import numpy as np

class Perceptron(object):
    """
    eta : 学習率
    n_iter : 訓練データの訓練回数
    random_state : 重みを初期化する乱数シード
    """
    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
    """
    w_ : 適合後の重み
    errors_ : 各エポック（訓練の最大回数）での誤分類（更新）の数
    """
    def fit(self, X, y):
        """
        X : 訓練データ shape=[n_examples, n_features]
        y : 目的変数 shape=[n_examples]
        """
        rgen = np.random.RandomState(self.random_state)
        self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1+X.shape[1])
        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):
        return np.dot(X, self.w_[1:]) + self.w_[0]

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

memo = """
import os
import pandas as pd
s=os.path.join('https://archive.ics.uci.edu', 'ml', 'machine-learning-databases','iris', 'iris.data')
s=r'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'
print('URL:',s)
df = pd.read_csv(s, header=None, encoding='utf-8')
df.tail()
"""

memo2 = """
import matplotlib.pyplot as plt
import numpy as np
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1 , 1)
X = df.iloc[0:100, [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('sepal length[cm]')
plt.ylabel('petal length[cm]')
plt.legend(loc='upper left')
plt.show()
"""

memo3 = """
from perceptron import Perceptron
ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(X, y)
plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Number of Update')
plt.show()
"""

memo4 = """
from pstest2 import plot_decision_regions
plot_decision_regions(X,y,classifier=ppn)
plt.xlabel('sepal length[cm]')
plt.ylabel('petal length[cm]')
plt.legend(loc='upper left')
plt.show()
"""
