In [1]:
import numpy as np
class Perceptron:
def __init__(self):
self._w = self._b = None
def fit(self, x, y, lr=0.01, epoch=1000):
x, y = np.asarray(x, np.float32), np.asarray(y, np.float32)
self._w = np.zeros(x.shape[1])
self._b = 0.
for _ in range(epoch):
err = -y * self.predict(x, True)
idx = np.argmax(err)
if err[idx] < 0:
break
delta = lr * y[idx]
self._w += delta * x[idx]
self._b += delta
def predict(self, x, raw=False):
x = np.asarray(x, np.float32)
y_pred = x.dot(self._w) + self._b
if raw:
return y_pred
return np.sign(y_pred).astype(np.float32)
In [2]:
from Util import gen_two_clusters
x, y = gen_two_clusters()
perceptron = Perceptron()
perceptron.fit(x, y)
print("准确率：{:8.6} %".format((perceptron.predict(x) == y).mean() * 100))
In [3]:
from Util import visualize2d
visualize2d(perceptron, x, y)
visualize2d(perceptron, x, y, True)