# 3.6.10.15. Example of linear and non-linear modelsΒΆ

This is an example plot from the tutorial which accompanies an explanation of the support vector machine GUI.

```import numpy as np
from matplotlib import pyplot as plt

from sklearn import svm
```

data that is linearly separable

```def linear_model(rseed=42, n_samples=30):
" Generate data according to a linear model"
np.random.seed(rseed)

data = np.random.normal(0, 10, (n_samples, 2))
data[:n_samples // 2] -= 15
data[n_samples // 2:] += 15

labels = np.ones(n_samples)
labels[:n_samples // 2] = -1

return data, labels

X, y = linear_model()
clf = svm.SVC(kernel='linear')
clf.fit(X, y)

plt.figure(figsize=(6, 4))
ax = plt.subplot(111, xticks=[], yticks=[])
ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.bone)

ax.scatter(clf.support_vectors_[:, 0],
clf.support_vectors_[:, 1],
s=80, edgecolors="k", facecolors="none")

delta = 1
y_min, y_max = -50, 50
x_min, x_max = -50, 50
x = np.arange(x_min, x_max + delta, delta)
y = np.arange(y_min, y_max + delta, delta)
X1, X2 = np.meshgrid(x, y)
Z = clf.decision_function(np.c_[X1.ravel(), X2.ravel()])
Z = Z.reshape(X1.shape)

ax.contour(X1, X2, Z, [-1.0, 0.0, 1.0], colors='k',
linestyles=['dashed', 'solid', 'dashed'])
```

data with a non-linear separation

```def nonlinear_model(rseed=42, n_samples=30):

theta = np.random.random(n_samples) * np.pi * 2

data = np.empty((n_samples, 2))
data[:, 0] = radius * np.cos(theta)
data[:, 1] = radius * np.sin(theta)

labels = np.ones(n_samples)
labels[far_pts] = -1

return data, labels

X, y = nonlinear_model()
clf = svm.SVC(kernel='rbf', gamma=0.001, coef0=0, degree=3)
clf.fit(X, y)

plt.figure(figsize=(6, 4))
ax = plt.subplot(1, 1, 1, xticks=[], yticks=[])
ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.bone, zorder=2)

ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
s=80, edgecolors="k", facecolors="none")

delta = 1
y_min, y_max = -50, 50
x_min, x_max = -50, 50
x = np.arange(x_min, x_max + delta, delta)
y = np.arange(y_min, y_max + delta, delta)
X1, X2 = np.meshgrid(x, y)
Z = clf.decision_function(np.c_[X1.ravel(), X2.ravel()])
Z = Z.reshape(X1.shape)

ax.contour(X1, X2, Z, [-1.0, 0.0, 1.0], colors='k',
linestyles=['dashed', 'solid', 'dashed'], zorder=1)

plt.show()
```

Total running time of the script: ( 0 minutes 0.047 seconds)

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