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clang-format and clang-tidy fixes for ca2a7c64

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2021-10-21 17:07:25 +00:00
parent ca2a7c6447
commit 8992a0922d
5 changed files with 164 additions and 148 deletions
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21
bit_manipulation/count_of_set_bits.cpp
View File

@@ -5,7 +5,8 @@
* integer.
*
* @details
* We are given an integer number. We need to calculate the number of set bits in it.
* We are given an integer number. We need to calculate the number of set bits
* in it.
*
* A binary number consists of two digits. They are 0 & 1. Digit 1 is known as
* set bit in computer terms.
@@ -15,7 +16,7 @@
* @author [Prashant Thakur](https://github.com/prashant-th18)
*/
#include <cassert> /// for assert
#include <iostream> /// for IO operations
#include <iostream> /// for IO operations
/**
* @namespace bit_manipulation
* @brief Bit manipulation algorithms
@@ -33,21 +34,21 @@ namespace count_of_set_bits {
* @param n is the number whose set bit will be counted
* @returns total number of set-bits in the binary representation of number `n`
*/
std::uint64_t countSetBits(std :: int64_t n) { // int64_t is preferred over int so that
// no Overflow can be there.
std::uint64_t countSetBits(
std ::int64_t n) { // int64_t is preferred over int so that
// no Overflow can be there.
int count = 0; // "count" variable is used to count number of set-bits('1') in
// binary representation of number 'n'
while (n != 0)
{
int count = 0; // "count" variable is used to count number of set-bits('1')
// in binary representation of number 'n'
while (n != 0) {
++count;
n = (n & (n - 1));
}
return count;
// Why this algorithm is better than the standard one?
// Because this algorithm runs the same number of times as the number of
// set-bits in it. Means if my number is having "3" set bits, then this while loop
// will run only "3" times!!
// set-bits in it. Means if my number is having "3" set bits, then this
// while loop will run only "3" times!!
}
} // namespace count_of_set_bits
} // namespace bit_manipulation

5
ciphers/atbash_cipher.cpp
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@@ -22,7 +22,8 @@
*/
namespace ciphers {
/** \namespace atbash
* \brief Functions for the [Atbash Cipher](https://en.wikipedia.org/wiki/Atbash) implementation
* \brief Functions for the [Atbash
* Cipher](https://en.wikipedia.org/wiki/Atbash) implementation
*/
namespace atbash {
std::map<char, char> atbash_cipher_map = {
@@ -43,7 +44,7 @@ std::map<char, char> atbash_cipher_map = {
* @param text Plaintext to be encrypted
* @returns encoded or decoded string
*/
std::string atbash_cipher(std::string text) {
std::string atbash_cipher(const std::string& text) {
std::string result;
for (char letter : text) {
result += atbash_cipher_map[letter];

154
data_structures/stack_using_queue.cpp
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@@ -3,13 +3,14 @@
* @details
* Using 2 Queues inside the Stack class, we can easily implement Stack
* data structure with heavy computation in push function.
*
* References used: [StudyTonight](https://www.studytonight.com/data-structures/stack-using-queue)
*
* References used:
* [StudyTonight](https://www.studytonight.com/data-structures/stack-using-queue)
* @author [tushar2407](https://github.com/tushar2407)
*/
#include <iostream> /// for IO operations
#include <queue> /// for queue data structure
#include <cassert> /// for assert
#include <cassert> /// for assert
#include <iostream> /// for IO operations
#include <queue> /// for queue data structure
/**
* @namespace data_strcutres
@@ -18,66 +19,59 @@
namespace data_structures {
/**
* @namespace stack_using_queue
* @brief Functions for the [Stack Using Queue](https://www.studytonight.com/data-structures/stack-using-queue) implementation
* @brief Functions for the [Stack Using
* Queue](https://www.studytonight.com/data-structures/stack-using-queue)
* implementation
*/
namespace stack_using_queue {
/**
* @brief Stack Class implementation for basic methods of Stack Data Structure.
*/
struct Stack {
std::queue<int64_t> main_q; ///< stores the current state of the stack
std::queue<int64_t> auxiliary_q; ///< used to carry out intermediate
///< operations to implement stack
uint32_t current_size = 0; ///< stores the current size of the stack
/**
* @brief Stack Class implementation for basic methods of Stack Data Structure.
* Returns the top most element of the stack
* @returns top element of the queue
*/
struct Stack
{
std::queue<int64_t> main_q; ///< stores the current state of the stack
std::queue<int64_t> auxiliary_q; ///< used to carry out intermediate operations to implement stack
uint32_t current_size = 0; ///< stores the current size of the stack
/**
* Returns the top most element of the stack
* @returns top element of the queue
*/
int top()
{
return main_q.front();
}
int top() { return main_q.front(); }
/**
* @brief Inserts an element to the top of the stack.
* @param val the element that will be inserted into the stack
* @returns void
*/
void push(int val)
{
auxiliary_q.push(val);
while(!main_q.empty())
{
auxiliary_q.push(main_q.front());
main_q.pop();
}
swap(main_q, auxiliary_q);
current_size++;
}
/**
* @brief Removes the topmost element from the stack
* @returns void
*/
void pop()
{
if(main_q.empty()) {
return;
}
/**
* @brief Inserts an element to the top of the stack.
* @param val the element that will be inserted into the stack
* @returns void
*/
void push(int val) {
auxiliary_q.push(val);
while (!main_q.empty()) {
auxiliary_q.push(main_q.front());
main_q.pop();
current_size--;
}
swap(main_q, auxiliary_q);
current_size++;
}
/**
* @brief Utility function to return the current size of the stack
* @returns current size of stack
*/
int size()
{
return current_size;
/**
* @brief Removes the topmost element from the stack
* @returns void
*/
void pop() {
if (main_q.empty()) {
return;
}
};
main_q.pop();
current_size--;
}
/**
* @brief Utility function to return the current size of the stack
* @returns current size of stack
*/
int size() { return current_size; }
};
} // namespace stack_using_queue
} // namespace data_structures
@@ -85,30 +79,29 @@ namespace stack_using_queue {
* @brief Self-test implementations
* @returns void
*/
static void test()
{
static void test() {
data_structures::stack_using_queue::Stack s;
s.push(1); /// insert an element into the stack
s.push(2); /// insert an element into the stack
s.push(3); /// insert an element into the stack
assert(s.size()==3); /// size should be 3
assert(s.top()==3); /// topmost element in the stack should be 3
s.pop(); /// remove the topmost element from the stack
assert(s.top()==2); /// topmost element in the stack should now be 2
s.pop(); /// remove the topmost element from the stack
assert(s.top()==1);
s.push(5); /// insert an element into the stack
assert(s.top()==5); /// topmost element in the stack should now be 5
s.pop(); /// remove the topmost element from the stack
assert(s.top()==1); /// topmost element in the stack should now be 1
assert(s.size()==1); /// size should be 1
s.push(1); /// insert an element into the stack
s.push(2); /// insert an element into the stack
s.push(3); /// insert an element into the stack
assert(s.size() == 3); /// size should be 3
assert(s.top() == 3); /// topmost element in the stack should be 3
s.pop(); /// remove the topmost element from the stack
assert(s.top() == 2); /// topmost element in the stack should now be 2
s.pop(); /// remove the topmost element from the stack
assert(s.top() == 1);
s.push(5); /// insert an element into the stack
assert(s.top() == 5); /// topmost element in the stack should now be 5
s.pop(); /// remove the topmost element from the stack
assert(s.top() == 1); /// topmost element in the stack should now be 1
assert(s.size() == 1); /// size should be 1
}
/**
@@ -119,8 +112,7 @@ static void test()
* declared above.
* @returns 0 on exit
*/
int main()
{
int main() {
test(); // run self-test implementations
return 0;
}

128
math/integral_approximation2.cpp
View File

@@ -1,29 +1,34 @@
/**
* @file
* @brief [Monte Carlo Integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)
* @brief [Monte Carlo
* Integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)
*
* @details
* In mathematics, Monte Carlo integration is a technique for numerical integration using random numbers.
* It is a particular Monte Carlo method that numerically computes a definite integral.
* While other algorithms usually evaluate the integrand at a regular grid, Monte Carlo randomly chooses points at which the integrand is evaluated.
* This method is particularly useful for higher-dimensional integrals.
* In mathematics, Monte Carlo integration is a technique for numerical
* integration using random numbers. It is a particular Monte Carlo method that
* numerically computes a definite integral. While other algorithms usually
* evaluate the integrand at a regular grid, Monte Carlo randomly chooses points
* at which the integrand is evaluated. This method is particularly useful for
* higher-dimensional integrals.
*
* This implementation supports arbitrary pdfs.
* These pdfs are sampled using the [Metropolis-Hastings algorithm](https://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm).
* This can be swapped out by every other sampling techniques for example the inverse method.
* Metropolis-Hastings was chosen because it is the most general and can also be extended for a higher dimensional sampling space.
* These pdfs are sampled using the [Metropolis-Hastings
* algorithm](https://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm). This
* can be swapped out by every other sampling techniques for example the inverse
* method. Metropolis-Hastings was chosen because it is the most general and can
* also be extended for a higher dimensional sampling space.
*
* @author [Domenic Zingsheim](https://github.com/DerAndereDomenic)
*/
#define _USE_MATH_DEFINES /// for M_PI on windows
#include <cmath> /// for math functions
#include <cstdint> /// for fixed size data types
#include <ctime> /// for time to initialize rng
#include <functional> /// for function pointers
#include <iostream> /// for std::cout
#include <random> /// for random number generation
#include <vector> /// for std::vector
#define _USE_MATH_DEFINES /// for M_PI on windows
#include <cmath> /// for math functions
#include <cstdint> /// for fixed size data types
#include <ctime> /// for time to initialize rng
#include <functional> /// for function pointers
#include <iostream> /// for std::cout
#include <random> /// for random number generation
#include <vector> /// for std::vector
/**
* @namespace math
@@ -32,25 +37,34 @@
namespace math {
/**
* @namespace monte_carlo
* @brief Functions for the [Monte Carlo Integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration) implementation
* @brief Functions for the [Monte Carlo
* Integration](https://en.wikipedia.org/wiki/Monte_Carlo_integration)
* implementation
*/
namespace monte_carlo {
using Function = std::function<double(double&)>; /// short-hand for std::functions used in this implementation
using Function = std::function<double(
double&)>; /// short-hand for std::functions used in this implementation
/**
* @brief Generate samples according to some pdf
* @details This function uses Metropolis-Hastings to generate random numbers. It generates a sequence of random numbers by using a markov chain.
* Therefore, we need to define a start_point and the number of samples we want to generate.
* Because the first samples generated by the markov chain may not be distributed according to the given pdf, one can specify how many samples
* @details This function uses Metropolis-Hastings to generate random numbers.
* It generates a sequence of random numbers by using a markov chain. Therefore,
* we need to define a start_point and the number of samples we want to
* generate. Because the first samples generated by the markov chain may not be
* distributed according to the given pdf, one can specify how many samples
* should be discarded before storing samples.
* @param start_point The starting point of the markov chain
* @param pdf The pdf to sample
* @param num_samples The number of samples to generate
* @param discard How many samples should be discarded at the start
* @returns A vector of size num_samples with samples distributed according to the pdf
* @returns A vector of size num_samples with samples distributed according to
* the pdf
*/
std::vector<double> generate_samples(const double& start_point, const Function& pdf, const uint32_t& num_samples, const uint32_t& discard = 100000) {
std::vector<double> generate_samples(const double& start_point,
const Function& pdf,
const uint32_t& num_samples,
const uint32_t& discard = 100000) {
std::vector<double> samples;
samples.reserve(num_samples);
@@ -61,19 +75,19 @@ std::vector<double> generate_samples(const double& start_point, const Function&
std::normal_distribution<double> normal(0.0, 1.0);
generator.seed(time(nullptr));
for(uint32_t t = 0; t < num_samples + discard; ++t) {
for (uint32_t t = 0; t < num_samples + discard; ++t) {
// Generate a new proposal according to some mutation strategy.
// This is arbitrary and can be swapped.
double x_dash = normal(generator) + x_t;
double acceptance_probability = std::min(pdf(x_dash)/pdf(x_t), 1.0);
double acceptance_probability = std::min(pdf(x_dash) / pdf(x_t), 1.0);
double u = uniform(generator);
// Accept "new state" according to the acceptance_probability
if(u <= acceptance_probability) {
if (u <= acceptance_probability) {
x_t = x_dash;
}
if(t >= discard) {
if (t >= discard) {
samples.push_back(x_t);
}
}
@@ -92,13 +106,17 @@ std::vector<double> generate_samples(const double& start_point, const Function&
* @param function The function to integrate
* @param pdf The pdf to sample
* @param num_samples The number of samples used to approximate the integral
* @returns The approximation of the integral according to 1/N \sum_{i}^N f(x_i) / p(x_i)
* @returns The approximation of the integral according to 1/N \sum_{i}^N f(x_i)
* / p(x_i)
*/
double integral_monte_carlo(const double& start_point, const Function& function, const Function& pdf, const uint32_t& num_samples = 1000000) {
double integral_monte_carlo(const double& start_point, const Function& function,
const Function& pdf,
const uint32_t& num_samples = 1000000) {
double integral = 0.0;
std::vector<double> samples = generate_samples(start_point, pdf, num_samples);
std::vector<double> samples =
generate_samples(start_point, pdf, num_samples);
for(double sample : samples) {
for (double sample : samples) {
integral += function(sample) / pdf(sample);
}
@@ -113,8 +131,13 @@ double integral_monte_carlo(const double& start_point, const Function& function,
* @returns void
*/
static void test() {
std::cout << "Disclaimer: Because this is a randomized algorithm," << std::endl;
std::cout << "it may happen that singular samples deviate from the true result." << std::endl << std::endl;;
std::cout << "Disclaimer: Because this is a randomized algorithm,"
<< std::endl;
std::cout
<< "it may happen that singular samples deviate from the true result."
<< std::endl
<< std::endl;
;
math::monte_carlo::Function f;
math::monte_carlo::Function pdf;
@@ -122,60 +145,58 @@ static void test() {
double lower_bound = 0, upper_bound = 0;
/* \int_{-2}^{2} -x^2 + 4 dx */
f = [&](double& x) {
return -x*x + 4.0;
};
f = [&](double& x) { return -x * x + 4.0; };
lower_bound = -2.0;
upper_bound = 2.0;
pdf = [&](double& x) {
if(x >= lower_bound && x <= -1.0) {
if (x >= lower_bound && x <= -1.0) {
return 0.1;
}
if(x <= upper_bound && x >= 1.0) {
if (x <= upper_bound && x >= 1.0) {
return 0.1;
}
if(x > -1.0 && x < 1.0) {
if (x > -1.0 && x < 1.0) {
return 0.4;
}
return 0.0;
};
integral = math::monte_carlo::integral_monte_carlo((upper_bound - lower_bound) / 2.0, f, pdf);
integral = math::monte_carlo::integral_monte_carlo(
(upper_bound - lower_bound) / 2.0, f, pdf);
std::cout << "This number should be close to 10.666666: " << integral << std::endl;
std::cout << "This number should be close to 10.666666: " << integral
<< std::endl;
/* \int_{0}^{1} e^x dx */
f = [&](double& x) {
return std::exp(x);
};
f = [&](double& x) { return std::exp(x); };
lower_bound = 0.0;
upper_bound = 1.0;
pdf = [&](double& x) {
if(x >= lower_bound && x <= 0.2) {
if (x >= lower_bound && x <= 0.2) {
return 0.1;
}
if(x > 0.2 && x <= 0.4) {
if (x > 0.2 && x <= 0.4) {
return 0.4;
}
if(x > 0.4 && x < upper_bound) {
if (x > 0.4 && x < upper_bound) {
return 1.5;
}
return 0.0;
};
integral = math::monte_carlo::integral_monte_carlo((upper_bound - lower_bound) / 2.0, f, pdf);
integral = math::monte_carlo::integral_monte_carlo(
(upper_bound - lower_bound) / 2.0, f, pdf);
std::cout << "This number should be close to 1.7182818: " << integral << std::endl;
std::cout << "This number should be close to 1.7182818: " << integral
<< std::endl;
/* \int_{-\infty}^{\infty} sinc(x) dx, sinc(x) = sin(pi * x) / (pi * x)
This is a difficult integral because of its infinite domain.
Therefore, it may deviate largely from the expected result.
*/
f = [&](double& x) {
return std::sin(M_PI * x) / (M_PI * x);
};
f = [&](double& x) { return std::sin(M_PI * x) / (M_PI * x); };
pdf = [&](double& x) {
return 1.0 / std::sqrt(2.0 * M_PI) * std::exp(-x * x / 2.0);
@@ -183,7 +204,8 @@ static void test() {
integral = math::monte_carlo::integral_monte_carlo(0.0, f, pdf, 10000000);
std::cout << "This number should be close to 1.0: " << integral << std::endl;
std::cout << "This number should be close to 1.0: " << integral
<< std::endl;
}
/**

4
range_queries/segtree.cpp
View File

@@ -144,7 +144,7 @@ void update(std::vector<int64_t> *segtree, std::vector<int64_t> *lazy,
* @returns void
*/
static void test() {
int64_t max = static_cast<int64_t>(2 * pow(2, ceil(log2(7))) - 1);
auto max = static_cast<int64_t>(2 * pow(2, ceil(log2(7))) - 1);
assert(max == 15);
std::vector<int64_t> arr{1, 2, 3, 4, 5, 6, 7}, lazy(max), segtree(max);
@@ -172,7 +172,7 @@ int main() {
uint64_t n = 0;
std::cin >> n;
uint64_t max = static_cast<uint64_t>(2 * pow(2, ceil(log2(n))) - 1);
auto max = static_cast<uint64_t>(2 * pow(2, ceil(log2(n))) - 1);
std::vector<int64_t> arr(n), lazy(max), segtree(max);
int choice = 0;