def feature_matrix2(trips, n=None):
velocities = [compute_velocity(trip) for trip in trips]
speeds = [compute_scalar(velocity) for velocity in velocities]
maxlen = len(max(speeds, key=len)) if n is None else n
print("maxlen:", maxlen)
res = np.array([np.fft.fft(s, n=maxlen) for s in speeds])
print(res.shape)
return res
def feature_matrix2(trips, n=None):
velocities = [compute_velocity(trip) for trip in trips]
speeds = [compute_scalar(velocity) for velocity in velocities]
maxlen = len(max(speeds, key=len)) if n is None else n
print("maxlen:", maxlen)
res = np.array([np.fft.fft(s, n=maxlen) for s in speeds])
print(res.shape)
return res
def feature_vector(trip):
velocity = compute_velocity(trip)
velocity5 = n_step_diff(trip, n=5)
acceleration = np.gradient(velocity)
acceleration5 = np.gradient(velocity5)
cos_dt = [cos_between(a, np.array([1, 0])) for a in acceleration5]
angle_dt = [np.arccos(c) for c in cos_dt]
speed = compute_scalar(velocity)
corner_features = corners_features(velocity)
acc_features = acceleration_features(velocity, angle_dt, cos_dt, speed)
return np.hstack((corner_features, acc_features))
def feature_vector(trip):
velocity = compute_velocity(trip)
velocity5 = n_step_diff(trip, n=5)
acceleration = np.gradient(velocity)
acceleration5 = np.gradient(velocity5)
cos_dt = [cos_between(a, np.array([1, 0])) for a in acceleration5]
angle_dt = [np.arccos(c) for c in cos_dt]
speed = compute_scalar(velocity)
corner_features = corners_features(velocity)
acc_features = acceleration_features(velocity, angle_dt, cos_dt, speed)
return np.hstack((corner_features, acc_features))
def main():
trips = read_trips(sys.argv[1])
velocities = [compute_velocity(trip) for trip in trips]
speeds = [compute_scalar(v) for v in velocities]
features = np.array([corners_features(v, s) for v, s in zip(velocities, speeds)])
#print(features)
n = random.randint(0, 199)
n = 29
print("Trip number is:", n)
(corners, angles) = identify_corners(velocities[n])
trip = trips[n]
import matplotlib.pyplot as plt
plt.scatter(trips[n][:,0], trips[n][:,1])#, c=speeds[n])
for start, end in corners:
t = trip[list(range(start, end+1))]
plt.plot(t[:,0], t[:,1], 'r', linewidth=4.0)
plt.axis('equal')
plt.show()
def corners_features(velocity, dists=None):
if dists is None:
dists = compute_scalar(velocity)
corners, angles = identify_corners(velocity, dists)
if len(corners) == 0:
return np.zeros(14)
corner_speeds = [dists[range(c1, c2+1)] for c1, c2 in corners]
corner_speeds_contig = list(chain(*corner_speeds))
bins = np.linspace(0, 14, 10)
corner_speed_hist, bs = np.histogram(corner_speeds_contig, bins=bins)
mean_corner_speeds = [np.mean(cs) for cs in corner_speeds]
max_speed = np.max(mean_corner_speeds)
min_speed = np.min(mean_corner_speeds)
mean_speed = np.mean(mean_corner_speeds)
res = np.hstack((
np.array([float(len(corners))/np.sum(dists), np.mean(angles), max_speed, min_speed, mean_speed]),
corner_speed_hist
))
return res
def main():
trips = read_trips(sys.argv[1])
velocities = [compute_velocity(trip) for trip in trips]
speeds = [compute_scalar(v) for v in velocities]
features = np.array(
[corners_features(v, s) for v, s in zip(velocities, speeds)])
#print(features)
n = random.randint(0, 199)
n = 29
print("Trip number is:", n)
(corners, angles) = identify_corners(velocities[n])
trip = trips[n]
import matplotlib.pyplot as plt
plt.scatter(trips[n][:, 0], trips[n][:, 1]) #, c=speeds[n])
for start, end in corners:
t = trip[list(range(start, end + 1))]
plt.plot(t[:, 0], t[:, 1], 'r', linewidth=4.0)
plt.axis('equal')
plt.show()
def corners_features(velocity, dists=None):
if dists is None:
dists = compute_scalar(velocity)
corners, angles = identify_corners(velocity, dists)
if len(corners) == 0:
return np.zeros(14)
corner_speeds = [dists[range(c1, c2 + 1)] for c1, c2 in corners]
corner_speeds_contig = list(chain(*corner_speeds))
bins = np.linspace(0, 14, 10)
corner_speed_hist, bs = np.histogram(corner_speeds_contig, bins=bins)
mean_corner_speeds = [np.mean(cs) for cs in corner_speeds]
max_speed = np.max(mean_corner_speeds)
min_speed = np.min(mean_corner_speeds)
mean_speed = np.mean(mean_corner_speeds)
res = np.hstack((np.array([
float(len(corners)) / np.sum(dists),
np.mean(angles), max_speed, min_speed, mean_speed
]), corner_speed_hist))
return res
def identify_corners(velocity, dist=None):
"""
Calculates where the corners are from a sequence of velocities
Arguments:
velocity: a sequence of 2D velocity vectors
dist: (Optional) a sequence of scalar distances if this has already been calculated.
Returns:
sequences of indices in a list. Each sequence corresponds to a corner where the indices are positions from the original trip array
"""
if dist is None:
dist = compute_scalar(velocity)
res = []
angles = []
i = 0
while i < len(velocity) - 1:
if angle_between(velocity[i], velocity[i + 1]) > np.radians(5):
start = i
start_vec = velocity[i]
prev_corner_angle = 0
currdist = dist[i]
if dist[i] < 1:
#if the distance from last is small it's probably noise
i += 1
continue
for j, (v, d) in enumerate(zip(velocity[i + 1:], dist[i + 1:])):
if d < 1:
#if the distance from last is small it's probably noise
continue
currdist += d
corner_angle = angle_between(start_vec, v)
if currdist > 70 or corner_angle <= prev_corner_angle + np.radians(
1):
if corner_angle > np.radians(45):
i = i + j
res.append([start, i])
angles.append(corner_angle)
break
else:
break
prev_corner_angle = corner_angle
i += 1
return (res, angles)
def identify_corners(velocity, dist=None):
"""
Calculates where the corners are from a sequence of velocities
Arguments:
velocity: a sequence of 2D velocity vectors
dist: (Optional) a sequence of scalar distances if this has already been calculated.
Returns:
sequences of indices in a list. Each sequence corresponds to a corner where the indices are positions from the original trip array
"""
if dist is None:
dist = compute_scalar(velocity)
res = []
angles = []
i = 0
while i < len(velocity) - 1:
if angle_between(velocity[i], velocity[i+1]) > np.radians(5):
start = i
start_vec = velocity[i]
prev_corner_angle = 0
currdist = dist[i]
if dist[i] < 1:
#if the distance from last is small it's probably noise
i+=1
continue
for j, (v, d) in enumerate(zip(velocity[i+1:], dist[i+1:])):
if d < 1:
#if the distance from last is small it's probably noise
continue
currdist += d
corner_angle = angle_between(start_vec, v)
if currdist > 70 or corner_angle <= prev_corner_angle + np.radians(1):
if corner_angle > np.radians(45):
i = i + j
res.append([start, i])
angles.append(corner_angle)
break
else:
break
prev_corner_angle = corner_angle
i += 1
return (res, angles)
def feature_vector(trip):
velocity = compute_velocity(trip)
speed = compute_scalar(velocity)
corner_features = corners_features(velocity)
acc_features = acceleration_features(speed)
return np.hstack((corner_features, acc_features))
def feature_vector(trip):
velocity = compute_velocity(trip)
speed = compute_scalar(velocity)
corner_features = corners_features(velocity)
acc_features = acceleration_features(speed)
return np.hstack((corner_features, acc_features))
