def test_strange_case(self):
xhat = reconstruct_srls(self.edm, self.all_points, W=self.weights)
print(xhat[0])
xhat = reconstruct_srls(self.edm,
self.all_points,
W=self.weights,
rescale=False,
z=self.all_points[0, 2])
print(xhat[0])
def zero_weights(self, noise=0.1):
print('TestSRLS:test_zero_weights({})'.format(noise))
index = np.arange(self.n)
other = np.delete(range(self.pts.N), index)
edm_noisy = create_noisy_edm(self.pts.edm, noise)
# missing anchors
N_missing = 2
indices = np.random.choice(other, size=N_missing, replace=False)
reduced_points = np.delete(self.pts.points, indices, axis=0)
points_missing = create_from_points(reduced_points, PointSet)
edm_anchors = np.delete(edm_noisy, indices, axis=0)
edm_anchors = np.delete(edm_anchors, indices, axis=1)
missing_anchors = reconstruct_srls(edm_anchors,
points_missing.points,
n=self.n,
W=None,
print_out=False)
# missing distances
weights = np.ones(edm_noisy.shape)
weights[indices, index] = 0.0
weights[index, indices] = 0.0
missing_distances = reconstruct_srls(edm_noisy,
self.pts.points,
n=self.n,
W=weights)
left_distances = np.delete(range(self.pts.N), indices)
self.assertTrue(
np.linalg.norm(missing_distances[left_distances, :] -
missing_anchors) < self.eps, 'anchors moved.')
self.assertTrue(
np.linalg.norm(missing_distances[index, :] -
missing_anchors[index, :]) < self.eps,
'point moved.')
if noise == 0.0:
error = np.linalg.norm(missing_anchors - points_missing.points)
u, s, v = np.linalg.svd(self.pts.points[other, :])
try:
self.assertTrue(error < self.eps, 'error {}'.format(error))
except:
print(
'failed with anchors (this can be due to unlucky geometry):',
points_missing.points)
raise
def call_method(self, method=''):
print('TestSRLS:call_method')
if method == '' or method == 'normal':
return reconstruct_srls(self.pts.edm,
self.pts.points,
W=np.ones(self.pts.edm.shape))
elif method == 'rescale':
return reconstruct_srls(self.pts.edm,
self.pts.points,
W=np.ones(self.pts.edm.shape),
rescale=True)
elif method == 'fixed' and self.pts.d == 3:
return reconstruct_srls(self.pts.edm,
self.pts.points,
W=np.ones(self.pts.edm.shape),
rescale=False,
z=self.pts.points[0, 2])
def test_srls_fixed(self):
self.create_points(N=10, d=3)
zreal = self.pts.points[0, 2]
xhat = reconstruct_srls(self.pts.edm,
self.pts.points,
n=self.n,
W=np.ones(self.pts.edm.shape),
rescale=False,
z=self.pts.points[0, 2])
self.assertEqual(xhat[0, 2], zreal)
np.testing.assert_allclose(xhat, self.pts.points)
def test_srls_fixed(self):
print('TestSRLS:test_srls_fixed')
self.create_points(N=10, d=3)
zreal = self.pts.points[0, 2]
xhat = reconstruct_srls(self.pts.edm,
self.pts.points,
W=np.ones(self.pts.edm.shape),
rescale=False,
z=zreal)
if xhat is not None:
np.testing.assert_allclose(xhat[0, 2], zreal)
np.testing.assert_allclose(xhat, self.pts.points)
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
from pylocus.point_set import PointSet
from pylocus.algorithms import reconstruct_srls
from pylocus.simulation import create_noisy_edm, create_mask, create_weights
from pylocus.basics import mse, rmse
points = PointSet(N=5, d=2)
points.set_points('random')
print("point to localize:", points.points[0, :])
print("anchors:", points.points[1:, :])
std = 0.1
edm_noisy = create_noisy_edm(points.edm, noise=std)
mask = create_mask(points.N, method='none')
weights = create_weights(points.N, method='one')
weights = np.multiply(mask, weights)
points_estimated = reconstruct_srls(edm_noisy, points.points, W=weights)
error = mse(points_estimated[0, :], points.points[0, :])
print("estimated point: {}, original point: {}, mse: {:2.2e}".format(
points_estimated[0, :], points.points[0, :], error))
def call_method(self):
print('TestSRLS:call_method')
return reconstruct_srls(self.pts.edm,
self.pts.points,
indices=[self.index],
W=np.ones(self.pts.edm.shape))