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feat: Modify search/text_search.cpp (#1662)

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* Modified search/text_search.cpp

* Added tests

* Added a few test cases

* Added a few more test cases and documentation

* Minor fix

* Minor fixes

* Minor fixes

* Minor output fixes

* Minor output fixes

* Minor readability fixes

* clang-format and clang-tidy fixes for a01765a6

* Restored original settings

* clang-format and clang-tidy fixes for 6a8f3a4e

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
Co-authored-by: David Leal <halfpacho@gmail.com>
This commit is contained in:
Anuran Roy
2021-10-15 00:04:55 +05:30
committed by GitHub
parent 5a654fb85b
commit 85721be69b
5 changed files with 217 additions and 123 deletions
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128
probability/geometric_dist.cpp
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@@ -1,13 +1,16 @@
/**
* @file
* @brief [Geometric Distribution](https://en.wikipedia.org/wiki/Geometric_distribution)
* @brief [Geometric
* Distribution](https://en.wikipedia.org/wiki/Geometric_distribution)
*
* @details
* The geometric distribution models the experiment of doing Bernoulli trials until a
* sucess was observed. There are two formulations of the geometric distribution:
* 1) The probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set { 1, 2, 3, ... }
* 2) The probability distribution of the number Y = X − 1 of failures before the first success, supported on the set { 0, 1, 2, 3, ... }
* Here, the first one is implemented.
* The geometric distribution models the experiment of doing Bernoulli trials
* until a sucess was observed. There are two formulations of the geometric
* distribution: 1) The probability distribution of the number X of Bernoulli
* trials needed to get one success, supported on the set { 1, 2, 3, ... } 2)
* The probability distribution of the number Y = X − 1 of failures before the
* first success, supported on the set { 0, 1, 2, 3, ... } Here, the first one
* is implemented.
*
* Common variables used:
* p - The success probability
@@ -16,14 +19,14 @@
* @author [Domenic Zingsheim](https://github.com/DerAndereDomenic)
*/
#include <cassert> /// for assert
#include <cmath> /// for math functions
#include <cstdint> /// for fixed size data types
#include <ctime> /// for time to initialize rng
#include <iostream> /// for std::cout
#include <limits> /// for std::numeric_limits
#include <random> /// for random numbers
#include <vector> /// for std::vector
#include <cassert> /// for assert
#include <cmath> /// for math functions
#include <cstdint> /// for fixed size data types
#include <ctime> /// for time to initialize rng
#include <iostream> /// for std::cout
#include <limits> /// for std::numeric_limits
#include <random> /// for random numbers
#include <vector> /// for std::vector
/**
* @namespace probability
@@ -32,12 +35,15 @@
namespace probability {
/**
* @namespace geometric_dist
* @brief Functions for the [Geometric Distribution](https://en.wikipedia.org/wiki/Geometric_distribution) algorithm implementation
* @brief Functions for the [Geometric
* Distribution](https://en.wikipedia.org/wiki/Geometric_distribution) algorithm
* implementation
*/
namespace geometric_dist {
/**
* @brief Returns a random number between [0,1]
* @returns A uniformly distributed random number between 0 (included) and 1 (included)
* @returns A uniformly distributed random number between 0 (included) and 1
* (included)
*/
float generate_uniform() {
return static_cast<float>(rand()) / static_cast<float>(RAND_MAX);
@@ -46,12 +52,11 @@ float generate_uniform() {
/**
* @brief A class to model the geometric distribution
*/
class geometric_distribution
{
private:
float p; ///< The succes probability p
class geometric_distribution {
private:
float p; ///< The succes probability p
public:
public:
/**
* @brief Constructor for the geometric distribution
* @param p The success probability
@@ -59,28 +64,24 @@ public:
explicit geometric_distribution(const float& p) : p(p) {}
/**
* @brief The expected value of a geometrically distributed random variable X
* @brief The expected value of a geometrically distributed random variable
* X
* @returns E[X] = 1/p
*/
float expected_value() const {
return 1.0f/ p;
}
float expected_value() const { return 1.0f / p; }
/**
* @brief The variance of a geometrically distributed random variable X
* @returns V[X] = (1 - p) / p^2
*/
float variance() const {
return (1.0f - p) / (p * p);
}
float variance() const { return (1.0f - p) / (p * p); }
/**
* @brief The standard deviation of a geometrically distributed random variable X
* @brief The standard deviation of a geometrically distributed random
* variable X
* @returns \sigma = \sqrt{V[X]}
*/
float standard_deviation() const {
return std::sqrt(variance());
}
float standard_deviation() const { return std::sqrt(variance()); }
/**
* @brief The probability density function
@@ -95,7 +96,8 @@ public:
/**
* @brief The cumulative distribution function
* @details The sum of all probabilities up to (and including) k trials. Basically CDF(k) = P(x <= k)
* @details The sum of all probabilities up to (and including) k trials.
* Basically CDF(k) = P(x <= k)
* @param k The number of trials in [1,\infty)
* @returns The probability to have success within k trials
*/
@@ -105,8 +107,9 @@ public:
/**
* @brief The inverse cumulative distribution function
* @details This functions answers the question: Up to how many trials are needed to have success with a probability of cdf?
* The exact floating point value is reported.
* @details This functions answers the question: Up to how many trials are
* needed to have success with a probability of cdf? The exact floating
* point value is reported.
* @param cdf The probability in [0,1]
* @returns The number of (exact) trials.
*/
@@ -115,26 +118,37 @@ public:
}
/**
* @brief Generates a (discrete) sample according to the geometrical distribution
* @brief Generates a (discrete) sample according to the geometrical
* distribution
* @returns A geometrically distributed number in [1,\infty)
*/
uint32_t draw_sample() const {
float uniform_sample = generate_uniform();
return static_cast<uint32_t>(inverse_cumulative_distribution(uniform_sample)) + 1;
return static_cast<uint32_t>(
inverse_cumulative_distribution(uniform_sample)) +
1;
}
/**
* @brief This function computes the probability to have success in a given range of tries
* @brief This function computes the probability to have success in a given
* range of tries
* @details Computes P(min_tries <= x <= max_tries).
* Can be used to calculate P(x >= min_tries) by not passing a second argument.
* Can be used to calculate P(x <= max_tries) by passing 1 as the first argument
* Can be used to calculate P(x >= min_tries) by not passing a second
* argument. Can be used to calculate P(x <= max_tries) by passing 1 as the
* first argument
* @param min_tries The minimum number of tries in [1,\infty) (inclusive)
* @param max_tries The maximum number of tries in [min_tries, \infty) (inclusive)
* @returns The probability of having success within a range of tries [min_tries, max_tries]
* @param max_tries The maximum number of tries in [min_tries, \infty)
* (inclusive)
* @returns The probability of having success within a range of tries
* [min_tries, max_tries]
*/
float range_tries(const uint32_t& min_tries = 1, const uint32_t& max_tries = std::numeric_limits<uint32_t>::max()) const {
float range_tries(const uint32_t& min_tries = 1,
const uint32_t& max_tries =
std::numeric_limits<uint32_t>::max()) const {
float cdf_lower = cumulative_distribution(min_tries - 1);
float cdf_upper = max_tries == std::numeric_limits<uint32_t>::max() ? 1.0f : cumulative_distribution(max_tries);
float cdf_upper = max_tries == std::numeric_limits<uint32_t>::max()
? 1.0f
: cumulative_distribution(max_tries);
return cdf_upper - cdf_lower;
}
};
@@ -144,10 +158,12 @@ public:
/**
* @brief Tests the sampling method of the geometric distribution
* @details Draws 1000000 random samples and estimates mean and variance
* These should be close to the expected value and variance of the given distribution to pass.
* These should be close to the expected value and variance of the given
* distribution to pass.
* @param dist The distribution to test
*/
void sample_test(const probability::geometric_dist::geometric_distribution& dist) {
void sample_test(
const probability::geometric_dist::geometric_distribution& dist) {
uint32_t n_tries = 1000000;
std::vector<float> tries;
tries.resize(n_tries);
@@ -165,11 +181,13 @@ void sample_test(const probability::geometric_dist::geometric_distribution& dist
var += (tries[i] - mean) * (tries[i] - mean);
}
//Unbiased estimate of variance
// Unbiased estimate of variance
var /= static_cast<float>(n_tries - 1);
std::cout << "This value should be near " << dist.expected_value() << ": " << mean << std::endl;
std::cout << "This value should be near " << dist.variance() << ": " << var << std::endl;
std::cout << "This value should be near " << dist.expected_value() << ": "
<< mean << std::endl;
std::cout << "This value should be near " << dist.variance() << ": " << var
<< std::endl;
}
/**
@@ -187,7 +205,9 @@ static void test() {
assert(std::abs(dist.standard_deviation() - 2.788866755) < threshold);
assert(std::abs(dist.probability_density(5) - 0.07203) < threshold);
assert(std::abs(dist.cumulative_distribution(6) - 0.882351) < threshold);
assert(std::abs(dist.inverse_cumulative_distribution(dist.cumulative_distribution(8)) - 8) < threshold);
assert(std::abs(dist.inverse_cumulative_distribution(
dist.cumulative_distribution(8)) -
8) < threshold);
assert(std::abs(dist.range_tries() - 1.0f) < threshold);
assert(std::abs(dist.range_tries(3) - 0.49f) < threshold);
assert(std::abs(dist.range_tries(5, 11) - 0.2203267f) < threshold);
@@ -202,7 +222,9 @@ static void test() {
assert(std::abs(dist.standard_deviation() - 1.4142135f) < threshold);
assert(std::abs(dist.probability_density(5) - 0.03125) < threshold);
assert(std::abs(dist.cumulative_distribution(6) - 0.984375) < threshold);
assert(std::abs(dist.inverse_cumulative_distribution(dist.cumulative_distribution(8)) - 8) < threshold);
assert(std::abs(dist.inverse_cumulative_distribution(
dist.cumulative_distribution(8)) -
8) < threshold);
assert(std::abs(dist.range_tries() - 1.0f) < threshold);
assert(std::abs(dist.range_tries(3) - 0.25f) < threshold);
assert(std::abs(dist.range_tries(5, 11) - 0.062011f) < threshold);
@@ -217,7 +239,9 @@ static void test() {
assert(std::abs(dist.standard_deviation() - 0.559016f) < threshold);
assert(std::abs(dist.probability_density(5) - 0.00128) < threshold);
assert(std::abs(dist.cumulative_distribution(6) - 0.999936) < threshold);
assert(std::abs(dist.inverse_cumulative_distribution(dist.cumulative_distribution(8)) - 8) < threshold);
assert(std::abs(dist.inverse_cumulative_distribution(
dist.cumulative_distribution(8)) -
8) < threshold);
assert(std::abs(dist.range_tries() - 1.0f) < threshold);
assert(std::abs(dist.range_tries(3) - 0.04f) < threshold);
assert(std::abs(dist.range_tries(5, 11) - 0.00159997f) < threshold);