博客

import pandas as pd

import numpy as np

import cv2

import random

import time

from sklearn.cross_validation import train_test_split

from sklearn.metrics import accuracy_score

class Perceptron(object):

def __init__(self):
    self.learning_step = 0.00001
    self.max_iteration = 5000

def predict_(self, x):
    wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
    return int(wx > 0)

def train(self, features, labels):
    self.w = [0.0] * (len(features[0]) + 1)

    correct_count = 0
    time = 0

    while time < self.max_iteration:
        index = random.randint(0, len(labels) - 1)
        x = list(features[index])
        x.append(1.0)
        y = 2 * labels[index] - 1
        wx = sum([self.w[j] * x[j] for j in range(len(self.w))])

        if wx * y > 0:
            correct_count += 1
            if correct_count > self.max_iteration:
                break
            continue

        for i in range(len(self.w)):
            self.w[i] += self.learning_step * (y * x[i])

def predict(self,features):
    labels = []
    for feature in features:
        x = list(feature)
        x.append(1)
        labels.append(self.predict_(x))
    return labels

if

name

== ‘

main

‘:

print( 'Start read data')

time_1 = time.time()

raw_data = pd.read_csv('D:/tjxlx/xlx/data/train_binary.csv', header=0)
data = raw_data.values

imgs = data[0::, 1::]
labels = data[::, 0]

# 选取 2/3 数据作为训练集， 1/3 数据作为测试集
train_features, test_features, train_labels, test_labels = train_test_split(
    imgs, labels, test_size=0.33, random_state=23323)
# print train_features.shape
# print train_features.shape

time_2 = time.time()
print ('read data cost ', time_2 - time_1, ' second', '\n')

print ('Start training')
p = Perceptron()
p.train(train_features, train_labels)

time_3 = time.time()
print ('training cost ', time_3 - time_2, ' second', '\n')

print ('Start predicting')
test_predict = p.predict(test_features)
time_4 = time.time()
print ('predicting cost ', time_4 - time_3, ' second', '\n')

score = accuracy_score(test_labels, test_predict)
print ("The accruacy socre is ", score)

Python 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)] on win32

Type “copyright”, “credits” or “license()” for more information.

================== RESTART: D:\tjxlx\xlx\perceptron\感知器.py ==================

Warning (from warnings module):

File “D:\li\anaconda\lib\site-packages\sklearn\cross_validation.py”, line 41

“This module will be removed in 0.20.”, DeprecationWarning)

DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.

Start read data

read data cost 12.797999858856201 second

Start training

training cost 1.4560000896453857 second

Start predicting

predicting cost 3.135999917984009 second

The accruacy socre is 0.9859307359307359