mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from sklearn.linear_model import SGDClassifier
|
|
from sklearn.datasets.samples_generator import make_blobs
|
|
|
|
def plot_sgd_separator():
|
|
# we create 50 separable points
|
|
X, Y = make_blobs(n_samples=50, centers=2,
|
|
random_state=0, cluster_std=0.60)
|
|
|
|
# fit the model
|
|
clf = SGDClassifier(loss="hinge", alpha=0.01,
|
|
n_iter=200, fit_intercept=True)
|
|
clf.fit(X, Y)
|
|
|
|
# plot the line, the points, and the nearest vectors to the plane
|
|
xx = np.linspace(-1, 5, 10)
|
|
yy = np.linspace(-1, 5, 10)
|
|
|
|
X1, X2 = np.meshgrid(xx, yy)
|
|
Z = np.empty(X1.shape)
|
|
for (i, j), val in np.ndenumerate(X1):
|
|
x1 = val
|
|
x2 = X2[i, j]
|
|
p = clf.decision_function([x1, x2])
|
|
Z[i, j] = p[0]
|
|
levels = [-1.0, 0.0, 1.0]
|
|
linestyles = ['dashed', 'solid', 'dashed']
|
|
colors = 'k'
|
|
|
|
ax = plt.axes()
|
|
ax.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
|
|
ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
|
|
|
|
ax.axis('tight')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
plot_sgd_separator()
|
|
plt.show()
|