end = partial_trip.TRUNC_GRID_POLYLINE[-k:]
# Create a DestinationGrid object
posterior = DestinationGrid(N, M)
# Match this segment with trips in the training set
for i, chunk in enumerate(train):
# Only look at trips that start from the same position
trips = chunk[chunk.START_CELL == partial_trip.GRID_POLYLINE[0]]
for idx, row in trips.iterrows():
if match(end, row.GRID_POLYLINE, k):
destination = row.GRID_POLYLINE[-1]
posterior._table[destination] += 1
posterior.normalizeProbs()
# Export the probabilities
dests = [id_to_nr(dest) for dest in posterior]
dists = [
haversine(coords_from_cell(dest), partial_trip.START_POINT)
for dest in posterior
]
probs = posterior._table.values()
df_probs = pd.DataFrame({
"TRIP_ID": partial_trip.TRIP_ID,
"DEST_CELL": dests,
"PROB": probs,
"DISTANCE": dists
})
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")
end = partial_trip.TRUNC_GRID_POLYLINE[-k:]
# Create a DestinationGrid object
posterior = DestinationGrid(N, M)
# Match this segment with trips in the training set
for i, chunk in enumerate(train):
# Only look at trips that start from the same position
trips = chunk[chunk.START_CELL == partial_trip.GRID_POLYLINE[0]]
for idx, row in trips.iterrows():
if match(end, row.GRID_POLYLINE, k):
destination = row.GRID_POLYLINE[-1]
posterior._table[destination] += 1
posterior.normalizeProbs()
# Export the probabilities
dests = [id_to_nr(dest) for dest in posterior]
dists = [haversine(coords_from_cell(dest), partial_trip.START_POINT) for dest in posterior]
probs = posterior._table.values()
df_probs = pd.DataFrame({"TRIP_ID": partial_trip.TRIP_ID,
"DEST_CELL": dests,
"PROB": probs,
"DISTANCE": dists})
# Filter out the cells that never are a destination
df_probs = df_probs[df_probs.PROB != 0.0]
if idx == 0:
"TRUNC_POLYLINE": lambda x: eval(x),
"TRUNC_GRID_POLYLINE": lambda x: eval(x)})
# Select a partial trip
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")
