def draw_fig(self):
plt.figure(1)
plt.subplot(131)
bp.bar_plot(self.prior,title="prior", ylim=(0,.4))
plt.subplot(132)
bp.bar_plot(self.posterior,title="posterior", ylim=(0,.4))
plt.subplot(133)
bp.bar_plot(self.prior2,title="prior2", ylim=(0,.4))
plt.show()
def draw_fig(self):
bp.bar_plot(self.belief)
import code.book_plots as bp
import code.gh_internal as gh
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np;
from filterpy.discrete_bayes import normalize
def scaled_update (hall, belief, z, prob):
scale_ = prob/(1-prob)
belief[hall==1] *=scale_
normalize(belief)
belief = np.array([0.1]*10)
hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])
reading = 1
scaled_update(hallway, belief, reading, prob=0.75)
belief /= sum(belief);
print("belief:", belief)
print ("sum = ", sum(belief))
plt.figure()
bp.bar_plot(belief).show()
import code.book_plots as bp
import code.gh_internal as gh
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
from filterpy.discrete_bayes import normalize
def perfect_predict(belief, move):
n = len(belief)
result = np.zeros(n)
for i in range(n):
result[i] = belief[(i - move) % n]
return result
plt.figure(1)
belief = np.array([.35, .1, .2, .3, 0, 0, 0, 0, 0, .05])
plt.subplot(121)
bp.bar_plot(belief, ylim=(0, .4))
newBelief = perfect_predict(belief, 1)
plt.subplot(122)
bp.bar_plot(newBelief, ylim=(0, .4))
plt.show()
def draw_fig_posterior(self):
bp.bar_plot(self.posterior,
title="posterior-" + str(self.loopIdx),
ylim=(0, .4))
def draw_fig(self):
bp.bar_plot(self.prior, ylim=(0,0.6))
def draw_fig(self):
bp.bar_plot(self.belief, ylim=(0, 0.6))
import matplotlib.pyplot as plt
import numpy as np;
from filterpy.discrete_bayes import normalize
def update(likelihood, belief):
return normalize( likelihood * belief)
def lh_hallway(hall, belief, z, prob):
try:
scale = prob / (1. - prob)
except ZeroDivisionError:
scale = 1e8
likelihood = np.ones(len(hall))
likelihood[hall==z] *= scale
return likelihood
belief = np.array([0.1]*10)
hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])
reading = 1
likelihood = lh_hallway(hallway, belief, reading, prob=0.75)
postreiorBelief = update(likelihood,belief)
print("belief:", postreiorBelief)
print ("sum = ", sum(postreiorBelief))
plt.figure()
bp.bar_plot(postreiorBelief).show()
import code.book_plots as bp
import code.gh_internal as gh
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
from filterpy.discrete_bayes import normalize
def scaled_update(hall, belief, z, prob):
scale_ = prob / (1. - prob)
likelihood = np.ones(len(hall))
print("su.likelihood:", likelihood)
likelihood[hall == z] *= scale_
print("su.likelihood:", likelihood)
print("su.belief:", belief)
newhyp = likelihood * belief
print("su.lik*bel:", newhyp)
n = normalize(newhyp)
print("su.posterior:", n)
return n
belief = np.array([0.1] * 10)
hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])
reading = 1
newhyp = scaled_update(hallway, belief, reading, prob=0.75)
plt.figure()
bp.bar_plot(newhyp).show()
# def predict( belief, move, p_correct, p_under, p_over): # n = len(belief) # result = np.zeros( n ) # w = [p_under, p_correct, p_over] # V = move # for i in range( n ): # result[i] = ( # belief[(i-V)%n] * p_correct + belief[(i-V-1)%n] * p_over + # belief[(i-V+1)%n] * p_under) # belief[:] = result # belief = np.array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.]) # predict( belief, 2, .8, .1, .1) # bp.bar_plot( belief ) # grid() # show() # # # # belief = np.array([1/10.]*10) np.set_printoptions(precision=3) print belief hallway = np.array( [1,1,0,0,0,0,0,0,1,0]) belief = hallway*1./3 set_figsize( y=2 ) bp.bar_plot( belief )
