# coding: utf-8

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import  load_iris
from matplotlib.colors import  ListedColormap

class Perceptron(object):
    """perceptron classifier

    Parameters
    ------------
    eta: float
        Learning rate between 0 and 1
    n_iter: int
        Passes over the training dataset

    Attributes
    ------------
    w_: 1d-array
        weights after fitting
    errors_:list
        Number of misclassification in every epoch
    """

    def __init__(self, eta = 0.01, n_iter = 10):
        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_featuers is the number of features.
        y: array-like, shape=[n_smaples]
            Target values.

        Returns
        ----------
        self: object
        """

        self.w_ = np.zeros(1 + X.shape[1]) #Add w_0
        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)#analoge ? : in C++

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header = None)
y = df.iloc[:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[:100, [0, 2]].values


plt.scatter(X[:50, 0],X[:50, 1], color = 'red', marker = 'o', label = 'setosa')
plt.scatter(X[50:100, 0], X[50:100, 1], color = 'blue', marker = 'x', label = 'versicolor')
plt.xlabel('pental length')
plt.ylabel('sepal length')
plt.legend(loc = 'upper left')
plt.show()



pct = Perceptron(eta = 0.1, n_iter = 10)
pct.fit(X, y)

plt.plot(range(1, len(pct.errors_) + 1), pct.errors_, marker = 'o')
plt.xlabel('Epoches')
plt.ylabel('Number of misclassification')
plt.show()

def plot_decision_region(X, y, classifier, resolution = 0.02):
        #setop marker generator and color map
        markers = ('s', 'x', 'o', '^', 'v')
        colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
        cmap = ListedColormap(colors[:len(np.unique(y))])

        #plot the decision surface
        x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
        x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1

        xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))

        Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
        Z = Z.reshape(xx1.shape)

        plt.contourf(xx1, xx2, Z, alpha = 0.4, cmap = cmap)
        plt.xlim(xx1.min(), xx1.max())
        plt.ylim(xx2.min(), xx2.max())

        #plot class samples
        for idx, cl in enumerate(np.unique(y)):
            plt.scatter(x = X[y == cl, 0], y = X[y == cl, 1],
                       alpha = 0.8, c = cmap(idx),
                       marker = markers[idx], label = cl)



plot_decision_region(X, y, pct, 0.01)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc = 'upper left')
plt.show()





