Pattern to detect an anomaly:

 

import numpy as np

import matplotlib.pyplot as plt

from sklearn import svm

from sklearn.datasets import make_blobs

 

 

# we create 40 separable points

X, y = make_blobs(n_samples=40, centers=2, random_state=6)

 

# fit the model, do not regularize for illustration purposes

clf = svm.SVC(kernel="linear", C=1000)

clf.fit(X, y)

 

plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)

 

# plot the decision function

ax = plt.gca()

xlim = ax.get_xlim()

ylim = ax.get_ylim()

 

# create grid to evaluate model

xx = np.linspace(xlim[0], xlim[1], 30)

yy = np.linspace(ylim[0], ylim[1], 30)

YY, XX = np.meshgrid(yy, xx)

xy = np.vstack([XX.ravel(), YY.ravel()]).T

Z = clf.decision_function(xy).reshape(XX.shape)

 

# plot decision boundary and margins

ax.contour(XX, YY, Z, colors="k", levels=[-1, 0, 1], alpha=0.5,

           linestyles=["--", "-", "--"])

# plot support vectors

ax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,

           linewidth=1, facecolors="none", edgecolors="k")

 

plt.savefig("SOSONEP04PyPlot01.pdf")