| Signature | Description | Parameters |
|---|---|---|
#include <DataFrame/DataFrameStatsVisitors.h> template<typename AV, typename T, typename I = unsigned long, std::size_t A = 0> struct BiasVisitor; // ------------------------------------- template<typename A, typename T, typename I = unsigned long, std::size_t A = 0> using bias_v = BiasVisitor<A, T, I, A>; |
This is a "single action visitor", meaning it is passed the whole data vector in one call and you must use the single_act_visit() interface. This visitor calculates rate of change between the source and a moving average. AV is the type of mean/average visitor This works with both scalar and multidimensional (i.e. vectors or arrays) datasets. get_result() returns the vector of change rates of a scalar column. In case of a multidimensional column, it returns a vector of vectors. Each inner vector is the length of data dimension. In case of a multidimensional column, the change rates are done per dimension
explicit
BiasVisitor(AV avg, size_t roll_period = 26)
|
A: Mean visitor type. T: Column data type. I: Index type. A: Memory alignment boundary for vectors. Default is system default alignment |
static void test_BiasVisitor() { using StrDataFrame = StdDataFrame<std::string>; std::cout << "\nTesting BiasVisitor{ } ..." << std::endl; StrDataFrame df; try { df.read("SHORT_IBM.csv", io_format::csv2); df.remove_data_by_loc<double, long>({ 0, 1500 }); using avg1 = MeanVisitor<double, std::string>; avg1 avg1_v; bias_v<avg1, double, std::string> bias1 (avg1_v); df.single_act_visit<double>("IBM_Close", bias1); assert(bias1.get_result().size() == 221); assert(std::isnan(bias1.get_result()[0])); assert(std::isnan(bias1.get_result()[24])); assert(std::abs(bias1.get_result()[25] - 0.0309) < 0.0001); assert(std::abs(bias1.get_result()[30] - 0.0477) < 0.0001); assert(std::abs(bias1.get_result()[35] - 0.0907) < 0.0001); assert(std::abs(bias1.get_result()[220] - -0.0698) < 0.0001); assert(std::abs(bias1.get_result()[215] - -0.049) < 0.0001); assert(std::abs(bias1.get_result()[210] - 0.0242) < 0.0001); using s_avg1 = StableMeanVisitor<double, std::string>; s_avg1 s_avg1_v; bias_v<s_avg1, double, std::string> s_bias1 (s_avg1_v); df.single_act_visit<double>("IBM_Close", s_bias1); assert(s_bias1.get_result().size() == 221); assert(std::isnan(s_bias1.get_result()[0])); assert(std::isnan(s_bias1.get_result()[24])); assert(std::abs(s_bias1.get_result()[25] - 0.0309) < 0.0001); assert(std::abs(s_bias1.get_result()[30] - 0.0477) < 0.0001); assert(std::abs(s_bias1.get_result()[35] - 0.0907) < 0.0001); assert(std::abs(s_bias1.get_result()[220] - -0.0698) < 0.0001); assert(std::abs(s_bias1.get_result()[215] - -0.049) < 0.0001); assert(std::abs(s_bias1.get_result()[210] - 0.0242) < 0.0001); using avg2 = WeightedMeanVisitor<double, std::string>; avg2 avg2_v; bias_v<avg2, double, std::string> bias2 (avg2_v); df.single_act_visit<double>("IBM_Close", bias2); assert(bias2.get_result().size() == 221); assert(std::isnan(bias2.get_result()[0])); assert(std::isnan(bias2.get_result()[24])); assert(std::abs(bias2.get_result()[25] - 0.0224) < 0.0001); assert(std::abs(bias2.get_result()[30] - 0.0381) < 0.0001); assert(std::abs(bias2.get_result()[35] - 0.068) < 0.0001); assert(std::abs(bias2.get_result()[220] - -0.0532) < 0.0001); assert(std::abs(bias2.get_result()[215] - -0.0496) < 0.0001); assert(std::abs(bias2.get_result()[210] - 0.0168) < 0.0001); using avg3 = GeometricMeanVisitor<double, std::string>; avg3 avg3_v; bias_v<avg3, double, std::string> bias3 (avg3_v); df.single_act_visit<double>("IBM_Close", bias3); assert(bias3.get_result().size() == 221); assert(std::isnan(bias3.get_result()[0])); assert(std::isnan(bias3.get_result()[24])); assert(std::abs(bias3.get_result()[25] - 0.0311) < 0.0001); assert(std::abs(bias3.get_result()[30] - 0.0479) < 0.0001); assert(std::abs(bias3.get_result()[35] - 0.0919) < 0.0001); assert(std::abs(bias3.get_result()[220] - -0.0685) < 0.0001); assert(std::abs(bias3.get_result()[215] - -0.0485) < 0.0001); assert(std::abs(bias3.get_result()[210] - 0.0245) < 0.0001); using avg4 = HarmonicMeanVisitor<double, std::string>; avg4 avg4_v; bias_v<avg4, double, std::string> bias4 (avg4_v); df.single_act_visit<double>("IBM_Close", bias4); assert(bias4.get_result().size() == 221); assert(std::isnan(bias4.get_result()[0])); assert(std::isnan(bias4.get_result()[24])); assert(std::abs(bias4.get_result()[25] - 0.0313) < 0.0001); assert(std::abs(bias4.get_result()[30] - 0.0481) < 0.0001); assert(std::abs(bias4.get_result()[35] - 0.093) < 0.0001); assert(std::abs(bias4.get_result()[220] - -0.0672) < 0.0001); assert(std::abs(bias4.get_result()[215] - -0.048) < 0.0001); assert(std::abs(bias4.get_result()[210] - 0.0248) < 0.0001); } catch (const DataFrameError &ex) { std::cout << ex.what() << std::endl; ::exit(-1); } // Now multidimensional data // RandGenParams<double> p; p.seed = 123; p.min_value = 1; 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)); MeanVisitor<ary_col_t, std::string> ary_mean_v; MeanVisitor<vec_col_t, std::string> vec_mean_v; GeometricMeanVisitor<ary_col_t, std::string> ary_geom_v; GeometricMeanVisitor<vec_col_t, std::string> vec_geom_v; const auto &ary_mean_res = df.single_act_visit<ary_col_t>("array_col", ary_mean_v).get_result(); const auto &vec_mean_res = df.single_act_visit<vec_col_t>("vector_col", vec_mean_v).get_result(); const auto &ary_geom_res = df.single_act_visit<ary_col_t>("array_col", ary_geom_v).get_result(); const auto &vec_geom_res = df.single_act_visit<vec_col_t>("vector_col", vec_geom_v).get_result(); assert(ary_mean_res.size() == 3); assert(vec_mean_res.size() == 3); assert(std::abs(ary_mean_res[0] - 5.47712) < 0.00001); assert(std::abs(ary_mean_res[2] - 5.67667) < 0.00001); assert(std::abs(vec_mean_res[0] - 5.47712) < 0.00001); assert(std::abs(vec_mean_res[2] - 5.67667) < 0.00001); assert(ary_geom_res.size() == 3); assert(vec_geom_res.size() == 3); assert(std::abs(ary_geom_res[0] - 4.80128) < 0.00001); assert(std::abs(ary_geom_res[2] - 4.97216) < 0.00001); assert(std::abs(vec_geom_res[0] - 4.80128) < 0.00001); assert(std::abs(vec_geom_res[2] - 4.97216) < 0.00001); using ary_mean_t = MeanVisitor<ary_col_t, std::string>; using vec_mean_t = MeanVisitor<vec_col_t, std::string>; bias_v<ary_mean_t, ary_col_t, std::string> ary_bias_v { ary_mean_v, 26 }; bias_v<vec_mean_t, vec_col_t, std::string> vec_bias_v { vec_mean_v, 26 }; df.single_act_visit<ary_col_t>("array_col", ary_bias_v); df.single_act_visit<vec_col_t>("vector_col", vec_bias_v); assert(ary_bias_v.get_result().size() == 221); for (std::size_t i { 0 }; const auto &vec : ary_bias_v.get_result()) { if (i++ < 25) assert(vec.empty()); else assert(vec.size() == dim); } assert(std::fabs(ary_bias_v.get_result()[25][0] - 0.182259) < 0.000001); assert(std::fabs(ary_bias_v.get_result()[35][1] - -0.461714) < 0.000001); assert(std::fabs(ary_bias_v.get_result()[36][2] - -0.184178) < 0.000001); assert(std::fabs(ary_bias_v.get_result()[220][1] - -0.845398) < 0.000001); assert(vec_bias_v.get_result().size() == 221); for (std::size_t i { 0 }; const auto &vec : vec_bias_v.get_result()) { if (i++ < 25) assert(vec.empty()); else assert(vec.size() == dim); } assert(std::fabs(vec_bias_v.get_result()[25][0] - 0.182259) < 0.000001); assert(std::fabs(vec_bias_v.get_result()[35][1] - -0.461714) < 0.000001); assert(std::fabs(vec_bias_v.get_result()[36][2] - -0.184178) < 0.000001); assert(std::fabs(vec_bias_v.get_result()[220][1] - -0.845398) < 0.000001); }
