'''
A script to plot a bunch of points
'''
from mayavi import mlab
from LogReader import LogReader
import numpy as np
def plot_points(points):
print '[plot_points] Plotting points!'
xs = np.array([int(point._x) for point in points])
ys = np.array([int(point._y) for point in points])
zs = np.array([int(point._z) for point in points])
labels = np.array([int(point._label) for point in points])
mlab.points3d(xs, ys, zs, labels, scale_factor = 0.4, mode='cube')
mlab.show()
def plot_predicted_labels(points, labels):
print '[plot_points] Plotting points!'
xs = np.array([int(point._x) for point in points])
ys = np.array([int(point._y) for point in points])
zs = np.array([int(point._z) for point in points])
mlab.points3d(xs, ys, zs, labels, scale_factor = .4, mode='cube')
mlab.show()
if __name__ == "__main__":
#Load a log
log_object = LogReader('../data/oakland_part3_an_rf.node_features')
points = log_object.read()
plot_points(points)
def rand_perm(n):
p = range(0, n)
for i in range(0, n):
r = random.randint(i, n - 1)
x = p[i]
p[i] = p[r]
p[r] = x
return p
if __name__ == "__main__":
train_log_object = LogReader('../data/oakland_part3_an_rf.node_features')
train_points = train_log_object.read()
test_log_object = LogReader('../data/oakland_part3_am_rf.node_features')
test_points = test_log_object.read()
X = np.array([point._feature for point in train_points])
Y = np.array([point._label for point in train_points])
perm_idx = np.random.permutation(X.shape[0]) #range(X.shape[0])
ds_train = Dataset(X[perm_idx,:],Y[perm_idx])
cv_fold = 5
param_num = 9
cm = np.zeros([cv_fold,cv_fold,5,5])
acc = np.zeros([cv_fold,cv_fold])
params = [[5,0.01],[10,0.01],[15,0.01],[5,0.001],[10,0.001],[15,0.001],[5,0.1],[10,0.1],[15,0.1]]
cum_cm = np.zeros([cv_fold,5,5])
mean_confusion_matrix = np.zeros([param_num,5,5])
