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- class Perceptron(object):
- """Perceptron classifier.
- Parameters
- ------------
- eta : float
- Learning rate (between 0.0 and 1.0)
- n_iter : int
- Passes over the training dataset.
- random_state : int
- Random number generator seed for random weight
- initialization.
- 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 and
- n_features is the number of features.
- y : array-like, shape = [n_samples]
- Target 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]) #Draw random samples from a normal (Gaussian) distribution.
- 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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