entry"><span style="width:0px;display:inline-block;"> </span><span id="arr_38_" class="arrow" onclick="toggleFolder('38_')">▼</span><span class="icona"><span class="icon">C</span></span><a class="el" href="dd/d2f/class_trie.html" target="_self">Trie</a></td><td class="desc"></td></tr>
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<tr id="row_38_0_" class="even"><td class="entry"><span style="width:32px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="de/d48/struct_trie_1_1_trie_node.html" target="_self">TrieNode</a></td><td class="desc"></td></tr>
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<tr id="row_39_"><td class="entry"><span style="width:16px;display:inline-block;"> </span><span class="icona"><span class="icon">C</span></span><a class="el" href="d4/dd9/structtrie.html" target="_self">trie</a></td><td class="desc"></td></tr>
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</table>
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</div><!-- directory -->
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</div><!-- contents -->
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@@ -38,6 +38,6 @@ var annotated_dup =
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[ "stack", "d1/dc2/classstack.html", "d1/dc2/classstack" ],
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[ "stack_linkedList", "d2/dc4/classstack__linked_list.html", "d2/dc4/classstack__linked_list" ],
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||||
[ "tower", "d2/d2c/structtower.html", "d2/d2c/structtower" ],
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[ "trie", "d4/dd9/structtrie.html", "d4/dd9/structtrie" ],
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[ "Trie", "dd/d2f/class_trie.html", "dd/d2f/class_trie" ]
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[ "Trie", "dd/d2f/class_trie.html", "dd/d2f/class_trie" ],
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[ "trie", "d4/dd9/structtrie.html", "d4/dd9/structtrie" ]
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];
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174
classes.html
174
classes.html
@@ -98,104 +98,106 @@ $(document).ready(function(){initNavTree('classes.html',''); initResizable(); })
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<table class="classindex">
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<tr><td rowspan="2" valign="bottom"><a name="letter_a"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  a  </div></td></tr></table>
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</td>
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<td valign="top"><a class="el" href="db/d19/structlinear__probing_1_1_entry.html">Entry</a> (<a class="el" href="d8/d89/namespacelinear__probing.html">linear_probing</a>)   </td>
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<td rowspan="2" valign="bottom"><a name="letter_l"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  l  </div></td></tr></table>
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<td rowspan="2" valign="bottom"><a name="letter_e"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  e  </div></td></tr></table>
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</td>
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<td valign="top"><a class="el" href="d9/d5a/structgeometry_1_1jarvis_1_1_point.html">Point</a> (geometry::jarvis)   </td>
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||||
<td valign="top"><a class="el" href="d7/d7c/classstatistics_1_1stats__computer1.html">stats_computer1</a> (<a class="el" href="d2/dcf/namespacestatistics.html">statistics</a>)   </td>
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<td valign="top"><a class="el" href="d6/d26/classciphers_1_1_hill_cipher.html">HillCipher</a> (<a class="el" href="d6/d4e/namespaceciphers.html">ciphers</a>)   </td>
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<td valign="top"><a class="el" href="db/d8b/struct_node.html">Node</a>   </td>
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<td valign="top"><a class="el" href="d1/d46/classdata__structure_1_1_skip_list.html">SkipList</a> (<a class="el" href="d1/db4/namespacedata__structure.html">data_structure</a>)   </td>
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||||
</tr>
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||||
<tr><td valign="top"><a class="el" href="d9/dde/structdouble__hashing_1_1_entry.html">Entry</a> (<a class="el" href="d0/d65/namespacedouble__hashing.html">double_hashing</a>)   </td>
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<td rowspan="2" valign="bottom"><a name="letter_q"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  q  </div></td></tr></table>
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<tr><td rowspan="2" valign="bottom"><a name="letter_i"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  i  </div></td></tr></table>
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</td>
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||||
<td valign="top"><a class="el" href="d8/dab/classstatistics_1_1stats__computer2.html">stats_computer2</a> (<a class="el" href="d2/dcf/namespacestatistics.html">statistics</a>)   </td>
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||||
<td valign="top"><a class="el" href="d5/da1/structnode.html">node</a>   </td>
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<td valign="top"><a class="el" href="dd/d4f/class_solution.html">Solution</a>   </td>
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||||
</tr>
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||||
<tr><td valign="top"><a class="el" href="d6/d30/classmachine__learning_1_1adaline.html">adaline</a> (<a class="el" href="d8/d77/namespacemachine__learning.html">machine_learning</a>)   </td>
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||||
<td valign="top"><a class="el" href="da/dd1/structquadratic__probing_1_1_entry.html">Entry</a> (<a class="el" href="d4/dd2/namespacequadratic__probing.html">quadratic_probing</a>)   </td>
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||||
<td valign="top"><a class="el" href="db/d82/classlarge__number.html">large_number</a>   </td>
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||||
<td rowspan="2" valign="bottom"><a name="letter_t"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  t  </div></td></tr></table>
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||||
</td>
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||||
<td valign="top"><a class="el" href="d7/d77/class_edge.html">Edge</a>   </td>
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||||
<td valign="top"><a class="el" href="d3/d6f/structdata__structure_1_1_node.html">Node</a> (<a class="el" href="d1/db4/namespacedata__structure.html">data_structure</a>)   </td>
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||||
<td valign="top"><a class="el" href="d1/dc2/classstack.html">stack</a>   </td>
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||||
</tr>
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||||
<tr><td rowspan="2" valign="bottom"><a name="letter_b"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  b  </div></td></tr></table>
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||||
</td>
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||||
<td rowspan="2" valign="bottom"><a name="letter_f"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  f  </div></td></tr></table>
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||||
</td>
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||||
<td valign="top"><a class="el" href="d0/dff/structlinkedlist.html">linkedlist</a>   </td>
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||||
<td valign="top"><a class="el" href="dd/d1b/structquery.html">query</a>   </td>
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||||
</tr>
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||||
<tr><td valign="top"><a class="el" href="d8/d10/structlist.html">list</a>   </td>
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||||
<td valign="top"><a class="el" href="dc/db5/struct_queue.html">Queue</a>   </td>
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||||
<td valign="top"><a class="el" href="d2/d2c/structtower.html">tower</a>   </td>
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||||
</tr>
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||||
<tr><td valign="top"><a class="el" href="d9/d90/struct_btree.html">Btree</a>   </td>
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||||
<td valign="top"><a class="el" href="dd/d91/class_fenwick_tree.html">FenwickTree</a>   </td>
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||||
<td valign="top"><a class="el" href="d9/d23/classgraph_1_1_lowest_common_ancestor.html">LowestCommonAncestor</a> (<a class="el" href="df/dce/namespacegraph.html">graph</a>)   </td>
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<td valign="top"><a class="el" href="db/da9/classqueue.html">queue</a>   </td>
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<td valign="top"><a class="el" href="d4/dd9/structtrie.html">trie</a>   </td>
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||||
</tr>
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||||
<tr><td rowspan="2" valign="bottom"><a name="letter_c"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  c  </div></td></tr></table>
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</td>
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<td rowspan="2" valign="bottom"><a name="letter_g"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  g  </div></td></tr></table>
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</td>
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<td rowspan="2" valign="bottom"><a name="letter_m"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  m  </div></td></tr></table>
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</td>
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<td valign="top"><a class="el" href="d0/dd2/class_queue___array.html">Queue_Array</a>   </td>
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<td valign="top"><a class="el" href="dd/d2f/class_trie.html">Trie</a>   </td>
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</tr>
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<tr><td rowspan="2" valign="bottom"><a name="letter_r"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  r  </div></td></tr></table>
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</td>
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<td valign="top"><a class="el" href="de/d48/struct_trie_1_1_trie_node.html">Trie::TrieNode</a>   </td>
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</tr>
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||||
<tr><td valign="top"><a class="el" href="d5/d15/classcll.html">cll</a>   </td>
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<td valign="top"><a class="el" href="da/d9a/class_graph.html">Graph</a>   </td>
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||||
<td valign="top"><a class="el" href="d2/d05/class_min_heap.html">MinHeap</a>   </td>
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||||
<td></td></tr>
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||||
<tr><td valign="top"><a class="el" href="d1/db3/structcompare.html">compare</a>   </td>
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||||
<td valign="top"><a class="el" href="de/d00/classgraph_1_1is__graph__bipartite_1_1_graph.html">Graph</a> (graph::is_graph_bipartite)   </td>
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||||
<td valign="top"><a class="el" href="d5/d29/struct_min_heap_node.html">MinHeapNode</a>   </td>
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||||
<td valign="top"><a class="el" href="d0/d58/classgraph_1_1_rooted_tree.html">RootedTree</a> (<a class="el" href="df/dce/namespacegraph.html">graph</a>)   </td>
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||||
<td></td></tr>
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||||
<tr><td valign="top"><a class="el" href="da/d5a/class_complex.html">Complex</a>   </td>
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||||
<td valign="top"><a class="el" href="dc/d61/classgraph_1_1_graph.html">Graph</a> (<a class="el" href="df/dce/namespacegraph.html">graph</a>)   </td>
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||||
<td valign="top"><a class="el" href="d1/d77/structmst.html">mst</a>   </td>
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||||
<td rowspan="2" valign="bottom"><a name="letter_s"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  s  </div></td></tr></table>
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||||
</td>
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||||
<td></td></tr>
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||||
<tr><td valign="top"><a class="el" href="d4/dde/classgeometry_1_1jarvis_1_1_convexhull.html">Convexhull</a> (geometry::jarvis)   </td>
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||||
<td rowspan="2" valign="bottom"><a name="letter_h"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  h  </div></td></tr></table>
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||||
</td>
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||||
<td rowspan="2" valign="bottom"><a name="letter_n"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  n  </div></td></tr></table>
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||||
</td>
|
||||
<td></td></tr>
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||||
<tr><td valign="top"><a class="el" href="d3/dbb/class_cycle_check.html">CycleCheck</a>   </td>
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||||
<td valign="top"><a class="el" href="d4/db4/struct_segment_intersection.html">SegmentIntersection</a>   </td>
|
||||
<td></td></tr>
|
||||
<tr><td rowspan="2" valign="bottom"><a name="letter_d"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  d  </div></td></tr></table>
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||||
</td>
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||||
<td valign="top"><a class="el" href="dd/d1c/classhash__chain.html">hash_chain</a>   </td>
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||||
<td valign="top"><a class="el" href="db/d8b/struct_node.html">Node</a>   </td>
|
||||
<td valign="top"><a class="el" href="d1/d46/classdata__structure_1_1_skip_list.html">SkipList</a> (<a class="el" href="d1/db4/namespacedata__structure.html">data_structure</a>)   </td>
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||||
<td></td></tr>
|
||||
<tr><td valign="top"><a class="el" href="d6/d26/classciphers_1_1_hill_cipher.html">HillCipher</a> (<a class="el" href="d6/d4e/namespaceciphers.html">ciphers</a>)   </td>
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||||
<td valign="top"><a class="el" href="d5/da1/structnode.html">node</a>   </td>
|
||||
<td valign="top"><a class="el" href="dd/d4f/class_solution.html">Solution</a>   </td>
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||||
<td></td></tr>
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||||
<tr><td valign="top"><a class="el" href="d9/dee/classdouble__linked__list.html">double_linked_list</a>   </td>
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||||
<td rowspan="2" valign="bottom"><a name="letter_i"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  i  </div></td></tr></table>
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||||
</td>
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||||
<td valign="top"><a class="el" href="d3/d6f/structdata__structure_1_1_node.html">Node</a> (<a class="el" href="d1/db4/namespacedata__structure.html">data_structure</a>)   </td>
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||||
<td valign="top"><a class="el" href="d1/dc2/classstack.html">stack</a>   </td>
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||||
<td></td></tr>
|
||||
<tr><td rowspan="2" valign="bottom"><a name="letter_e"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  e  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="db/d19/structlinear__probing_1_1_entry.html">Entry</a> (<a class="el" href="d8/d89/namespacelinear__probing.html">linear_probing</a>)   </td>
|
||||
<td valign="top"><a class="el" href="db/d66/struct_item.html">Item</a>   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_p"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  p  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="d2/dc4/classstack__linked_list.html">stack_linkedList</a>   </td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="d9/dde/structdouble__hashing_1_1_entry.html">Entry</a> (<a class="el" href="d0/d65/namespacedouble__hashing.html">double_hashing</a>)   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_l"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  l  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="d7/d7c/classstatistics_1_1stats__computer1.html">stats_computer1</a> (<a class="el" href="d2/dcf/namespacestatistics.html">statistics</a>)   </td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="d9/d90/struct_btree.html">Btree</a>   </td>
|
||||
<td valign="top"><a class="el" href="da/dd1/structquadratic__probing_1_1_entry.html">Entry</a> (<a class="el" href="d4/dd2/namespacequadratic__probing.html">quadratic_probing</a>)   </td>
|
||||
<td valign="top"><a class="el" href="d8/dc8/struct_point.html">Point</a>   </td>
|
||||
<td valign="top"><a class="el" href="d8/dab/classstatistics_1_1stats__computer2.html">stats_computer2</a> (<a class="el" href="d2/dcf/namespacestatistics.html">statistics</a>)   </td>
|
||||
</tr>
|
||||
<tr><td rowspan="2" valign="bottom"><a name="letter_c"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  c  </div></td></tr></table>
|
||||
</td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_f"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  f  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="db/d82/classlarge__number.html">large_number</a>   </td>
|
||||
<td valign="top"><a class="el" href="d9/d5a/structgeometry_1_1jarvis_1_1_point.html">Point</a> (geometry::jarvis)   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_t"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  t  </div></td></tr></table>
|
||||
</td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="d0/dff/structlinkedlist.html">linkedlist</a>   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_q"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  q  </div></td></tr></table>
|
||||
</td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="d5/d15/classcll.html">cll</a>   </td>
|
||||
<td valign="top"><a class="el" href="dd/d91/class_fenwick_tree.html">FenwickTree</a>   </td>
|
||||
<td valign="top"><a class="el" href="d8/d10/structlist.html">list</a>   </td>
|
||||
<td valign="top"><a class="el" href="d2/d2c/structtower.html">tower</a>   </td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="d1/db3/structcompare.html">compare</a>   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_g"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  g  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="d9/d23/classgraph_1_1_lowest_common_ancestor.html">LowestCommonAncestor</a> (<a class="el" href="df/dce/namespacegraph.html">graph</a>)   </td>
|
||||
<td valign="top"><a class="el" href="dd/d1b/structquery.html">query</a>   </td>
|
||||
<td valign="top"><a class="el" href="dd/d2f/class_trie.html">Trie</a>   </td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="da/d5a/class_complex.html">Complex</a>   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_m"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  m  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="dc/db5/struct_queue.html">Queue</a>   </td>
|
||||
<td valign="top"><a class="el" href="d4/dd9/structtrie.html">trie</a>   </td>
|
||||
</tr>
|
||||
<tr><td valign="top"><a class="el" href="d4/dde/classgeometry_1_1jarvis_1_1_convexhull.html">Convexhull</a> (geometry::jarvis)   </td>
|
||||
<td valign="top"><a class="el" href="da/d9a/class_graph.html">Graph</a>   </td>
|
||||
<td valign="top"><a class="el" href="db/da9/classqueue.html">queue</a>   </td>
|
||||
<td valign="top"><a class="el" href="de/d48/struct_trie_1_1_trie_node.html">Trie::TrieNode</a>   </td>
|
||||
</tr>
|
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<tr><td valign="top"><a class="el" href="d3/dbb/class_cycle_check.html">CycleCheck</a>   </td>
|
||||
<td valign="top"><a class="el" href="de/d00/classgraph_1_1is__graph__bipartite_1_1_graph.html">Graph</a> (graph::is_graph_bipartite)   </td>
|
||||
<td valign="top"><a class="el" href="d2/d05/class_min_heap.html">MinHeap</a>   </td>
|
||||
<td valign="top"><a class="el" href="d0/dd2/class_queue___array.html">Queue_Array</a>   </td>
|
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<td></td></tr>
|
||||
<tr><td rowspan="2" valign="bottom"><a name="letter_d"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  d  </div></td></tr></table>
|
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</td>
|
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<td valign="top"><a class="el" href="dc/d61/classgraph_1_1_graph.html">Graph</a> (<a class="el" href="df/dce/namespacegraph.html">graph</a>)   </td>
|
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<td valign="top"><a class="el" href="d5/d29/struct_min_heap_node.html">MinHeapNode</a>   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_r"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  r  </div></td></tr></table>
|
||||
</td>
|
||||
<td></td></tr>
|
||||
<tr><td rowspan="2" valign="bottom"><a name="letter_h"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  h  </div></td></tr></table>
|
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</td>
|
||||
<td valign="top"><a class="el" href="d1/d77/structmst.html">mst</a>   </td>
|
||||
<td></td></tr>
|
||||
<tr><td valign="top"><a class="el" href="dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">DenseLayer</a> (machine_learning::neural_network::layers)   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_n"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  n  </div></td></tr></table>
|
||||
</td>
|
||||
<td valign="top"><a class="el" href="d0/d58/classgraph_1_1_rooted_tree.html">RootedTree</a> (<a class="el" href="df/dce/namespacegraph.html">graph</a>)   </td>
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<td></td></tr>
|
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<tr><td valign="top"><a class="el" href="d9/dee/classdouble__linked__list.html">double_linked_list</a>   </td>
|
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<td valign="top"><a class="el" href="dd/d1c/classhash__chain.html">hash_chain</a>   </td>
|
||||
<td rowspan="2" valign="bottom"><a name="letter_s"></a><table border="0" cellspacing="0" cellpadding="0"><tr><td><div class="ah">  s  </div></td></tr></table>
|
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</td>
|
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<td></td></tr>
|
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<tr><td></td><td></td><td valign="top"><a class="el" href="d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">NeuralNetwork</a> (machine_learning::neural_network)   </td>
|
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<td></td></tr>
|
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<tr><td></td><td></td><td></td><td valign="top"><a class="el" href="d4/db4/struct_segment_intersection.html">SegmentIntersection</a>   </td>
|
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<td></td></tr>
|
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<tr><td valign="top"><a class="el" href="db/d66/struct_item.html">Item</a>   </td>
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<td></td><td></td></tr>
|
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<tr><td valign="top"><a class="el" href="d7/d77/class_edge.html">Edge</a>   </td>
|
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<td></td><td valign="top"><a class="el" href="d8/dc8/struct_point.html">Point</a>   </td>
|
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<td></td><td></td></tr>
|
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<tr><td></td><td></td><td></td><td></td><td></td></tr>
|
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</table>
|
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<a href="#func-members">Functions</a> </div>
|
||||
<div class="headertitle">
|
||||
<div class="title">neural_network.cpp File Reference</div> </div>
|
||||
</div><!--header-->
|
||||
<div class="contents">
|
||||
|
||||
<p>Implementation of <a href="https://en.wikipedia.org/wiki/Multilayer_perceptron">Multilayer Perceptron</a>.
|
||||
<a href="#details">More...</a></p>
|
||||
<div class="textblock"><code>#include "<a class="el" href="../../d8/d95/vector__ops_8hpp_source.html">vector_ops.hpp</a>"</code><br />
|
||||
<code>#include <iostream></code><br />
|
||||
<code>#include <valarray></code><br />
|
||||
<code>#include <vector></code><br />
|
||||
<code>#include <cmath></code><br />
|
||||
<code>#include <algorithm></code><br />
|
||||
<code>#include <chrono></code><br />
|
||||
<code>#include <string></code><br />
|
||||
<code>#include <fstream></code><br />
|
||||
<code>#include <sstream></code><br />
|
||||
<code>#include <cassert></code><br />
|
||||
</div><div class="textblock"><div class="dynheader">
|
||||
Include dependency graph for neural_network.cpp:</div>
|
||||
<div class="dyncontent">
|
||||
<div class="center"><div class="zoom"><iframe scrolling="no" frameborder="0" src="../../d2/d23/neural__network_8cpp__incl.svg" width="100%" height="451"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe></div>
|
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</div>
|
||||
</div>
|
||||
</div><table class="memberdecls">
|
||||
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
|
||||
Classes</h2></td></tr>
|
||||
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td></tr>
|
||||
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
|
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<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a></td></tr>
|
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<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
|
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="namespaces"></a>
|
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Namespaces</h2></td></tr>
|
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<tr class="memitem:d8/d77/namespacemachine__learning"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d8/d77/namespacemachine__learning.html">machine_learning</a></td></tr>
|
||||
<tr class="memdesc:d8/d77/namespacemachine__learning"><td class="mdescLeft"> </td><td class="mdescRight">Machine learning algorithms. <br /></td></tr>
|
||||
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:d0/d2e/namespaceneural__network"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d0/d2e/namespaceneural__network.html">neural_network</a></td></tr>
|
||||
<tr class="memdesc:d0/d2e/namespaceneural__network"><td class="mdescLeft"> </td><td class="mdescRight">Neural Network or Multilayer Perceptron. <br /></td></tr>
|
||||
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:d5/d39/namespaceactivations"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d5/d39/namespaceactivations.html">activations</a></td></tr>
|
||||
<tr class="memdesc:d5/d39/namespaceactivations"><td class="mdescLeft"> </td><td class="mdescRight">Various activation functions used in Neural network. <br /></td></tr>
|
||||
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:d3/d17/namespaceutil__functions"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d3/d17/namespaceutil__functions.html">util_functions</a></td></tr>
|
||||
<tr class="memdesc:d3/d17/namespaceutil__functions"><td class="mdescLeft"> </td><td class="mdescRight">Various utility functions used in Neural network. <br /></td></tr>
|
||||
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:d5/d2c/namespacelayers"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d5/d2c/namespacelayers.html">layers</a></td></tr>
|
||||
<tr class="memdesc:d5/d2c/namespacelayers"><td class="mdescLeft"> </td><td class="mdescRight">This namespace contains layers used in MLP. <br /></td></tr>
|
||||
<tr class="separator:"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
</table><table class="memberdecls">
|
||||
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
|
||||
Functions</h2></td></tr>
|
||||
<tr class="memitem:a23aa9d32bcbcd65cfc85f0a41e2afadc"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#a23aa9d32bcbcd65cfc85f0a41e2afadc">machine_learning::neural_network::activations::sigmoid</a> (const double &x)</td></tr>
|
||||
<tr class="separator:a23aa9d32bcbcd65cfc85f0a41e2afadc"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:a76eb66212d577f948a457b6e29d87c46"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#a76eb66212d577f948a457b6e29d87c46">machine_learning::neural_network::activations::dsigmoid</a> (const double &x)</td></tr>
|
||||
<tr class="separator:a76eb66212d577f948a457b6e29d87c46"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:af8f264600754602b6a9ea19cc690e50e"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#af8f264600754602b6a9ea19cc690e50e">machine_learning::neural_network::activations::relu</a> (const double &x)</td></tr>
|
||||
<tr class="separator:af8f264600754602b6a9ea19cc690e50e"><td class="memSeparator" colspan="2"> </td></tr>
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||||
<tr class="memitem:aa69e95a34054d7989bf446f96b2ffaf9"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#aa69e95a34054d7989bf446f96b2ffaf9">machine_learning::neural_network::activations::drelu</a> (const double &x)</td></tr>
|
||||
<tr class="separator:aa69e95a34054d7989bf446f96b2ffaf9"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:a371aa7dd5d5add0143d1756bb0a1b32f"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#a371aa7dd5d5add0143d1756bb0a1b32f">machine_learning::neural_network::activations::tanh</a> (const double &x)</td></tr>
|
||||
<tr class="separator:a371aa7dd5d5add0143d1756bb0a1b32f"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:a2a5e874b9774aa5362dbcf288828b95c"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#a2a5e874b9774aa5362dbcf288828b95c">machine_learning::neural_network::activations::dtanh</a> (const double &x)</td></tr>
|
||||
<tr class="separator:a2a5e874b9774aa5362dbcf288828b95c"><td class="memSeparator" colspan="2"> </td></tr>
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||||
<tr class="memitem:a45d3e30406712ada3d9713ece3c1b153"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#a45d3e30406712ada3d9713ece3c1b153">machine_learning::neural_network::util_functions::square</a> (const double &x)</td></tr>
|
||||
<tr class="separator:a45d3e30406712ada3d9713ece3c1b153"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:a32c00da08f2cf641dd336270f6e3c407"><td class="memItemLeft" align="right" valign="top">double </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#a32c00da08f2cf641dd336270f6e3c407">machine_learning::neural_network::util_functions::identity_function</a> (const double &x)</td></tr>
|
||||
<tr class="separator:a32c00da08f2cf641dd336270f6e3c407"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:aa8dca7b867074164d5f45b0f3851269d"><td class="memItemLeft" align="right" valign="top">static void </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a> ()</td></tr>
|
||||
<tr class="separator:aa8dca7b867074164d5f45b0f3851269d"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
<tr class="memitem:ae66f6b31b5ad750f1fe042a706a4e3d4"><td class="memItemLeft" align="right" valign="top">int </td><td class="memItemRight" valign="bottom"><a class="el" href="../../d2/d58/neural__network_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a> ()</td></tr>
|
||||
<tr class="separator:ae66f6b31b5ad750f1fe042a706a4e3d4"><td class="memSeparator" colspan="2"> </td></tr>
|
||||
</table>
|
||||
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
|
||||
<div class="textblock"><p>Implementation of <a href="https://en.wikipedia.org/wiki/Multilayer_perceptron">Multilayer Perceptron</a>. </p>
|
||||
<dl class="section author"><dt>Author</dt><dd><a href="https://github.com/imdeep2905">Deep Raval</a></dd></dl>
|
||||
<p>A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons (with threshold activation). Multilayer perceptrons are sometimes colloquially referred to as "vanilla" neural networks, especially when they have a single hidden layer.</p>
|
||||
<p>An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.</p>
|
||||
<p>See <a href="https://en.wikipedia.org/wiki/Backpropagation">Backpropagation</a> for training algorithm.</p>
|
||||
<dl class="section note"><dt>Note</dt><dd>This implementation uses mini-batch gradient descent as optimizer and MSE as loss function. Bias is also not included. </dd></dl>
|
||||
</div><h2 class="groupheader">Function Documentation</h2>
|
||||
<a id="aa69e95a34054d7989bf446f96b2ffaf9"></a>
|
||||
<h2 class="memtitle"><span class="permalink"><a href="#aa69e95a34054d7989bf446f96b2ffaf9">◆ </a></span>drelu()</h2>
|
||||
|
||||
<div class="memitem">
|
||||
<div class="memproto">
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||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">double machine_learning::neural_network::activations::drelu </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const double & </td>
|
||||
<td class="paramname"><em>x</em></td><td>)</td>
|
||||
<td></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div><div class="memdoc">
|
||||
<p>Derivative of relu function </p><dl class="params"><dt>Parameters</dt><dd>
|
||||
<table class="params">
|
||||
<tr><td class="paramname">X</td><td>Value </td></tr>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>derivative of relu(x) </dd></dl>
|
||||
<div class="fragment"><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  {</div>
|
||||
<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="keywordflow">return</span> x >= 0.0 ? 1.0 : 0.0;</div>
|
||||
<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  }</div>
|
||||
</div><!-- fragment --><div class="dynheader">
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||||
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||||
<div class="dyncontent">
|
||||
<div class="center"><iframe scrolling="no" frameborder="0" src="../../d2/d58/neural__network_8cpp_aa69e95a34054d7989bf446f96b2ffaf9_cgraph.svg" width="194" height="76"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
<a id="a76eb66212d577f948a457b6e29d87c46"></a>
|
||||
<h2 class="memtitle"><span class="permalink"><a href="#a76eb66212d577f948a457b6e29d87c46">◆ </a></span>dsigmoid()</h2>
|
||||
|
||||
<div class="memitem">
|
||||
<div class="memproto">
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||||
<table class="memname">
|
||||
<tr>
|
||||
<td class="memname">double machine_learning::neural_network::activations::dsigmoid </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const double & </td>
|
||||
<td class="paramname"><em>x</em></td><td>)</td>
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||||
<td></td>
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||||
</tr>
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||||
</table>
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||||
</div><div class="memdoc">
|
||||
<p>Derivative of sigmoid function </p><dl class="params"><dt>Parameters</dt><dd>
|
||||
<table class="params">
|
||||
<tr><td class="paramname">X</td><td>Value </td></tr>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Returns derivative of sigmoid(x) </dd></dl>
|
||||
<div class="fragment"><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  {</div>
|
||||
<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordflow">return</span> x * (1 - x);</div>
|
||||
<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  }</div>
|
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d2/d58/neural__network_8cpp_a76eb66212d577f948a457b6e29d87c46_cgraph.svg" width="178" height="91"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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</div>
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||||
</div>
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||||
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||||
</div>
|
||||
</div>
|
||||
<a id="a2a5e874b9774aa5362dbcf288828b95c"></a>
|
||||
<h2 class="memtitle"><span class="permalink"><a href="#a2a5e874b9774aa5362dbcf288828b95c">◆ </a></span>dtanh()</h2>
|
||||
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||||
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||||
<div class="memproto">
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<table class="memname">
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||||
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||||
<td class="memname">double machine_learning::neural_network::activations::dtanh </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const double & </td>
|
||||
<td class="paramname"><em>x</em></td><td>)</td>
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<td></td>
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</tr>
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||||
</table>
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||||
</div><div class="memdoc">
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||||
<p>Derivative of Sigmoid function </p><dl class="params"><dt>Parameters</dt><dd>
|
||||
<table class="params">
|
||||
<tr><td class="paramname">X</td><td>Value </td></tr>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Returns derivative of tanh(x) </dd></dl>
|
||||
<div class="fragment"><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  {</div>
|
||||
<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordflow">return</span> 1 - x * x;</div>
|
||||
<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  }</div>
|
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</div><!-- fragment --><div class="dynheader">
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d2/d58/neural__network_8cpp_a2a5e874b9774aa5362dbcf288828b95c_cgraph.svg" width="198" height="76"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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</div>
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</div>
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||||
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||||
</div>
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||||
</div>
|
||||
<a id="a32c00da08f2cf641dd336270f6e3c407"></a>
|
||||
<h2 class="memtitle"><span class="permalink"><a href="#a32c00da08f2cf641dd336270f6e3c407">◆ </a></span>identity_function()</h2>
|
||||
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||||
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||||
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||||
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||||
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||||
<td class="memname">double machine_learning::neural_network::util_functions::identity_function </td>
|
||||
<td>(</td>
|
||||
<td class="paramtype">const double & </td>
|
||||
<td class="paramname"><em>x</em></td><td>)</td>
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||||
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||||
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||||
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|
||||
<p>Identity function </p><dl class="params"><dt>Parameters</dt><dd>
|
||||
<table class="params">
|
||||
<tr><td class="paramname">X</td><td>Value </td></tr>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Returns x </dd></dl>
|
||||
<div class="fragment"><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  {</div>
|
||||
<div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordflow">return</span> x;</div>
|
||||
<div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  }</div>
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d2/d58/neural__network_8cpp_a32c00da08f2cf641dd336270f6e3c407_cgraph.svg" width="178" height="91"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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</div>
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||||
</div>
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||||
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||||
</div>
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||||
</div>
|
||||
<a id="ae66f6b31b5ad750f1fe042a706a4e3d4"></a>
|
||||
<h2 class="memtitle"><span class="permalink"><a href="#ae66f6b31b5ad750f1fe042a706a4e3d4">◆ </a></span>main()</h2>
|
||||
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||||
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||||
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||||
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||||
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||||
<td class="memname">int main </td>
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||||
<td>(</td>
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||||
<td class="paramname"></td><td>)</td>
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<td></td>
|
||||
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||||
</div><div class="memdoc">
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||||
<p>Driver Code </p>
|
||||
<div class="fragment"><div class="line"><a name="l00786"></a><span class="lineno"> 786</span>  {</div>
|
||||
<div class="line"><a name="l00787"></a><span class="lineno"> 787</span>  <span class="comment">// Testing</span></div>
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<div class="line"><a name="l00788"></a><span class="lineno"> 788</span>  <a class="code" href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a>();</div>
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<div class="line"><a name="l00789"></a><span class="lineno"> 789</span>  <span class="keywordflow">return</span> 0;</div>
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<div class="line"><a name="l00790"></a><span class="lineno"> 790</span> }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#af8f264600754602b6a9ea19cc690e50e">◆ </a></span>relu()</h2>
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<td class="memname">double machine_learning::neural_network::activations::relu </td>
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<td>(</td>
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<td class="paramtype">const double & </td>
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<p>Relu function </p><dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<dl class="section return"><dt>Returns</dt><dd>relu(x) </dd></dl>
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<div class="fragment"><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  {</div>
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<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">return</span> <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/algorithm/max.html">std::max</a>(0.0, x);</div>
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<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a23aa9d32bcbcd65cfc85f0a41e2afadc">◆ </a></span>sigmoid()</h2>
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<td class="memname">double machine_learning::neural_network::activations::sigmoid </td>
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<td>(</td>
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<td class="paramtype">const double & </td>
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<td class="paramname"><em>x</em></td><td>)</td>
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<p>Sigmoid function </p><dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">X</td><td>Value </td></tr>
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<dl class="section return"><dt>Returns</dt><dd>Returns sigmoid(x) </dd></dl>
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<div class="fragment"><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  {</div>
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<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">return</span> 1.0 / (1.0 + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a>(-x));</div>
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<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a45d3e30406712ada3d9713ece3c1b153">◆ </a></span>square()</h2>
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<td class="memname">double machine_learning::neural_network::util_functions::square </td>
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<td>(</td>
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<td class="paramtype">const double & </td>
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<td class="paramname"><em>x</em></td><td>)</td>
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<p>Square function </p><dl class="params"><dt>Parameters</dt><dd>
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<table class="params">
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<tr><td class="paramname">X</td><td>Value </td></tr>
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||||
</table>
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||||
</dd>
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||||
</dl>
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||||
<dl class="section return"><dt>Returns</dt><dd>Returns x * x </dd></dl>
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||||
<div class="fragment"><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  {</div>
|
||||
<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="keywordflow">return</span> x * x;</div>
|
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<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a371aa7dd5d5add0143d1756bb0a1b32f">◆ </a></span>tanh()</h2>
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<td class="memname">double machine_learning::neural_network::activations::tanh </td>
|
||||
<td>(</td>
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<td class="paramtype">const double & </td>
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||||
<td class="paramname"><em>x</em></td><td>)</td>
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<p>Tanh function </p><dl class="params"><dt>Parameters</dt><dd>
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||||
<table class="params">
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||||
<tr><td class="paramname">X</td><td>Value </td></tr>
|
||||
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||||
</dd>
|
||||
</dl>
|
||||
<dl class="section return"><dt>Returns</dt><dd>Returns tanh(x) </dd></dl>
|
||||
<div class="fragment"><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  {</div>
|
||||
<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="keywordflow">return</span> 2 / (1 + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a>(-2 * x)) - 1;</div>
|
||||
<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#aa8dca7b867074164d5f45b0f3851269d">◆ </a></span>test()</h2>
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<td class="memname">static void test </td>
|
||||
<td>(</td>
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||||
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||||
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||||
<span class="mlabels"><span class="mlabel">static</span></span> </td>
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||||
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||||
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||||
</div><div class="memdoc">
|
||||
<p>Function to test neural network </p><dl class="section return"><dt>Returns</dt><dd>none </dd></dl>
|
||||
<div class="fragment"><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  {</div>
|
||||
<div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <span class="comment">// Creating network with 3 layers for "iris.csv"</span></div>
|
||||
<div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  <a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a> myNN =</div>
|
||||
<div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a>({</div>
|
||||
<div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  {4, <span class="stringliteral">"none"</span>}, <span class="comment">// First layer with 3 neurons and "none" as activation</span></div>
|
||||
<div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  {6, <span class="stringliteral">"relu"</span>}, <span class="comment">// Second layer with 6 neurons and "relu" as activation</span></div>
|
||||
<div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  {3, <span class="stringliteral">"sigmoid"</span>} <span class="comment">// Third layer with 3 neurons and "sigmoid" as activation</span></div>
|
||||
<div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  });</div>
|
||||
<div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  <span class="comment">// Printing summary of model</span></div>
|
||||
<div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">summary</a>();</div>
|
||||
<div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  <span class="comment">// Training Model</span></div>
|
||||
<div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">fit_from_csv</a>(<span class="stringliteral">"iris.csv"</span>, <span class="keyword">true</span>, 100, 0.3, <span class="keyword">false</span>, 2, 32, <span class="keyword">true</span>);</div>
|
||||
<div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  <span class="comment">// Testing predictions of model</span></div>
|
||||
<div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">single_predict</a>({{5,3.4,1.6,0.4}})) == 0);</div>
|
||||
<div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">single_predict</a>({{6.4,2.9,4.3,1.3}})) == 1);</div>
|
||||
<div class="line"><a name="l00781"></a><span class="lineno"> 781</span>  assert(<a class="code" href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a>(myNN.<a class="code" href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">single_predict</a>({{6.2,3.4,5.4,2.3}})) == 2);</div>
|
||||
<div class="line"><a name="l00782"></a><span class="lineno"> 782</span>  <span class="keywordflow">return</span>;</div>
|
||||
<div class="line"><a name="l00783"></a><span class="lineno"> 783</span> }</div>
|
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<div class="ttc" id="anamespacemachine__learning_html_a1b42d24ad7bedbfa8e5b59fe96987a44"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a1b42d24ad7bedbfa8e5b59fe96987a44">machine_learning::argmax</a></div><div class="ttdeci">size_t argmax(const std::vector< std::valarray< T >> &A)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:296</div></div>
|
||||
<div class="ttc" id="aneural__network_8cpp_html_aa8dca7b867074164d5f45b0f3851269d"><div class="ttname"><a href="../../d2/d58/neural__network_8cpp.html#aa8dca7b867074164d5f45b0f3851269d">test</a></div><div class="ttdeci">static void test()</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:766</div></div>
|
||||
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a26680e7a28b3925f83b984d2dfa52256"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a26680e7a28b3925f83b984d2dfa52256">machine_learning::neural_network::NeuralNetwork::single_predict</a></div><div class="ttdeci">std::vector< std::valarray< double > > single_predict(const std::vector< std::valarray< double >> &X)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:453</div></div>
|
||||
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a5172a6791b9bd24f4232bab8d6b81fff"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a5172a6791b9bd24f4232bab8d6b81fff">machine_learning::neural_network::NeuralNetwork::fit_from_csv</a></div><div class="ttdeci">void fit_from_csv(const std::string &file_name, const bool &last_label, const int &epochs, const double &learning_rate, const bool &normalize, const int &slip_lines=1, const size_t &batch_size=32, const bool &shuffle=true)</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:574</div></div>
|
||||
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html">machine_learning::neural_network::NeuralNetwork</a></div><div class="ttdef"><b>Definition:</b> neural_network.cpp:261</div></div>
|
||||
<div class="ttc" id="aclassmachine__learning_1_1neural__network_1_1_neural_network_html_a61d30113d13304c664057118b92a5931"><div class="ttname"><a href="../../d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a61d30113d13304c664057118b92a5931">machine_learning::neural_network::NeuralNetwork::summary</a></div><div class="ttdeci">void summary()</div><div class="ttdef"><b>Definition:</b> neural_network.cpp:742</div></div>
|
||||
<div class="ttc" id="aexp_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/numeric/math/exp.html">std::exp</a></div><div class="ttdeci">T exp(T... args)</div></div>
|
||||
<div class="ttc" id="amax_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/algorithm/max.html">std::max</a></div><div class="ttdeci">T max(T... args)</div></div>
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15
d2/d58/neural__network_8cpp.js
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15
d2/d58/neural__network_8cpp.js
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@@ -0,0 +1,15 @@
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var neural__network_8cpp =
|
||||
[
|
||||
[ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer" ],
|
||||
[ "NeuralNetwork", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network" ],
|
||||
[ "drelu", "d2/d58/neural__network_8cpp.html#aa69e95a34054d7989bf446f96b2ffaf9", null ],
|
||||
[ "dsigmoid", "d2/d58/neural__network_8cpp.html#a76eb66212d577f948a457b6e29d87c46", null ],
|
||||
[ "dtanh", "d2/d58/neural__network_8cpp.html#a2a5e874b9774aa5362dbcf288828b95c", null ],
|
||||
[ "identity_function", "d2/d58/neural__network_8cpp.html#a32c00da08f2cf641dd336270f6e3c407", null ],
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<p>This is the complete list of members for <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a>, including all inherited members.</p>
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<table class="directory">
|
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<tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>activation</b> (defined in <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a>)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
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<tr bgcolor="#f0f0f0"><td class="entry"><b>activation_function</b> (defined in <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a>)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
|
||||
<tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>dactivation_function</b> (defined in <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a>)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
|
||||
<tr><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a51c2b942ecf10625780c6bb9d5c50ff1">DenseLayer</a>(const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernal_shape, const bool &random_kernal)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a04b8e21316458436c8851959928c3964">DenseLayer</a>(const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernal)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"><span class="mlabel">inline</span></td></tr>
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<tr><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a2871146feaaa453558239df67b21e0d2">DenseLayer</a>(const DenseLayer &layer)=default</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
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<tr class="even"><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a6c859e3737aa88b29854df0347b29f4e">DenseLayer</a>(DenseLayer &&)=default</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
|
||||
<tr bgcolor="#f0f0f0"><td class="entry"><b>kernal</b> (defined in <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a>)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
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<tr bgcolor="#f0f0f0" class="even"><td class="entry"><b>neurons</b> (defined in <a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a>)</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a8809e6df990f37c85c06474dd955cb2b">operator=</a>(const DenseLayer &layer)=default</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
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<tr><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ac9cda9453c4a0caf5bae7f9213b019a0">~DenseLayer</a>()=default</td><td class="entry"><a class="el" href="../../dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html">machine_learning::neural_network::layers::DenseLayer</a></td><td class="entry"></td></tr>
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@@ -153,7 +153,7 @@ Functions</h2></td></tr>
|
||||
<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">if</span> (number < 0) {</div>
|
||||
<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
|
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<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  }</div>
|
||||
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordtype">int</span> sum = 0;</div>
|
||||
<div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordtype">int</span> <a class="code" href="../../d8/d77/namespacemachine__learning.html#a5305cc18d11943d3dc0d66e70d7e9c44">sum</a> = 0;</div>
|
||||
<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordtype">int</span> temp = number;</div>
|
||||
<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="comment">// Finding the total number of digits in the number</span></div>
|
||||
<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordtype">int</span> total_digits = <a class="code" href="../../d4/d5d/math_2armstrong__number_8cpp.html#aab733299b170fa5a752503f95f090fcd">number_of_digits</a>(number);</div>
|
||||
@@ -161,15 +161,15 @@ Functions</h2></td></tr>
|
||||
<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keywordtype">int</span> rem = temp % 10;</div>
|
||||
<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="comment">// Finding each digit raised to the power total digit and add it to the</span></div>
|
||||
<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="comment">// total sum</span></div>
|
||||
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  sum = sum + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/pow.html">std::pow</a>(rem, total_digits);</div>
|
||||
<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="../../d8/d77/namespacemachine__learning.html#a5305cc18d11943d3dc0d66e70d7e9c44">sum</a> = <a class="code" href="../../d8/d77/namespacemachine__learning.html#a5305cc18d11943d3dc0d66e70d7e9c44">sum</a> + <a class="codeRef" target="_blank" href="http://en.cppreference.com/w/cpp/numeric/math/pow.html">std::pow</a>(rem, total_digits);</div>
|
||||
<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  temp = temp / 10;</div>
|
||||
<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  }</div>
|
||||
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">return</span> number == sum;</div>
|
||||
<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keywordflow">return</span> number == <a class="code" href="../../d8/d77/namespacemachine__learning.html#a5305cc18d11943d3dc0d66e70d7e9c44">sum</a>;</div>
|
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<div class="line"><a name="l00053"></a><span class="lineno"> 53</span> }</div>
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</div><!-- fragment --><div class="dynheader">
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Here is the call graph for this function:</div>
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<div class="dyncontent">
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d4/d5d/math_2armstrong__number_8cpp_a9cebd92bc70890c3cfa261d68a5016e7_cgraph.svg" width="278" height="88"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d4/d5d/math_2armstrong__number_8cpp_a9cebd92bc70890c3cfa261d68a5016e7_cgraph.svg" width="312" height="139"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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@@ -197,7 +197,7 @@ Here is the call graph for this function:</div>
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</div><!-- fragment --><div class="dynheader">
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Here is the call graph for this function:</div>
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<div class="dyncontent">
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d4/d5d/math_2armstrong__number_8cpp_ae66f6b31b5ad750f1fe042a706a4e3d4_cgraph.svg" width="468" height="88"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d4/d5d/math_2armstrong__number_8cpp_ae66f6b31b5ad750f1fe042a706a4e3d4_cgraph.svg" width="503" height="139"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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@@ -268,7 +268,7 @@ Here is the call graph for this function:</div>
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</div><!-- fragment --><div class="dynheader">
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Here is the call graph for this function:</div>
|
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<div class="dyncontent">
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d4/d5d/math_2armstrong__number_8cpp_ae1a3968e7947464bee7714f6d43b7002_cgraph.svg" width="370" height="88"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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<div class="center"><iframe scrolling="no" frameborder="0" src="../../d4/d5d/math_2armstrong__number_8cpp_ae1a3968e7947464bee7714f6d43b7002_cgraph.svg" width="404" height="139"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
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</div>
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</div>
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@@ -279,6 +279,7 @@ Here is the call graph for this function:</div>
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<div class="ttc" id="amath_2armstrong__number_8cpp_html_aab733299b170fa5a752503f95f090fcd"><div class="ttname"><a href="../../d4/d5d/math_2armstrong__number_8cpp.html#aab733299b170fa5a752503f95f090fcd">number_of_digits</a></div><div class="ttdeci">int number_of_digits(int num)</div><div class="ttdef"><b>Definition:</b> armstrong_number.cpp:21</div></div>
|
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<div class="ttc" id="amath_2armstrong__number_8cpp_html_a9cebd92bc70890c3cfa261d68a5016e7"><div class="ttname"><a href="../../d4/d5d/math_2armstrong__number_8cpp.html#a9cebd92bc70890c3cfa261d68a5016e7">is_armstrong</a></div><div class="ttdeci">bool is_armstrong(int number)</div><div class="ttdef"><b>Definition:</b> armstrong_number.cpp:36</div></div>
|
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<div class="ttc" id="amath_2armstrong__number_8cpp_html_ae1a3968e7947464bee7714f6d43b7002"><div class="ttname"><a href="../../d4/d5d/math_2armstrong__number_8cpp.html#ae1a3968e7947464bee7714f6d43b7002">test</a></div><div class="ttdeci">void test()</div><div class="ttdef"><b>Definition:</b> armstrong_number.cpp:59</div></div>
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<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  <span class="keywordflow">if</span> (num < 0) {</div>
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<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  sum = sum + (num % 10);</div>
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<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  num = num / 10;</div>
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<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  }</div>
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<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">return</span> sum;</div>
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<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">return</span> <a class="code" href="../../d8/d77/namespacemachine__learning.html#a5305cc18d11943d3dc0d66e70d7e9c44">sum</a>;</div>
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@@ -272,7 +278,7 @@ Here is the call graph for this function:</div>
|
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</div><!-- fragment --><div class="dynheader">
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Here is the call graph for this function:</div>
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||||
@@ -286,6 +292,7 @@ Here is the call graph for this function:</div>
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<div class="ttc" id="aendl_html"><div class="ttname"><a href="http://en.cppreference.com/w/cpp/io/manip/endl.html">std::endl</a></div><div class="ttdeci">T endl(T... args)</div></div>
|
||||
<div class="ttc" id="asum__of__digits_8cpp_html_a4619c78b6ad985713024f930f31c4395"><div class="ttname"><a href="../../d4/d83/sum__of__digits_8cpp.html#a4619c78b6ad985713024f930f31c4395">sum_of_digits</a></div><div class="ttdeci">int sum_of_digits(int num)</div><div class="ttdef"><b>Definition:</b> sum_of_digits.cpp:23</div></div>
|
||||
<div class="ttc" id="asum__of__digits_8cpp_html_a1440a7779ac56f47a3f355ce4a8c7da0"><div class="ttname"><a href="../../d4/d83/sum__of__digits_8cpp.html#a1440a7779ac56f47a3f355ce4a8c7da0">test1</a></div><div class="ttdeci">void test1()</div><div class="ttdef"><b>Definition:</b> sum_of_digits.cpp:40</div></div>
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<div class="ttc" id="anamespacemachine__learning_html_a5305cc18d11943d3dc0d66e70d7e9c44"><div class="ttname"><a href="../../d8/d77/namespacemachine__learning.html#a5305cc18d11943d3dc0d66e70d7e9c44">machine_learning::sum</a></div><div class="ttdeci">T sum(const std::vector< std::valarray< T >> &A)</div><div class="ttdef"><b>Definition:</b> vector_ops.hpp:228</div></div>
|
||||
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