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65 lines
1.7 KiB
65 lines
1.7 KiB
import numpy as np
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class Perceptron(object):
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"""
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Perceptron classifier
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___________________
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parameters
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__________________
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eta: float
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learning rate betweeen 0.0 and 1.0
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n_iter: int
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Passes over the training dataset
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___________________
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Attributes
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___________________
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w_: 1d array
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weights after training
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errors_: list
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Number of msiclassifications for every epoch
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"""
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def __init__(self, eta, n_iter):
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self.eta = eta
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self.n_iter = n_iter
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def fit(self, X, y):
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"""
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Fit training data
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______________________
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parameters
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______________________
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X: {array-like}, shape = [n_samples, n_features]
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Training vectors where n_samples is the number of samples
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and n_features is the number of features
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y: array-like, shape = [n_samples]
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Target values
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Returns
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self: object
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"""
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self.w_ = np.zeros(1 + X.shape[1])
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self.errors_ = []
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for _ in range(self.n_iter):
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errors = 0
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for xi,target in zip(X, y):
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update = self.eta * (target - self.predict(xi))
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self.w_[1:] += update * xi
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self.w_[0] += update
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errors += int(update != 0.0)
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self.errors_.append(errors)
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return self
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def net_input(self, X):
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"""
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Calculate net input
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"""
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return np.dot(X, self.w_[1:]) + self.w_[0]
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def predict(self, X):
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"""
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Return class label after unit step
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"""
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return np.where(self.net_input(X) >= 0.0, 1, -1)
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