Made graphs smaller.
Most of the graphs were really too tall. Well, we can quibble about that but I made them smaller to reduce the PDF size and thus print size of the full book.
This commit is contained in:
Roger Labbe
parent
f6dfbbbd1e
commit
6b7368f91b
39874
01_g-h_filter.ipynb
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01_g-h_filter.ipynb
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04_Gaussians.ipynb
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04_Gaussians.ipynb
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05_Kalman_Filters.ipynb
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13_Smoothing.ipynb
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13_Smoothing.ipynb
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@ -281,444 +281,402 @@
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],
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||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7219898>"
|
||||
"<matplotlib.figure.Figure at 0x740ab38>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@ -37,20 +37,22 @@ def equal_axis():
|
||||
pylab.rcParams['figure.figsize'] = 10,10
|
||||
plt.axis('equal')
|
||||
|
||||
|
||||
def reset_axis():
|
||||
pylab.rcParams['figure.figsize'] = 11, 5.5
|
||||
pylab.rcParams['figure.figsize'] = 11, 4
|
||||
|
||||
def set_figsize(x, y):
|
||||
def set_figsize(x=11, y=4):
|
||||
pylab.rcParams['figure.figsize'] = x, y
|
||||
|
||||
|
||||
@contextmanager
|
||||
def figsize(x,y):
|
||||
def figsize(x=11, y=4):
|
||||
"""Temporarily set the figure size using 'with figsize(a,b):'"""
|
||||
|
||||
set_figsize(x,y)
|
||||
size = pylab.rcParams['figure.figsize']
|
||||
set_figsize(x, y)
|
||||
yield
|
||||
reset_axis()
|
||||
pylab.rcParams['figure.figsize'] = size
|
||||
|
||||
@contextmanager
|
||||
def numpy_precision(precision):
|
||||
|
||||
@ -9,7 +9,6 @@ from __future__ import print_function
|
||||
|
||||
import copy
|
||||
import numpy as np
|
||||
import bar_plot
|
||||
import numpy.random as random
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
@ -17,6 +16,19 @@ import matplotlib.pyplot as plt
|
||||
problem specific
|
||||
'''
|
||||
|
||||
|
||||
def bar_plot(pos, ylim=(0,1), title=None):
|
||||
plt.cla()
|
||||
ax = plt.gca()
|
||||
x = np.arange(len(pos))
|
||||
ax.bar(x, pos, color='#30a2da')
|
||||
if ylim:
|
||||
plt.ylim(ylim)
|
||||
plt.xticks(x+0.4, x)
|
||||
if title is not None:
|
||||
plt.title(title)
|
||||
|
||||
|
||||
class DiscreteBayes1D(object):
|
||||
|
||||
def __init__(self, world_map, belief=None):
|
||||
@ -89,7 +101,7 @@ def animate_three_doors (loops=5):
|
||||
f.sense (m, .8, .2)
|
||||
f.update(1, (.05, .9, .05))
|
||||
|
||||
bar_plot.plot(f.belief)
|
||||
bar_plot(f.belief)
|
||||
plt.pause(0.01)
|
||||
|
||||
|
||||
@ -142,7 +154,7 @@ if __name__ == "__main__":
|
||||
|
||||
|
||||
|
||||
#animate_three_doors(loops=1)
|
||||
animate_three_doors(loops=1)
|
||||
|
||||
|
||||
|
||||
|
||||
199
experiments/train.py
Normal file
199
experiments/train.py
Normal file
@ -0,0 +1,199 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat Apr 4 19:02:39 2015
|
||||
|
||||
@author: Roger
|
||||
"""
|
||||
|
||||
|
||||
import numpy as np
|
||||
import numpy.random as random
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
def bar_plot(pos, ylim=(0,1), title=None):
|
||||
plt.cla()
|
||||
ax = plt.gca()
|
||||
x = np.arange(len(pos))
|
||||
ax.bar(x, pos, color='#30a2da')
|
||||
if ylim:
|
||||
plt.ylim(ylim)
|
||||
plt.xticks(x+0.4, x)
|
||||
if title is not None:
|
||||
plt.title(title)
|
||||
|
||||
|
||||
|
||||
def normalize(belief):
|
||||
s = sum(belief)
|
||||
belief /= s
|
||||
|
||||
def update(map_, belief, z, p_hit, p_miss):
|
||||
for i, val in enumerate(map_):
|
||||
if val == z:
|
||||
belief[i] *= p_hit
|
||||
else:
|
||||
belief[i] *= p_miss
|
||||
|
||||
belief = normalize(belief)
|
||||
|
||||
|
||||
def predict(belief, U, kernel):
|
||||
N = len(belief)
|
||||
kN = len(kernel)
|
||||
width = int((kN - 1) / 2)
|
||||
|
||||
belief_k = np.zeros(N)
|
||||
|
||||
for i in range(N):
|
||||
for k in range (kN):
|
||||
index = (i + (width-k)-U) % N
|
||||
belief_k[i] += belief[index] * kernel[k]
|
||||
belief[:] = belief_k[:]
|
||||
|
||||
|
||||
def add_noise (Z, count):
|
||||
n= len(Z)
|
||||
for i in range(count):
|
||||
j = random.randint(0,n)
|
||||
Z[j] = random.randint(0,2)
|
||||
|
||||
|
||||
def animate_three_doors (loops=5):
|
||||
world = np.array([1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0])
|
||||
#world = np.array([1,1,1,1,1])
|
||||
#world = np.array([1,0,1,0,1,0])
|
||||
|
||||
|
||||
f = DiscreteBayes1D(world)
|
||||
|
||||
measurements = np.tile(world, loops)
|
||||
add_noise(measurements, 50)
|
||||
|
||||
for m in measurements:
|
||||
f.sense (m, .8, .2)
|
||||
f.update(1, (.05, .9, .05))
|
||||
|
||||
bar_plot(f.belief)
|
||||
plt.pause(0.01)
|
||||
|
||||
|
||||
def animate_book(loops=5):
|
||||
world = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])
|
||||
#world = np.array([1,1,1,1,1])
|
||||
#world = np.array([1,0,1,0,1,0])
|
||||
|
||||
N = len(world)
|
||||
belief = np.array([1./N]*N)
|
||||
|
||||
measurements = np.tile(world, loops)
|
||||
add_noise(measurements, 5)
|
||||
|
||||
for m in measurements:
|
||||
update(world, belief, m, .8, .2)
|
||||
predict(belief, 1, (.05, .9, .05))
|
||||
|
||||
bar_plot(belief)
|
||||
plt.pause(0.01)
|
||||
print(f.belief)
|
||||
|
||||
|
||||
import random
|
||||
|
||||
class Train(object):
|
||||
|
||||
def __init__(self, track, move_error=.1, sense_error=.1, no_sense_error=.05):
|
||||
self.track = track
|
||||
self.pos = 0
|
||||
self.move_error = move_error
|
||||
self.sense_error = sense_error
|
||||
self.no_sense_error = no_sense_error
|
||||
|
||||
|
||||
def move(self, distance=1):
|
||||
""" move in the specified direction with some small chance of error"""
|
||||
|
||||
self.pos += distance
|
||||
|
||||
# insert random movement error
|
||||
r = random.random()
|
||||
if r < self.move_error:
|
||||
self.pos -= 1
|
||||
elif r > 1 - self.move_error:
|
||||
self.pos += 1
|
||||
|
||||
self.pos = self.pos % len(self.track)
|
||||
return self.pos
|
||||
|
||||
def sense(self):
|
||||
pos = self.pos
|
||||
|
||||
# insert random sensor error
|
||||
r = random.random()
|
||||
if r < self.sense_error:
|
||||
if random.random() > 0.5:
|
||||
pos += 1
|
||||
else:
|
||||
pos -= 1
|
||||
print('sense error')
|
||||
return pos
|
||||
|
||||
|
||||
def animate_train(loops=5):
|
||||
world = np.array([1,2,3,4,5,6,7,8,9,10])
|
||||
#world = np.array([1,1,1,1,1])
|
||||
#world = np.array([1,0,1,0,1,0])
|
||||
|
||||
N = len(world)
|
||||
belief = np.zeros(N)
|
||||
belief[0] = 1.0
|
||||
|
||||
robot = Train(world, .0, 0, 0)
|
||||
|
||||
for i in range(N*loops):
|
||||
robot.move(1)
|
||||
m = robot.sense()
|
||||
update(world, belief, m, .9, .1)
|
||||
predict(belief, 1, (.05, .9, .05))
|
||||
|
||||
bar_plot(belief)
|
||||
plt.pause(0.5)
|
||||
print(belief)
|
||||
|
||||
#animate_train(1)
|
||||
|
||||
world = np.array([1,2,3,4,5,6,7,8,9,10])
|
||||
#world = np.array([1,1,1,1,1])
|
||||
#world = np.array([1,0,1,0,1,0])
|
||||
|
||||
|
||||
def predict_old(pos_belief, move, p_correct, p_under, p_over):
|
||||
n = len(pos_belief)
|
||||
result = np.zeros(n)
|
||||
for i in range(n):
|
||||
result[i] = (
|
||||
pos_belief[(i-move) % n] * p_correct +
|
||||
pos_belief[(i-move-1) % n] * p_over +
|
||||
pos_belief[(i-move+1) % n] * p_under)
|
||||
pos_belief[:] = result
|
||||
|
||||
|
||||
N = len(world)
|
||||
|
||||
belief = np.ones(N)
|
||||
belief[0] = 10.0
|
||||
normalize(belief)
|
||||
|
||||
belief2 = belief.copy()
|
||||
|
||||
predict(belief, 3, (.05, .9, .05))
|
||||
predict_old(belief2, 3, .9, .05, .05)
|
||||
|
||||
'''
|
||||
print(belief)
|
||||
update(world, belief, 1, .8, .2)
|
||||
print(belief)
|
||||
|
||||
predict(belief, 3, (.05, .9, .05))
|
||||
print(belief)
|
||||
'''
|
||||
Loading…
Reference in New Issue
Block a user