def read_projectile_trajectories(csv_file="Dataset.csv"):
projectiles = []
with open(csv_file, 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
projectile = Projectile()
first_iteration = True
for row in reader:
time, along_x, along_y = int(row[0]), float(row[1]), float(row[2])
if not(first_iteration) and time == 0:
projectiles.append(projectile)
projectile = Projectile()
projectile.add_trajectory_point(time, along_x, along_y)
continue
projectile.add_trajectory_point(time, along_x, along_y)
first_iteration = False
if projectile.trajectory_points_count() != 0:
projectiles.append(projectile)
return projectiles
projectiles = read_projectile_trajectories()
base_features, label_x, label_y = build_training_sets(projectiles)
projectile_tracker = ProjectileTracker()
# Build a model on how a projectile behaves.
projectile_tracker.fit(base_features, label_x, label_y)
# Create the test projectile.
test_projectile = Projectile()
test_thorow_vx = 1.0 * cos(pi / 4.0)
test_thorow_vy = 1.0 * sin(pi / 4.0)
# print "Test Projectile(thrown in 45 degree angle at velocity 10 m/s):"
for i in xrange(101):
base_feat = [[test_thorow_vx, test_thorow_vy, i]]
prediction = projectile_tracker.predict(base_feat)
pred_x = prediction[0][0]
pred_y = prediction[0][1]
if i != 0 and pred_y < 0:
break
test_projectile.add_trajectory_point(i, pred_x, pred_y)
print ', '.join([str(elm) for elm in [i, pred_x, pred_y]])
# test_projectile.plot_trajectory()
