Made into example rather than test

release/4.3a0
Frank Dellaert 2019-05-16 10:46:10 -04:00
parent 245d7eb849
commit fd21f2ec71
2 changed files with 118 additions and 109 deletions

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"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
Example comparing DoglegOptimizer with Levenberg-Marquardt.
Author: Frank Dellaert
"""
# pylint: disable=no-member, invalid-name
import math
import argparse
import gtsam
import matplotlib.pyplot as plt
import numpy as np
def run(args):
"""Test Dogleg vs LM, inspired by issue #452."""
# print parameters
print("num samples = {}, deltaInitial = {}".format(
args.num_samples, args.delta))
# Ground truth solution
T11 = gtsam.Pose2(0, 0, 0)
T12 = gtsam.Pose2(1, 0, 0)
T21 = gtsam.Pose2(0, 1, 0)
T22 = gtsam.Pose2(1, 1, 0)
# Factor graph
graph = gtsam.NonlinearFactorGraph()
# Priors
prior = gtsam.noiseModel_Isotropic.Sigma(3, 1)
graph.add(gtsam.PriorFactorPose2(11, T11, prior))
graph.add(gtsam.PriorFactorPose2(21, T21, prior))
# Odometry
model = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.01, 0.01, 0.3]))
graph.add(gtsam.BetweenFactorPose2(11, 12, T11.between(T12), model))
graph.add(gtsam.BetweenFactorPose2(21, 22, T21.between(T22), model))
# Range
model_rho = gtsam.noiseModel_Isotropic.Sigma(1, 0.01)
graph.add(gtsam.RangeFactorPose2(12, 22, 1.0, model_rho))
params = gtsam.DoglegParams()
params.setDeltaInitial(args.delta) # default is 10
# Add progressively more noise to ground truth
sigmas = [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20]
n = len(sigmas)
p_dl, s_dl, p_lm, s_lm = [0]*n, [0]*n, [0]*n, [0]*n
for i, sigma in enumerate(sigmas):
dl_fails, lm_fails = 0, 0
# Attempt num_samples optimizations for both DL and LM
for _attempt in range(args.num_samples):
initial = gtsam.Values()
initial.insert(11, T11.retract(np.random.normal(0, sigma, 3)))
initial.insert(12, T12.retract(np.random.normal(0, sigma, 3)))
initial.insert(21, T21.retract(np.random.normal(0, sigma, 3)))
initial.insert(22, T22.retract(np.random.normal(0, sigma, 3)))
# Run dogleg optimizer
dl = gtsam.DoglegOptimizer(graph, initial, params)
result = dl.optimize()
dl_fails += graph.error(result) > 1e-9
# Run
lm = gtsam.LevenbergMarquardtOptimizer(graph, initial)
result = lm.optimize()
lm_fails += graph.error(result) > 1e-9
# Calculate Bayes estimate of success probability
# using a beta prior of alpha=0.5, beta=0.5
alpha, beta = 0.5, 0.5
v = args.num_samples+alpha+beta
p_dl[i] = (args.num_samples-dl_fails+alpha)/v
p_lm[i] = (args.num_samples-lm_fails+alpha)/v
def stddev(p):
"""Calculate standard deviation."""
return math.sqrt(p*(1-p)/(1+v))
s_dl[i] = stddev(p_dl[i])
s_lm[i] = stddev(p_lm[i])
fmt = "sigma= {}:\tDL success {:.2f}% +/- {:.2f}%, LM success {:.2f}% +/- {:.2f}%"
print(fmt.format(sigma,
100*p_dl[i], 100*s_dl[i],
100*p_lm[i], 100*s_lm[i]))
if args.plot:
fig, ax = plt.subplots()
dl_plot = plt.errorbar(sigmas, p_dl, yerr=s_dl, label="Dogleg")
lm_plot = plt.errorbar(sigmas, p_lm, yerr=s_lm, label="LM")
plt.title("Dogleg emprical success vs. LM")
plt.legend(handles=[dl_plot, lm_plot])
ax.set_xlim(0, sigmas[-1]+1)
ax.set_ylim(0, 1)
plt.show()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Compare Dogleg and LM success rates")
parser.add_argument("-n", "--num_samples", type=int, default=1000,
help="Number of samples for each sigma")
parser.add_argument("-d", "--delta", type=float, default=10.0,
help="Initial delta for dogleg")
parser.add_argument("-p", "--plot", action="store_true",
help="Flag to plot results")
args = parser.parse_args()
run(args)

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"""
GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
See LICENSE for the license information
DoglegOptimizer unit tests.
Author: Frank Dellaert
"""
# pylint: disable=no-member, invalid-name
import math
import unittest
import gtsam
import matplotlib.pyplot as plt
import numpy as np
from gtsam.utils.test_case import GtsamTestCase
class TestDoglegOptimizer(GtsamTestCase):
"""Test Dogleg vs LM, isnpired by issue #452."""
def test_DoglegOptimizer(self):
# Ground truth solution
T11 = gtsam.Pose2(0, 0, 0)
T12 = gtsam.Pose2(1, 0, 0)
T21 = gtsam.Pose2(0, 1, 0)
T22 = gtsam.Pose2(1, 1, 0)
# Factor graph
graph = gtsam.NonlinearFactorGraph()
# Priors
prior = gtsam.noiseModel_Isotropic.Sigma(3, 1)
graph.add(gtsam.PriorFactorPose2(11, T11, prior))
graph.add(gtsam.PriorFactorPose2(21, T21, prior))
# Odometry
model = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.01, 0.01, 0.3]))
graph.add(gtsam.BetweenFactorPose2(11, 12, T11.between(T12), model))
graph.add(gtsam.BetweenFactorPose2(21, 22, T21.between(T22), model))
# Range
model_rho = gtsam.noiseModel_Isotropic.Sigma(1, 0.01)
graph.add(gtsam.RangeFactorPose2(12, 22, 1.0, model_rho))
num_samples = 1000
print("num samples = {}%".format(num_samples))
params = gtsam.DoglegParams()
params.setDeltaInitial(10) # default was 1.0
# Add progressively more noise to ground truth
sigmas = [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20]
n = len(sigmas)
p_dl, s_dl, p_lm, s_lm = [0]*n, [0]*n, [0]*n, [0]*n
for i, sigma in enumerate(sigmas):
dl_fails, lm_fails = 0, 0
# Attempt num_samples optimizations for both DL and LM
for _attempt in range(num_samples):
initial = gtsam.Values()
initial.insert(11, T11.retract(np.random.normal(0, sigma, 3)))
initial.insert(12, T12.retract(np.random.normal(0, sigma, 3)))
initial.insert(21, T21.retract(np.random.normal(0, sigma, 3)))
initial.insert(22, T22.retract(np.random.normal(0, sigma, 3)))
# Run dogleg optimizer
dl = gtsam.DoglegOptimizer(graph, initial, params)
result = dl.optimize()
dl_fails += graph.error(result) > 1e-9
# Run
lm = gtsam.LevenbergMarquardtOptimizer(graph, initial)
result = lm.optimize()
lm_fails += graph.error(result) > 1e-9
# Calculate Bayes estimate of success probability
# using a beta prior of alpha=0.5, beta=0.5
alpha, beta = 0.5, 0.5
v = num_samples+alpha+beta
p_dl[i] = (num_samples-dl_fails+alpha)/v
p_lm[i] = (num_samples-lm_fails+alpha)/v
def stddev(p):
"""Calculate standard deviation."""
return math.sqrt(p*(1-p)/(1+v))
s_dl[i] = stddev(p_dl[i])
s_lm[i] = stddev(p_lm[i])
fmt = "sigma= {}:\tDL success {:.2f}% +/- {:.2f}%, LM success {:.2f}% +/- {:.2f}%"
print(fmt.format(sigma,
100*p_dl[i], 100*s_dl[i],
100*p_lm[i], 100*s_lm[i]))
fig, ax = plt.subplots()
dl_plot = plt.errorbar(sigmas, p_dl, yerr=s_dl, label="Dogleg")
lm_plot = plt.errorbar(sigmas, p_lm, yerr=s_lm, label="LM")
plt.title("Dogleg emprical success vs. LM")
plt.legend(handles=[dl_plot, lm_plot])
ax.set_xlim(0, sigmas[-1]+1)
ax.set_ylim(0, 1)
plt.show()
if __name__ == "__main__":
unittest.main()