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[cpp fixes] tidied up code based on error reports by clang-tidy (#950)

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* tidied up code based on error reports by clang-tidy

* added doc for activation function
This commit is contained in:
Krishna Vedala
2020-07-13 07:52:38 -04:00
committed by GitHub
parent 1f32b4e412
commit 8ab9a2ae93
6 changed files with 243 additions and 172 deletions
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88
machine_learning/kohonen_som_trace.cpp
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@@ -20,6 +20,7 @@
*/
#define _USE_MATH_DEFINES // required for MS Visual C++
#include <algorithm>
#include <array>
#include <cmath>
#include <cstdlib>
#include <ctime>
@@ -71,12 +72,14 @@ int save_nd_data(const char *fname,
for (int i = 0; i < num_points; i++) {
// for each feature in the array
for (int j = 0; j < num_features; j++) {
fp << X[i][j]; // print the feature value
if (j < num_features - 1) // if not the last feature
fp << ","; // suffix comma
fp << X[i][j]; // print the feature value
if (j < num_features - 1) { // if not the last feature
fp << ","; // suffix comma
}
}
if (i < num_points - 1) { // if not the last row
fp << "\n"; // start a new line
}
if (i < num_points - 1) // if not the last row
fp << "\n"; // start a new line
}
fp.close();
@@ -100,9 +103,9 @@ namespace machine_learning {
void update_weights(const std::valarray<double> &x,
std::vector<std::valarray<double>> *W,
std::valarray<double> *D, double alpha, int R) {
int j, k;
int num_out = W->size(); // number of SOM output nodes
int num_features = x.size(); // number of data features
int j = 0, k = 0;
int num_out = W->size(); // number of SOM output nodes
// int num_features = x.size(); // number of data features
#ifdef _OPENMP
#pragma omp for
@@ -117,7 +120,7 @@ void update_weights(const std::valarray<double> &x,
// step 2: get closest node i.e., node with snallest Euclidian distance to
// the current pattern
auto result = std::min_element(std::begin(*D), std::end(*D));
double d_min = *result;
// double d_min = *result;
int d_min_idx = std::distance(std::begin(*D), result);
// step 3a: get the neighborhood range
@@ -129,9 +132,10 @@ void update_weights(const std::valarray<double> &x,
#ifdef _OPENMP
#pragma omp for
#endif
for (j = from_node; j < to_node; j++)
for (j = from_node; j < to_node; j++) {
// update weights of nodes in the neighborhood
(*W)[j] += alpha * (x - (*W)[j]);
}
}
/**
@@ -145,16 +149,16 @@ void update_weights(const std::valarray<double> &x,
void kohonen_som_tracer(const std::vector<std::valarray<double>> &X,
std::vector<std::valarray<double>> *W,
double alpha_min) {
int num_samples = X.size(); // number of rows
int num_features = X[0].size(); // number of columns
int num_out = W->size(); // number of rows
int num_samples = X.size(); // number of rows
// int num_features = X[0].size(); // number of columns
int num_out = W->size(); // number of rows
int R = num_out >> 2, iter = 0;
double alpha = 1.f;
std::valarray<double> D(num_out);
// Loop alpha from 1 to slpha_min
for (; alpha > alpha_min; alpha -= 0.01, iter++) {
do {
// Loop for each sample pattern in the data set
for (int sample = 0; sample < num_samples; sample++) {
// update weights for the current input pattern sample
@@ -162,9 +166,13 @@ void kohonen_som_tracer(const std::vector<std::valarray<double>> &X,
}
// every 10th iteration, reduce the neighborhood range
if (iter % 10 == 0 && R > 1)
if (iter % 10 == 0 && R > 1) {
R--;
}
}
alpha -= 0.01;
iter++;
} while (alpha > alpha_min);
}
} // namespace machine_learning
@@ -190,7 +198,7 @@ void test_circle(std::vector<std::valarray<double>> *data) {
const double R = 0.75, dr = 0.3;
double a_t = 0., b_t = 2.f * M_PI; // theta random between 0 and 2*pi
double a_r = R - dr, b_r = R + dr; // radius random between R-dr and R+dr
int i;
int i = 0;
#ifdef _OPENMP
#pragma omp for
@@ -223,24 +231,26 @@ void test_circle(std::vector<std::valarray<double>> *data) {
* output](https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/machine_learning/kohonen/test1.svg)
*/
void test1() {
int j, N = 500;
int j = 0, N = 500;
int features = 2;
int num_out = 50;
std::vector<std::valarray<double>> X(N);
std::vector<std::valarray<double>> W(num_out);
for (int i = 0; i < std::max(num_out, N); i++) {
// loop till max(N, num_out)
if (i < N) // only add new arrays if i < N
if (i < N) { // only add new arrays if i < N
X[i] = std::valarray<double>(features);
}
if (i < num_out) { // only add new arrays if i < num_out
W[i] = std::valarray<double>(features);
#ifdef _OPENMP
#pragma omp for
#endif
for (j = 0; j < features; j++)
for (j = 0; j < features; j++) {
// preallocate with random initial weights
W[i][j] = _random(-1, 1);
}
}
}
@@ -267,7 +277,7 @@ void test1() {
void test_lamniscate(std::vector<std::valarray<double>> *data) {
const int N = data->size();
const double dr = 0.2;
int i;
int i = 0;
#ifdef _OPENMP
#pragma omp for
@@ -303,24 +313,26 @@ void test_lamniscate(std::vector<std::valarray<double>> *data) {
* output](https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/machine_learning/kohonen/test2.svg)
*/
void test2() {
int j, N = 500;
int j = 0, N = 500;
int features = 2;
int num_out = 20;
std::vector<std::valarray<double>> X(N);
std::vector<std::valarray<double>> W(num_out);
for (int i = 0; i < std::max(num_out, N); i++) {
// loop till max(N, num_out)
if (i < N) // only add new arrays if i < N
if (i < N) { // only add new arrays if i < N
X[i] = std::valarray<double>(features);
}
if (i < num_out) { // only add new arrays if i < num_out
W[i] = std::valarray<double>(features);
#ifdef _OPENMP
#pragma omp for
#endif
for (j = 0; j < features; j++)
for (j = 0; j < features; j++) {
// preallocate with random initial weights
W[i][j] = _random(-1, 1);
}
}
}
@@ -347,18 +359,18 @@ void test2() {
void test_3d_classes(std::vector<std::valarray<double>> *data) {
const int N = data->size();
const double R = 0.1; // radius of cluster
int i;
int i = 0;
const int num_classes = 8;
const double centres[][3] = {
const std::array<const std::array<double, 3>, num_classes> centres = {
// centres of each class cluster
{.5, .5, .5}, // centre of class 0
{.5, .5, -.5}, // centre of class 1
{.5, -.5, .5}, // centre of class 2
{.5, -.5, -.5}, // centre of class 3
{-.5, .5, .5}, // centre of class 4
{-.5, .5, -.5}, // centre of class 5
{-.5, -.5, .5}, // centre of class 6
{-.5, -.5, -.5} // centre of class 7
std::array<double, 3>({.5, .5, .5}), // centre of class 0
std::array<double, 3>({.5, .5, -.5}), // centre of class 1
std::array<double, 3>({.5, -.5, .5}), // centre of class 2
std::array<double, 3>({.5, -.5, -.5}), // centre of class 3
std::array<double, 3>({-.5, .5, .5}), // centre of class 4
std::array<double, 3>({-.5, .5, -.5}), // centre of class 5
std::array<double, 3>({-.5, -.5, .5}), // centre of class 6
std::array<double, 3>({-.5, -.5, -.5}) // centre of class 7
};
#ifdef _OPENMP
@@ -400,24 +412,26 @@ void test_3d_classes(std::vector<std::valarray<double>> *data) {
* output](https://raw.githubusercontent.com/TheAlgorithms/C-Plus-Plus/docs/images/machine_learning/kohonen/test3.svg)
*/
void test3() {
int j, N = 200;
int j = 0, N = 200;
int features = 3;
int num_out = 20;
std::vector<std::valarray<double>> X(N);
std::vector<std::valarray<double>> W(num_out);
for (int i = 0; i < std::max(num_out, N); i++) {
// loop till max(N, num_out)
if (i < N) // only add new arrays if i < N
if (i < N) { // only add new arrays if i < N
X[i] = std::valarray<double>(features);
}
if (i < num_out) { // only add new arrays if i < num_out
W[i] = std::valarray<double>(features);
#ifdef _OPENMP
#pragma omp for
#endif
for (j = 0; j < features; j++)
for (j = 0; j < features; j++) {
// preallocate with random initial weights
W[i][j] = _random(-1, 1);
}
}
}