def test_perc_not(self):
p_not = neurons.Perceptron([-1], 0.5)
self.assertEqual(p_not.feed([0]), 1)
self.assertEqual(p_not.feed([1]), 0)
def test_perc_nand(self):
p_nand = neurons.Perceptron([-2, -2], 3)
self.assertEqual(p_nand.feed([0, 0]), 1)
self.assertEqual(p_nand.feed([0, 1]), 1)
self.assertEqual(p_nand.feed([1, 0]), 1)
self.assertEqual(p_nand.feed([1, 1]), 0)
def test_perc_or(self):
p_or = neurons.Perceptron([1, 1], -0.5)
self.assertEqual(p_or.feed([0, 0]), 0)
self.assertEqual(p_or.feed([0, 1]), 1)
self.assertEqual(p_or.feed([1, 0]), 1)
self.assertEqual(p_or.feed([1, 1]), 1)
def test_perc_and(self):
p_and = neurons.Perceptron([1, 1], -1.5)
self.assertEqual(p_and.feed([0, 0]), 0)
self.assertEqual(p_and.feed([0, 1]), 0)
self.assertEqual(p_and.feed([1, 0]), 0)
self.assertEqual(p_and.feed([1, 1]), 1)
import pandas as pd
# Needed for VScode to function correctly, can be deleted for normal operation
os.chdir(
'/home/cameron/Dropbox/University/PhD/Teaching/COMP219-AI/PythonMachineLearning/chapter02'
)
import neurons
import plot
df = pd.read_csv('iris.data', header=None)
# Select setosa and versicolor
y = df.iloc[0:150, 4].values
y = np.where(y == 'Iris-virginica', -1, 1)
# extract sepal length and petal length
X = df.iloc[0:150, [1, 3]].values
plot.plotSetosaVersicolor(X)
ppn = neurons.Perceptron(eta=0.1, n_iter=10)
ppn.fit(X, y)
plot.plotEpochUpdates(ppn)
# plot.plotDecisionRegions(X, y, ppn)
#ada = neurons.AdalineGD(eta=0.1, n_iter=10)
#ada.fit(X, y)
# plot.plotDecisionRegions(X, y, ppn)