Skip to content

Instantly share code, notes, and snippets.

@jiaxianhua

jiaxianhua/test_slmethod_perceptron.py

Created June 5, 2019 07:37
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save jiaxianhua/3442e76aefff8202a6e33532e74d0ae5 to your computer and use it in GitHub Desktop.
Save jiaxianhua/3442e76aefff8202a6e33532e74d0ae5 to your computer and use it in GitHub Desktop.
Download ZIP
Raw
test_slmethod_perceptron.py
import numpy as np
from sklearn.datasets import make_classification
from slmethod.perceptron import Perceptron
separable = False
while not separable:
samples = make_classification(n_samples=100,
n_features=2,
n_redundant=0,
n_informative=1,
n_clusters_per_class=1,
flip_y=-1)
red = samples[0][samples[1] == 0]
blue = samples[0][samples[1] == 1]
separable = any([
red[:, k].max() < blue[:, k].min()
or red[:, k].min() > blue[:, k].max() for k in range(2)
])
X = samples[0]
y = samples[1]
y = np.array([1 if i == 1 else -1 for i in y])
minX = np.min(X[:, 0])
maxX = np.max(X[:, 0])
x_points = np.array([minX, maxX])
origin_clf = Perceptron(dual=False)
origin_clf.fit(X, y)
print(origin_clf.w)
print(origin_clf.b)
origin_clf.show2d('slmethod_perceprton.gif')
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment