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- import numpy as np
- class Perceptron(object):
- """
- Perceptron classifier
- ___________________
- parameters
- __________________
- eta: float
- learning rate betweeen 0.0 and 1.0
- n_iter: int
- Passes over the training dataset
-
- ___________________
- Attributes
- ___________________
- w_: 1d array
- weights after training
- errors_: list
- Number of msiclassifications for every epoch
- """
- def __init__(self, eta, n_iter):
- self.eta = eta
- self.n_iter = n_iter
-
- 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
- and n_features is the number of features
- y: array-like, shape = [n_samples]
- Target values
-
- ____________________
- Returns
- ____________________
- self: object
- """
- self.w_ = np.zeros(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):
- """
- Calculate net input
- """
- return np.dot(X, self.w_[1:]) + self.w_[0]
-
- def predict(self, X):
- """
- Return class label after unit step
- """
- return np.where(self.net_input(X) >= 0.0, 1, -1)
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