From 43362d3b501168a8b6e356e0525c84cdad57e14f Mon Sep 17 00:00:00 2001
From: Krishna Vedala <7001608+kvedala@users.noreply.github.com>
Date: Fri, 5 Jun 2020 20:26:34 -0400
Subject: [PATCH] add author name
---
machine_learning/adaline_learning.cpp | 2 ++
machine_learning/kohonen_som_trace.cpp | 2 ++
math/fibonacci_fast.cpp | 1 +
math/fibonacci_large.cpp | 1 +
math/large_factorial.cpp | 1 +
math/large_number.h | 1 +
math/realtime_stats.cpp | 1 +
math/sqrt_double.cpp | 4 ++--
numerical_methods/durand_kerner_roots.cpp | 1 +
numerical_methods/newton_raphson_method.cpp | 4 +++-
numerical_methods/ordinary_least_squares_regressor.cpp | 1 +
sorting/shell_sort2.cpp | 1 +
12 files changed, 17 insertions(+), 3 deletions(-)
diff --git a/machine_learning/adaline_learning.cpp b/machine_learning/adaline_learning.cpp
index 077b9408e..eafeca826 100644
--- a/machine_learning/adaline_learning.cpp
+++ b/machine_learning/adaline_learning.cpp
@@ -3,6 +3,8 @@
* \brief [Adaptive Linear Neuron
* (ADALINE)](https://en.wikipedia.org/wiki/ADALINE) implementation
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
+ *
* 
diff --git a/machine_learning/kohonen_som_trace.cpp b/machine_learning/kohonen_som_trace.cpp
index 4929eaef3..31e9421bf 100644
--- a/machine_learning/kohonen_som_trace.cpp
+++ b/machine_learning/kohonen_som_trace.cpp
@@ -8,6 +8,8 @@
* follows the given data points. This this creates a chain of nodes that
* resembles the given input shape.
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
+ *
* \note This C++ version of the program is considerable slower than its [C
* counterpart](https://github.com/kvedala/C/blob/master/machine_learning/kohonen_som_trace.c)
* \note The compiled code is much slower when compiled with MS Visual C++ 2019
diff --git a/math/fibonacci_fast.cpp b/math/fibonacci_fast.cpp
index 08cced351..8fdb20058 100644
--- a/math/fibonacci_fast.cpp
+++ b/math/fibonacci_fast.cpp
@@ -11,6 +11,7 @@
* found if we have already found n/2th or (n+1)/2th fibonacci It is a property
* of fibonacci similar to matrix exponentiation.
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
* @see fibonacci_large.cpp, fibonacci.cpp, string_fibonacci.cpp
*/
diff --git a/math/fibonacci_large.cpp b/math/fibonacci_large.cpp
index d9dbff799..e4f4e5eaf 100644
--- a/math/fibonacci_large.cpp
+++ b/math/fibonacci_large.cpp
@@ -7,6 +7,7 @@
* Took 0.608246 seconds to compute 50,000^th Fibonacci
* number that contains 10450 digits!
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
* @see fibonacci.cpp, fibonacci_fast.cpp, string_fibonacci.cpp
*/
diff --git a/math/large_factorial.cpp b/math/large_factorial.cpp
index 1027f41ab..20c677cdc 100644
--- a/math/large_factorial.cpp
+++ b/math/large_factorial.cpp
@@ -2,6 +2,7 @@
* @file
* @brief Compute factorial of any arbitratily large number/
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
* @see factorial.cpp
*/
#include
diff --git a/math/large_number.h b/math/large_number.h
index c1a3665e4..bffb764d0 100644
--- a/math/large_number.h
+++ b/math/large_number.h
@@ -2,6 +2,7 @@
* @file
* @brief Library to perform arithmatic operations on arbitrarily large
* numbers.
+ * \author [Krishna Vedala](https://github.com/kvedala)
*/
#ifndef MATH_LARGE_NUMBER_H_
diff --git a/math/realtime_stats.cpp b/math/realtime_stats.cpp
index 26b923625..5f353ac4d 100644
--- a/math/realtime_stats.cpp
+++ b/math/realtime_stats.cpp
@@ -5,6 +5,7 @@
* This algorithm is really beneficial to compute statistics on data read in
* realtime. For example, devices reading biometrics data. The algorithm is
* simple enough to be easily implemented in an embedded system.
+ * \author [Krishna Vedala](https://github.com/kvedala)
*/
#include
#include
diff --git a/math/sqrt_double.cpp b/math/sqrt_double.cpp
index 1521b500a..c4beec9d8 100644
--- a/math/sqrt_double.cpp
+++ b/math/sqrt_double.cpp
@@ -1,7 +1,7 @@
/**
* @file
- * @brief Calculate the square root of any positive number in \f$O(\log N)\f$
- * time, with precision fixed using [bisection
+ * @brief Calculate the square root of any positive real number in \f$O(\log
+ * N)\f$ time, with precision fixed using [bisection
* method](https://en.wikipedia.org/wiki/Bisection_method) of root-finding.
*
* @see Can be implemented using faster and better algorithms like
diff --git a/numerical_methods/durand_kerner_roots.cpp b/numerical_methods/durand_kerner_roots.cpp
index 80464412a..897141143 100644
--- a/numerical_methods/durand_kerner_roots.cpp
+++ b/numerical_methods/durand_kerner_roots.cpp
@@ -3,6 +3,7 @@
* \brief Compute all possible approximate roots of any given polynomial using
* [Durand Kerner
* algorithm](https://en.wikipedia.org/wiki/Durand%E2%80%93Kerner_method)
+ * \author [Krishna Vedala](https://github.com/kvedala)
*
* Test the algorithm online:
* https://gist.github.com/kvedala/27f1b0b6502af935f6917673ec43bcd7
diff --git a/numerical_methods/newton_raphson_method.cpp b/numerical_methods/newton_raphson_method.cpp
index 318363a39..d086123ca 100644
--- a/numerical_methods/newton_raphson_method.cpp
+++ b/numerical_methods/newton_raphson_method.cpp
@@ -1,13 +1,15 @@
/**
* \file
* \brief Solve the equation \f$f(x)=0\f$ using [Newton-Raphson
- * method](https://en.wikipedia.org/wiki/Newton%27s_method)
+ * method](https://en.wikipedia.org/wiki/Newton%27s_method) for both real and
+ * complex solutions
*
* The \f$(i+1)^\text{th}\f$ approximation is given by:
* \f[
* x_{i+1} = x_i - \frac{f(x_i)}{f'(x_i)}
* \f]
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
* \see bisection_method.cpp, false_position.cpp
*/
#include
diff --git a/numerical_methods/ordinary_least_squares_regressor.cpp b/numerical_methods/ordinary_least_squares_regressor.cpp
index de02b27bb..43979d0ea 100644
--- a/numerical_methods/ordinary_least_squares_regressor.cpp
+++ b/numerical_methods/ordinary_least_squares_regressor.cpp
@@ -3,6 +3,7 @@
* \brief Linear regression example using [Ordinary least
* squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
*
+ * \author [Krishna Vedala](https://github.com/kvedala)
* Program that gets the number of data samples and number of features per
* sample along with output per sample. It applies OLS regression to compute
* the regression output for additional test data samples.
diff --git a/sorting/shell_sort2.cpp b/sorting/shell_sort2.cpp
index 85186e752..96ef25580 100644
--- a/sorting/shell_sort2.cpp
+++ b/sorting/shell_sort2.cpp
@@ -1,6 +1,7 @@
/**
* \file
* \brief [Shell sort](https://en.wikipedia.org/wiki/Shell_sort) algorithm
+ * \author [Krishna Vedala](https://github.com/kvedala)
*/
#include
#include