# SGD: PenaltiesΒΆ

Plot the contours of the three penalties.

All of the above are supported by `sklearn.linear_model.stochastic_gradient`.

```from __future__ import division
print(__doc__)

import numpy as np
import matplotlib.pyplot as plt

def l1(xs):
return np.array([np.sqrt((1 - np.sqrt(x ** 2.0)) ** 2.0) for x in xs])

def l2(xs):
return np.array([np.sqrt(1.0 - x ** 2.0) for x in xs])

def el(xs, z):
return np.array([(2 - 2 * x - 2 * z + 4 * x * z -
(4 * z ** 2
- 8 * x * z ** 2
+ 8 * x ** 2 * z ** 2
- 16 * x ** 2 * z ** 3
+ 8 * x * z ** 3 + 4 * x ** 2 * z ** 4) ** (1. / 2)
- 2 * x * z ** 2) / (2 - 4 * z) for x in xs])

def cross(ext):
plt.plot([-ext, ext], [0, 0], "k-")
plt.plot([0, 0], [-ext, ext], "k-")

xs = np.linspace(0, 1, 100)

alpha = 0.501  # 0.5 division throuh zero

cross(1.2)

l1_color = "navy"
l2_color = "c"
elastic_net_color = "darkorange"
lw = 2

plt.plot(xs, l1(xs), color=l1_color, label="L1", lw=lw)
plt.plot(xs, -1.0 * l1(xs), color=l1_color, lw=lw)
plt.plot(-1 * xs, l1(xs), color=l1_color, lw=lw)
plt.plot(-1 * xs, -1.0 * l1(xs), color=l1_color, lw=lw)

plt.plot(xs, l2(xs), color=l2_color, label="L2", lw=lw)
plt.plot(xs, -1.0 * l2(xs), color=l2_color, lw=lw)
plt.plot(-1 * xs, l2(xs), color=l2_color, lw=lw)
plt.plot(-1 * xs, -1.0 * l2(xs), color=l2_color, lw=lw)

plt.plot(xs, el(xs, alpha), color=elastic_net_color, label="Elastic Net", lw=lw)
plt.plot(xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)
plt.plot(-1 * xs, el(xs, alpha), color=elastic_net_color, lw=lw)
plt.plot(-1 * xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)

plt.xlabel(r"\$w_0\$")
plt.ylabel(r"\$w_1\$")
plt.legend()

plt.axis("equal")
plt.show()
```

Total running time of the script: ( 0 minutes 0.066 seconds)

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