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scientificComputing/regression/lecture/cubicerrors.py

67 lines
2.2 KiB
Python

import matplotlib.pyplot as plt
import numpy as np
from plotstyle import *
def create_data():
# wikipedia:
# Generally, males vary in total length from 250 to 390 cm and
# weigh between 90 and 306 kg
c = 6
x = np.arange(2.2, 3.9, 0.05)
y = c * x**3.0
rng = np.random.RandomState(32281)
noise = rng.randn(len(x))*50
y += noise
return x, y, c
def plot_data_errors(ax, x, y, c):
ax.set_xlabel('Size x', 'm')
ax.set_ylabel('Weight y', 'kg')
ax.set_xlim(2, 4)
ax.set_ylim(0, 400)
ax.set_xticks(np.arange(2.0, 4.1, 0.5))
ax.set_yticks(np.arange(0, 401, 100))
ax.annotate('Error',
xy=(x[28]+0.05, y[28]+60), xycoords='data',
xytext=(3.4, 70), textcoords='data', ha='left',
arrowprops=dict(arrowstyle="->", relpos=(0.9,1.0),
connectionstyle="angle3,angleA=50,angleB=-30") )
ax.plot(x[:40], y[:40], zorder=0, **psAm)
inxs = [3, 10, 11, 17, 18, 21, 28, 30, 33]
ax.plot(x[inxs], y[inxs], zorder=10, **psA)
xx = np.linspace(2.1, 3.9, 100)
ax.plot(xx, c*xx**3.0, **lsBm)
for i in inxs :
xx = [x[i], x[i]]
yy = [c*x[i]**3.0, y[i]]
ax.plot(xx, yy, zorder=5, **lsDm)
def plot_error_hist(ax, x, y, c):
ax.set_xlabel('Squared error')
ax.set_ylabel('Frequency')
bins = np.arange(0.0, 11000.0, 750)
ax.set_xlim(bins[0], bins[-1])
ax.set_ylim(0, 15)
ax.set_xticks(np.arange(bins[0], bins[-1], 5000))
ax.set_yticks(np.arange(0, 16, 5))
errors = (y-(c*x**3.0))**2.0
mls = np.mean(errors)
ax.annotate('Mean\nsquared\nerror',
xy=(mls, 0.5), xycoords='data',
xytext=(4500, 6), textcoords='data', ha='left',
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
connectionstyle="angle3,angleA=10,angleB=90") )
ax.hist(errors, bins, **fsC)
if __name__ == "__main__":
x, y, c = create_data()
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=cm_size(figure_width, 0.9*figure_height))
fig.subplots_adjust(wspace=0.5, **adjust_fs(left=6.0, right=1.2))
plot_data_errors(ax1, x, y, c)
plot_error_hist(ax2, x, y, c)
fig.savefig("cubicerrors.pdf")
plt.close()