lumpur 0.0.6

learn to use methods for processing unclear response

lumpur

learn to use methods for processing unclear response

contribute

• Learn the instructions on first-contributions.
• Apply to this repository what you learn there.

features

• plot_binary() function in viz.plot.binary module.
• plot_polynomial() function in viz.plot.polynomial module.
• Polynomial class in num.polynomial module.
• binary()function in dat.clasdata module.
• abbr() function in use.misc.info module.

examples

Following are some examples of lumpur.

polynomial

from lumpur.num.polynomial import Polynomial

p1 = Polynomial([1, 2, 3])
print('y1 =', p1)
p2 = Polynomial([0, -2, 5, 6, 9])
print('y2 =', p2)
p3 = p1 + p2
print('y3 =', p3)

y1 = 1 + 2x + 3x^2
y2 = -2x + 5x^2 + 6x^3 + 9x^4
y3 = 1 + 8x^2 + 6x^3 + 9x^4

from lumpur.num.polynomial import Polynomial

p1 = Polynomial([1, -2, 3])
print('y1 =', p1)
p2 = Polynomial([-2, 1])
print('y2 =', p2)
p3 = p1 * p2
print('y3 =', p3)

y1 = 1 - 2x + 3x^2
y2 = -2 + x^1
y3 = -2 + 5x - 8x^2 + 3x^3

from lumpur.num.polynomial import Polynomial
from lumpur.viz.plot.polynomial import plot_polynomial

p1 = Polynomial([-1, 1])
p2 = Polynomial([-3, 1])
p3 = Polynomial([-5, 1])
p4 = Polynomial([-7, 1])
p = p1 * p2 * p3 * p4
dp = p.differentiate()
d2p = dp.differentiate()
d3p = d2p.differentiate()
d4p = d3p.differentiate()
d5p = d4p.differentiate()
x = [0.1*i for i in range(10, 71)]

plot_polynomial(x, p, label='p')
plot_polynomial(x, dp, label='dpdx')
plot_polynomial(x, d2p, label='d2p/dx2')
plot_polynomial(x, d3p, label='d3p/dx3')
plot_polynomial(x, d4p, label='d4p/dx4')

circular decision boundary

$$ 0.41 - 0.8x - 1.2y + x^2 + y^2 = 0 $$

import lumpur.dat.clasdata as ldc
from lumpur.viz.plot.binary import plot_binary

coeffs = [[0.41], [-0.8, -1.2], [1, 0, 1]]
r1 = [0, 1.05, 0.05]
r2 = [0, 1.05, 0.05]
df = ldc.binary(coeffs, r1=r1, r2=r2)
plot_binary(df)

linear decision boundary

$$ -x + y = 0 $$

import lumpur.dat.clasdata as ldc
from lumpur.viz.plot.binary import plot_binary

coeffs = [[0], [-1, 1]]
df = ldc.binary(coeffs)
plot_binary(df)

abbreviation

import lumpur.use.misc.info as info

print(info.abbrv())

learn to use methods for processing unclear response