def main():
# loop over all experiments
for expt in session.query(models.Experiment):
print("In experiment '{}'".format(expt.id))
dt = 1 / expt.sampling_frequency
for traj in session.query(
models.Trajectory).filter_by(experiment=expt):
positions = traj.positions(session)
velocities = traj.velocities(session)
# calculate kinematic quantities
velocities_a = kinematics.norm(velocities)
accelerations = kinematics.acceleration(velocities, dt)
accelerations_a = kinematics.norm(accelerations)
headings = kinematics.heading(velocities)
angular_velocities = kinematics.angular_velocity(velocities, dt)
angular_velocities_a = kinematics.norm(angular_velocities)
angular_accelerations = kinematics.acceleration(
angular_velocities, dt)
angular_accelerations_a = kinematics.norm(angular_accelerations)
distance_from_wall = kinematics.distance_from_wall(
positions, WALL_BOUNDS)
# store kinematic quantities in timepoints
for ctr, tp in enumerate(traj.timepoints(session)):
tp.velocity_a = velocities_a[ctr]
tp.acceleration_x, tp.acceleration_y, tp.acceleration_z = accelerations[
ctr]
tp.acceleration_a = accelerations_a[ctr]
tp.heading_xy, tp.heading_xz, tp.heading_xyz = headings[ctr]
tp.angular_velocity_x, tp.angular_velocity_y, tp.angular_velocity_z = angular_velocities[
ctr]
tp.angular_velocity_a = angular_velocities_a[ctr]
tp.angular_acceleration_x, tp.angular_acceleration_y, tp.angular_acceleration_z = angular_accelerations[
ctr]
tp.angular_acceleration_a = angular_accelerations_a[ctr]
tp.distance_from_wall = distance_from_wall[ctr]
session.add(tp)
commit(session)
def main():
# loop over all experiments
for expt in session.query(models.Experiment):
print("In experiment '{}'".format(expt.id))
dt = 1 / expt.sampling_frequency
for traj in session.query(models.Trajectory).filter_by(experiment=expt):
positions = traj.positions(session)
velocities = traj.velocities(session)
# calculate kinematic quantities
velocities_a = kinematics.norm(velocities)
accelerations = kinematics.acceleration(velocities, dt)
accelerations_a = kinematics.norm(accelerations)
headings = kinematics.heading(velocities)
angular_velocities = kinematics.angular_velocity(velocities, dt)
angular_velocities_a = kinematics.norm(angular_velocities)
angular_accelerations = kinematics.acceleration(angular_velocities, dt)
angular_accelerations_a = kinematics.norm(angular_accelerations)
distance_from_wall = kinematics.distance_from_wall(positions, WALL_BOUNDS)
# store kinematic quantities in timepoints
for ctr, tp in enumerate(traj.timepoints(session)):
tp.velocity_a = velocities_a[ctr]
tp.acceleration_x, tp.acceleration_y, tp.acceleration_z = accelerations[ctr]
tp.acceleration_a = accelerations_a[ctr]
tp.heading_xy, tp.heading_xz, tp.heading_xyz = headings[ctr]
tp.angular_velocity_x, tp.angular_velocity_y, tp.angular_velocity_z = angular_velocities[ctr]
tp.angular_velocity_a = angular_velocities_a[ctr]
tp.angular_acceleration_x, tp.angular_acceleration_y, tp.angular_acceleration_z = angular_accelerations[ctr]
tp.angular_acceleration_a = angular_accelerations_a[ctr]
tp.distance_from_wall = distance_from_wall[ctr]
session.add(tp)
commit(session)
def train(self, positives, negatives):
"""
Train this ubersimple classifier.
:param positives:
:param negatives:
:return:
"""
speeds_pos = [kinematics.norm(positive).mean() for positive in positives]
speeds_neg = [kinematics.norm(negative).mean() for negative in negatives]
self.mean_pos = np.mean(speeds_pos)
self.mean_neg = np.mean(speeds_neg)
self.std_pos = np.std(speeds_pos)
self.std_neg = np.std(speeds_neg)
def test_example_norm_calculated_and_ints_handled_correctly(self):
v = np.array([[1, 1, 1],
[2, 3, 4],
[5, 1, -1]])
n_correct = np.array([np.sqrt(3), 5.3851648071345037, 5.196152422706632])
np.testing.assert_array_almost_equal(n_correct, kinematics.norm(v))
def train(self, positives, negatives):
"""
Train this ubersimple classifier.
:param positives:
:param negatives:
:return:
"""
speeds_pos = [
kinematics.norm(positive).mean() for positive in positives
]
speeds_neg = [
kinematics.norm(negative).mean() for negative in negatives
]
self.mean_pos = np.mean(speeds_pos)
self.mean_neg = np.mean(speeds_neg)
self.std_pos = np.std(speeds_pos)
self.std_neg = np.std(speeds_neg)
def predict(self, tss):
"""
Make predictions on a set of time-series.
:param tss:
:return:
"""
predictions = []
for ts in tss:
mean_speed = kinematics.norm(ts).mean()
l_pos = stats.norm.pdf(mean_speed, self.mean_pos, self.std_pos)
l_neg = stats.norm.pdf(mean_speed, self.mean_neg, self.std_neg)
if l_pos > l_neg:
predictions.append(1)
else:
predictions.append(-1)
return predictions
def predict(self, tss):
"""
Make predictions on a set of time-series.
:param tss:
:return:
"""
predictions = []
for ts in tss:
mean_speed = kinematics.norm(ts).mean()
l_pos = stats.norm.pdf(mean_speed, self.mean_pos, self.std_pos)
l_neg = stats.norm.pdf(mean_speed, self.mean_neg, self.std_neg)
if l_pos > l_neg:
predictions.append(1)
else:
predictions.append(-1)
return predictions
def test_tiling_works_correctly(self):
v = np.random.normal(0, 1, (100, 5))
self.assertEqual(v.shape, kinematics.norm(v, '2darray').shape)
def test_example_norm_calculated_and_ints_handled_correctly(self):
v = np.array([[1, 1, 1], [2, 3, 4], [5, 1, -1]])
n_correct = np.array(
[np.sqrt(3), 5.3851648071345037, 5.196152422706632])
np.testing.assert_array_almost_equal(n_correct, kinematics.norm(v))
def test_tiling_works_correctly(self):
v = np.random.normal(0, 1, (100, 5))
self.assertEqual(v.shape, kinematics.norm(v, '2darray').shape)
