yafl/tests/src/yaflpy_compile_unscented.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
    Copyright 2020 anonimous <shkolnick-kun@gmail.com> and contributors.

    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

    Unless required by applicable law or agreed to in writing,
    software distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

    See the License for the specific language governing permissions
"""
import matplotlib.pyplot as plt
import numpy as np
import pyximport
import scipy.stats
import sys
import time

sys.path.insert(0,'../../src/yaflpy')

pyximport.install(
    build_dir='../projects/obj',
    pyimport=True,
    reload_support=True,
    language_level=3,
    setup_args={
        'include_dirs': [np.get_include(), '../../src', '../../src/yaflpy'],
        }
    )

from yaflpy import MerweSigmaPoints as SP
#from yaflpy import Unscented as KF
#from yaflpy import UnscentedAdaptive as KF
#from yaflpy import UnscentedBierman as KF
from yaflpy import UnscentedAdaptiveBierman as KF


def _fx(x, dt, **fx_args):
    x = x.copy()
    x[0] += x[1] * dt
    x[2] += x[3] * dt
    return x


def _hx(x, **hx_args):
    if hx_args:
        print(hx_args)
    return np.array([x[0], x[2]])


def _zrf(a,b):
    #print(a - b)
    return a - b

STD = 100.

sp = SP(4, 0.1, 2., 0)
kf = KF(4, 2, 1., _hx, _fx, sp)
#kf = KF(4, 2, 1., _hx, _fx, sp, residual_z=_zrf)

kf.x[0] = 0.
kf.x[1] = 0.3
kf.Dp *= .00001
kf.Dq *= 1.0e-8
#This is robust filter, so no square here
kf.Dr *= STD*STD

kf.Dr[0] *= .75
kf.Ur += .5

#kf.chi2 = 8.807468393511947

# sp = MerweScaledSigmaPoints(4, alpha=.1, beta=2., kappa=0)
# kf = UnscentedKalmanFilter(4, 2, dt=1., fx=_fx, hx=_hx, points=sp)

# kf.P *= .1
# kf.Q *= 1e-8
# kf.R *= STD*STD


print(kf.x)

N = 6000

clean = np.zeros((N, 2))
noisy = np.zeros((N, 2))
t     = np.zeros((N,), dtype=np.float)
# for i in range(1, len(clean)//2):
#     clean[i] = clean[i-1] + np.array([1.5,1.])
#     noisy[i] = clean[i]   + np.random.normal(scale=STD, size=2)
#     t[i] = i

# for i in range(i, len(clean)):
#     clean[i] = clean[i-1] + np.array([1.,1.5])
#     noisy[i] = clean[i]   + np.random.normal(scale=STD, size=2)
#     t[i] = i

for i in range(1, len(clean)):
    clean[i] = clean[i-1] + np.array([1.,1.])
    noisy[i] = clean[i]   + np.random.normal(scale=STD, size=2)
    t[i] = i

kf_out = np.zeros((N, 2))



start = time.time()
for i, z in enumerate(noisy):
    kf.predict()
    kf.update(z)
    kf_out[i] = kf.zp
end = time.time()
print(end - start)



plt.plot(t, noisy - kf_out)
plt.show()

plt.plot(t, clean - kf_out)
plt.show()

plt.plot(clean[:,0], clean[:,1], kf_out[:,0], kf_out[:,1])
plt.show()

plt.plot(noisy[:,0], noisy[:,1],  kf_out[:,0], kf_out[:,1])
plt.show()

plt.plot(t, noisy[:,1], t, kf_out[:,1], t, clean[:,1])
plt.show()

plt.plot(t, noisy[:,0], t, kf_out[:,0], t, clean[:,0])
plt.show()


print('Done!')