def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
measurement = []
predicted_measurement = []
# zt_component => measurement to a specific detected cylinder in the scan from particle p.
for zt_component, landmark in assignment:
measurement.append(zt_component)
predicted_measurement.append(ParticleFilter.h(
p, landmark, self.scanner_displacement))
weights.append(self.probability_of_measurement(
measurement, predicted_measurement))
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
#print p
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
#print cylinders
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
#print assignment
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
part_wt = 1
for i in assignment:
measurement, landmark = i
predicted_measurement = self.h(p, landmark,
self.scanner_displacement)
part_wt *= self.probability_of_measurement(
measurement, predicted_measurement)
#print p1
weights.append(part_wt)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, return list of weights."""
# --->>> Put the code of a previous solution here.
# pass # Remove.
weights = []
for p in self.particles: # Loop in particles.
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(
cylinders, p, self.scanner_displacement,
landmarks) # Assign once for each particle.
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
# weights.append(1.0) # Replace this.
prob = 1.0
for e in assignment:
true_measurement, landmark = e
predicted_measurement = self.h(p, landmark,
scanner_displacement)
prob *= self.probability_of_measurement(
true_measurement, predicted_measurement)
#if prob == 1.0:
# prob = 0.0
weights.append(prob)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
measurement = []
predicted_measurement = []
# zt_component => measurement to a specific detected cylinder in the scan from particle p.
for zt_component, landmark in assignment:
measurement.append(zt_component)
predicted_measurement.append(
ParticleFilter.h(p, landmark, self.scanner_displacement))
weights.append(
self.probability_of_measurement(measurement,
predicted_measurement))
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weights.append(1.0) # Replace this.
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weights.append(1.0) # Replace this.
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weight = 1
for assign in assignment:
predicted_measurement = self.h(p, assign[1], self.scanner_displacement)
weight *= self.probability_of_measurement(assign[0], predicted_measurement)
weights.append(weight)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, return list of weights."""
weights = []
for p in self.particles:
weight = 1
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p, self.scanner_displacement, landmarks)
for a in assignment:
d_,alpha_ = self.h(p,a[1],self.scanner_displacement)
weight = weight*self.probability_of_measurement((d_,alpha_),a[0])
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weights.append(weight) # Replace this.
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
wt = 1.0
for z, landmark in assignment:
# Get measurement for given landmark
z_pred = self.h(p, landmark, self.scanner_displacement)
wt *= self.probability_of_measurement(z, z_pred)
weights.append(wt)
total = sum(weights)
return [w / total for w in weights]
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
w = 1.
for pair in assignment:
predicted_measurement = self.h(p,pair[1],self.scanner_displacement)
w = w * self.probability_of_measurement(pair[0],predicted_measurement)
weights.append(w) # Replace this.
normalizing_factor = 1/sum(weights)
weights = [normalizing_factor * w for w in weights]
return weights
def compute_weights(self, cylinders, landmarks): """Computes one weight for each particle, returns list of weights.""" weights = [] for p in self.particles: # Get list of tuples: # [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ] assignment = assign_cylinders(cylinders, p, self.scanner_displacement, landmarks) prob = 1. for tupl in assignment: meas_prime = self.h(p, tupl[1], self.scanner_displacement) prob *= self.probability_of_measurement(tupl[0], meas_prime) weights.append(prob) weights = np.array(weights) total = sum(weights) return weights/total
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
Final_prob = 1
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
for i in range(len((assignment))):
predicted_landmarks = self.h(p, assignment[i][1],
self.scanner_displacement)
Final_prob *= self.probability_of_measurement(
assignment[i][0], predicted_landmarks)
weights.append(Final_prob)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
w_p = 1
for z, landmark in assignment:
z_pred = self.h(p, landmark, self.scanner_displacement)
w_p *= self.probability_of_measurement(z, z_pred)
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weights.append(w_p) # Replace this.
totalsum = sum(weights)
return [w / totalsum for w in weights]
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
Final_prob = 1
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
for i in range(len((assignment))):
predicted_landmarks = self.h(p, assignment[i][1],
self.scanner_displacement)
Final_prob *= self.probability_of_measurement(
assignment[i][0], predicted_landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weights.append(Final_prob)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
total_weight = 1
for pairing in assignment:
expected_measurement = [0, 0]
expected_measurement[0] = ( (p[0]-pairing[1][0])**2 + (p[1]-pairing[1][1])**2 )**0.5
expected_measurement[1] = atan2( (pairing[1][1] - p[1]), (pairing[1][0] - p[0]) ) - p[2]
total_weight *= self.probability_of_measurement(pairing[0], expected_measurement)
weights.append(total_weight)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, returns list of weights."""
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
P= 1
for i in xrange(len(assignment)):
p_measurement = self.h(p,assignment[i][1],self.scanner_displacement)
po = self.probability_of_measurement(assignment[i][0],p_measurement)
P *= po
weights.append(P)
return weights
def compute_weights(self, cylinders, landmarks):
"""Computes one weight for each particle, return list of weights."""
# --->>> Put the code of a previous solution here.
weights = []
for p in self.particles:
# Get list of tuples:
# [ ((range_0, bearing_0), (landmark_x, landmark_y)), ... ]
assignment = assign_cylinders(cylinders, p,
self.scanner_displacement, landmarks)
# --->>> Insert code to compute weight for particle p here.
# This will require a loop over all (measurement, landmark)
# in assignment. Append weight to the list of weights.
weight = 1.0
for pair in assignment:
measurement, landmark = pair
predicted_measurement = self.h(p, landmark,
self.scanner_displacement)
weight *= self.probability_of_measurement(
measurement, predicted_measurement)
weights.append(weight) # Replace this.
return weights
