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100644 functions_m.html create mode 100644 functions_n.html create mode 100644 functions_o.html create mode 100644 functions_p.html create mode 100644 functions_q.html create mode 100644 functions_r.html create mode 100644 functions_s.html create mode 100644 functions_t.html create mode 100644 functions_u.html create mode 100644 functions_v.html create mode 100644 functions_w.html create mode 100644 functions_x.html create mode 100644 functions_y.html create mode 100644 functions_~.html create mode 100644 inherit_graph_47.map create mode 100644 inherit_graph_47.md5 create mode 100644 inherit_graph_47.svg create mode 100644 inherit_graph_48.map create mode 100644 inherit_graph_48.md5 create mode 100644 inherit_graph_48.svg create mode 100644 search/namespaces_d.html create mode 100644 search/namespaces_d.js create mode 100644 search/namespaces_e.html create mode 100644 search/namespaces_e.js diff --git a/annotated.html b/annotated.html index 3cc5186be..75d13a5d3 100644 --- a/annotated.html +++ b/annotated.html @@ -95,7 +95,7 @@ $(document).ready(function(){initNavTree('annotated.html',''); initResizable();
Here are the classes, structs, unions and interfaces with brief descriptions:
-
[detail level 123]
+
[detail level 1234]
@@ -116,7 +116,11 @@ $(document).ready(function(){initNavTree('annotated.html',''); initResizable(); - + + + + + @@ -151,9 +155,9 @@ $(document).ready(function(){initNavTree('annotated.html',''); initResizable(); - - - + + +
 ▼NciphersAlgorithms for encryption and decryption
 CHillCipherImplementation of Hill Cipher algorithm
 ▼Ndata_structureData-structure algorithms
 ▼Nlinear_probingAn implementation of hash table using linear probing algorithm
 CEntry
 ▼Nmachine_learningMachine learning algorithms
 Cadaline
 ▼Nneural_network
 ▼Nlayers
 CDenseLayer
 CNeuralNetwork
 Cadaline
 ▼Nquadratic_probingAn implementation of hash table using quadratic probing algorithm
 CEntry
 ▼NstatisticsStatistical algorithms
 Cstack
 Cstack_linkedList
 Ctower
 Ctrie
 ▼CTrie
 CTrieNode
 ▼CTrie
 CTrieNode
 Ctrie
diff --git a/annotated_dup.js b/annotated_dup.js index 944777508..cf7b0ce82 100644 --- a/annotated_dup.js +++ b/annotated_dup.js @@ -38,6 +38,6 @@ var annotated_dup = [ "stack", "d1/dc2/classstack.html", "d1/dc2/classstack" ], [ "stack_linkedList", "d2/dc4/classstack__linked_list.html", "d2/dc4/classstack__linked_list" ], [ "tower", "d2/d2c/structtower.html", "d2/d2c/structtower" ], - [ "trie", "d4/dd9/structtrie.html", "d4/dd9/structtrie" ], - [ "Trie", "dd/d2f/class_trie.html", "dd/d2f/class_trie" ] + [ "Trie", "dd/d2f/class_trie.html", "dd/d2f/class_trie" ], + [ "trie", "d4/dd9/structtrie.html", "d4/dd9/structtrie" ] ]; \ No newline at end of file diff --git a/classes.html b/classes.html index 143eb8b12..a8cb9fc85 100644 --- a/classes.html +++ b/classes.html @@ -98,104 +98,106 @@ $(document).ready(function(){initNavTree('classes.html',''); initResizable(); }) - - - - + + + - - - + + - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - -
  a  
Entry (linear_probing)   
  l  
+
  e  
Point (geometry::jarvis)   stats_computer1 (statistics)   HillCipher (ciphers)   Node   SkipList (data_structure)   
Entry (double_hashing)   
  q  
+
  i  
stats_computer2 (statistics)   node   Solution   
adaline (machine_learning)   Entry (quadratic_probing)   large_number   
  t  
-		Edge   Node (data_structure)   stack   
  b  
  f  
-		linkedlist   query   
list   Queue   tower   
Btree   FenwickTree   LowestCommonAncestor (graph)   queue   trie   
  c  
-
  g  
-
  m  
-		Queue_Array   Trie   
  r  
-		Trie::TrieNode   
cll   Graph   MinHeap   
compare   Graph (graph::is_graph_bipartite)   MinHeapNode   RootedTree (graph)   
Complex   Graph (graph)   mst   
  s  
-
Convexhull (geometry::jarvis)   
  h  
-
  n  
-
CycleCheck   SegmentIntersection   
  d  
-		hash_chain   Node   SkipList (data_structure)   
HillCipher (ciphers)   node   Solution   
double_linked_list   
  i  
-		Node (data_structure)   stack   
  e  
-		Entry (linear_probing)   Item   
  p  
 stack_linkedList   
Entry (double_hashing)   
  l  
+		stats_computer1 (statistics)   
Btree   Entry (quadratic_probing)   Point   stats_computer2 (statistics)   
  c  
+
  f  
+		large_number   Point (geometry::jarvis)   
  t  
+
linkedlist   
  q  
+
cll   FenwickTree   list   tower   
compare   
  g  
+		LowestCommonAncestor (graph)   query   Trie   
Complex   
  m  
+		Queue   trie   
Convexhull (geometry::jarvis)   Graph   queue   Trie::TrieNode   
CycleCheck   Graph (graph::is_graph_bipartite)   MinHeap   Queue_Array   
  d  
+		Graph (graph)   MinHeapNode   
  r  
+
  h  
+		mst   
DenseLayer (machine_learning::neural_network::layers)   
  n  
+		RootedTree (graph)   
double_linked_list   hash_chain   
  s  
+
NeuralNetwork (machine_learning::neural_network)   
SegmentIntersection   
Item   
Edge   Point   
a | b | c | d | e | f | g | h | i | l | m | n | p | q | r | s | t
diff --git a/d0/d24/classgraph_1_1_rooted_tree__coll__graph.map b/d0/d24/classgraph_1_1_rooted_tree__coll__graph.map index f99dd2cd2..5b312166b 100644 --- a/d0/d24/classgraph_1_1_rooted_tree__coll__graph.map +++ b/d0/d24/classgraph_1_1_rooted_tree__coll__graph.map @@ -1,6 +1,6 @@ - - - - + + + + diff --git a/d0/d24/classgraph_1_1_rooted_tree__coll__graph.md5 b/d0/d24/classgraph_1_1_rooted_tree__coll__graph.md5 index 8ac5403eb..181fe8f8e 100644 --- a/d0/d24/classgraph_1_1_rooted_tree__coll__graph.md5 +++ b/d0/d24/classgraph_1_1_rooted_tree__coll__graph.md5 @@ -1 +1 @@ -749777ad85452feddc9d7cd0fbff0c07 \ No newline at end of file +fdd5ab72963604e19437e70aa71723c5 \ No newline at end of file diff --git a/d0/d24/classgraph_1_1_rooted_tree__coll__graph.svg b/d0/d24/classgraph_1_1_rooted_tree__coll__graph.svg index d2370c877..6be3a70c2 100644 --- a/d0/d24/classgraph_1_1_rooted_tree__coll__graph.svg +++ b/d0/d24/classgraph_1_1_rooted_tree__coll__graph.svg @@ -4,17 +4,17 @@ - - + + graph::RootedTree - + Node1 - -graph::RootedTree + +graph::RootedTree @@ -22,50 +22,57 @@ Node2 - -graph::Graph + +graph::Graph Node2->Node1 - - + + Node3 - -std::vector< std::vector -< int > > + +std::vector< std::vector +< int > > Node3->Node2 - - - neighbors + + + neighbors Node4 - -std::vector< int > + +std::vector< int > - + Node4->Node1 - - - parent -level + + + parent +level + + + +Node4->Node3 + + + elements diff --git a/d0/d2e/namespaceneural__network.html b/d0/d2e/namespaceneural__network.html new file mode 100644 index 000000000..bb63c85fc --- /dev/null +++ b/d0/d2e/namespaceneural__network.html @@ -0,0 +1,114 @@ + + + + + + + +Algorithms_in_C++: neural_network Namespace Reference + + + + + + + + + + + + + + + +
+
+ + + + + + +
+
Algorithms_in_C++ +  1.0.0 +
+
Set of algorithms implemented in C++.
+
+
+ + + + + + + +
+
+ +
+
+
+ +
+ +
+
+ + +
+ +
+ +
+
+
neural_network Namespace Reference
+
+
+ +

Neural Network or Multilayer Perceptron. +More...

+

Detailed Description

+

Neural Network or Multilayer Perceptron.

+
+
+ + + + diff --git a/d0/d58/classgraph_1_1_rooted_tree.html b/d0/d58/classgraph_1_1_rooted_tree.html index f9fcf8078..3d5b12c53 100644 --- a/d0/d58/classgraph_1_1_rooted_tree.html +++ b/d0/d58/classgraph_1_1_rooted_tree.html @@ -108,7 +108,7 @@ Inheritance diagram for graph::RootedTree:
Collaboration diagram for graph::RootedTree:
-
+
[legend]
diff --git a/d0/de2/gaussian__elimination_8cpp.html b/d0/de2/gaussian__elimination_8cpp.html index 07908112e..b4bc917f6 100644 --- a/d0/de2/gaussian__elimination_8cpp.html +++ b/d0/de2/gaussian__elimination_8cpp.html @@ -175,15 +175,15 @@ Functions
51  std::cout << std::endl
52  << "Value of the Gaussian Elimination method: " << std::endl;
53  for (i = mat_size - 1; i >= 0; i--) {
-
54  double sum = 0;
+
54  double sum = 0;
55  for (j = mat_size - 1; j > i; j--) {
56  x[i][j] = x[j][j] * x[i][j];
-
57  sum = x[i][j] + sum;
+
57  sum = x[i][j] + sum;
58  }
59  if (x[i][i] == 0)
60  x[i][i] = 0;
61  else
-
62  x[i][i] = (x[i][mat_size] - sum) / (x[i][i]);
+
62  x[i][i] = (x[i][mat_size] - sum) / (x[i][i]);
63 
64  std::cout << "x" << i << "= " << x[i][i] << std::endl;
65  }
@@ -201,7 +201,7 @@ Functions
Here is the call graph for this function:
-
+
@@ -213,6 +213,7 @@ Here is the call graph for this function:
std::cout
std::endl
T endl(T... args)
std::cin
+
machine_learning::sum
T sum(const std::vector< std::valarray< T >> &A)
Definition: vector_ops.hpp:228
std::vector
STL class.
std::vector::size
T size(T... args)
std::cerr
-
std::valarray
STL class.
+
std::valarray< double >
lu_decomposition
int lu_decomposition(const matrix< T > &A, matrix< double > *L, matrix< double > *U)
Definition: lu_decomposition.h:29
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@@ -452,14 +452,14 @@ template<typename T >
201  for (int kk = 0; kk < ROWS; kk++) {
202  col_vector2[kk] = A[kk][k];
203  }
-
204  R[0][i][k] = (col_vector * col_vector2).sum();
+
204  R[0][i][k] = (col_vector * col_vector2).sum();
205  }
206  }
207 }
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@@ -626,6 +626,7 @@ Here is the call graph for this function:
std::setw
T setw(T... args)
std::numeric_limits
std::ostream::precision
T precision(T... args)
+
machine_learning::sum
T sum(const std::vector< std::valarray< T >> &A)
Definition: vector_ops.hpp:228
std::cout
std::ofstream
STL class.
std::perror
T perror(T... args)
-
std::valarray
STL class.
+
std::valarray< double >
std::sin
T sin(T... args)
save_exact_solution
void save_exact_solution(const double &X0, const double &X_MAX, const double &step_size, const std::valarray< double > &Y0)
Definition: ode_semi_implicit_euler.cpp:153
exact_solution
void exact_solution(const double &x, std::valarray< double > *y)
Exact solution of the problem. Used for solution comparison.
Definition: ode_semi_implicit_euler.cpp:66
diff --git a/d3/d17/namespaceutil__functions.html b/d3/d17/namespaceutil__functions.html new file mode 100644 index 000000000..cf983908a --- /dev/null +++ b/d3/d17/namespaceutil__functions.html @@ -0,0 +1,114 @@ + + + + + + + +Algorithms_in_C++: util_functions Namespace Reference + + + + + + + + + + + + + + + +
+
+
+ + + + + +
+
Algorithms_in_C++ +  1.0.0 +
+
Set of algorithms implemented in C++.
+
+
+ + + + + + + + +
+ +
+
+
+ +
+ +
+
+ + +
+ +
+ +
+
+
util_functions Namespace Reference
+
+
+ +

Various utility functions used in Neural network. +More...

+

Detailed Description

+

Various utility functions used in Neural network.

+
+
+ + + + diff --git a/d3/d24/qr__decomposition_8cpp.html b/d3/d24/qr__decomposition_8cpp.html index 32f12f3ba..100dc52be 100644 --- a/d3/d24/qr__decomposition_8cpp.html +++ b/d3/d24/qr__decomposition_8cpp.html @@ -176,7 +176,7 @@ Functions
Here is the call graph for this function:
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+cb02e1f826a5c9132042f629176d9e40 \ No newline at end of file diff --git a/d5/d08/classgraph_1_1_graph__coll__graph.svg b/d5/d08/classgraph_1_1_graph__coll__graph.svg index 5c6d850ff..07c69e1ac 100644 --- a/d5/d08/classgraph_1_1_graph__coll__graph.svg +++ b/d5/d08/classgraph_1_1_graph__coll__graph.svg @@ -4,11 +4,11 @@ - - + + graph::Graph - + Node1 @@ -35,5 +35,21 @@ neighbors + + +Node3 + + +std::vector< int > + + + + + +Node3->Node2 + + + elements + diff --git a/d5/d2c/namespacelayers.html b/d5/d2c/namespacelayers.html new file mode 100644 index 000000000..d718ac136 --- /dev/null +++ b/d5/d2c/namespacelayers.html @@ -0,0 +1,114 @@ + + + + + + + +Algorithms_in_C++: layers Namespace Reference + + + + + + + + + + + + + + + +
+
+ + + + + + +
+
Algorithms_in_C++ +  1.0.0 +
+
Set of algorithms implemented in C++.
+
+
+ + + + + + + +
+
+ +
+
+
+ +
+ +
+
+ + +
+ +
+ +
+
+
layers Namespace Reference
+
+
+ +

This namespace contains layers used in MLP. +More...

+

Detailed Description

+

This namespace contains layers used in MLP.

+
+
+ + + + diff --git a/d5/d39/namespaceactivations.html b/d5/d39/namespaceactivations.html new file mode 100644 index 000000000..ea980eec5 --- /dev/null +++ b/d5/d39/namespaceactivations.html @@ -0,0 +1,114 @@ + + + + + + + +Algorithms_in_C++: activations Namespace Reference + + + + + + + + + + + + + + + +
+
+ + + + + + +
+
Algorithms_in_C++ +  1.0.0 +
+
Set of algorithms implemented in C++.
+
+
+ + + + + + + +
+
+ +
+
+
+ +
+ +
+
+ + +
+ +
+ +
+
+
activations Namespace Reference
+
+
+ +

Various activation functions used in Neural network. +More...

+

Detailed Description

+

Various activation functions used in Neural network.

+
+
+ + + + diff --git a/d5/df6/check__amicable__pair_8cpp.html b/d5/df6/check__amicable__pair_8cpp.html index fccc74221..09f041937 100644 --- a/d5/df6/check__amicable__pair_8cpp.html +++ b/d5/df6/check__amicable__pair_8cpp.html @@ -164,7 +164,7 @@ Functions
Here is the call graph for this function:
-
+
@@ -193,7 +193,7 @@ Here is the call graph for this function:
Here is the call graph for this function:
-
+
@@ -223,7 +223,7 @@ Here is the call graph for this function:
Returns
Sum of the proper divisor of the number.
21  {
22  // Variable to store the sum of all proper divisors.
-
23  int sum = 0;
+
23  int sum = 0;
24  // Below loop condition helps to reduce Time complexity by a factor of
25  // square root of the number.
26  for (int div = 2; div * div <= num; ++div) {
@@ -231,16 +231,22 @@ Here is the call graph for this function:
28  if (num % div == 0) {
29  // If both divisor are same, add once to 'sum'
30  if (div == (num / div)) {
-
31  sum += div;
+
31  sum += div;
32  } else {
33  // If both divisor are not the same, add both to 'sum'.
-
34  sum += (div + (num / div));
+
34  sum += (div + (num / div));
35  }
36  }
37  }
-
38  return sum + 1;
+
38  return sum + 1;
39 }
- +
+Here is the call graph for this function:
+
+
+
+
+ @@ -269,7 +275,7 @@ Here is the call graph for this function:
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+
@@ -283,6 +289,7 @@ Here is the call graph for this function:
std::endl
T endl(T... args)
test
void test()
Definition: check_amicable_pair.cpp:56
are_amicable
bool are_amicable(int x, int y)
Definition: check_amicable_pair.cpp:48
+
machine_learning::sum
T sum(const std::vector< std::valarray< T >> &A)
Definition: vector_ops.hpp:228
std::ofstream
STL class.
std::ofstream::close
T close(T... args)
std::perror
T perror(T... args)
-
std::valarray
STL class.
+
std::valarray< double >
std::ofstream::open
T open(T... args)
problem
void problem(const double &x, std::valarray< double > *y, std::valarray< double > *dy)
Problem statement for a system with first-order differential equations. Updates the system differenti...
Definition: ode_semi_implicit_euler.cpp:53
problem
void problem(const double &x, std::valarray< double > *y, std::valarray< double > *dy)
Problem statement for a system with first-order differential equations. Updates the system differenti...
Definition: ode_forward_euler.cpp:54
diff --git a/d6/dd3/ode__midpoint__euler_8cpp.html b/d6/dd3/ode__midpoint__euler_8cpp.html index 88e51f11a..fd8260e44 100644 --- a/d6/dd3/ode__midpoint__euler_8cpp.html +++ b/d6/dd3/ode__midpoint__euler_8cpp.html @@ -396,7 +396,7 @@ Here is the call graph for this function:
std::cout
std::ofstream
STL class.
std::perror
T perror(T... args)
-
std::valarray
STL class.
+
std::valarray< double >
std::sin
T sin(T... args)
midpoint_euler
double midpoint_euler(double dx, double x0, double x_max, std::valarray< double > *y, bool save_to_file=false)
Compute approximation using the midpoint-Euler method in the given limits.
Definition: ode_midpoint_euler.cpp:107
std::cin
diff --git a/d7/d40/class_solution__coll__graph.map b/d7/d40/class_solution__coll__graph.map index cf9eeebf0..10ea5f137 100644 --- a/d7/d40/class_solution__coll__graph.map +++ b/d7/d40/class_solution__coll__graph.map @@ -1,6 +1,6 @@ - - - - + + + + diff --git a/d7/d40/class_solution__coll__graph.md5 b/d7/d40/class_solution__coll__graph.md5 index 83a128604..c3aad1e61 100644 --- a/d7/d40/class_solution__coll__graph.md5 +++ b/d7/d40/class_solution__coll__graph.md5 @@ -1 +1 @@ -9e693fb7fbd874f7ac6c139d189b702e \ No newline at end of file +3e541ed381f3c66f8083b8b7f87a52bf \ No newline at end of file diff --git a/d7/d40/class_solution__coll__graph.svg b/d7/d40/class_solution__coll__graph.svg index 046608243..12bcc5169 100644 --- a/d7/d40/class_solution__coll__graph.svg +++ b/d7/d40/class_solution__coll__graph.svg @@ -4,17 +4,17 @@ - - + + Solution - + Node1 - -Solution + +Solution @@ -22,52 +22,59 @@ Node2 - -std::vector< std::vector -< int > > + +std::vector< std::vector +< int > > Node2->Node1 - - - bridge -graph + + + bridge +graph Node3 - -std::vector< int > + +std::vector< int > - + Node3->Node1 - - - out_time -in_time + + + out_time +in_time + + + +Node3->Node2 + + + elements Node4 - -std::vector< bool > + +std::vector< bool > - + Node4->Node1 - - - visited + + + visited diff --git a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.map b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.map new file mode 100644 index 000000000..608c6e430 --- /dev/null +++ b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.map @@ -0,0 +1,8 @@ + + + + + + + + diff --git a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5 b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5 new file mode 100644 index 000000000..358d45712 --- /dev/null +++ b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5 @@ -0,0 +1 @@ +040d8fc937b68336edec7878d1b4b3a4 \ No newline at end of file diff --git a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg new file mode 100644 index 000000000..f222571ac --- /dev/null +++ b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg @@ -0,0 +1,107 @@ + + + + + + +machine_learning::neural_network::NeuralNetwork + + + +Node1 + + +machine_learning::neural +_network::NeuralNetwork + + + + + +Node2 + + +std::vector< machine +_learning::neural_network +::layers::DenseLayer > + + + + + +Node2->Node1 + + + layers + + + +Node3 + + +machine_learning::neural +_network::layers::DenseLayer + + + + + +Node3->Node2 + + + elements + + + +Node4 + + +std::string + + + + + +Node4->Node3 + + + activation + + + +Node5 + + +std::vector< std::valarray +< double > > + + + + + +Node5->Node3 + + + kernal + + + +Node6 + + +std::valarray< double > + + + + + +Node6->Node5 + + + elements + + + diff --git a/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html b/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html new file mode 100644 index 000000000..29965585a --- /dev/null +++ b/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html @@ -0,0 +1,131 @@ + + + + + + + +Algorithms_in_C++: Member List + + + + + + + + + + + + + + + +
+
+ + + + + + +
+
Algorithms_in_C++ +  1.0.0 +
+
Set of algorithms implemented in C++.
+
+
+ + + + + + + +
+
+ +
+
+
+ +
+ +
+
+ + +
+ +
+ +
+
+
machine_learning::neural_network::NeuralNetwork Member List
+
+
+ +

This is the complete list of members for machine_learning::neural_network::NeuralNetwork, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + +
__detailed_single_prediction(const std::vector< std::valarray< double >> &X)machine_learning::neural_network::NeuralNetworkinlineprivate
batch_predict(const std::vector< std::vector< std::valarray< double >>> &X)machine_learning::neural_network::NeuralNetworkinline
evaluate(const std::vector< std::vector< std::valarray< double >>> &X, const std::vector< std::vector< std::valarray< double >>> &Y)machine_learning::neural_network::NeuralNetworkinline
evaluate_from_csv(const std::string &file_name, const bool &last_label, const bool &normalize, const int &slip_lines=1)machine_learning::neural_network::NeuralNetworkinline
fit(const std::vector< std::vector< std::valarray< double >>> &X_, const std::vector< std::vector< std::valarray< double >>> &Y_, const int &epochs=100, const double &learning_rate=0.01, const size_t &batch_size=32, const bool &shuffle=true)machine_learning::neural_network::NeuralNetworkinline
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)machine_learning::neural_network::NeuralNetworkinline
get_XY_from_csv(const std::string &file_name, const bool &last_label, const bool &normalize, const int &slip_lines=1)machine_learning::neural_network::NeuralNetworkinline
layers (defined in machine_learning::neural_network::NeuralNetwork)machine_learning::neural_network::NeuralNetworkprivate
load_model(const std::string &file_name)machine_learning::neural_network::NeuralNetworkinline
NeuralNetwork(const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernals)machine_learning::neural_network::NeuralNetworkinlineprivate
NeuralNetwork()=defaultmachine_learning::neural_network::NeuralNetwork
NeuralNetwork(const std::vector< std::pair< int, std::string >> &config)machine_learning::neural_network::NeuralNetworkinlineexplicit
NeuralNetwork(const NeuralNetwork &model)=defaultmachine_learning::neural_network::NeuralNetwork
NeuralNetwork(NeuralNetwork &&)=defaultmachine_learning::neural_network::NeuralNetwork
operator=(const NeuralNetwork &model)=defaultmachine_learning::neural_network::NeuralNetwork
operator=(NeuralNetwork &&)=defaultmachine_learning::neural_network::NeuralNetwork
save_model(const std::string &_file_name)machine_learning::neural_network::NeuralNetworkinline
single_predict(const std::vector< std::valarray< double >> &X)machine_learning::neural_network::NeuralNetworkinline
summary()machine_learning::neural_network::NeuralNetworkinline
~NeuralNetwork()=defaultmachine_learning::neural_network::NeuralNetwork
+
+ + + + diff --git a/d8/d77/namespacemachine__learning.html b/d8/d77/namespacemachine__learning.html index 6e62bbc8c..2b7f60564 100644 --- a/d8/d77/namespacemachine__learning.html +++ b/d8/d77/namespacemachine__learning.html @@ -119,6 +119,72 @@ Functions   void kohonen_som_tracer (const std::vector< std::valarray< double >> &X, std::vector< std::valarray< double >> *W, double alpha_min)   +template<typename T > +std::ostream & operator<< (std::ostream &out, std::vector< std::valarray< T >> const &A) +  +template<typename T > +std::ostream & operator<< (std::ostream &out, const std::pair< T, T > &A) +  +template<typename T > +std::ostream & operator<< (std::ostream &out, const std::valarray< T > &A) +  +template<typename T > +std::valarray< T > insert_element (const std::valarray< T > &A, const T &ele) +  +template<typename T > +std::valarray< T > pop_front (const std::valarray< T > &A) +  +template<typename T > +std::valarray< T > pop_back (const std::valarray< T > &A) +  +template<typename T > +void equal_shuffle (std::vector< std::vector< std::valarray< T >> > &A, std::vector< std::vector< std::valarray< T >> > &B) +  +template<typename T > +void uniform_random_initialization (std::vector< std::valarray< T >> &A, const std::pair< size_t, size_t > &shape, const T &low, const T &high) +  +template<typename T > +void unit_matrix_initialization (std::vector< std::valarray< T >> &A, const std::pair< size_t, size_t > &shape) +  +template<typename T > +void zeroes_initialization (std::vector< std::valarray< T >> &A, const std::pair< size_t, size_t > &shape) +  +template<typename T > +T sum (const std::vector< std::valarray< T >> &A) +  +template<typename T > +std::pair< size_t, size_t > get_shape (const std::vector< std::valarray< T >> &A) +  +template<typename T > +std::vector< std::vector< std::valarray< T > > > minmax_scaler (const std::vector< std::vector< std::valarray< T >>> &A, const T &low, const T &high) +  +template<typename T > +size_t argmax (const std::vector< std::valarray< T >> &A) +  +template<typename T > +std::vector< std::valarray< T > > apply_function (const std::vector< std::valarray< T >> &A, T(*func)(const T &)) +  +template<typename T > +std::vector< std::valarray< T > > operator* (const std::vector< std::valarray< T >> &A, const T &val) +  +template<typename T > +std::vector< std::valarray< T > > operator/ (const std::vector< std::valarray< T >> &A, const T &val) +  +template<typename T > +std::vector< std::valarray< T > > transpose (const std::vector< std::valarray< T >> &A) +  +template<typename T > +std::vector< std::valarray< T > > operator+ (const std::vector< std::valarray< T >> &A, const std::vector< std::valarray< T >> &B) +  +template<typename T > +std::vector< std::valarray< T > > operator- (const std::vector< std::valarray< T >> &A, const std::vector< std::valarray< T >> &B) +  +template<typename T > +std::vector< std::valarray< T > > multiply (const std::vector< std::valarray< T >> &A, const std::vector< std::valarray< T >> &B) +  +template<typename T > +std::vector< std::valarray< T > > hadamard_product (const std::vector< std::valarray< T >> &A, const std::vector< std::valarray< T >> &B) +  @@ -127,7 +193,337 @@ Variables

Variables

Detailed Description

Machine learning algorithms.

+

Machine Learning algorithms.

Function Documentation

+ +

◆ apply_function()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::apply_function (const std::vector< std::valarray< T >> & A,
T(*)(const T &) func 
)
+
+

Function which applys supplied function to every element of 2D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
A2D vector on which function will be applied
funcFunction to be applied
+
+
+
Returns
new resultant vector
+
316  {
+
317  std::vector<std::valarray<double>> B = A; // New vector to store resultant vector
+
318  for(auto &b : B) { // For every row in vector
+
319  b = b.apply(func); // Apply function to that row
+
320  }
+
321  return B; // Return new resultant 2D vector
+
322 }
+
+
+
+ +

◆ argmax()

+ +
+
+
+template<typename T >
+ + + + + + + + +
size_t machine_learning::argmax (const std::vector< std::valarray< T >> & A)
+
+

Function to get index of maximum element in 2D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + +
A2D vector for which maximum index is required
+
+
+
Returns
index of maximum element
+
296  {
+
297  const auto shape = get_shape(A);
+
298  // As this function is used on predicted (or target) vector, shape should be (1, X)
+
299  if(shape.first != 1) {
+
300  std::cerr << "ERROR: (argmax) Supplied vector is ineligible for argmax" << std::endl;
+
301  std::exit(EXIT_FAILURE);
+
302  }
+
303  // Return distance of max element from first element (i.e. index)
+
304  return std::distance(std::begin(A[0]), std::max_element(std::begin(A[0]), std::end(A[0])));
+
305 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ equal_shuffle()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
void machine_learning::equal_shuffle (std::vector< std::vector< std::valarray< T >> > & A,
std::vector< std::vector< std::valarray< T >> > & B 
)
+
+

Function to equally shuffle two 3D vectors (used for shuffling training data)

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
AFirst 3D vector
BSecond 3D vector
+
+
+
134  {
+
135  // If two vectors have different sizes
+
136  if(A.size() != B.size())
+
137  {
+
138  std::cerr << "ERROR : Can not equally shuffle two vectors with different sizes: ";
+
139  std::cerr << A.size() << " and " << B.size() << std::endl;
+
140  std::exit(EXIT_FAILURE);
+
141  }
+
142  for(size_t i = 0; i < A.size(); i++) { // For every element in A and B
+
143  // Genrating random index < size of A and B
+
144  std::srand(std::chrono::system_clock::now().time_since_epoch().count());
+
145  size_t random_index = std::rand() % A.size();
+
146  // Swap elements in both A and B with same random index
+
147  std::swap(A[i], A[random_index]);
+
148  std::swap(B[i], B[random_index]);
+
149  }
+
150  return;
+
151 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ get_shape()

+ +
+
+
+template<typename T >
+ + + + + + + + +
std::pair<size_t, size_t> machine_learning::get_shape (const std::vector< std::valarray< T >> & A)
+
+

Function to get shape of given 2D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + +
A2D vector for which shape is required
+
+
+
Returns
shape as pair
+
243  {
+
244  const size_t sub_size = (*A.begin()).size();
+
245  for(const auto &a : A) {
+
246  // If supplied vector don't have same shape in all rows
+
247  if(a.size() != sub_size) {
+
248  std::cerr << "ERROR: (get_shape) Supplied vector is not 2D Matrix" << std::endl;
+
249  std::exit(EXIT_FAILURE);
+
250  }
+
251  }
+
252  return std::make_pair(A.size(), sub_size); // Return shape as pair
+
253 }
+
+
+
+ +

◆ hadamard_product()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::hadamard_product (const std::vector< std::valarray< T >> & A,
const std::vector< std::valarray< T >> & B 
)
+
+

Function to get hadamard product of two 2D vectors

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
AFirst 2D vector
BSecond 2D vector
+
+
+
Returns
new resultant vector
+
466  {
+
467  const auto shape_a = get_shape(A);
+
468  const auto shape_b = get_shape(B);
+
469  // If vectors are not eligible for hadamard product
+
470  if(shape_a.first != shape_b.first || shape_a.second != shape_b.second) {
+
471  std::cerr << "ERROR: (hadamard_product) Supplied vectors have different shapes ";
+
472  std::cerr << shape_a << " and " << shape_b << std::endl;
+
473  std::exit(EXIT_FAILURE);
+
474  }
+
475  std::vector<std::valarray<T>> C; // Vector to store result
+
476  for(size_t i = 0; i < A.size(); i++) {
+
477  C.push_back(A[i] * B[i]); // Elementwise multiplication
+
478  }
+
479  return C; // Return new resultant 2D vector
+
480 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ insert_element()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::valarray<T> machine_learning::insert_element (const std::valarray< T > & A,
const T & ele 
)
+
+

Function to insert element into 1D vector

Template Parameters
+ + +
Ttypename of the 1D vector and the element
+
+
+
Parameters
+ + + +
A1D vector in which element will to be inserted
eleelement to be inserted
+
+
+
Returns
new resultant vector
+
84  {
+
85  std::valarray <T> B; // New 1D vector to store resultant vector
+
86  B.resize(A.size() + 1); // Resizing it accordingly
+
87  for(size_t i = 0; i < A.size(); i++) { // For every element in A
+
88  B[i] = A[i]; // Copy element in B
+
89  }
+
90  B[B.size() - 1] = ele; // Inserting new element in last position
+
91  return B; // Return resultant vector
+
92 }
+
+
+

◆ kohonen_som()

@@ -213,7 +609,7 @@ Variables
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+
@@ -288,10 +684,651 @@ Here is the call graph for this function:
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+
+
+ + +

◆ minmax_scaler()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + + + + + + + +
std::vector<std::vector<std::valarray<T> > > machine_learning::minmax_scaler (const std::vector< std::vector< std::valarray< T >>> & A,
const T & low,
const T & high 
)
+
+

Function to scale given 3D vector using min-max scaler

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + + +
A3D vector which will be scaled
lownew minimum value
highnew maximum value
+
+
+
Returns
new scaled 3D vector
+
265  {
+
266  std::vector<std::vector<std::valarray<T>>> B = A; // Copying into new vector B
+
267  const auto shape = get_shape(B[0]); // Storing shape of B's every element
+
268  // As this function is used for scaling training data vector should be of shape (1, X)
+
269  if(shape.first != 1) {
+
270  std::cerr << "ERROR: (MinMax Scaling) Supplied vector is not supported for minmax scaling, shape: ";
+
271  std::cerr << shape << std::endl;
+
272  std::exit(EXIT_FAILURE);
+
273  }
+
274  for(size_t i = 0; i < shape.second; i++) {
+
275  T min = B[0][0][i], max = B[0][0][i];
+
276  for(size_t j = 0; j < B.size(); j++) {
+
277  // Updating minimum and maximum values
+
278  min = std::min(min, B[j][0][i]);
+
279  max = std::max(max, B[j][0][i]);
+
280  }
+
281  for(size_t j = 0; j < B.size(); j++) {
+
282  // Applying min-max scaler formula
+
283  B[j][0][i] = ((B[j][0][i] - min) / (max - min)) * (high - low) + low;
+
284  }
+
285  }
+
286  return B; // Return new resultant 3D vector
+
287 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ multiply()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::multiply (const std::vector< std::valarray< T >> & A,
const std::vector< std::valarray< T >> & B 
)
+
+

Function to multiply two 2D vectors

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
AFirst 2D vector
BSecond 2D vector
+
+
+
Returns
new resultant vector
+
434  {
+
435  const auto shape_a = get_shape(A);
+
436  const auto shape_b = get_shape(B);
+
437  // If vectors are not eligible for multiplication
+
438  if(shape_a.second != shape_b.first ) {
+
439  std::cerr << "ERROR: (multiply) Supplied vectors are not eligible for multiplication ";
+
440  std::cerr << shape_a << " and " << shape_b << std::endl;
+
441  std::exit(EXIT_FAILURE);
+
442  }
+
443  std::vector<std::valarray<T>> C; // Vector to store result
+
444  // Normal matrix multiplication
+
445  for (size_t i = 0; i < shape_a.first; i++) {
+
446  std::valarray<T> row;
+
447  row.resize(shape_b.second);
+
448  for(size_t j = 0; j < shape_b.second; j++) {
+
449  for(size_t k = 0; k < shape_a.second; k++) {
+
450  row[j] += A[i][k] * B[k][j];
+
451  }
+
452  }
+
453  C.push_back(row);
+
454  }
+
455  return C; // Return new resultant 2D vector
+
456 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ operator*()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::operator* (const std::vector< std::valarray< T >> & A,
const T & val 
)
+
+

Overloaded operator "*" to multiply given 2D vector with scaler

Template Parameters
+ + +
Ttypename of both vector and the scaler
+
+
+
Parameters
+ + + +
A2D vector to which scaler will be multiplied
valScaler value which will be multiplied
+
+
+
Returns
new resultant vector
+
332  {
+
333  std::vector<std::valarray<double>> B = A; // New vector to store resultant vector
+
334  for(auto &b : B) { // For every row in vector
+
335  b = b * val; // Multiply row with scaler
+
336  }
+
337  return B; // Return new resultant 2D vector
+
338 }
+
+
+
+ +

◆ operator+()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::operator+ (const std::vector< std::valarray< T >> & A,
const std::vector< std::valarray< T >> & B 
)
+
+

Overloaded operator "+" to add two 2D vectors

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
AFirst 2D vector
BSecond 2D vector
+
+
+
Returns
new resultant vector
+
386  {
+
387  const auto shape_a = get_shape(A);
+
388  const auto shape_b = get_shape(B);
+
389  // If vectors don't have equal shape
+
390  if(shape_a.first != shape_b.first || shape_a.second != shape_b.second) {
+
391  std::cerr << "ERROR: (vector addition) Supplied vectors have different shapes ";
+
392  std::cerr << shape_a << " and " << shape_b << std::endl;
+
393  std::exit(EXIT_FAILURE);
+
394  }
+
395  std::vector<std::valarray <T>> C;
+
396  for(size_t i = 0; i < A.size(); i++) { // For every row
+
397  C.push_back(A[i] + B[i]); // Elementwise addition
+
398  }
+
399  return C; // Return new resultant 2D vector
+
400 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ operator-()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::operator- (const std::vector< std::valarray< T >> & A,
const std::vector< std::valarray< T >> & B 
)
+
+

Overloaded operator "-" to add subtract 2D vectors

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
AFirst 2D vector
BSecond 2D vector
+
+
+
Returns
new resultant vector
+
410  {
+
411  const auto shape_a = get_shape(A);
+
412  const auto shape_b = get_shape(B);
+
413  // If vectors don't have equal shape
+
414  if(shape_a.first != shape_b.first || shape_a.second != shape_b.second) {
+
415  std::cerr << "ERROR: (vector subtraction) Supplied vectors have different shapes ";
+
416  std::cerr << shape_a << " and " << shape_b << std::endl;
+
417  std::exit(EXIT_FAILURE);
+
418  }
+
419  std::vector<std::valarray<T>> C; // Vector to store result
+
420  for(size_t i = 0; i < A.size(); i++) { // For every row
+
421  C.push_back(A[i] - B[i]); // Elementwise substraction
+
422  }
+
423  return C; // Return new resultant 2D vector
+
424 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ operator/()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::vector<std::valarray <T> > machine_learning::operator/ (const std::vector< std::valarray< T >> & A,
const T & val 
)
+
+

Overloaded operator "/" to divide given 2D vector with scaler

Template Parameters
+ + +
Ttypename of the vector and the scaler
+
+
+
Parameters
+ + + +
A2D vector to which scaler will be divided
valScaler value which will be divided
+
+
+
Returns
new resultant vector
+
348  {
+
349  std::vector<std::valarray<double>> B = A; // New vector to store resultant vector
+
350  for(auto &b : B) { // For every row in vector
+
351  b = b / val; // Divide row with scaler
+
352  }
+
353  return B; // Return new resultant 2D vector
+
354 }
+
+
+
+ +

◆ operator<<() [1/3]

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::ostream& machine_learning::operator<< (std::ostream & out,
const std::pair< T, T > & A 
)
+
+

Overloaded operator "<<" to print a pair

Template Parameters
+ + +
Ttypename of the pair
+
+
+
Parameters
+ + + +
outstd::ostream to output
APair to be printed
+
+
+
51  {
+
52  // Setting output precision to 4 in case of floating point numbers
+
53  out.precision(4);
+
54  // printing pair in the form (p, q)
+
55  std::cerr << "(" << A.first << ", " << A.second << ")";
+
56  return out;
+
57 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ operator<<() [2/3]

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::ostream& machine_learning::operator<< (std::ostream & out,
const std::valarray< T > & A 
)
+
+

Overloaded operator "<<" to print a 1D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
outstd::ostream to output
A1D vector to be printed
+
+
+
66  {
+
67  // Setting output precision to 4 in case of floating point numbers
+
68  out.precision(4);
+
69  for(const auto &a : A) { // For every element in the vector.
+
70  std::cerr << a << ' '; // Print element
+
71  }
+
72  std::cerr << std::endl;
+
73  return out;
+
74 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ operator<<() [3/3]

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
std::ostream& machine_learning::operator<< (std::ostream & out,
std::vector< std::valarray< T >> const & A 
)
+
+

Overloaded operator "<<" to print 2D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
outstd::ostream to output
A2D vector to be printed
+
+
+
32  {
+
33  // Setting output precision to 4 in case of floating point numbers
+
34  out.precision(4);
+
35  for(const auto &a : A) { // For each row in A
+
36  for(const auto &x : a) { // For each element in row
+
37  std::cerr << x << ' '; // print element
+
38  }
+
39  std::cerr << std::endl;
+
40  }
+
41  return out;
+
42 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ pop_back()

+ +
+
+
+template<typename T >
+ + + + + + + + +
std::valarray<T> machine_learning::pop_back (const std::valarray< T > & A)
+
+

Function to remove last element from 1D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + +
A1D vector from which last element will be removed
+
+
+
Returns
new resultant vector
+
117  {
+
118  std::valarray <T> B; // New 1D vector to store resultant vector
+
119  B.resize(A.size() - 1); // Resizing it accordingly
+
120  for(size_t i = 0; i < A.size() - 1; i ++) { // For every (except last) element in A
+
121  B[i] = A[i]; // Copy element in B
+
122  }
+
123  return B; // Return resultant vector
+
124 }
+
+
+
+ +

◆ pop_front()

+ +
+
+
+template<typename T >
+ + + + + + + + +
std::valarray<T> machine_learning::pop_front (const std::valarray< T > & A)
+
+

Function to remove first element from 1D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + +
A1D vector from which first element will be removed
+
+
+
Returns
new resultant vector
+
101  {
+
102  std::valarray <T> B; // New 1D vector to store resultant vector
+
103  B.resize(A.size() - 1); // Resizing it accordingly
+
104  for(size_t i = 1; i < A.size(); i ++) { // // For every (except first) element in A
+
105  B[i - 1] = A[i]; // Copy element in B with left shifted position
+
106  }
+
107  return B; // Return resultant vector
+
108 }
+
@@ -383,6 +1420,230 @@ Here is the call graph for this function: + + + +

◆ sum()

+ +
+
+
+template<typename T >
+ + + + + + + + +
T machine_learning::sum (const std::vector< std::valarray< T >> & A)
+
+

Function to get sum of all elements in 2D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + +
A2D vector for which sum is required
+
+
+
Returns
returns sum of all elements of 2D vector
+
228  {
+
229  T cur_sum = 0; // Initially sum is zero
+
230  for(const auto &a : A) { // For every row in A
+
231  cur_sum += a.sum(); // Add sum of that row to current sum
+
232  }
+
233  return cur_sum; // Return sum
+
234 }
+
+
+
+ +

◆ transpose()

+ +
+
+
+template<typename T >
+ + + + + + + + +
std::vector<std::valarray <T> > machine_learning::transpose (const std::vector< std::valarray< T >> & A)
+
+

Function to get transpose of 2D vector

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + +
A2D vector which will be transposed
+
+
+
Returns
new resultant vector
+
363  {
+
364  const auto shape = get_shape(A); // Current shape of vector
+
365  std::vector <std::valarray <T> > B; // New vector to store result
+
366  // Storing transpose values of A in B
+
367  for(size_t j = 0; j < shape.second; j++) {
+
368  std::valarray <T> row;
+
369  row.resize(shape.first);
+
370  for(size_t i = 0; i < shape.first; i++) {
+
371  row[i] = A[i][j];
+
372  }
+
373  B.push_back(row);
+
374  }
+
375  return B; // Return new resultant 2D vector
+
376 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ uniform_random_initialization()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void machine_learning::uniform_random_initialization (std::vector< std::valarray< T >> & A,
const std::pair< size_t, size_t > & shape,
const T & low,
const T & high 
)
+
+

Function to initialize given 2D vector using uniform random initialization

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + + + +
A2D vector to be initialized
shaperequired shape
lowlower limit on value
highupper limit on value
+
+
+
165  {
+
166  A.clear(); // Making A empty
+
167  // Uniform distribution in range [low, high]
+
168  std::default_random_engine generator(std::chrono::system_clock::now().time_since_epoch().count());
+
169  std::uniform_real_distribution <T> distribution(low, high);
+
170  for(size_t i = 0; i < shape.first; i++) { // For every row
+
171  std::valarray <T> row; // Making empty row which will be inserted in vector
+
172  row.resize(shape.second);
+
173  for(auto &r : row) { // For every element in row
+
174  r = distribution(generator); // copy random number
+
175  }
+
176  A.push_back(row); // Insert new row in vector
+
177  }
+
178  return;
+
179 }
+
+Here is the call graph for this function:
+
+
+
+
+ +
+
+ +

◆ unit_matrix_initialization()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
void machine_learning::unit_matrix_initialization (std::vector< std::valarray< T >> & A,
const std::pair< size_t, size_t > & shape 
)
+
+

Function to Intialize 2D vector as unit matrix

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
A2D vector to be initialized
shaperequired shape
+
+
+
191  {
+
192  A.clear(); // Making A empty
+
193  for(size_t i = 0; i < shape.first; i++) {
+
194  std::valarray <T> row; // Making empty row which will be inserted in vector
+
195  row.resize(shape.second);
+
196  row[i] = T(1); // Insert 1 at ith position
+
197  A.push_back(row); // Insert new row in vector
+
198  }
+
199  return;
+
200 }
+
@@ -556,7 +1817,7 @@ Here is the call graph for this function:
218  // compute Euclidian distance of each output
219  // point from the current sample
220  auto d = ((*W)[x][y] - X);
-
221  (*D)[x][y] = (d * d).sum();
+
221  (*D)[x][y] = (d * d).sum();
222  (*D)[x][y] = std::sqrt((*D)[x][y]);
223  }
224  }
@@ -598,10 +1859,62 @@ Here is the call graph for this function:
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-
+
+
+ + +

◆ zeroes_initialization()

+ +
+
+
+template<typename T >
+ + + + + + + + + + + + + + + + + + +
void machine_learning::zeroes_initialization (std::vector< std::valarray< T >> & A,
const std::pair< size_t, size_t > & shape 
)
+
+

Function to Intialize 2D vector as zeroes

Template Parameters
+ + +
Ttypename of the vector
+
+
+
Parameters
+ + + +
A2D vector to be initialized
shaperequired shape
+
+
+
211  {
+
212  A.clear(); // Making A empty
+
213  for(size_t i = 0; i < shape.first; i++) {
+
214  std::valarray <T> row; // Making empty row which will be inserted in vector
+
215  row.resize(shape.second); // By default all elements are zero
+
216  A.push_back(row); // Insert new row in vector
+
217  }
+
218  return;
+
219 }
+

Variable Documentation

@@ -630,25 +1943,39 @@ Here is the call graph for this function: +
std::srand
T srand(T... args)
+
std::max_element
T max_element(T... args)
machine_learning::update_weights
void update_weights(const std::valarray< double > &x, std::vector< std::valarray< double >> *W, std::valarray< double > *D, double alpha, int R)
Definition: kohonen_som_trace.cpp:103
std::vector
STL class.
std::vector::size
T size(T... args)
+
std::default_random_engine
std::distance
T distance(T... args)
std::strerror
T strerror(T... args)
machine_learning::update_weights
double update_weights(const std::valarray< double > &X, std::vector< std::vector< std::valarray< double >>> *W, std::vector< std::valarray< double >> *D, double alpha, int R)
Definition: kohonen_som_topology.cpp:200
get_min_2d
void get_min_2d(const std::vector< std::valarray< double >> &X, double *val, int *x_idx, int *y_idx)
Definition: kohonen_som_topology.cpp:105
std::sqrt
T sqrt(T... args)
-
std::cout
+
std::vector::clear
T clear(T... args)
+
std::uniform_real_distribution
+
std::vector::push_back
T push_back(T... args)
+
std::cerr
std::ofstream
STL class.
std::min_element
T min_element(T... args)
std::valarray
STL class.
+
std::rand
T rand(T... args)
+
std::swap
T swap(T... args)
std::min
T min(T... args)
std::endl
T endl(T... args)
std::exp
T exp(T... args)
std::begin
T begin(T... args)
+
machine_learning::get_shape
std::pair< size_t, size_t > get_shape(const std::vector< std::valarray< T >> &A)
Definition: vector_ops.hpp:243
+
std::make_pair
T make_pair(T... args)
std::end
T end(T... args)
std::max
T max(T... args)
+
std::exit
T exit(T... args)
+
std::ostream::precision
T precision(T... args)
+
machine_learning::sum
T sum(const std::vector< std::valarray< T >> &A)
Definition: vector_ops.hpp:228
std::pow
T pow(T... args)
+
std::chrono::system_clock::now
T now(T... args)