diff --git a/README.md b/README.md
index afd302cd..2ab5cc54 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,15 @@
# The Algorithms - C # {#mainpage}
-[](https://gitpod.io/#https://github.com/kvedala/C)
+[](https://gitpod.io/#https://github.com/TheAlgorithms/C)
[](https://gitter.im/TheAlgorithms)
-[](https://github.com/kvedala/C-Plus-Plus/blob/master/CONTRIBUTING.md)
-
-
-
-
+[](https://github.com/TheAlgorithms/C-Plus-Plus/blob/master/CONTRIBUTING.md)
+
+
+
+
-[Online Documentation](https://kvedala.github.io/C).
+[Online Documentation](https://TheAlgorithms.github.io/C).
-Click on [Files menu](https://kvedala.github.io/C/files.html) to see the list of all the files documented with the code.
+Click on [Files menu](https://TheAlgorithms.github.io/C/files.html) to see the list of all the files documented with the code.
All the code can be executed and tested online: [](https://colab.research.google.com/gist/kvedala/27f1b0b6502af935f6917673ec43bcd7/plot-durand_kerner-log.ipynb)
diff --git a/machine_learning/kohonen_som_topology.c b/machine_learning/kohonen_som_topology.c
index a024ae04..3820d713 100644
--- a/machine_learning/kohonen_som_topology.c
+++ b/machine_learning/kohonen_som_topology.c
@@ -11,7 +11,7 @@
* data points in a much higher dimesional space by creating an equivalent in a
* 2-dimensional space.
* 
* \warning MSVC 2019 compiler generates code that does not execute as expected.
* However, MinGW, Clang for GCC and Clang for MSVC compilers on windows perform
@@ -23,18 +23,18 @@
#include
#include
#include
-#ifdef _OPENMP // check if OpenMP based parallellization is available
+#ifdef _OPENMP // check if OpenMP based parallellization is available
#include
#endif
#ifndef max
-#define max(a, b) \
- (((a) > (b)) ? (a) : (b)) /**< shorthand for maximum value \
+#define max(a, b) \
+ (((a) > (b)) ? (a) : (b)) /**< shorthand for maximum value \
*/
#endif
#ifndef min
-#define min(a, b) \
- (((a) < (b)) ? (a) : (b)) /**< shorthand for minimum value \
+#define min(a, b) \
+ (((a) < (b)) ? (a) : (b)) /**< shorthand for minimum value \
*/
#endif
@@ -98,7 +98,7 @@ int save_2d_data(const char *fname, double **X, int num_points,
int num_features)
{
FILE *fp = fopen(fname, "wt");
- if (!fp) // error with fopen
+ if (!fp) // error with fopen
{
char msg[120];
sprintf(msg, "File error (%s): ", fname);
@@ -106,16 +106,16 @@ int save_2d_data(const char *fname, double **X, int num_points,
return -1;
}
- for (int i = 0; i < num_points; i++) // for each point in the array
+ for (int i = 0; i < num_points; i++) // for each point in the array
{
- for (int j = 0; j < num_features; j++) // for each feature in the array
+ for (int j = 0; j < num_features; j++) // for each feature in the array
{
- fprintf(fp, "%.4g", X[i][j]); // print the feature value
- if (j < num_features - 1) // if not the last feature
- fputc(',', fp); // suffix comma
+ fprintf(fp, "%.4g", X[i][j]); // print the feature value
+ if (j < num_features - 1) // if not the last feature
+ fputc(',', fp); // suffix comma
}
- if (i < num_points - 1) // if not the last row
- fputc('\n', fp); // start a new line
+ if (i < num_points - 1) // if not the last row
+ fputc('\n', fp); // start a new line
}
fclose(fp);
return 0;
@@ -134,7 +134,7 @@ int save_2d_data(const char *fname, double **X, int num_points,
int save_u_matrix(const char *fname, struct array_3d *W)
{
FILE *fp = fopen(fname, "wt");
- if (!fp) // error with fopen
+ if (!fp) // error with fopen
{
char msg[120];
sprintf(msg, "File error (%s): ", fname);
@@ -144,9 +144,9 @@ int save_u_matrix(const char *fname, struct array_3d *W)
int R = max(W->dim1 >> 3, 2); /* neighborhood range */
- for (int i = 0; i < W->dim1; i++) // for each x
+ for (int i = 0; i < W->dim1; i++) // for each x
{
- for (int j = 0; j < W->dim2; j++) // for each y
+ for (int j = 0; j < W->dim2; j++) // for each y
{
double distance = 0.f;
int k;
@@ -159,12 +159,12 @@ int save_u_matrix(const char *fname, struct array_3d *W)
#ifdef _OPENMP
#pragma omp parallel for reduction(+ : distance)
#endif
- for (l = from_x; l < to_x; l++) // scan neighborhoor in x
+ for (l = from_x; l < to_x; l++) // scan neighborhoor in x
{
- for (int m = from_y; m < to_y; m++) // scan neighborhood in y
+ for (int m = from_y; m < to_y; m++) // scan neighborhood in y
{
double d = 0.f;
- for (k = 0; k < W->dim3; k++) // for each feature
+ for (k = 0; k < W->dim3; k++) // for each feature
{
double *w1 = data_3d(W, i, j, k);
double *w2 = data_3d(W, l, m, k);
@@ -176,13 +176,13 @@ int save_u_matrix(const char *fname, struct array_3d *W)
}
}
- distance /= R * R; // mean distance from neighbors
- fprintf(fp, "%.4g", distance); // print the mean separation
- if (j < W->dim2 - 1) // if not the last column
- fputc(',', fp); // suffix comma
+ distance /= R * R; // mean distance from neighbors
+ fprintf(fp, "%.4g", distance); // print the mean separation
+ if (j < W->dim2 - 1) // if not the last column
+ fputc(',', fp); // suffix comma
}
- if (i < W->dim1 - 1) // if not the last row
- fputc('\n', fp); // start a new line
+ if (i < W->dim1 - 1) // if not the last row
+ fputc('\n', fp); // start a new line
}
fclose(fp);
return 0;
@@ -198,14 +198,14 @@ int save_u_matrix(const char *fname, struct array_3d *W)
*/
void get_min_2d(double **X, int N, double *val, int *x_idx, int *y_idx)
{
- val[0] = INFINITY; // initial min value
+ val[0] = INFINITY; // initial min value
- for (int i = 0; i < N; i++) // traverse each x-index
+ for (int i = 0; i < N; i++) // traverse each x-index
{
- for (int j = 0; j < N; j++) // traverse each y-index
+ for (int j = 0; j < N; j++) // traverse each y-index
{
- if (X[i][j] < val[0]) // if a lower value is found
- { // save the value and its index
+ if (X[i][j] < val[0]) // if a lower value is found
+ { // save the value and its index
x_idx[0] = i;
y_idx[0] = j;
val[0] = X[i][j];
@@ -314,7 +314,7 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
for (int i = 0; i < num_out; i++)
D[i] = (double *)malloc(num_out * sizeof(double));
- double dmin = 1.f; // average minimum distance of all samples
+ double dmin = 1.f; // average minimum distance of all samples
// Loop alpha from 1 to slpha_min
for (double alpha = 1.f; alpha > alpha_min && dmin > 1e-3;
@@ -339,8 +339,7 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
}
putchar('\n');
- for (int i = 0; i < num_out; i++)
- free(D[i]);
+ for (int i = 0; i < num_out; i++) free(D[i]);
free(D);
}
@@ -356,15 +355,15 @@ void kohonen_som(double **X, struct array_3d *W, int num_samples,
*/
void test_2d_classes(double *const *data, int N)
{
- const double R = 0.3; // radius of cluster
+ const double R = 0.3; // radius of cluster
int i;
const int num_classes = 4;
const double centres[][2] = {
// centres of each class cluster
- {.5, .5}, // centre of class 1
- {.5, -.5}, // centre of class 2
- {-.5, .5}, // centre of class 3
- {-.5, -.5} // centre of class 4
+ {.5, .5}, // centre of class 1
+ {.5, -.5}, // centre of class 2
+ {-.5, .5}, // centre of class 3
+ {-.5, -.5} // centre of class 4
};
#ifdef _OPENMP
@@ -372,7 +371,8 @@ void test_2d_classes(double *const *data, int N)
#endif
for (i = 0; i < N; i++)
{
- int class = rand() % num_classes; // select a random class for the point
+ int class =
+ rand() % num_classes; // select a random class for the point
// create random coordinates (x,y,z) around the centre of the class
data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
@@ -397,7 +397,7 @@ void test1()
{
int j, N = 300;
int features = 2;
- int num_out = 30; // image size - N x N
+ int num_out = 30; // image size - N x N
// 2D space, hence size = number of rows * 2
double **X = (double **)malloc(N * sizeof(double *));
@@ -408,13 +408,13 @@ void test1()
W.dim2 = num_out;
W.dim3 = features;
W.data = (double *)malloc(num_out * num_out * features *
- sizeof(double)); // assign rows
+ sizeof(double)); // assign rows
- for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ for (int i = 0; i < 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] = (double *)malloc(features * sizeof(double));
- if (i < num_out) // only add new arrays if i < num_out
+ if (i < num_out) // only add new arrays if i < num_out
{
for (int k = 0; k < num_out; k++)
{
@@ -431,14 +431,13 @@ void test1()
}
}
- test_2d_classes(X, N); // create test data around circumference of a circle
- save_2d_data("test1.csv", X, N, features); // save test data points
- save_u_matrix("w11.csv", &W); // save initial random weights
- kohonen_som(X, &W, N, features, num_out, 1e-4); // train the SOM
- save_u_matrix("w12.csv", &W); // save the resultant weights
+ test_2d_classes(X, N); // create test data around circumference of a circle
+ save_2d_data("test1.csv", X, N, features); // save test data points
+ save_u_matrix("w11.csv", &W); // save initial random weights
+ kohonen_som(X, &W, N, features, num_out, 1e-4); // train the SOM
+ save_u_matrix("w12.csv", &W); // save the resultant weights
- for (int i = 0; i < N; i++)
- free(X[i]);
+ for (int i = 0; i < N; i++) free(X[i]);
free(X);
free(W.data);
}
@@ -455,15 +454,15 @@ void test1()
*/
void test_3d_classes1(double *const *data, int N)
{
- const double R = 0.2; // radius of cluster
+ const double R = 0.2; // radius of cluster
int i;
const int num_classes = 4;
const double centres[][3] = {
// centres of each class cluster
- {.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 1
+ {.5, -.5, -.5}, // centre of class 2
+ {-.5, .5, .5}, // centre of class 3
+ {-.5, -.5 - .5} // centre of class 4
};
#ifdef _OPENMP
@@ -471,7 +470,8 @@ void test_3d_classes1(double *const *data, int N)
#endif
for (i = 0; i < N; i++)
{
- int class = rand() % num_classes; // select a random class for the point
+ int class =
+ rand() % num_classes; // select a random class for the point
// create random coordinates (x,y,z) around the centre of the class
data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
@@ -497,7 +497,7 @@ void test2()
{
int j, N = 500;
int features = 3;
- int num_out = 30; // image size - N x N
+ int num_out = 30; // image size - N x N
// 3D space, hence size = number of rows * 3
double **X = (double **)malloc(N * sizeof(double *));
@@ -508,13 +508,13 @@ void test2()
W.dim2 = num_out;
W.dim3 = features;
W.data = (double *)malloc(num_out * num_out * features *
- sizeof(double)); // assign rows
+ sizeof(double)); // assign rows
- for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ for (int i = 0; i < 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] = (double *)malloc(features * sizeof(double));
- if (i < num_out) // only add new arrays if i < num_out
+ if (i < num_out) // only add new arrays if i < num_out
{
for (int k = 0; k < num_out; k++)
{
@@ -522,7 +522,7 @@ void test2()
#pragma omp for
#endif
for (j = 0; j < features; j++)
- { // preallocate with random initial weights
+ { // preallocate with random initial weights
double *w = data_3d(&W, i, k, j);
w[0] = _random(-5, 5);
}
@@ -530,14 +530,13 @@ void test2()
}
}
- test_3d_classes1(X, N); // create test data
- save_2d_data("test2.csv", X, N, features); // save test data points
- save_u_matrix("w21.csv", &W); // save initial random weights
- kohonen_som(X, &W, N, features, num_out, 1e-4); // train the SOM
- save_u_matrix("w22.csv", &W); // save the resultant weights
+ test_3d_classes1(X, N); // create test data
+ save_2d_data("test2.csv", X, N, features); // save test data points
+ save_u_matrix("w21.csv", &W); // save initial random weights
+ kohonen_som(X, &W, N, features, num_out, 1e-4); // train the SOM
+ save_u_matrix("w22.csv", &W); // save the resultant weights
- for (int i = 0; i < N; i++)
- free(X[i]);
+ for (int i = 0; i < N; i++) free(X[i]);
free(X);
free(W.data);
}
@@ -554,19 +553,19 @@ void test2()
*/
void test_3d_classes2(double *const *data, int N)
{
- const double R = 0.2; // radius of cluster
+ const double R = 0.2; // radius of cluster
int i;
const int num_classes = 8;
const double centres[][3] = {
// centres of each class cluster
- {.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
- {-.5, -.5, -.5} // centre of class 8
+ {.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
+ {-.5, -.5, -.5} // centre of class 8
};
#ifdef _OPENMP
@@ -574,7 +573,8 @@ void test_3d_classes2(double *const *data, int N)
#endif
for (i = 0; i < N; i++)
{
- int class = rand() % num_classes; // select a random class for the point
+ int class =
+ rand() % num_classes; // select a random class for the point
// create random coordinates (x,y,z) around the centre of the class
data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
@@ -609,13 +609,13 @@ void test3()
W.dim2 = num_out;
W.dim3 = features;
W.data = (double *)malloc(num_out * num_out * features *
- sizeof(double)); // assign rows
+ sizeof(double)); // assign rows
- for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ for (int i = 0; i < 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] = (double *)malloc(features * sizeof(double));
- if (i < num_out) // only add new arrays if i < num_out
+ if (i < num_out) // only add new arrays if i < num_out
{
for (int k = 0; k < num_out; k++)
{
@@ -632,14 +632,13 @@ void test3()
}
}
- test_3d_classes2(X, N); // create test data around the lamniscate
- save_2d_data("test3.csv", X, N, features); // save test data points
- save_u_matrix("w31.csv", &W); // save initial random weights
- kohonen_som(X, &W, N, features, num_out, 0.01); // train the SOM
- save_u_matrix("w32.csv", &W); // save the resultant weights
+ test_3d_classes2(X, N); // create test data around the lamniscate
+ save_2d_data("test3.csv", X, N, features); // save test data points
+ save_u_matrix("w31.csv", &W); // save initial random weights
+ kohonen_som(X, &W, N, features, num_out, 0.01); // train the SOM
+ save_u_matrix("w32.csv", &W); // save the resultant weights
- for (int i = 0; i < N; i++)
- free(X[i]);
+ for (int i = 0; i < N; i++) free(X[i]);
free(X);
free(W.data);
}
diff --git a/machine_learning/kohonen_som_trace.c b/machine_learning/kohonen_som_trace.c
index 476f364f..3569f974 100644
--- a/machine_learning/kohonen_som_trace.c
+++ b/machine_learning/kohonen_som_trace.c
@@ -16,18 +16,18 @@
#include
#include
#include
-#ifdef _OPENMP // check if OpenMP based parallellization is available
+#ifdef _OPENMP // check if OpenMP based parallellization is available
#include
#endif
#ifndef max
-#define max(a, b) \
- (((a) > (b)) ? (a) : (b)) /**< shorthand for maximum value \
+#define max(a, b) \
+ (((a) > (b)) ? (a) : (b)) /**< shorthand for maximum value \
*/
#endif
#ifndef min
-#define min(a, b) \
- (((a) < (b)) ? (a) : (b)) /**< shorthand for minimum value \
+#define min(a, b) \
+ (((a) < (b)) ? (a) : (b)) /**< shorthand for minimum value \
*/
#endif
@@ -64,7 +64,7 @@ int save_nd_data(const char *fname, double **X, int num_points,
int num_features)
{
FILE *fp = fopen(fname, "wt");
- if (!fp) // error with fopen
+ if (!fp) // error with fopen
{
char msg[120];
sprintf(msg, "File error (%s): ", fname);
@@ -72,16 +72,16 @@ int save_nd_data(const char *fname, double **X, int num_points,
return -1;
}
- for (int i = 0; i < num_points; i++) // for each point in the array
+ for (int i = 0; i < num_points; i++) // for each point in the array
{
- for (int j = 0; j < num_features; j++) // for each feature in the array
+ for (int j = 0; j < num_features; j++) // for each feature in the array
{
- fprintf(fp, "%.4g", X[i][j]); // print the feature value
- if (j < num_features - 1) // if not the last feature
- fprintf(fp, ","); // suffix comma
+ fprintf(fp, "%.4g", X[i][j]); // print the feature value
+ if (j < num_features - 1) // if not the last feature
+ fprintf(fp, ","); // suffix comma
}
- if (i < num_points - 1) // if not the last row
- fprintf(fp, "\n"); // start a new line
+ if (i < num_points - 1) // if not the last row
+ fprintf(fp, "\n"); // start a new line
}
fclose(fp);
return 0;
@@ -96,12 +96,12 @@ int save_nd_data(const char *fname, double **X, int num_points,
*/
void get_min_1d(double const *X, int N, double *val, int *idx)
{
- val[0] = INFINITY; // initial min value
+ val[0] = INFINITY; // initial min value
- for (int i = 0; i < N; i++) // check each value
+ for (int i = 0; i < N; i++) // check each value
{
- if (X[i] < val[0]) // if a lower value is found
- { // save the value and its index
+ if (X[i] < val[0]) // if a lower value is found
+ { // save the value and its index
idx[0] = i;
val[0] = X[i];
}
@@ -212,8 +212,8 @@ void kohonen_som_tracer(double **X, double *const *W, int num_samples,
void test_circle(double *const *data, int N)
{
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
+ 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;
#ifdef _OPENMP
@@ -221,9 +221,9 @@ void test_circle(double *const *data, int N)
#endif
for (i = 0; i < N; i++)
{
- double r = _random(a_r, b_r); // random radius
- double theta = _random(a_t, b_t); // random theta
- data[i][0] = r * cos(theta); // convert from polar to cartesian
+ double r = _random(a_r, b_r); // random radius
+ double theta = _random(a_t, b_t); // random theta
+ data[i][0] = r * cos(theta); // convert from polar to cartesian
data[i][1] = r * sin(theta);
}
}
@@ -245,7 +245,7 @@ void test_circle(double *const *data, int N)
* "w12.csv" title "w2"
* ```
* 
+ * output](https://raw.githubusercontent.com/TheAlgorithms/C/docs/images/machine_learning/kohonen/test1.svg)
*/
void test1()
{
@@ -259,28 +259,28 @@ void test1()
// number of clusters nodes * 2
double **W = (double **)malloc(num_out * sizeof(double *));
- for (int i = 0; i < max(num_out, N); i++) // loop till max(N, num_out)
+ for (int i = 0; i < 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] = (double *)malloc(features * sizeof(double));
- if (i < num_out) // only add new arrays if i < num_out
+ if (i < num_out) // only add new arrays if i < num_out
{
W[i] = (double *)malloc(features * sizeof(double));
#ifdef _OPENMP
#pragma omp for
#endif
// preallocate with random initial weights
- for (j = 0; j < features; j++)
- W[i][j] = _random(-1, 1);
+ for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
}
}
- test_circle(X, N); // create test data around circumference of a circle
- save_nd_data("test1.csv", X, N, features); // save test data points
+ test_circle(X, N); // create test data around circumference of a circle
+ save_nd_data("test1.csv", X, N, features); // save test data points
save_nd_data("w11.csv", W, num_out,
- features); // save initial random weights
- kohonen_som_tracer(X, W, N, features, num_out, 0.1); // train the SOM
- save_nd_data("w12.csv", W, num_out, features); // save the resultant weights
+ features); // save initial random weights
+ kohonen_som_tracer(X, W, N, features, num_out, 0.1); // train the SOM
+ save_nd_data("w12.csv", W, num_out,
+ features); // save the resultant weights
for (int i = 0; i < max(num_out, N); i++)
{
@@ -315,10 +315,10 @@ void test_lamniscate(double *const *data, int N)
#endif
for (i = 0; i < N; i++)
{
- double dx = _random(-dr, dr); // random change in x
- double dy = _random(-dr, dr); // random change in y
- double theta = _random(0, M_PI); // random theta
- data[i][0] = dx + cos(theta); // convert from polar to cartesian
+ double dx = _random(-dr, dr); // random change in x
+ double dy = _random(-dr, dr); // random change in y
+ double theta = _random(0, M_PI); // random theta
+ data[i][0] = dx + cos(theta); // convert from polar to cartesian
data[i][1] = dy + sin(2. * theta) / 2.f;
}
}
@@ -342,7 +342,7 @@ void test_lamniscate(double *const *data, int N)
* "w22.csv" title "w2"
* ```
* 
+ * output](https://raw.githubusercontent.com/TheAlgorithms/C/docs/images/machine_learning/kohonen/test2.svg)
*/
void test2()
{
@@ -353,9 +353,9 @@ void test2()
double **W = (double **)malloc(num_out * sizeof(double *));
for (int i = 0; i < max(num_out, N); i++)
{
- if (i < N) // only add new arrays if i < N
+ if (i < N) // only add new arrays if i < N
X[i] = (double *)malloc(features * sizeof(double));
- if (i < num_out) // only add new arrays if i < num_out
+ if (i < num_out) // only add new arrays if i < num_out
{
W[i] = (double *)malloc(features * sizeof(double));
@@ -363,17 +363,17 @@ void test2()
#pragma omp for
#endif
// preallocate with random initial weights
- for (j = 0; j < features; j++)
- W[i][j] = _random(-1, 1);
+ for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
}
}
- test_lamniscate(X, N); // create test data around the lamniscate
- save_nd_data("test2.csv", X, N, features); // save test data points
+ test_lamniscate(X, N); // create test data around the lamniscate
+ save_nd_data("test2.csv", X, N, features); // save test data points
save_nd_data("w21.csv", W, num_out,
- features); // save initial random weights
- kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
- save_nd_data("w22.csv", W, num_out, features); // save the resultant weights
+ features); // save initial random weights
+ kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
+ save_nd_data("w22.csv", W, num_out,
+ features); // save the resultant weights
for (int i = 0; i < max(num_out, N); i++)
{
@@ -398,15 +398,15 @@ void test2()
*/
void test_3d_classes(double *const *data, int N)
{
- const double R = 0.1; // radius of cluster
+ const double R = 0.1; // radius of cluster
int i;
const int num_classes = 4;
const double centres[][3] = {
// centres of each class cluster
- {.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 1
+ {.5, -.5, -.5}, // centre of class 2
+ {-.5, .5, .5}, // centre of class 3
+ {-.5, -.5 - .5} // centre of class 4
};
#ifdef _OPENMP
@@ -414,7 +414,8 @@ void test_3d_classes(double *const *data, int N)
#endif
for (i = 0; i < N; i++)
{
- int class = rand() % num_classes; // select a random class for the point
+ int class =
+ rand() % num_classes; // select a random class for the point
// create random coordinates (x,y,z) around the centre of the class
data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
@@ -445,7 +446,7 @@ void test_3d_classes(double *const *data, int N)
* "w32.csv" title "w2"
* ```
* 
+ * output](https://raw.githubusercontent.com/TheAlgorithms/C/docs/images/machine_learning/kohonen/test3.svg)
*/
void test3()
{
@@ -456,9 +457,9 @@ void test3()
double **W = (double **)malloc(num_out * sizeof(double *));
for (int i = 0; i < max(num_out, N); i++)
{
- if (i < N) // only add new arrays if i < N
+ if (i < N) // only add new arrays if i < N
X[i] = (double *)malloc(features * sizeof(double));
- if (i < num_out) // only add new arrays if i < num_out
+ if (i < num_out) // only add new arrays if i < num_out
{
W[i] = (double *)malloc(features * sizeof(double));
@@ -466,17 +467,17 @@ void test3()
#pragma omp for
#endif
// preallocate with random initial weights
- for (j = 0; j < features; j++)
- W[i][j] = _random(-1, 1);
+ for (j = 0; j < features; j++) W[i][j] = _random(-1, 1);
}
}
- test_3d_classes(X, N); // create test data around the lamniscate
- save_nd_data("test3.csv", X, N, features); // save test data points
+ test_3d_classes(X, N); // create test data around the lamniscate
+ save_nd_data("test3.csv", X, N, features); // save test data points
save_nd_data("w31.csv", W, num_out,
- features); // save initial random weights
- kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
- save_nd_data("w32.csv", W, num_out, features); // save the resultant weights
+ features); // save initial random weights
+ kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
+ save_nd_data("w32.csv", W, num_out,
+ features); // save the resultant weights
for (int i = 0; i < max(num_out, N); i++)
{
@@ -524,7 +525,8 @@ int main(int argc, char **argv)
end_clk = clock();
printf("Test 3 completed in %.4g sec\n",
get_clock_diff(start_clk, end_clk));
- printf("(Note: Calculated times include: creating test sets, training "
- "model and writing files to disk.)\n\n");
+ printf(
+ "(Note: Calculated times include: creating test sets, training "
+ "model and writing files to disk.)\n\n");
return 0;
}
diff --git a/numerical_methods/durand_kerner_roots.c b/numerical_methods/durand_kerner_roots.c
index 440fad1c..8b032aa7 100644
--- a/numerical_methods/durand_kerner_roots.c
+++ b/numerical_methods/durand_kerner_roots.c
@@ -21,10 +21,10 @@
* Sample implementation results to compute approximate roots of the equation
* \f$x^4-1=0\f$:\n
* 
@@ -53,8 +53,7 @@ long double complex poly_function(double *coeffs, unsigned int degree,
long double complex out = 0.;
unsigned int n;
- for (n = 0; n < degree; n++)
- out += coeffs[n] * cpow(x, degree - n - 1);
+ for (n = 0; n < degree; n++) out += coeffs[n] * cpow(x, degree - n - 1);
return out;
}
@@ -102,8 +101,9 @@ int main(int argc, char **argv)
if (argc < 2)
{
- printf("Please pass the coefficients of the polynomial as commandline "
- "arguments.\n");
+ printf(
+ "Please pass the coefficients of the polynomial as commandline "
+ "arguments.\n");
return 0;
}
@@ -224,8 +224,7 @@ int main(int argc, char **argv)
if (iter % 500 == 0)
{
printf("Iter: %lu\t", iter);
- for (n = 0; n < degree - 1; n++)
- printf("\t%s", complex_str(s0[n]));
+ for (n = 0; n < degree - 1; n++) printf("\t%s", complex_str(s0[n]));
printf("\t\tabsolute average change: %.4g\n", tol_condition);
}
@@ -241,8 +240,7 @@ end:
#endif
printf("\nIterations: %lu\n", iter);
- for (n = 0; n < degree - 1; n++)
- printf("\t%s\n", complex_str(s0[n]));
+ for (n = 0; n < degree - 1; n++) printf("\t%s\n", complex_str(s0[n]));
printf("absolute average change: %.4g\n", tol_condition);
printf("Time taken: %.4g sec\n",
(end_time - start_time) / (double)CLOCKS_PER_SEC);
diff --git a/numerical_methods/ode_forward_euler.c b/numerical_methods/ode_forward_euler.c
index 0f8292fd..ee4451b8 100644
--- a/numerical_methods/ode_forward_euler.c
+++ b/numerical_methods/ode_forward_euler.c
@@ -22,7 +22,7 @@
* The computation results are stored to a text file `forward_euler.csv` and the
* exact soltuion results in `exact.csv` for comparison.
* 
*
* To implement [Van der Pol
@@ -54,9 +54,9 @@
*/
void problem(const double *x, double *y, double *dy)
{
- const double omega = 1.F; // some const for the problem
- dy[0] = y[1]; // x dot
- dy[1] = -omega * omega * y[0]; // y dot
+ const double omega = 1.F; // some const for the problem
+ dy[0] = y[1]; // x dot
+ dy[1] = -omega * omega * y[0]; // y dot
}
/**
@@ -83,8 +83,7 @@ void forward_euler_step(const double dx, const double *x, double *y, double *dy)
{
int o;
problem(x, y, dy);
- for (o = 0; o < order; o++)
- y[o] += dx * dy[o];
+ for (o = 0; o < order; o++) y[o] += dx * dy[o];
}
/**
@@ -116,13 +115,13 @@ double forward_euler(double dx, double x0, double x_max, double *y,
/* start integration */
clock_t t1 = clock();
double x = x0;
- do // iterate for each step of independent variable
+ do // iterate for each step of independent variable
{
if (save_to_file && fp)
- fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
- forward_euler_step(dx, &x, y, dy); // perform integration
- x += dx; // update step
- } while (x <= x_max); // till upper limit of independent variable
+ fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
+ forward_euler_step(dx, &x, y, dy); // perform integration
+ x += dx; // update step
+ } while (x <= x_max); // till upper limit of independent variable
/* end of integration */
clock_t t2 = clock();
@@ -169,7 +168,7 @@ int main(int argc, char *argv[])
do
{
- fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
+ fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
exact_solution(&x, y);
x += step_size;
} while (x <= X_MAX);
diff --git a/numerical_methods/ode_midpoint_euler.c b/numerical_methods/ode_midpoint_euler.c
index 0e823432..eaf72307 100644
--- a/numerical_methods/ode_midpoint_euler.c
+++ b/numerical_methods/ode_midpoint_euler.c
@@ -21,7 +21,7 @@
* \f}
* The computation results are stored to a text file `midpoint_euler.csv` and
* the exact soltuion results in `exact.csv` for comparison. 
*
* To implement [Van der Pol
@@ -53,9 +53,9 @@
*/
void problem(const double *x, double *y, double *dy)
{
- const double omega = 1.F; // some const for the problem
- dy[0] = y[1]; // x dot
- dy[1] = -omega * omega * y[0]; // y dot
+ const double omega = 1.F; // some const for the problem
+ dy[0] = y[1]; // x dot
+ dy[1] = -omega * omega * y[0]; // y dot
}
/**
@@ -86,13 +86,11 @@ void midpoint_euler_step(double dx, double *x, double *y, double *dy)
double tmp_x = (*x) + 0.5 * dx;
double tmp_y[order];
int o;
- for (o = 0; o < order; o++)
- tmp_y[o] = y[o] + 0.5 * dx * dy[o];
+ for (o = 0; o < order; o++) tmp_y[o] = y[o] + 0.5 * dx * dy[o];
problem(&tmp_x, tmp_y, dy);
- for (o = 0; o < order; o++)
- y[o] += dx * dy[o];
+ for (o = 0; o < order; o++) y[o] += dx * dy[o];
}
/**
@@ -124,13 +122,13 @@ double midpoint_euler(double dx, double x0, double x_max, double *y,
/* start integration */
clock_t t1 = clock();
double x = x0;
- do // iterate for each step of independent variable
+ do // iterate for each step of independent variable
{
if (save_to_file && fp)
- fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
- midpoint_euler_step(dx, &x, y, dy); // perform integration
- x += dx; // update step
- } while (x <= x_max); // till upper limit of independent variable
+ fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
+ midpoint_euler_step(dx, &x, y, dy); // perform integration
+ x += dx; // update step
+ } while (x <= x_max); // till upper limit of independent variable
/* end of integration */
clock_t t2 = clock();
@@ -177,7 +175,7 @@ int main(int argc, char *argv[])
do
{
- fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
+ fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
exact_solution(&x, y);
x += step_size;
} while (x <= X_MAX);
diff --git a/numerical_methods/ode_semi_implicit_euler.c b/numerical_methods/ode_semi_implicit_euler.c
index ce629941..e87d2a91 100644
--- a/numerical_methods/ode_semi_implicit_euler.c
+++ b/numerical_methods/ode_semi_implicit_euler.c
@@ -21,7 +21,7 @@
* \f}
* The computation results are stored to a text file `semi_implicit_euler.csv`
* and the exact soltuion results in `exact.csv` for comparison. 
*
* To implement [Van der Pol
@@ -33,7 +33,7 @@
* dy[1] = mu * (1.f - y[0] * y[0]) * y[1] - y[0];
* ```
* 
*
* \see ode_forward_euler.c, ode_midpoint_euler.c
@@ -57,9 +57,9 @@
*/
void problem(const double *x, double *y, double *dy)
{
- const double omega = 1.F; // some const for the problem
- dy[0] = y[1]; // x dot
- dy[1] = -omega * omega * y[0]; // y dot
+ const double omega = 1.F; // some const for the problem
+ dy[0] = y[1]; // x dot
+ dy[1] = -omega * omega * y[0]; // y dot
}
/**
@@ -86,13 +86,13 @@ void semi_implicit_euler_step(double dx, double *x, double *y, double *dy)
{
int o;
- problem(x, y, dy); // update dy once
- y[0] += dx * dy[0]; // update y0
+ problem(x, y, dy); // update dy once
+ y[0] += dx * dy[0]; // update y0
- problem(x, y, dy); // update dy once more
+ problem(x, y, dy); // update dy once more
for (o = 1; o < order; o++)
- y[o] += dx * dy[o]; // update remaining using new dy
+ y[o] += dx * dy[o]; // update remaining using new dy
*x += dx;
}
@@ -125,13 +125,13 @@ double semi_implicit_euler(double dx, double x0, double x_max, double *y,
/* start integration */
clock_t t1 = clock();
double x = x0;
- do // iterate for each step of independent variable
+ do // iterate for each step of independent variable
{
if (save_to_file && fp)
- fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
- semi_implicit_euler_step(dx, &x, y, dy); // perform integration
- x += dx; // update step
- } while (x <= x_max); // till upper limit of independent variable
+ fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
+ semi_implicit_euler_step(dx, &x, y, dy); // perform integration
+ x += dx; // update step
+ } while (x <= x_max); // till upper limit of independent variable
/* end of integration */
clock_t t2 = clock();
@@ -178,7 +178,7 @@ int main(int argc, char *argv[])
do
{
- fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
+ fprintf(fp, "%.4g,%.4g,%.4g\n", x, y[0], y[1]); // write to file
exact_solution(&x, y);
x += step_size;
} while (x <= X_MAX);