In the field of Machine Learning, the Perceptron is a Supervised Learning Algorithm for binary classifiers. The Perceptron Model implements the following function:

For a particular choice of the weight vector and bias parameter , the model predicts output for the corresponding input vector .

**OR** logical function truth table for * 2-bit binary variables*, i.e, the input vector and the corresponding output –

0 | 0 | 0 |

0 | 1 | 1 |

1 | 0 | 1 |

1 | 1 | 1 |

Now for the corresponding weight vector of the input vector , the associated Perceptron Function can be defined as:

For the implementation, considered weight parameters are and the bias parameter is .

**Python Implementation:**

`# importing Python library` `import` `numpy as np` ` ` `# define Unit Step Function` `def` `unitStep(v):` ` ` `if` `v >` `=` `0` `:` ` ` `return` `1` ` ` `else` `:` ` ` `return` `0` ` ` `# design Perceptron Model` `def` `perceptronModel(x, w, b):` ` ` `v ` `=` `np.dot(w, x) ` `+` `b` ` ` `y ` `=` `unitStep(v)` ` ` `return` `y` ` ` `# OR Logic Function` `# w1 = 1, w2 = 1, b = -0.5` `def` `OR_logicFunction(x):` ` ` `w ` `=` `np.array([` `1` `, ` `1` `])` ` ` `b ` `=` `-` `0.5` ` ` `return` `perceptronModel(x, w, b)` ` ` `# testing the Perceptron Model` `test1 ` `=` `np.array([` `0` `, ` `1` `])` `test2 ` `=` `np.array([` `1` `, ` `1` `])` `test3 ` `=` `np.array([` `0` `, ` `0` `])` `test4 ` `=` `np.array([` `1` `, ` `0` `])` ` ` `print` `(` `"OR({}, {}) = {}"` `.` `format` `(` `0` `, ` `1` `, OR_logicFunction(test1)))` `print` `(` `"OR({}, {}) = {}"` `.` `format` `(` `1` `, ` `1` `, OR_logicFunction(test2)))` `print` `(` `"OR({}, {}) = {}"` `.` `format` `(` `0` `, ` `0` `, OR_logicFunction(test3)))` `print` `(` `"OR({}, {}) = {}"` `.` `format` `(` `1` `, ` `0` `, OR_logicFunction(test4)))` |

**Output:**

OR(0, 1) = 1 OR(1, 1) = 1 OR(0, 0) = 0 OR(1, 0) = 1

Here, the model predicted output () for each of the test inputs are exactly matched with the OR logic gate conventional output () according to the truth table for 2-bit binary input.

Hence, it is verified that the perceptron algorithm for OR logic gate is correctly implemented.