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fix: symbol shadowing error on clang-16 fixed

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Mateusz Pusz
2024-05-29 20:57:00 +02:00
parent 9c4a87e2e4
commit 1d1057aa44
8 changed files with 24 additions and 24 deletions
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6
example/kalman_filter/kalman_filter-example_1.cpp
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@@ -61,11 +61,11 @@ int main()
print_header(initial_guess); print_header(initial_guess);
state next = initial_guess; state next = initial_guess;
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const state& previous = next; const state& previous = next;
const quantity gain = 1. / index * one; const quantity gain = 1. / index * one;
const state current = state_update(previous, m, gain); const state current = state_update(previous, measurement, gain);
next = current; next = current;
print(index++, gain, m, current, next); print(index++, gain, measurement, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_2.cpp
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@@ -67,10 +67,10 @@ int main()
print_header(initial); print_header(initial);
state next = state_extrapolation(initial, interval); state next = state_extrapolation(initial, interval);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const state& previous = next; const state& previous = next;
const state current = state_update(previous, m, gain, interval); const state current = state_update(previous, measurement, gain, interval);
next = state_extrapolation(current, interval); next = state_extrapolation(current, interval);
print(index++, m, current, next); print(index++, measurement, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_3.cpp
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@@ -67,10 +67,10 @@ int main()
print_header(initial); print_header(initial);
state next = state_extrapolation(initial, interval); state next = state_extrapolation(initial, interval);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const state& previous = next; const state& previous = next;
const state current = state_update(previous, m, gain, interval); const state current = state_update(previous, measurement, gain, interval);
next = state_extrapolation(current, interval); next = state_extrapolation(current, interval);
print(index++, m, current, next); print(index++, measurement, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_4.cpp
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@@ -69,10 +69,10 @@ int main()
print_header(initial); print_header(initial);
state next = state_extrapolation(initial, interval); state next = state_extrapolation(initial, interval);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const state& previous = next; const state& previous = next;
const state current = state_update(previous, m, gain, interval); const state current = state_update(previous, measurement, gain, interval);
next = state_extrapolation(current, interval); next = state_extrapolation(current, interval);
print(index++, m, current, next); print(index++, measurement, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_5.cpp
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@@ -70,11 +70,11 @@ int main()
print_header(initial); print_header(initial);
estimate next = predict(initial); estimate next = predict(initial);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const estimate& previous = next; const estimate& previous = next;
const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance); const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance);
const estimate current = state_estimate_update(previous, m, gain); const estimate current = state_estimate_update(previous, measurement, gain);
next = predict(current); next = predict(current);
print(index++, m, gain, current, next); print(index++, measurement, gain, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_6.cpp
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@@ -75,11 +75,11 @@ int main()
print_header(initial); print_header(initial);
estimate next = predict(initial); estimate next = predict(initial);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const estimate& previous = next; const estimate& previous = next;
const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance); const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance);
const estimate current = state_estimate_update(previous, m, gain); const estimate current = state_estimate_update(previous, measurement, gain);
next = predict(current); next = predict(current);
print(index++, m, gain, current, next); print(index++, measurement, gain, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_7.cpp
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@@ -75,11 +75,11 @@ int main()
print_header(initial); print_header(initial);
estimate next = predict(initial); estimate next = predict(initial);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const estimate& previous = next; const estimate& previous = next;
const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance); const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance);
const estimate current = state_estimate_update(previous, m, gain); const estimate current = state_estimate_update(previous, measurement, gain);
next = predict(current); next = predict(current);
print(index++, m, gain, current, next); print(index++, measurement, gain, current, next);
} }
} }

6
example/kalman_filter/kalman_filter-example_8.cpp
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@@ -75,11 +75,11 @@ int main()
print_header(initial); print_header(initial);
estimate next = predict(initial); estimate next = predict(initial);
for (int index = 1; const auto& m : measurements) { for (int index = 1; const auto& measurement : measurements) {
const estimate& previous = next; const estimate& previous = next;
const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance); const quantity gain = kalman::kalman_gain(previous.variance(), measurement_variance);
const estimate current = state_estimate_update(previous, m, gain); const estimate current = state_estimate_update(previous, measurement, gain);
next = predict(current); next = predict(current);
print(index++, m, gain, current, next); print(index++, measurement, gain, current, next);
} }
} }