python实现感知机模型的示例

from sklearn.linear_model import Perceptron

import argparse #一个好用的参数传递模型

import numpy as np

from sklearn.datasets import load_iris #数据集

from sklearn.model_selection import train_test_split #训练集和测试集分割

from loguru import logger #日志输出,不清楚用法

#python is also oop

class PerceptronToby():

"""

n_epoch:迭代次数

learning_rate:学习率

loss_tolerance:损失阈值,即损失函数达到极小值的变化量

"""

def __init__(self, n_epoch = 500, learning_rate = 0.1, loss_tolerance = 0.01):

self._n_epoch = n_epoch

self._lr = learning_rate

self._loss_tolerance = loss_tolerance

"""训练模型,即找到每个数据最合适的权重以得到最小的损失函数"""

def fit(self, X, y):

# X:训练集,即数据集,每一行是样本,每一列是数据或标签,一样本包括一数据和一标签

# y:标签,即1或-1

n_sample, n_feature = X.shape #剥离矩阵的方法真帅

#均匀初始化参数

rnd_val = 1/np.sqrt(n_feature)

rng = np.random.default_rng()

self._w = rng.uniform(-rnd_val,rnd_val,size = n_feature)

#偏置初始化为0

self._b = 0

#开始训练了,迭代n_epoch次

num_epoch = 0 #记录迭代次数

prev_loss = 0 #前损失值

while True:

curr_loss = 0 #现在损失值

wrong_classify = 0 #误分类样本

#一次迭代对每个样本操作一次

for i in range(n_sample):

#输出函数

y_pred = np.dot(self._w,X[i]) + self._b

#损失函数

curr_loss += -y[i] * y_pred

# 感知机只对误分类样本进行参数更新,使用梯度下降法

if y[i] * y_pred <= 0:

self._w += self._lr * y[i] * X[i]

self._b += self._lr * y[i]

wrong_classify += 1

num_epoch += 1

loss_diff = curr_loss - prev_loss

prev_loss = curr_loss

# 训练终止条件:

# 1. 训练epoch数达到指定的epoch数时停止训练

# 2. 本epoch损失与上一个epoch损失差异小于指定的阈值时停止训练

# 3. 训练过程中不再存在误分类点时停止训练

if num_epoch >= self._n_epoch or abs(loss_diff) < self._loss_tolerance or wrong_classify == 0:

break

"""预测模型,顾名思义"""

def predict(self, x):

"""给定输入样本,预测其类别"""

y_pred = np.dot(self._w, x) + self._b

return 1 if y_pred >= 0 else -1

#主函数

def main():

#参数数组生成

parser = argparse.ArgumentParser(description="感知机算法实现命令行参数")

parser.add_argument("--nepoch", type=int, default=500, help="训练多少个epoch后终止训练")

parser.add_argument("--lr", type=float, default=0.1, help="学习率")

parser.add_argument("--loss_tolerance", type=float, default=0.001, help="当前损失与上一个epoch损失之差的绝对值小于该值时终止训练")

args = parser.parse_args()

#导入数据

X, y = load_iris(return_X_y=True)

# print(y)

y[:50] = -1

# 分割数据

xtrain, xtest, ytrain, ytest = train_test_split(X[:100], y[:100], train_size=0.8, shuffle=True)

# print(xtest)

#调用并训练模型

model = PerceptronToby(args.nepoch, args.lr, args.loss_tolerance)

model.fit(xtrain, ytrain)

n_test = xtest.shape[0]

# print(n_test)

n_right = 0

for i in range(n_test):

y_pred = model.predict(xtest[i])

if y_pred == ytest[i]:

n_right += 1

else:

logger.info("该样本真实标签为:{},但是toby模型预测标签为:{}".format(ytest[i], y_pred))

logger.info("toby模型在测试集上的准确率为:{}%".format(n_right * 100 / n_test))

skmodel = Perceptron(max_iter=args.nepoch)

skmodel.fit(xtrain, ytrain)

logger.info("sklearn模型在测试集上准确率为:{}%".format(100 * skmodel.score(xtest, ytest)))

if __name__ == "__main__":

main()```

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