# Perceptron in Python - ML From Scratch 06

Implement a single-layer Perceptron algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.

In this Machine Learning from Scratch Tutorial, we are going to implement a single-layer Perceptron algorithm using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm.

All algorithms from this course can be found on GitHub together with example tests.

https://sebastianraschka.com/Articles/2015_singlelayer_neurons.html

## Implementation¶

``````import numpy as np

class Perceptron:

def __init__(self, learning_rate=0.01, n_iters=1000):
self.lr = learning_rate
self.n_iters = n_iters
self.activation_func = self._unit_step_func
self.weights = None
self.bias = None

def fit(self, X, y):
n_samples, n_features = X.shape

# init parameters
self.weights = np.zeros(n_features)
self.bias = 0

y_ = np.array([1 if i > 0 else 0 for i in y])

for _ in range(self.n_iters):

for idx, x_i in enumerate(X):

linear_output = np.dot(x_i, self.weights) + self.bias
y_predicted = self.activation_func(linear_output)

# Perceptron update rule
update = self.lr * (y_[idx] - y_predicted)

self.weights += update * x_i
self.bias += update

def predict(self, X):
linear_output = np.dot(X, self.weights) + self.bias
y_predicted = self.activation_func(linear_output)
return y_predicted

def _unit_step_func(self, x):
return np.where(x>=0, 1, 0)
``````

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