AttributeError: 'Perception' object has no attribute 'w_'

我的代码

# -*- coding: utf-8 -*-

"""

Created on Fri Sep 29 20:32:51 2017

@author: yangqiusong

"""

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from matplotlib.colors import ListedColormap

def plot_decision_regions(X, Y, classifier, resolution = 0.02):

    markers =('s','x','o','v')

    colors = ('read', 'blue', 'lightgreen', 'gray', 'cyan')

    cmap = ListedColormap(colors[:len(np.unique(Y))])

    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max()

    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max()

    #print x1_min, x2_min

   # print x1_max, x2_max

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

                           np.arange(x2_min, x2_max, resolution))

    #print xx1

    #print xx2

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

    print xx1.ravel()

    print xx2.ravel()

    print Z

    Z = Z.reshape(xx1.shape)

    plt.contourf(xx1, xx2, alpha = 0.4, cmap = cmap)

    plt.xlim(xx1.min(), xx1.max())

    plt.ylim(xx2.min(), xx2.max())

    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(idex),

                    marker = markers[idx], label = cl)

class Perception(object):

    """

eta:学习率

n_iter:权重向量的训练次数

w_：神经分叉权重向量

errors_: 用于记录神经元判断出错次数

"""

    def __init__(self, eta = 0.01, n_iter = 10):

        self.eta = eta

        self.n_iter = n_iter

        #self.w_ = np.zero(1+X.shape[1])

        #self.errors_ = []

    def fit(self, X, Y):

        """

        输入训练数据，培训神经元，X输入样本向量，Y对应样本分类

        X:shape[n_samples, n_features]

        X:[[1,2,3], [4,5,6]]

        n_samples: 2

        n_features: 3

        y:[1,-1]

        初始化权重向量为0

        加一是因为前面算法提到的w0,也就是步调函数阈值

        """

        self.w_ = np.zeros(1+X.shape[1])

        self.errors_ = []

        for _ in range(self.n_iter):

            errors = 0

            """

            X:[[1,2,3],[4,5,6]]

            Y:[1,-1]

            zip(X,Y) = [[1,2,3,1],[4,5,6,-1]]

            """

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

                """

                update = eta*(Y - Y')

                """

                update = self.eta*(target - self.predict(xi))

                """

                xi 是一个向量

                update * xi 等价:

                [da_w(1) = X[1]*update,da_w(2) = X[2]*update,da_w(3) = X[3]*update]

                """

                self.w_[1:] += update * xi

                self.w_[0] += update

                errors += int(update != 0.0)

                self.errors_.append(errors)

    def net_input(self,X):

        """

        z = w0*1 + w1*X1 + ... + wn*Xn

        """

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

    def predict(self, X):

        return np.where(self.net_input(X) >= 0, 1, -1)

file = 'data.txt'

df = pd.read_csv(file, header = None)

Y = df.loc[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('花瓣长度')

plt.ylabel('花经长度')

plt.legend(loc = 'upper left')

#plt.show() 

"""

ppn = Perception(eta = 0.1, n_iter = 10)

plot_decision_regions(X, Y, ppn, resolution = 0.02)

plt.xlabel('花瓣长度')

plt.ylabel('花经长度')

plt.legend(loc = 'upper left')

plt.show()