Tests for Python Perceptron v.1
""" | |
MIT License | |
Copyright (c) 2018 Thomas Countz | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: | |
The above copyright notice and this permission notice shall be included in all | |
copies or substantial portions of the Software. | |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
SOFTWARE. | |
""" | |
import unittest | |
import numpy as np | |
from perceptron import Perceptron | |
class PerceptronTest(unittest.TestCase): | |
def test_mimics_logical_and(self): | |
weights = np.array([-1, 1, 1]) | |
a = 1 | |
b = 1 | |
inputs = np.array([a, b]) | |
perceptron = Perceptron(inputs.size) | |
perceptron.weights = weights | |
output = perceptron.predict(inputs) | |
self.assertEqual(output, a & b) | |
def test_trains_for_logical_and(self): | |
labels = np.array([1, 0, 0, 0]) | |
input_matrix = [] | |
input_matrix.append(np.array([1, 1])) | |
input_matrix.append(np.array([1, 0])) | |
input_matrix.append(np.array([0, 1])) | |
input_matrix.append(np.array([0, 0])) | |
perceptron = Perceptron(2, threshold=10, learning_rate=1) | |
perceptron.train(input_matrix, labels) | |
a = 1 | |
b = 1 | |
inputs = np.array([a, b]) | |
output = perceptron.predict(inputs) | |
self.assertEqual(output, a & b) | |
if __name__ == '__main__': | |
unittest.main() |
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