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

import?numpy?as?np

class?Perceptron（object）:
????“““
????eta:?學(xué)習(xí)率（01）一般是使用者自定義，然后根據(jù)經(jīng)驗(yàn)調(diào)整
????n_iter:?權(quán)重向量的訓(xùn)練次數(shù)，這里只訓(xùn)練10次
????w_:?神經(jīng)分叉權(quán)重向量
????errors_:?用于記錄神經(jīng)元判斷錯(cuò)誤的次數(shù)
????“““
????def?__init__（self?eta=0.01?n_iter=10）:
????????self.eta?=?eta
????????self.n_iter?=?n_iter
????????
????def?fit（self?x?y）:
????????“““
????????輸入訓(xùn)練數(shù)據(jù)，訓(xùn)練神經(jīng)元
????????x：?輸入樣本的向量
????????y：?樣本對應(yīng)的分類
????????x:?shape[n_sample?n_features]:?樣本數(shù)量，每個(gè)樣本的特征維度
????????x:[[123][456]]，有2個(gè)樣本，每個(gè)樣本有3個(gè)特征
????????“““
????????#初始化權(quán)重向量的每個(gè)分量，這里初始化的值為0
????????#+1是為了w0，權(quán)重向量W的維度加1，多出的那一維用于計(jì)算激活函數(shù)的閾值
????????self.w_?=?np.zeros（1+x.shape[1]）
????????self.errors_?=?[]
????????
????????“““
????????開始訓(xùn)練
????????每次出現(xiàn)預(yù)測錯(cuò)誤的時(shí)候，把樣本重新輸入神經(jīng)元，更新權(quán)重向量
????????這里設(shè)置的迭代次數(shù)我n_iter=10
????????如果迭代10次都無法分類正確，則終止
????????“““
????????for?_?in?range（self.n_iter）:
????????????errors?=?0
????????????“““
????????????x:[[123][456]]
????????????y:[1-1]
????????????zip（xy）:?[（[1?2?3]?1）?（[4?5?6]?-1）]
????????????“““
????????????for?xi?target?in?zip（x?y）:
????????????????update?=?self.eta?*?（target?-?self.predict（xi））
????????????????
????????????????#xi是向量，target是實(shí)數(shù)
????????????????#更新權(quán)值
????????????????self.w_[1:]?+=?update?*?xi
????????????????
????????????????#更新閾值
????????????????self.w_[0]?+=?update;
????????????????
????????????????“““
????????????????統(tǒng)計(jì)預(yù)測的錯(cuò)誤次數(shù)
????????????????int（True）=1
????????????????int（False）=0
????????????????“““
????????????????errors?+=?int（update?!=0.0）
????????????????self.errors_.append（errors）
????????????????
????def?net_input（self?x）:
????????“““
????????輸入向量與權(quán)重向量做點(diǎn)積求和
????????“““
????????return?np.dot（x?self.w_[1:]）?+?self.w_[0]
????????????
????def?predict（selfx）:
????????“““
????????激活函數(shù)得到輸出為1或-1的分類
????????大于等于0輸出為1
????????小于0輸出為-1
????????“““
????????return?np.where（self.net_input（x）?>=?0.0??1-1）

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----

?????文件???????2492??2017-09-17?20:21??neuralnetwork\ganzhiqi.py

?????文件????????837??2017-09-18?15:05??neuralnetwork\main.py

?????文件????????786??2017-09-17?22:05??neuralnetwork\matplotlib.py

?????文件???????1691??2017-09-18?14:58??neuralnetwork\matplotlib_listencolormap.py

?????文件????????459??2017-09-17?20:33??neuralnetwork\pandas.py

?????文件???????4700??2017-09-16?15:44??neuralnetwork\test.csv

?????文件????????552??2017-09-18?13:36??neuralnetwork\use_ganzhiqi.py

?????文件???????1994??2017-09-18?13:29??neuralnetwork\__pycache__\ganzhiqi.cpython-36.pyc

?????文件???????1261??2017-09-18?15:01??neuralnetwork\__pycache__\matplotlib_listencolormap.cpython-36.pyc

?????目錄??????????0??2017-09-18?15:01??neuralnetwork\__pycache__

?????目錄??????????0??2017-09-18?15:18??neuralnetwork

-----------?---------??----------?-----??----

????????????????14772????????????????????11