def test_fit(self):
p = Perceptron()
p.fit([[1, 2], [3, 4]], [0, 1])
self.assertEqual(p.is_trained, True)
self.assertEqual(len(p.weights), 2)
from perceptron.perceptron import Perceptron
import numpy as np
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [1]])
print("[INFO] training perceptron...")
p = Perceptron(X.shape[1], alpha=0.1)
p.fit(X, y, epochs=20)
print("[INFO[ testing perceptron...")
for (x, target) in zip(X, y):
pred = p.predict(x)
print("[INFO] data = {}. ground truth={}, pred = {}".format(
x, target[0], pred))
from perceptron.perceptron import Perceptron
from linear_discriminant_analysis.fisher_lda import FisherLDA
import pandas as pd
import numpy as np
# Pereceptron Test
df = pd.read_csv('datasets/dataset_1.csv', names=['X1', 'X2', 'y'])
df['y'] = df['y'].replace(0, -1)
print(df.head())
p = Perceptron(0.01, 20, 2, 0, '3', './images/')
X = np.array(df[['X1', 'X2']])
y = df['y']
p.fit(X, y)
# Fishers LDA Test
disc = FisherLDA(dataset=1)
disc.visualize()