Native tests for VelocityTracker::getEstimate
Earlier commit broke VelocityTracker::getEstimate, which also broke
pointer location. In additional VelocityTracker::getEstimate is an @hide
api that could be potentially accessed by app.
Add native tests for getEstimate (and specifically, for the currently
used LeastSquares lsq2 strategy) to prevent future regressions.
Bug: 114017699
Test: atest libinput_tests
Change-Id: I69d0c40283b9589b8559e698b66d758a6c838f19
diff --git a/libs/input/tests/VelocityTracker_test.cpp b/libs/input/tests/VelocityTracker_test.cpp
index 43b6012..9da2e2a 100644
--- a/libs/input/tests/VelocityTracker_test.cpp
+++ b/libs/input/tests/VelocityTracker_test.cpp
@@ -16,6 +16,7 @@
#define LOG_TAG "VelocityTracker_test"
+#include <array>
#include <math.h>
#include <android-base/stringprintf.h>
@@ -32,29 +33,35 @@
// here EV = expected value, tol = VELOCITY_TOLERANCE
constexpr float VELOCITY_TOLERANCE = 0.2;
+// estimate coefficients must be within 0.001% of the target value
+constexpr float COEFFICIENT_TOLERANCE = 0.00001;
+
// --- VelocityTrackerTest ---
class VelocityTrackerTest : public testing::Test { };
-static void checkVelocity(float Vactual, float Vtarget) {
- // Compare directions
- if ((Vactual > 0 && Vtarget <= 0) || (Vactual < 0 && Vtarget >= 0)) {
- FAIL() << StringPrintf("Velocity %f does not have the same direction"
- " as the target velocity %f", Vactual, Vtarget);
- }
+/*
+ * Similar to EXPECT_NEAR, but ensures that the difference between the two float values
+ * is at most a certain fraction of the target value.
+ * If fraction is zero, require exact match.
+ */
+static void EXPECT_NEAR_BY_FRACTION(float actual, float target, float fraction) {
+ float tolerance = fabsf(target * fraction);
- // Compare magnitudes
- const float Vlower = fabsf(Vtarget * (1 - VELOCITY_TOLERANCE));
- const float Vupper = fabsf(Vtarget * (1 + VELOCITY_TOLERANCE));
- if (fabsf(Vactual) < Vlower) {
- FAIL() << StringPrintf("Velocity %f is more than %.0f%% below target velocity %f",
- Vactual, VELOCITY_TOLERANCE * 100, Vtarget);
+ if (target == 0 && fraction != 0) {
+ // If target is zero, this would force actual == target, which is too harsh.
+ // Relax this requirement a little. The value is determined empirically from the
+ // coefficients computed by the quadratic least squares algorithms.
+ tolerance = 1E-6;
}
- if (fabsf(Vactual) > Vupper) {
- FAIL() << StringPrintf("Velocity %f is more than %.0f%% above target velocity %f",
- Vactual, VELOCITY_TOLERANCE * 100, Vtarget);
- }
- SUCCEED() << StringPrintf("Velocity %f within %.0f%% of target %f)",
- Vactual, VELOCITY_TOLERANCE * 100, Vtarget);
+ EXPECT_NEAR(actual, target, tolerance);
+}
+
+static void checkVelocity(float Vactual, float Vtarget) {
+ EXPECT_NEAR_BY_FRACTION(Vactual, Vtarget, VELOCITY_TOLERANCE);
+}
+
+static void checkCoefficient(float actual, float target) {
+ EXPECT_NEAR_BY_FRACTION(actual, target, COEFFICIENT_TOLERANCE);
}
void failWithMessage(std::string message) {
@@ -123,6 +130,19 @@
delete event;
}
+static void computeAndCheckQuadraticEstimate(const Position* positions, size_t numSamples,
+ const std::array<float, 3>& coefficients) {
+ VelocityTracker vt("lsq2");
+ MotionEvent* event = createSimpleMotionEvent(positions, numSamples);
+ vt.addMovement(event);
+ VelocityTracker::Estimator estimator;
+ EXPECT_TRUE(vt.getEstimator(0, &estimator));
+ for (size_t i = 0; i< coefficients.size(); i++) {
+ checkCoefficient(estimator.xCoeff[i], coefficients[i]);
+ checkCoefficient(estimator.yCoeff[i], coefficients[i]);
+ }
+}
+
/*
* ================== VelocityTracker tests generated manually =====================================
*/
@@ -660,5 +680,114 @@
computeAndCheckVelocity(values, count, AMOTION_EVENT_AXIS_Y, 28354.796875); // lsq2
}
+/*
+ * Special care must be taken when constructing tests for LeastSquaresVelocityTrackerStrategy
+ * getEstimator function. In particular:
+ * - inside the function, time gets converted from nanoseconds to seconds
+ * before being used in the fit.
+ * - any values that are older than 100 ms are being discarded.
+ * - the newest time gets subtracted from all of the other times before being used in the fit.
+ * So these tests have to be designed with those limitations in mind.
+ *
+ * General approach for the tests below:
+ * We only used timestamps in milliseconds, 0 ms, 1 ms, and 2 ms, to be sure that
+ * we are well within the HORIZON range.
+ * When specifying the expected values of the coefficients, we treat the x values as if
+ * they were in ms. Then, to adjust for the time units, the coefficients get progressively
+ * multiplied by powers of 1E3.
+ * For example:
+ * data: t(ms), x
+ * 1 ms, 1
+ * 2 ms, 4
+ * 3 ms, 9
+ * The coefficients are (0, 0, 1).
+ * In the test, we would convert these coefficients to (0*(1E3)^0, 0*(1E3)^1, 1*(1E3)^2).
+ */
+TEST_F(VelocityTrackerTest, LeastSquaresVelocityTrackerStrategyEstimator_Constant) {
+ Position values[] = {
+ { 0000000, 1, 1 }, // 0 s
+ { 1000000, 1, 1 }, // 0.001 s
+ { 2000000, 1, 1 }, // 0.002 s
+ };
+ // The data used for the fit will be as follows:
+ // time(s), position
+ // -0.002, 1
+ // -0.001, 1
+ // -0.000, 1
+ size_t count = sizeof(values) / sizeof(Position);
+ computeAndCheckQuadraticEstimate(values, count, std::array<float, 3>({1, 0, 0}));
+}
+
+/*
+ * Straight line y = x :: the constant and quadratic coefficients are zero.
+ */
+TEST_F(VelocityTrackerTest, LeastSquaresVelocityTrackerStrategyEstimator_Linear) {
+ Position values[] = {
+ { 0000000, -2, -2 },
+ { 1000000, -1, -1 },
+ { 2000000, -0, -0 },
+ };
+ // The data used for the fit will be as follows:
+ // time(s), position
+ // -0.002, -2
+ // -0.001, -1
+ // -0.000, 0
+ size_t count = sizeof(values) / sizeof(Position);
+ computeAndCheckQuadraticEstimate(values, count, std::array<float, 3>({0, 1E3, 0}));
+}
+
+/*
+ * Parabola
+ */
+TEST_F(VelocityTrackerTest, LeastSquaresVelocityTrackerStrategyEstimator_Parabolic) {
+ Position values[] = {
+ { 0000000, 1, 1 },
+ { 1000000, 4, 4 },
+ { 2000000, 8, 8 },
+ };
+ // The data used for the fit will be as follows:
+ // time(s), position
+ // -0.002, 1
+ // -0.001, 4
+ // -0.000, 8
+ size_t count = sizeof(values) / sizeof(Position);
+ computeAndCheckQuadraticEstimate(values, count, std::array<float, 3>({8, 4.5E3, 0.5E6}));
+}
+
+/*
+ * Parabola
+ */
+TEST_F(VelocityTrackerTest, LeastSquaresVelocityTrackerStrategyEstimator_Parabolic2) {
+ Position values[] = {
+ { 0000000, 1, 1 },
+ { 1000000, 4, 4 },
+ { 2000000, 9, 9 },
+ };
+ // The data used for the fit will be as follows:
+ // time(s), position
+ // -0.002, 1
+ // -0.001, 4
+ // -0.000, 9
+ size_t count = sizeof(values) / sizeof(Position);
+ computeAndCheckQuadraticEstimate(values, count, std::array<float, 3>({9, 6E3, 1E6}));
+}
+
+/*
+ * Parabola :: y = x^2 :: the constant and linear coefficients are zero.
+ */
+TEST_F(VelocityTrackerTest, LeastSquaresVelocityTrackerStrategyEstimator_Parabolic3) {
+ Position values[] = {
+ { 0000000, 4, 4 },
+ { 1000000, 1, 1 },
+ { 2000000, 0, 0 },
+ };
+ // The data used for the fit will be as follows:
+ // time(s), position
+ // -0.002, 4
+ // -0.001, 1
+ // -0.000, 0
+ size_t count = sizeof(values) / sizeof(Position);
+ computeAndCheckQuadraticEstimate(values, count, std::array<float, 3>({0, 0E3, 1E6}));
+}
} // namespace android