Actual source code: ex_sharks.c

  1: /* Example inspired by the toy example in https://www.r-bloggers.com/2020/06/understanding-lasso-and-ridge-regression-2/
  2:  * blog post by Dr. Atakan Ekiz.
  3:  * Here we wish to predict the number of shark attacks (that is, this number is our response variable),
  4:  * using the following predictor variables:
  5:  * - percentage of swimmers who watched the movie Jaws
  6:  * - the number of swimmers in the water
  7:  * - the average temperature of the day
  8:  * - the price of your favorite tech stock of the day (totally uncorrelated variable) */

 10: static char help[] = "Tests basic creation and destruction of PetscRegressor objects.\n\n";

 12: #include <petscregressor.h>

 14: int main(int argc, char **args)
 15: {
 16:   PetscRegressor regressor;
 17:   PetscMPIInt    rank;
 18:   Mat            X;
 19:   Vec            y, y_predicted, coefficients;
 20:   PetscScalar    intercept;

 22:   PetscScalar y_array[20] = {98, 53, 39, 127, 73, 42, 71, 61, 83, 74, 85, 82, 62, 60, 43, 69, 67, 69, 85, 3}; // Number of shark attacks

 24:   PetscScalar X_array[80] = {37.92934, 513, 92.89899, 137.2139, // % watched Jaws, #swimmers, temperature, stock price
 25:                              52.77429, 451, 87.86271, 145.7987, //
 26:                              60.84441, 456, 88.28927, 149.7299, //
 27:                              26.54302, 546, 89.43875, 147.1180, //
 28:                              54.29125, 431, 88.01132, 124.3068, //
 29:                              55.06056, 355, 88.06297, 114.1730, //
 30:                              44.25260, 557, 87.78536, 112.5773, //
 31:                              44.53368, 398, 87.49603, 125.1628, //
 32:                              44.35548, 498, 88.95234, 124.8483, //
 33:                              41.09962, 406, 89.00630, 115.9223, //
 34:                              45.22807, 610, 86.38794, 148.1111, //
 35:                              40.01614, 452, 88.83585, 131.7050, //
 36:                              42.23746, 429, 87.78222, 106.3717, //
 37:                              50.64459, 450, 87.97008, 121.1523, //
 38:                              59.59494, 337, 89.67538, 145.7158, //
 39:                              48.89715, 383, 91.12611, 123.3896, //
 40:                              44.88990, 282, 93.29563, 145.4085, //
 41:                              40.88805, 366, 88.45329, 129.8872, //
 42:                              41.62828, 471, 93.21182, 131.5871, //
 43:                              74.15835, 453, 87.68438, 143.4579};

 45:   PetscInt rows_ix[20] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19};
 46:   PetscInt cols_ix[4]  = {0, 1, 2, 3};

 48:   PetscCall(PetscInitialize(&argc, &args, (char *)0, help));
 49:   PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));

 51:   PetscCall(VecCreate(PETSC_COMM_WORLD, &y));
 52:   PetscCall(VecSetSizes(y, PETSC_DECIDE, 20));
 53:   PetscCall(VecSetFromOptions(y));
 54:   PetscCall(VecDuplicate(y, &y_predicted));
 55:   PetscCall(MatCreate(PETSC_COMM_WORLD, &X));
 56:   PetscCall(MatSetSizes(X, PETSC_DECIDE, PETSC_DECIDE, 20, 4));
 57:   PetscCall(MatSetFromOptions(X));
 58:   PetscCall(MatSetUp(X));

 60:   if (!rank) {
 61:     PetscCall(VecSetValues(y, 20, rows_ix, y_array, INSERT_VALUES));
 62:     PetscCall(MatSetValues(X, 20, rows_ix, 4, cols_ix, X_array, ADD_VALUES));
 63:   }
 64:   PetscCall(VecAssemblyBegin(y));
 65:   PetscCall(VecAssemblyEnd(y));
 66:   PetscCall(MatAssemblyBegin(X, MAT_FINAL_ASSEMBLY));
 67:   PetscCall(MatAssemblyEnd(X, MAT_FINAL_ASSEMBLY));

 69:   PetscCall(PetscRegressorCreate(PETSC_COMM_WORLD, &regressor));
 70:   PetscCall(PetscRegressorSetType(regressor, PETSCREGRESSORLINEAR));
 71:   PetscRegressorSetFromOptions(regressor);
 72:   PetscCall(PetscRegressorFit(regressor, X, y));
 73:   PetscCall(PetscRegressorPredict(regressor, X, y_predicted));
 74:   PetscCall(PetscRegressorLinearGetIntercept(regressor, &intercept));
 75:   PetscCall(PetscRegressorLinearGetCoefficients(regressor, &coefficients));

 77:   PetscCall(PetscPrintf(PETSC_COMM_WORLD, "Intercept is %lf\n", intercept));
 78:   PetscCall(PetscPrintf(PETSC_COMM_WORLD, "Coefficients are\n"));
 79:   PetscCall(VecView(coefficients, PETSC_VIEWER_STDOUT_WORLD));
 80:   PetscCall(PetscPrintf(PETSC_COMM_WORLD, "Predicted values are\n"));
 81:   PetscCall(VecView(y_predicted, PETSC_VIEWER_STDOUT_WORLD));

 83:   PetscCall(PetscRegressorDestroy(&regressor));

 85:   PetscCall(PetscFinalize());
 86:   return 0;
 87: }