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https://github.com/TheAlgorithms/C
synced 2024-11-25 06:49:36 +03:00
added 3D test case
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@ -349,6 +349,115 @@ void test2()
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free(W);
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}
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/** Creates a random set of points distributed *near* the locus
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* of the [Lamniscate of
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* Gerono](https://en.wikipedia.org/wiki/Lemniscate_of_Gerono) and trains an SOM
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* that finds that circular pattern. \param[out] data matrix to store data in
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* \param[in] N number of points required
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*/
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void test_3d_classes(double *const *data, int N)
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{
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const double R = 0.1; // radius of cluster
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int i;
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const int num_classes = 4;
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const double centres[][3] = {
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// centres of each class cluster
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{.5, .5, .5}, // centre of class 1
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{.5, -.5, -.5}, // centre of class 2
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{-.5, .5, .5}, // centre of class 3
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{-.5, -.5 - .5} // centre of class 4
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};
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#ifdef _OPENMP
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#pragma omp for
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#endif
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for (i = 0; i < N; i++)
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{
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int class = rand() % num_classes; // select a random class for the point
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// create random coordinates (x,y,z) around the centre of the class
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data[i][0] = _random(centres[class][0] - R, centres[class][0] + R);
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data[i][1] = _random(centres[class][1] - R, centres[class][1] + R);
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data[i][2] = _random(centres[class][2] - R, centres[class][2] + R);
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/* The follosing can also be used
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for (int j = 0; j < 3; j++)
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data[i][j] = _random(centres[class][j] - R, centres[class][j] + R);
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*/
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}
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}
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/** Test that creates a random set of points distributed in six clusters in
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* 3D space. The following
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* [CSV](https://en.wikipedia.org/wiki/Comma-separated_values) files are created
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* to validate the execution:
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* * `test3.csv`: random test samples points with a circular pattern
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* * `w31.csv`: initial random map
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* * `w32.csv`: trained SOM map
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*
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* The outputs can be readily plotted in [gnuplot](https:://gnuplot.info) using
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* the following snippet
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* ```gnuplot
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* set datafile separator ','
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* plot "test3.csv" title "original", \
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* "w31.csv" title "w1", \
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* "w32.csv" title "w2"
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* ```
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*/
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void test3()
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{
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int j, N = 200;
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int features = 3;
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int num_out = 20;
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double **X = (double **)malloc(N * sizeof(double *));
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double **W = (double **)malloc(num_out * sizeof(double *));
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for (int i = 0; i < (num_out > N ? num_out : N); i++)
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{
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if (i < N) // only add new arrays if i < N
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X[i] = (double *)malloc(features * sizeof(double));
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if (i < num_out) // only add new arrays if i < num_out
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{
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W[i] = (double *)malloc(features * sizeof(double));
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#ifdef _OPENMP
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#pragma omp for
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#endif
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// preallocate with random initial weights
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for (j = 0; j < features; j++)
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W[i][j] = _random(-1, 1);
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}
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}
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test_3d_classes(X, N); // create test data around the lamniscate
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save_nd_data("test3.csv", X, N, features); // save test data points
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save_nd_data("w31.csv", W, num_out,
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features); // save initial random weights
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kohonen_som_tracer(X, W, N, features, num_out, 0.01); // train the SOM
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save_nd_data("w32.csv", W, num_out, features); // save the resultant weights
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for (int i = 0; i < (num_out > N ? num_out : N); i++)
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{
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if (i < N)
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free(X[i]);
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if (i < num_out)
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free(W[i]);
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}
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free(X);
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free(W);
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}
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/**
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* Convert clock cycle difference to time in seconds
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*
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* \param[in] start_t start clock
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* \param[in] start_t end clock
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* \returns time difference in seconds
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*/
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inline double get_clock_diff(clock_t start_t, clock_t end_t)
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{
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return (double)(end_t - start_t) / (double)CLOCKS_PER_SEC;
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}
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/** Main function */
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int main(int argc, char **argv)
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{
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@ -367,6 +476,11 @@ int main(int argc, char **argv)
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end_clk = clock();
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printf("Test 2 completed in %.4g sec\n",
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get_clock_diff(start_clk, end_clk));
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start_clk = clock();
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test3();
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end_clk = clock();
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printf("Test 3 completed in %.4g sec\n",
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get_clock_diff(start_clk, end_clk));
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printf("(Note: Calculated times include: creating test sets, training "
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"model and writing files to disk.)\n\n");
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return 0;
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