import numpy as np
from . __base import OnlineLearningModel
[docs]class Perceptron(OnlineLearningModel):
"""The Perceptron model.
Rosenblatt, F.,
The perceptron: a probabilistic model for information storage
and organization in the brain., Psychological review,
American Psychological Association, 1958, 65, 386
Attributes:
num_iterations (:obj:`int`, optional): Number of iterations
to run the training for. Defaults to 1.
random_state (:obj:`int`, optional): The random seed to use
with the pseudo-random generator. Defaults to `None`.
positive_label (:obj:`int`, optional): The number in the output
field that represents the positive label. The value passed
should be different than -1. Defaults to 1.
class_weight (:obj:`dict`, optional): Represents the relative
weight of the labels in the data. Useful for imbalanced
classification tasks.
Raises:
AssertionError: if `positive_label` is equal to -1.
"""
def __init__(
self,
num_iterations=1,
random_state=None,
positive_label=1,
class_weight=None
):
super().__init__(
num_iterations=num_iterations,
random_state=random_state,
positive_label=positive_label,
class_weight=class_weight
)
def _update(self, x: np.ndarray, y: int):
"""Updates the weight vector in case a mistake occured.
When presented with a data point, this method evaluates
the error and based on the result, updates or not the
weights vector.
Args:
x (:obj:`np.ndarray` or `list`): An array representing
one single data point. Array needs to be 2D.
y (`int`): Output value for the data point. Takes value
between 1 and -1.
Returns:
None
Raises:
IndexError: if the value x is not 2D.
"""
prediction = -1 if self.weights.dot(x) < 0 else 1
if y != prediction:
self.weights = self.weights + y * x * self.class_weight_[y]