← Back to Documentations

Signature Description Parameters 
#include <DataFrame/DataFrameStatsVisitors.h>

template<typename T, typename I = unsigned long>
struct SEMVisitor;

// -------------------------------------

template<typename T, typename I = unsigned long>
using sem_v = SEMVisitor<T, I>;
This functor class calculates the Standard Error of the Mean for a given column.
This works with both scalar and multidimensional (i.e. vector and arrays) datasets. For multidimensiional datasetes, you must use the single_act_visit() interface.
    explicit
    SEMVisitor (bool bias = true);
bias: If true it divides by n - 1, otherwise by n.

There are also the following member functions:
get_result(): Returns the SEM.
              In case of scalar dataset, SEM is a single number. In case of multidimensional dataset,
              SEM is a square matrix of data dimension rows and columns.
get_per_dim_SEM(): Returns per dimension SEMs in a vector of data dimension length. This is
                   the diagonal of the full matrix SEM. In case of scalar data, this returns an empty vector.
T: Column data type.
I: Index type. 
static void test_SEMVisitor()  {

    std::cout << "\nTesting SEMVisitor{ } ..." << std::endl;

    using MyDataFrame = StdDataFrame<unsigned long>;

    std::vector<unsigned long>  idx =
        { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
          21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 };
    std::vector<double>         d1 =
        { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
          21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 };
    MyDataFrame                 df;

    df.load_data(std::move(idx), std::make_pair("col_1", d1));

    SEMVisitor<double>  sem_visitor;
    const auto          result = df.visit<double>("col_1", sem_visitor).get_result();

    assert(fabs(result - 1.84842) < 0.00001);

    // Now multidimensional data
    //
    RandGenParams<double>   p;

    p.seed = 123;
    p.min_value = 0;
    p.max_value = 10.0;

    constexpr std::size_t   dim { 3 };

    using ary_col_t = std::array<double, dim>;
    using vec_col_t = std::vector<double>;

    // Generate and load 3 random columns
    //
    auto    rand_vec = gen_uniform_real_dist<double>(df.get_index().size() * dim, p);

    std::vector<ary_col_t>  array_col(df.get_index().size());
    std::vector<vec_col_t>  vector_col(df.get_index().size());

    for (std::size_t i { 0 }, j { 0 }; j < rand_vec.size(); ++i)  {
        vector_col[i].resize(dim);
        for (std::size_t d { 0 }; d < dim; ++d)
            array_col[i][d] = vector_col[i][d] = rand_vec[j++];
    }
    df.load_column<ary_col_t>("array_col", std::move(array_col));
    df.load_column<vec_col_t>("vector_col", std::move(vector_col));

    SEMVisitor<ary_col_t>   ary_sem;
    SEMVisitor<vec_col_t>   vec_sem;

    df.single_act_visit<ary_col_t>("array_col", ary_sem);
    df.single_act_visit<vec_col_t>("vector_col", vec_sem);

    const auto  &ary_dm_result = ary_sem.get_result();
    const auto  &vec_dm_result = vec_sem.get_result();

    assert(ary_dm_result.rows() == 3);
    assert(ary_dm_result.cols() == 3);
    assert(vec_dm_result.rows() == 3);
    assert(vec_dm_result.cols() == 3);
    assert(fabs(ary_dm_result(0, 0) - 0.202278) < 0.000001);
    assert(fabs(ary_dm_result(1, 2) - 0.012392) < 0.000001);
    assert(fabs(ary_dm_result(2, 1) - 0.012392) < 0.000001);
    assert(fabs(vec_dm_result(0, 0) - 0.202278) < 0.000001);
    assert(fabs(vec_dm_result(1, 2) - 0.012392) < 0.000001);
    assert(fabs(vec_dm_result(2, 1) - 0.012392) < 0.000001);

    const auto  &ary_per_dim = ary_sem.get_per_dim_SEM();
    const auto  &vec_per_dim = vec_sem.get_per_dim_SEM();

    assert(ary_per_dim.size() == 3);
    assert(vec_per_dim.size() == 3);
    assert(fabs(ary_per_dim[0] - 2.80871) < 0.00001);
    assert(fabs(ary_per_dim[2] - 2.77477) < 0.00001);
    assert(fabs(vec_per_dim[0] - 2.80871) < 0.00001);
    assert(fabs(vec_per_dim[2] - 2.77477) < 0.00001);
}

C++ DataFrame