def plot_errorbar1():
with figsize(y=2):
plt.figure()
plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
xlims=(145, 185), ylims=(-1, 1))
plt.show()
plt.savefig('../figs/gh_errorbar1.png', pad_inches=0.)
def plot_hypothesis5():
weights = [
158.0, 164.2, 160.3, 159.9, 162.1, 164.6, 169.6, 167.4, 166.4, 171.0,
171.2, 172.6
]
xs = range(1, len(weights) + 1)
line = np.poly1d(np.polyfit(xs, weights, 1))
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 13),
weights,
label='weights',
yerr=5,
fmt='o',
capthick=2,
capsize=10)
plt.plot(xs, line(xs), c='r', label='hypothesis')
plt.xlim(0, 13)
plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
show_legend()
plt.savefig('../figs/gh_hypothesis5.png', pad_inches=0.1)
def plot_estimate_chart_3():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->',
ec='r', lw=3, shrinkA=6, shrinkB=5))
ax.annotate('', xy=[1,159], xytext=[1,164.2],
arrowprops=dict(arrowstyle='-',
ec='k', lw=3, shrinkA=8, shrinkB=8))
est_y = (158 + .4*(164.2-158))
plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
plt.text (0.95, est_y, "new estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.savefig('../figs/gh_estimate3.png', pad_inches=0.1)
def plot_errorbar1():
with figsize(y=2):
plt.figure()
plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
xlims=(145, 185),
ylims=(-1, 1))
plt.show()
def plot_hypothesis2():
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 10], [169, 170.5], color='g', ls='--')
plt.xlim(0, 11); plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
def plot_hypothesis2():
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 10], [169, 170.5], color='g', ls='--')
plt.xlim(0, 11); plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.savefig('../figs/gh_hypothesis2.png', pad_inches=0.1)
def plot_estimate_chart_1():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
plt.scatter ([0], [158], c='b')
plt.scatter ([1], [159], c='r')
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.tight_layout()
def plot_hypothesis3():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 13), weights,
xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
def plot_hypothesis4():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
with figsize(y=2.5):
plt.figure()
ave = np.sum(weights) / len(weights)
plt.errorbar(range(1,13), weights, label='weights',
yerr=6, fmt='o', capthick=2, capsize=10)
plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
show_legend()
def plot_hypothesis1():
with figsize(y=2.5):
plt.figure()
plt.errorbar([1, 2, 3], [170, 161, 169],
xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 3], [180, 160], color='g', ls='--')
plt.plot([1, 3], [170, 170], color='g', ls='--')
plt.plot([1, 3], [160, 175], color='g', ls='--')
plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
plt.xlim(0,4); plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.tight_layout()
def plot_hypothesis3():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 13), weights,
xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
plt.savefig('../figs/gh_hypothesis3.png', pad_inches=0.1)
def plot_estimate_chart_1():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
plt.scatter ([0], [158], c='b')
plt.scatter ([1], [159], c='r')
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
plt.tight_layout()
plt.savefig('../figs/gh_estimate1.png', pad_inches=0.1)
def plot_hypothesis4():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
with figsize(y=2.5):
plt.figure()
ave = np.sum(weights) / len(weights)
plt.errorbar(range(1,13), weights, label='weights',
yerr=6, fmt='o', capthick=2, capsize=10)
plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
show_legend()
plt.savefig('../figs/gh_hypothesis4.png', pad_inches=0.1)
def plot_hypothesis1():
with figsize(y=2.5):
plt.figure()
plt.errorbar([1, 2, 3], [170, 161, 169],
xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 3], [180, 160], color='g', ls='--')
plt.plot([1, 3], [170, 170], color='g', ls='--')
plt.plot([1, 3], [160, 175], color='g', ls='--')
plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
plt.xlim(0,4); plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.tight_layout()
plt.savefig('../figs/gh_hypothesis1.png', pad_inches=0.1)
def plot_hypothesis5():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
xs = range(1, len(weights)+1)
line = np.poly1d(np.polyfit(xs, weights, 1))
with figsize(y=2.5):
plt.figure()
plt.errorbar(range(1, 13), weights, label='weights',
yerr=5, fmt='o', capthick=2, capsize=10)
plt.plot (xs, line(xs), c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
show_legend()
def plot_estimate_chart_2():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('', xy=[1,159], xytext=[0,158],
arrowprops=dict(arrowstyle='->',
ec='r', lw=3, shrinkA=6, shrinkB=5))
plt.scatter ([0], [158.0], c='k',s=128)
plt.scatter ([1], [164.2], c='b',s=128)
plt.scatter ([1], [159], c='r', s=128)
plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
plt.text (0, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
def plot_estimate_chart_2():
with figsize(y=2.5):
plt.figure()
ax = plt.axes()
ax.annotate('',
xy=[1, 159],
xytext=[0, 158],
arrowprops=dict(arrowstyle='->',
ec='r',
lw=3,
shrinkA=6,
shrinkB=5))
plt.scatter([0], [158.0], c='k', s=128)
plt.scatter([1], [164.2], c='b', s=128)
plt.scatter([1], [159], c='r', s=128)
plt.text(1.0,
158.8,
"prediction ($x_t)$",
ha='center',
va='top',
fontsize=18,
color='red')
plt.text(1.0,
164.4,
"measurement ($z$)",
ha='center',
va='bottom',
fontsize=18,
color='blue')
plt.text(0,
157.8,
"estimate ($\hat{x}_{t-1}$)",
ha='center',
va='top',
fontsize=18)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
ax.xaxis.grid(True, which="major", linestyle='dotted')
ax.yaxis.grid(True, which="major", linestyle='dotted')
def plot_errorbar3():
with book_format.figsize(y=1.5):
book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')],
xlims=(145, 185), ylims=(-1, 2))
def plot_errorbar3():
with book_format.figsize(y=1.5):
book_plots.plot_errorbars([(160, 1, 'A'), (170, 9, 'B')],
xlims=(145, 185),
ylims=(-1, 2))
def plot_errorbar1():
with book_format.figsize(y=1.5):
book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
xlims=(145, 185))
def plot_errorbar1():
with book_format.figsize(y=1.5):
book_plots.plot_errorbars([(160, 8, 'A'), (170, 8, 'B')],
xlims=(145, 185))
def plot_errorbar2():
with figsize(y=2):
plt.figure()
plot_errorbars([(160, 3, 'A'), (170, 9, 'B')],
xlims=(145, 185), ylims=(-1, 1))
#!/usr/bin/env python
# coding: utf-8
# mus and sigmas - не достаточно
# fixme: похоже чтобы воспользоваться корреляцией
# нужно знать ковариационную матрицу
import numpy as np
from code.gaussian_internal import plot_correlated_data
import code.mkf_internal as mkf_internal
height = [60, 62, 63, 65, 65.1, 68, 69, 70, 72, 74]
weight = [95, 120, 127, 119, 151, 143, 173, 171, 180, 210]
plot_correlated_data(height, weight, 'Height (in)', 'Weight (lbs)', False)
X = np.linspace( 1, 10, 100 )
Y = np.linspace( 1, 10, 100 )
print np.cov( X, Y )
mean = (2, 17)
cov = [[10.,0],
[0,4.]]
# join prob.
#mk.plot_3d_covariance( mean, cov )
from book_format import set_figsize, figsize
with figsize(y=4):
mkf_internal.plot_3_covariances()
