def print_correlations(samples, size, rho):
print('size = ' + str(size) + ', rho = ' + str(rho))
for sample in samples:
print(' $ ' + str(round(Correlation.mean(sample), 4)) + ' $ ', end='&')
print()
for sample in samples:
print(' $ ' + str(round(Correlation.square_mean(sample), 4)) + ' $ ',
end='&')
print()
for sample in samples:
print(' $ ' + str(round(Correlation.variance(sample), 4)) + ' $ ',
end='&')
print()
def plot(self):
correlation = Correlation()
pearson = correlation.pearson_correlation(self.x, self.y)
mean_x = Correlation.mean(self.x)
mean_y = Correlation.mean(self.y)
variance_x = np.sqrt(Correlation.variance(self.x))
variance_y = np.sqrt(Correlation.variance(self.y))
ell_radius_x = np.sqrt(1 + pearson)
ell_radius_y = np.sqrt(1 - pearson)
alpha = 1 / 2 * np.arctan(
(2 * pearson * variance_x * variance_y) /
(variance_x * variance_x - variance_y * variance_y))
alpha = alpha if alpha > 0 else alpha + np.pi / 2
scale_x = 3 * variance_x
scale_y = 3 * variance_y
ellipse = Elp((mean_x, mean_y),
2 * ell_radius_x * scale_x,
2 * ell_radius_y * scale_y,
np.degrees(alpha),
facecolor='none',
edgecolor='red')
fig, ax = plt.subplots()
ax.add_artist(ellipse)
plt.plot(self.x, self.y, 'o')
plt.xlim(-4, 4)
plt.ylim(-4, 4)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Size = ' + str(self.size) + ', rho = ' + str(self.rho))
plt.savefig('images/' + 'Ellipse' + str(self.size) + 'r' +
str(self.rho) + '.png')
plt.show()
return
def print_correlation(sample, name, size, rho):
print(name + ', ' + 'size = ' + str(size) + ', ' + 'rho = ' + str(rho))
print(Correlation.mean(sample))
print(Correlation.square_mean(sample))
print(Correlation.variance(sample))
print()
