PerceptronLearning.py

# define a class named Perceptron, which has a "fit" method, and "predict" method.
import numpy as np

class Perceptron(object):
    """Perceptron classifier.

    Parameters
    -------------
    eta : float
      Learning rate (between 0 and 1)
    n_iter : int
      Passes over the training dataset
    random_state : int
      Random number generator seed for random weight in the beginning

    Attributes
    -------------
    w_ : 1d-array
      Weights after fitting
    errors_ : list
      Number of misclassifications(updates) in each epoch

    """

    def __init__(self, eta = 0.01, n_iter = 50, random_state = 1):
        self.eta = eta
        self.n_iter = n_iter
        self.random_state = random_state

    def fit(self, x, y):
        """Fit training data.

        Parameters
        ______________
        x : (array-like), shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples(rows) and
            n_features is the number of features(columns)
        y : array-like, shape = [n_samples]
            Traget values

        Returns
        ______________
        self : object
        """
        rgen = np.random.RandomState(self.random_state)
        self.w_ = rgen.normal(loc = 0.0, scale = 0.01, size = 1 + x.shape[1])
        self.errors_ = []

        for _ in range(self.n_iter):
            errors = 0
            for xi, target in zip(x, y):
                update = self.eta*(target - self.predict(xi))
                self.w_[1:] += update*xi
                self.w_[0] += update
                errors += int(update != 0.0)
            self.errors_.append(errors)
        return self

    def net_input(self, x):
        return np.dot(x, self.w_[1:]) + self.w_[0]

    def predict(self, x):
        return np.where(self.net_input(x) >= 0.0, 1, -1)


import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',header=None)
df.tail()