def main():
location = "/home/joe/drivers/"
z = sys.argv[1]
k = int(sys.argv[2])
trips = read_trips(location + z)
fmatrix = feature_matrix(trips)
n_rows, n_comps = fmatrix.shape
train_fmatrix = fmatrix[: int(len(fmatrix) * 0.9)]
train_targets = np.ones(len(train_fmatrix))
test_fmatrix = fmatrix[int(len(fmatrix) * 0.9) :]
test_targets = np.ones(len(test_fmatrix))
targets = np.ones(len(fmatrix))
i = k
j = 0
while i < (k + 20) and j < 10000:
j += 1
try:
trips = read_trips(location + str(i))
fm = feature_matrix(trips)
train_fm = fm[:9]
test_fm = fm[9:10]
train_fmatrix = np.vstack((train_fmatrix, train_fm))
test_fmatrix = np.vstack((test_fmatrix, test_fm))
fmatrix = np.vstack((fmatrix, fm))
targets = np.hstack((targets, np.zeros(len(fm)) * i))
train_targets = np.hstack((train_targets, np.zeros(len(train_fm)) * i))
test_targets = np.hstack((test_targets, np.zeros(len(test_fm)) * i))
print(i)
i += 1
except IOError:
pass
pipeline = Pipeline([("scale", StandardScaler()), ("ICA", PCA(n_components=50))])
pipeline.fit(fmatrix)
train_trans = pipeline.transform(train_fmatrix)
test_trans = pipeline.transform(test_fmatrix)
print("point teng", num_t_targets / len(test_targets))
gb = RandomForestClassifier(n_estimators=50)
gb.fit(train_fmatrix, train_targets)
gb_score = gb.score(test_fmatrix, test_targets)
print("gb", gb_score)
dt = DecisionTreeClassifier()
dt.fit(train_fmatrix, train_targets)
dt_score = dt.score(test_fmatrix, test_targets)
print("dt", dt_score)
svc = SVC()
svc.fit(train_fmatrix, train_targets)
svc_score = svc.score(test_fmatrix, test_targets)
print("svc", svc_score)
# rfs = [RandomForestClassifier(n_estimators=40) for i in range(20)]
rfs = [GradientBoostingClassifier(n_estimators=100) for i in range(20)]
scores = []
for i, rf in enumerate(rfs):
rf.fit(train_fmatrix, train_targets)
scores.append(rf.score(test_fmatrix, test_targets))
print(i, scores[i])
print("average", np.mean(scores))
def main():
location = "/home/joe/drivers/"
z = sys.argv[1]
k = int(sys.argv[2])
trips = read_trips(location+z)
fmatrix = feature_matrix(trips)
n_rows, n_comps = fmatrix.shape
train_fmatrix = fmatrix[:int(len(fmatrix)*0.9)]
train_targets = np.ones(len(train_fmatrix))
test_fmatrix = fmatrix[int(len(fmatrix)*0.9):]
test_targets = np.ones(len(test_fmatrix))
targets = np.ones(len(fmatrix))
i = k
j = 0
while i < (k+20) and j < 10000:
j +=1
try:
trips = read_trips(location + str(i))
fm = feature_matrix(trips)
train_fm = fm[:9]
test_fm = fm[9:10]
train_fmatrix = np.vstack((train_fmatrix, train_fm))
test_fmatrix = np.vstack((test_fmatrix, test_fm))
fmatrix = np.vstack((fmatrix, fm))
targets = np.hstack((targets, np.zeros(len(fm)) * i))
train_targets = np.hstack((train_targets, np.zeros(len(train_fm)) * i))
test_targets = np.hstack((test_targets, np.zeros(len(test_fm)) * i))
print(i)
i += 1
except IOError:
pass
pipeline = Pipeline([('scale', StandardScaler()), ('ICA', PCA(n_components=50))])
pipeline.fit(fmatrix)
train_trans = pipeline.transform(train_fmatrix)
test_trans = pipeline.transform(test_fmatrix)
print("point teng", num_t_targets/len(test_targets))
gb = RandomForestClassifier(n_estimators=50)
gb.fit(train_fmatrix, train_targets)
gb_score = gb.score(test_fmatrix, test_targets)
print("gb", gb_score)
dt = DecisionTreeClassifier()
dt.fit(train_fmatrix, train_targets)
dt_score = dt.score(test_fmatrix, test_targets)
print("dt", dt_score)
svc = SVC()
svc.fit(train_fmatrix, train_targets)
svc_score = svc.score(test_fmatrix, test_targets)
print("svc", svc_score)
#rfs = [RandomForestClassifier(n_estimators=40) for i in range(20)]
rfs = [GradientBoostingClassifier(n_estimators=100) for i in range(20)]
scores = []
for i, rf in enumerate(rfs):
rf.fit(train_fmatrix, train_targets)
scores.append(rf.score(test_fmatrix, test_targets))
print(i, scores[i])
print("average", np.mean(scores))
def main():
location = sys.argv[1]
write_location = sys.argv[2]
until_driver = sys.argv[3]
trips = read_trips(location+"1")
fmatrix = feature_matrix(trips)
targets = np.ones(len(fmatrix))
for i in range(1, until_driver + 1):
try:
trips = read_trips(location + str(i))
fm = feature_matrix(trips)
fmatrix = np.vstack((fmatrix, fm))
np.savetxt(write_location + str(i)+'.csv', fmatrix, header='', delimiter=',', fmt="%10.5f")
except IOError:
print("error tengaleng")
def main():
location = sys.argv[1]
write_location = sys.argv[2]
until_driver = sys.argv[3]
trips = read_trips(location + "1")
fmatrix = feature_matrix(trips)
targets = np.ones(len(fmatrix))
for i in range(1, until_driver + 1):
try:
trips = read_trips(location + str(i))
fm = feature_matrix(trips)
fmatrix = np.vstack((fmatrix, fm))
np.savetxt(write_location + str(i) + '.csv',
fmatrix,
header='',
delimiter=',',
fmt="%10.5f")
except IOError:
print("error tengaleng")
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 write_files():
pathread = sys.argv[1]
pathwrite = sys.argv[2]
dirs = os.listdir(pathread)
corners_headers = ["num_corners", "avg_corner_angle", "max_corner_speed", "min_corner_speed", "avg_corner_speed"]
accn_headers1 = ["time_fast", "time_slow", "fraction_fast", "fraction_slow", "stops", "fraction_stationary"]
accn_headers2 = ["speed" + str(i) for i in range(1, 51)]
accn_headers3 = ["speedxangle" + str(i) for i in range(1, 51)]
accn_headers4 = ["acc" + str(i) for i in range(1, 51)]
accn_headers5 = ["acc2" + str(i) for i in range(1, 51)]
accn_headers5 = ["speedxcos(angle)" + str(i) for i in range(1, 51)]
accn_headers5 = ["accxangle" + str(i) for i in range(1, 51)]
headers = corners_headers + accn_headers1 + accn_headers2 + accn_headers3 + accn_headers4 + accn_headers5
header_str = ",".join(headers)
for d in dirs:
print("processing driver", d)
trips = read_trips(os.path.join(pathread, d))
fmatrix = feature_matrix(trips)
np.save(os.path.join(pathwrite, d + ".npy"), fmatrix)
def write_files():
pathread = sys.argv[1]
pathwrite = sys.argv[2]
dirs = os.listdir(pathread)
corners_headers = ['num_corners', 'avg_corner_angle', 'max_corner_speed', 'min_corner_speed', 'avg_corner_speed']
accn_headers1 = ['time_fast', 'time_slow', 'fraction_fast', 'fraction_slow', 'stops', 'fraction_stationary']
accn_headers2 = ['speed'+str(i) for i in range(1, 51)]
accn_headers3 = ['speedxangle'+str(i) for i in range(1, 51)]
accn_headers4 = ['acc'+str(i) for i in range(1, 51)]
accn_headers5 = ['acc2'+str(i) for i in range(1, 51)]
accn_headers5 = ['speedxcos(angle)'+str(i) for i in range(1, 51)]
accn_headers5 = ['accxangle'+str(i) for i in range(1, 51)]
headers = corners_headers + accn_headers1 + accn_headers2 + accn_headers3 + accn_headers4 + accn_headers5
header_str = ','.join(headers)
for d in dirs:
print("processing driver", d)
trips = read_trips(os.path.join(pathread, d))
fmatrix = feature_matrix(trips)
np.save(os.path.join(pathwrite, d + '.npy'), fmatrix)
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 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([acceleration_features(speed) for speed in speeds])
print(features)
