1 files changed, 100 insertions, 0 deletions
diff --git a/ink_stroke_modeler/internal/prediction/kalman_filter/axis_predictor_test.cc b/ink_stroke_modeler/internal/prediction/kalman_filter/axis_predictor_test.cc
new file mode 100644
index 0000000..40ba9f4
--- /dev/null
+++ b/ink_stroke_modeler/internal/prediction/kalman_filter/axis_predictor_test.cc
@@ -0,0 +1,100 @@
+// Copyright 2022 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ink_stroke_modeler/internal/prediction/kalman_filter/axis_predictor.h"
+
+#include <vector>
+
+#include "gtest/gtest.h"
+
+namespace ink {
+namespace stroke_model {
+namespace {
+
+constexpr int kStableIterNum = 4;
+
+constexpr double kProcessNoise = 0.01;
+constexpr double kMeasurementNoise = 1.0;
+
+}  // namespace
+
+struct DataSet {
+  double initial_observation;
+  std::vector<double> observation;
+  std::vector<double> position;
+  std::vector<double> velocity;
+  std::vector<double> acceleration;
+  std::vector<double> jerk;
+};
+
+void ValidateAxisPredictor(AxisPredictor* predictor, const DataSet& data) {
+  predictor->Reset();
+  predictor->Update(data.initial_observation);
+  for (decltype(data.observation.size()) i = 0; i < data.observation.size();
+       i++) {
+    predictor->Update(data.observation[i]);
+    EXPECT_NEAR(data.position[i], predictor->GetPosition(), 0.0001);
+    EXPECT_NEAR(data.velocity[i], predictor->GetVelocity(), 0.0001);
+    EXPECT_NEAR(data.acceleration[i], predictor->GetAcceleration(), 0.0001);
+    EXPECT_NEAR(data.jerk[i], predictor->GetJerk(), 0.0001);
+  }
+}
+
+// Test that the predictor will stable.
+TEST(AxisPredictorTest, ShouldStable) {
+  AxisPredictor predictor(kProcessNoise, kMeasurementNoise, kStableIterNum);
+  for (int i = 0; i < kStableIterNum; i++) {
+    EXPECT_FALSE(predictor.Stable());
+    predictor.Update(1);
+  }
+  EXPECT_TRUE(predictor.Stable());
+}
+
+// Test the kalman filter behavior. The data set is generated by a "known to
+// work" kalman filter.
+TEST(AxisPredictorTest, PredictedValue) {
+  AxisPredictor predictor(kProcessNoise, kMeasurementNoise, kStableIterNum);
+  DataSet data;
+  data.initial_observation = 0;
+  data.observation = {1, 2, 3, 4, 5, 6};
+  data.position = {0.6949411066858742, 1.8880162111305765, 3.0596776689233476,
+                   4.080666568886563,  5.039574058758894,  5.990101744132957};
+  data.velocity = {0.48326413015846115, 1.349212968908908,  1.5150757723942188,
+                   1.2449353797925855,  0.9823147273054352, 0.831418084705206};
+  data.acceleration = {0.20388102703160751, 0.6602537865634062,
+                       0.46392675203046707, 0.0691864035645362,
+                       -0.1571001901104591, -0.2303438651979314};
+  data.jerk = {0.051351580374544535, 0.17805019978769315,
+               0.06592110190532013,  -0.06063794909774803,
+               -0.10198612906906362, -0.09541445938944032};
+
+  ValidateAxisPredictor(&predictor, data);
+
+  data.initial_observation = 0;
+  data.observation = {1, 2, 4, 8, 16, 32};
+  data.position = {0.6949411066858742, 1.8880162111305765, 3.9597202826804603,
+                   7.9052737853848285, 15.720340533540115, 31.24662046486774};
+  data.velocity = {0.48326413015846115, 1.349212968908908, 2.492271225870179,
+                   4.610844489557212,   8.828231877380588, 16.987494416071463};
+  data.acceleration = {0.20388102703160751, 0.6602537865634062,
+                       1.090991623810185,   1.885675547541351,
+                       3.4586206593783526,  6.34082285106952};
+  data.jerk = {0.051351580374544535, 0.17805019978769315, 0.25373225050247916,
+               0.4023497012294069,   0.6945464157568688,  1.1947316519015612};
+
+  ValidateAxisPredictor(&predictor, data);
+}
+
+}  // namespace stroke_model
+}  // namespace ink