wlk = Walker(lattice = ltc, start = start_simulation, dest_threshold = 1)
# Simulate a random walk and record the destination
destinations.append(wlk.simulateWalker())
# Use pandas facilities
table = pd.DataFrame({"DEST": pd.Series(destinations)})
table["COUNT"] = 1
prob_table = table.groupby(["DEST"], as_index = False).aggregate({"COUNT": "sum"})
prob_table.COUNT = prob_table.COUNT / np.sum(prob_table.COUNT)
# Store the probabilities in a DestinationGrid object
posterior = DestinationGrid(N, M)
for idx, row in prob_table.iterrows():
posterior.setProb(row.DEST, row.COUNT)
# Plot the new distribution
plt.subplot(1,2,1)
plt.imshow(np.log(posterior.as_array() + trip_to_array(partial_trip.GRID_POLYLINE, N, M)), interpolation = "nearest")
plt.title("Complete trip superimposed")
plt.subplot(1,2,2)
plt.imshow(np.log(posterior.as_array() + trip_to_array(partial_trip.TRUNC_GRID_POLYLINE, N, M)), interpolation = "nearest")
plt.title("Partial trip superimposed")
# Simulate a random walk and record the destination
destinations.append(wlk.simulateWalker())
# Use pandas facilities
table = pd.DataFrame({"DEST": pd.Series(destinations)})
table["COUNT"] = 1
prob_table = table.groupby(["DEST"],
as_index=False).aggregate({"COUNT": "sum"})
prob_table.COUNT = prob_table.COUNT / np.sum(prob_table.COUNT)
# Store the probabilities in a DestinationGrid object
posterior = DestinationGrid(N, M)
for idx, row in prob_table.iterrows():
posterior.setProb(row.DEST, row.COUNT)
# Plot the new distribution
plt.subplot(1, 2, 1)
plt.imshow(np.log(posterior.as_array() +
trip_to_array(partial_trip.GRID_POLYLINE, N, M)),
interpolation="nearest")
plt.title("Complete trip superimposed")
plt.subplot(1, 2, 2)
plt.imshow(np.log(posterior.as_array() +
trip_to_array(partial_trip.TRUNC_GRID_POLYLINE, N, M)),
interpolation="nearest")
plt.title("Partial trip superimposed")
partial_trip = test.loc[0]
# Select the last segment of the partial trip
end = partial_trip.TRUNC_GRID_POLYLINE[-k:]
# Create a DestinationGrid object
grid = DestinationGrid(N, M)
# Match this segment with trips in the training set
for i, chunk in enumerate(trips):
for idx, row in chunk.iterrows():
if match(end, row.GRID_POLYLINE, k):
dest = row.GRID_POLYLINE[-1]
grid._table[dest] += 1
print "Processed chunk %d" % i
grid.normalizeProbs()
# Plot the new distribution
plt.subplot(1, 2, 1)
plt.imshow(np.log(grid.as_array() +
trip_to_array(partial_trip.GRID_POLYLINE, N, M)),
interpolation="nearest")
plt.title("Complete trip superimposed")
plt.subplot(1, 2, 2)
plt.imshow(np.log(grid.as_array() +
trip_to_array(partial_trip.TRUNC_GRID_POLYLINE, N, M)),
interpolation="nearest")
plt.title("Partial trip superimposed")
