import numpy as np

class Perceptron(object):

    """Perceptron classifier.

    Parameters

    ------------

    eta:float,Learning rate (between 0.0 and 1.0)

    n_iter:int,Passes over the training dataset.

    Attributes

    -------------

    w_: 1d-array,Weights after fitting.

    errors_: list,Numebr of misclassifications 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.先对权重参数初始化，然后对训练集中每一个样本循环，根据感知机算法学习规则对权重进行更新

        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
　　　　　#初始化权重。数据集特征维数+1。

        self.errors_ = []#用于记录每一轮中误分类的样本数

        for _ in range(self.n_iter):

            errors = 0

            for xi, target in zip(X,y):

                update = self.eta * (target - self.predict(xi))#调用了predict()函数

                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]#计算向量点乘

    def predict(self,X):#预测类别标记

        """return class label after unit step"""

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

import pandas as pd#用pandas读取数据

import matplotlib.pyplot as plt

import numpy as np

from matplotlib.colors import ListedColormap

from Perceptron_1 import Perceptron

df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',header=None)#读取数据还可以用request这个包

print(df.tail())#输出最后五行数据，看一下Iris数据集格式

"""抽取出前100条样本，这正好是Setosa和Versicolor对应的样本，我们将Versicolor

对应的数据作为类别1，Setosa对应的作为-1。对于特征，我们抽取出sepal length和petal

length两维度特征，然后用散点图对数据进行可视化"""   

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('petal length')

plt.ylabel('sepal lenght')

plt.legend(loc='upper left')

plt.show()

#train our perceptron model now

#为了更好地了解感知机训练过程，我们将每一轮的误分类

#数目可视化出来，检查算法是否收敛和找到分界线

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('Epoches')

plt.ylabel('Number of misclassifications')

plt.show()

#画分界线超平面

def plot_decision_region(X,y,classifier,resolution=0.02):

    #setup 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 desicion 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.contour(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,classifier=ppn)

plt.xlabel('sepal length [cm]')

plt.ylabel('petal length [cm]')

plt.legend(loc='upperleft')

plt.show()