df = pd.read_csv('../data/iris.csv')
df_1 = df.iloc[0:100]

d = {'setosa': 1,
 'versicolor': 0}
df_1['class'] = df_1['class'].map(d)

X = df_1[['sepal_length', 'petal_width']].values
y = df_1['class'].values

# standardize
X[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std()
X[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std()

import numpy as np

class Perceptron(object):

    def __init__(self, eta=0.01, epochs=50):
        self.eta = eta
        self.epochs = epochs

    def fit(self, X, y):

        # Initialize weights to 0
        self.w_ = np.zeros(1 + X.shape[1])

        self.cost_ = []

        for i in range(self.epochs):

            # update weights
            y_pred = self._dot_prod(X)
            errors = (y - y_pred)
            self.w_[1:] += self.eta * X.T.dot(errors)
            self.w_[0] += self.eta * errors.sum()

            cost = (errors**2).sum() / 2.0
            self.cost_.append(cost)

        return self

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

    def predict(self, X):
        return np.where(self._dot_prod(X) >= 0.0, 1, 0)

ppn = Perceptron(epochs=15, eta=0.01)
ppn.fit(X, y)

array([ 0.5       ,  0.02486745, -0.51024596])