static apop_data *colmeans(apop_data *in){
Get_vmsizes(in); //maxsize
apop_data *sums = apop_data_summarize(in);
Apop_col_tv(sums, "mean", means);
apop_data *out = apop_matrix_to_data(apop_vector_to_matrix(means, 'r'));
apop_name_stack(out->names, in->names, 'c', 'c');
apop_data *cov = apop_data_add_page(out, apop_data_covariance(in), "<Covariance>");
gsl_matrix_scale(cov->matrix, 1/sqrt(maxsize));
return out;
}
int main(){
int len = 3000;
gsl_vector *v = gsl_vector_alloc(len);
for (double i=0; i< len; i++) gsl_vector_set(v, i, 1./(i+1));
double square;
gsl_blas_ddot(v, v, &square);
printf("1 + (1/2)^2 + (1/3)^2 + ...= %g\n", square);
double pi_over_six = gsl_pow_2(M_PI)/6.;
Diff(square, pi_over_six);
/* Now using apop_dot, in a few forms.
First, vector-as-data dot itself.
If one of the inputs is a vector,
apop_dot puts the output in a vector-as-data:*/
apop_data *v_as_data = &(apop_data){.vector=v};
apop_data *vdotv = apop_dot(v_as_data, v_as_data);
Diff(gsl_vector_get(vdotv->vector, 0), pi_over_six);
/* Wrap matrix in an apop_data set. */
gsl_matrix *v_as_matrix = apop_vector_to_matrix(v);
apop_data dm = (apop_data){.matrix=v_as_matrix};
// (1 X len) vector dot (len X 1) matrix --- produce a scalar (one item vector).
apop_data *mdotv = apop_dot(v_as_data, &dm);
double scalarval = apop_data_get(mdotv);
Diff(scalarval, pi_over_six);
//(len X 1) dot (len X 1) --- bad dimensions.
apop_opts.verbose=-1; //don't print an error.
apop_data *mdotv2 = apop_dot(&dm, v_as_data);
apop_opts.verbose=0; //back to safety.
assert(mdotv2->error);
// If we want (len X 1) dot (1 X len) --> (len X len),
// use apop_vector_to_matrix.
apop_data dmr = (apop_data){.matrix=apop_vector_to_matrix(v, .row_col='r')};
apop_data *product_matrix = apop_dot(&dm, &dmr);
//The trace is the sum of squares:
gsl_vector_view trace = gsl_matrix_diagonal(product_matrix->matrix);
double tracesum = apop_sum(&trace.vector);
Diff(tracesum, pi_over_six);
apop_data_free(product_matrix);
gsl_matrix_free(dmr.matrix);
}
