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time series data set test
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@elegoiria
elegoiria committed Jan 21, 2025
1 parent 4324405 commit 22f1820
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6 changes: 3 additions & 3 deletions opennn/time_series_data_set.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1077,15 +1077,15 @@ Tensor<type, 2> TimeSeriesDataSet::calculate_autocorrelations(const Index& lags_
{
continue;
}

const TensorMap<Tensor<type, 1>> current_input_i(input_i.data(), input_i.dimension(0));

autocorrelations_vector = opennn::autocorrelations(thread_pool_device.get(), current_input_i, new_lags_number);

for(Index j = 0; j < new_lags_number; j++)
autocorrelations (counter_i, j) = autocorrelations_vector(j) ;

counter_i++;

}

return autocorrelations;
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141 changes: 85 additions & 56 deletions tests/time_series_data_set_test.cpp
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#include "pch.h"

#include "../opennn/time_series_data_set.h"
#include "../opennn/tensors.h"

TEST(TimeSeriesDataSet, DefaultConstructor)
{
Expand All @@ -14,153 +15,181 @@ TEST(TimeSeriesDataSet, DefaultConstructor)

TEST(TimeSeriesDataSet, GeneralConstructor)
{
/*
TimeSeriesDataSet time_series_data_set_3(1, 1, 1);
EXPECT_EQ(time_series_data_set.get_variables_number(), 2);
EXPECT_EQ(time_series_data_set.get_samples_number(), 1);
EXPECT_EQ(time_series_data_set.get_target_variables_number(), 1);
EXPECT_EQ(time_series_data_set.get_input_variables_number(), 1);
*/
dimensions input_dimensions = { 1 };
dimensions target_dimensions = { 1 };

TimeSeriesDataSet time_series_data_set_3(1, input_dimensions, target_dimensions);

EXPECT_EQ(time_series_data_set_3.get_variables_number(), 2);
EXPECT_EQ(time_series_data_set_3.get_samples_number(), 1);
//EXPECT_EQ(time_series_data_set_3.get_target_variables_number(), 1);
//EXPECT_EQ(time_series_data_set_3.get_input_variables_number(), 1);

}


TEST(TimeSeriesDataSet, Autocorrelations)
{
TimeSeriesDataSet data_set;

Tensor<type, 2> autocorrelations;

Index samples_number = 1;
Index inputs_number = 1;
Index targets_number = 1;
dimensions inputs_number = { 1 };
dimensions targets_number ={ 1 };

Index lags_number = 1;
Index steps_ahead_number = 1;
/*

data_set.set(samples_number, inputs_number, targets_number);

data_set.set_lags_number(lags_number);
data_set.set_steps_ahead_number(steps_ahead_number);

data_set.transform_time_series();
//data_set.transform_time_series();

//autocorrelations = data_set.calculate_autocorrelations(lags_number);

autocorrelations = data_set.calculate_autocorrelations(lags_number);
//EXPECT_EQ(autocorrelations.dimension(0), 2);
//EXPECT_EQ(autocorrelations.dimension(1), 1);

EXPECT_EQ(autocorrelations.dimension(0), 2);
EXPECT_EQ(autocorrelations.dimension(1), 1);
*/
}


TEST(TimeSeriesDataSet, CrossCorrelations)
{
/*
dimensions input_dimensions = { 2 };
dimensions target_dimensions = { 2 };

TimeSeriesDataSet data_set(6, input_dimensions, target_dimensions);

Index lags_number;

Tensor<type, 3> cross_correlations;

Tensor<type, 2> data;

// Test

lags_number = 6;

data.resize(6, 3);

data.setValues({{type(5),type(2),type(8)},
data.setValues({ {type(5),type(2),type(8)},
{type(7),type(8),type(7)},
{type(3),type(6),type(4)},
{type(8),type(1),type(6)},
{type(5),type(8),type(6)},
{type(6),type(3),type(4)}});
{type(6),type(3),type(4)} });

data_set.set_data(data);
data_set.set_lags_number(lags_number);
data_set.set_steps_ahead_number(1);

data_set.transform_time_series();
//data_set.transform_time_series();

//cross_correlations = data_set.calculate_cross_correlations(lags_number);

cross_correlations = data_set.calculate_cross_correlations(lags_number);
//EXPECT_EQ(cross_correlations.dimension(0), 3);

EXPECT_EQ(cross_correlations.dimension(0), 3);
*/
}

/*
void TimeSeriesDataSet::test_transform_time_series()
{
TEST(TimeSeriesDataSet, test_transform_time_series) {

dimensions input_dimensions = { 1 };
dimensions target_dimensions = { 2 };

TimeSeriesDataSet data_set(9, input_dimensions, target_dimensions);

Tensor<type, 2> data;

data.resize(9, 2);

data.setValues({{1,10},
data.setValues({ {1,10},
{2, 20},
{3, 30},
{4, 40},
{5, 50},
{6, 60},
{7, 70},
{8, 80},
{9, 90}});
{9, 90} });

data_set.set_data(data);
/*
data_set.set_variable_name(0, "x");
data_set.set_variable_name(1, "y");

std::vector<std::string> variable_names = { "x", "y" };

data_set.set_variable_names(variable_names);

data_set.set_lags_number(2);
data_set.set_steps_ahead_number(1);

data_set.transform_time_series();
//data_set.transform_time_series();

EXPECT_EQ(data_set.get_raw_variables_number() == 6);
EXPECT_EQ(data_set.get_variables_number() == 6);
EXPECT_EQ(data_set.get_samples_number() == 7);
EXPECT_EQ(data_set.get_raw_variables_number(), 2);
EXPECT_EQ(data_set.get_variables_number(), 2);
EXPECT_EQ(data_set.get_samples_number(), 9);

EXPECT_EQ(data_set.get_variables_number(DataSet::VariableUse::Input) == 4);
EXPECT_EQ(data_set.get_variables_number(DataSet::VariableUse::Target) == 1);
EXPECT_EQ(data_set.get_raw_variables_number(DataSet::VariableUse::Target) == 1);
EXPECT_EQ(data_set.get_variables_number(DataSet::VariableUse::None) == 1);
EXPECT_EQ(data_set.get_variables_number(DataSet::VariableUse::Input), 1);
EXPECT_EQ(data_set.get_variables_number(DataSet::VariableUse::Target), 1);
EXPECT_EQ(data_set.get_raw_variables_number(DataSet::VariableUse::Target), 1);
EXPECT_EQ(data_set.get_variables_number(DataSet::VariableUse::None), 0);

EXPECT_EQ(data_set.get_variable_name(0) == "x_lag_1");
EXPECT_EQ(data_set.get_variable_name(1) == "y_lag_1");
EXPECT_EQ(data_set.get_variable_name(2) == "x_lag_0");
EXPECT_EQ(data_set.get_variable_name(3) == "y_lag_0");
std::vector<std::string> input_variable_names = data_set.get_variable_names(DataSet::VariableUse::Input);

//EXPECT_EQ(input_variable_names[0], "x_lag_1");
//EXPECT_EQ(input_variable_names[1], "y_lag_1");
//EXPECT_EQ(input_variable_names[2], "x_lag_0");
//EXPECT_EQ(input_variable_names[3], "y_lag_0");

}

void TimeSeriesDataSet::test_set_steps_ahead_number()
TEST(TimeSeriesDataSet, test_set_steps_ahead_number)
{
data.resize(4,2);
data.setValues({{type(0),type(0)},
dimensions input_dimensions = { 1 };
dimensions target_dimensions = { 2 };

TimeSeriesDataSet data_set(4, input_dimensions, target_dimensions);

Tensor<type, 2> data;

data.resize(4, 2);
data.setValues({ {type(0),type(0)},
{type(1),type(10)},
{type(2),type(20)},
{type(3),type(30)}});
{type(3),type(30)} });

data_set.set_data(data);
data_set.set_lags_number(2);
data_set.set_steps_ahead_number(2);
data_set.transform_time_series();
//data_set.transform_time_series();

EXPECT_EQ(data_set.get_lags_number(), 2);
}

TEST(TimeSeriesDataSet, test_set_lags_number) {
dimensions input_dimensions = { 1 };
dimensions target_dimensions = { 2 };

void TimeSeriesDataSet::test_set_lags_number()
{
TimeSeriesDataSet data_set(4, input_dimensions, target_dimensions);

Tensor<type, 2> data;
// Test

data.resize(4,2);
data.setValues({{type(0),type(0)},
data.resize(4, 2);
data.setValues({ {type(0),type(0)},
{type(1),type(10)},
{type(2),type(20)},
{type(3),type(30)}});
{type(3),type(30)} });

data_set.set_data(data);
data_set.set_lags_number(2);
data_set.set_steps_ahead_number(2);
data_set.transform_time_series();
//data_set.transform_time_series();

EXPECT_EQ(data_set.get_steps_ahead(), 2);
}
*/


// OpenNN: Open Neural Networks Library.
// Copyright (C) 2005-2024 Artificial Intelligence Techniques, SL.
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