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perceptron.py
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perceptron.py
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# encoding=utf-8
import pandas as pd
import random
import time
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
class Perceptron(object):
def __init__(self):
self.learning_step = 0.001 # 学习率
self.max_iteration = 5000 # 分类正确上界,当分类正确的次数超过上界时,认为已训练好,退出训练
def train(self, features, labels):
# 初始化w,b为0,b在最后一位
self.w = [0.0] * (len(features[0]) + 1)
correct_count = 0 # 分类正确的次数
while correct_count < self.max_iteration:
# 随机选取数据(xi,yi)
index = random.randint(0, len(labels) - 1)
x = list(features[index])
x.append(1.0) # 加上1是为了与b相乘
y = 2 * labels[index] - 1 # label为1转化为正实例点+1,label为0转化为负实例点-1
# 计算w*xi+b
wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
# 如果yi(w*xi+b) > 0 则分类正确的次数加1
if wx * y > 0:
correct_count += 1
continue
# 如果yi(w*xi+b) <= 0 则更新w(最后一位实际上b)的值
for i in range(len(self.w)):
self.w[i] += self.learning_step * (y * x[i])
def predict_(self, x):
wx = sum([self.w[j] * x[j] for j in range(len(self.w))])
return int(wx > 0) # w*xi+b>0则返回返回1,否则返回0
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('train_binary.csv', header=0) # 读取csv数据,并将第一行视为表头,返回DataFrame类型
data = raw_data.values
features = data[::, 1::]
labels = data[::, 0]
# 避免过拟合,采用交叉验证,随机选取33%数据作为测试集,剩余为训练集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33,
random_state=0)
time_2 = time.time()
print('read data cost %f seconds' % (time_2 - time_1))
print('Start training')
p = Perceptron()
p.train(train_features, train_labels)
time_3 = time.time()
print('training cost %f seconds' % (time_3 - time_2))
print('Start predicting')
test_predict = p.predict(test_features)
time_4 = time.time()
print('predicting cost %f seconds' % (time_4 - time_3))
score = accuracy_score(test_labels, test_predict)
print("The accruacy score is %f" % score)