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我正在尝试在支持向量机（SVM）上实现粒子群优化以优化成本和伽马参数（径向基函数）以提高准确性。SVM 程序正在从 mysql db 获取数据并且工作正常。现在我需要一个用于优化成本和伽马参数的 PSO 代码并在 svm 程序中使用它。以下是 svm 和 pso 代码。Svm 工作正常，但 PSO 代码不起作用。代码是用python编写的。请帮忙..

支持向量机代码：

import MySQLdb
import cgi
import cgitb
import pandas as pd 
import numpy as np 
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing, cross_validation, svm 
from sklearn.svm import SVR
import mysql.connector as sql
import pandas as pd
cgitb.enable()
print 'Content-type: text/html\r\n\r'
form = cgi.FieldStorage()
e1= form.getvalue('EARNING_PER_SHARE', '')
e2 = form.getvalue('CASH_INVESTMENT', '')
e3 = form.getvalue('CURRENT_LIABILITY', '')
e4 = form.getvalue('TOTAL_REVENUE', '')
e5 = form.getvalue('GROSS_PROFIT', '')
db = MySQLdb.connect(host="127.0.0.1", db="cisco", user="root", passwd="")
cursor = db.cursor()
cursor.execute("""
INSERT INTO table1 (EARNING_PER_SHARE, CASH_INVESTMENT, CURRENT_LIABILITY, 
TOTAL_REVENUE,GROSS_PROFIT) 
VALUES (%s, %s, %s, %s, %s)
""", (e1, e2, e3, e4,e5))
db.commit()
db.close()
db_connection = sql.connect(host='127.0.0.1', database='cisco', user='root', 
password='')
db_cursor = db_connection.cursor()
db_cursor.execute('SELECT * FROM table1')
table_rows = db_cursor.fetchall()
df = pd.DataFrame(table_rows)
np = df.as_matrix()
X = df.drop([4], 1)
np1 = X.as_matrix()
y = df[4]
np2 = y.as_matrix()
x_train, x_test, y_train, y_test = train_test_split(np1, np2, test_size=0.3)
clf = svm.SVR()
clf.fit(np1, np2)
confidence = clf.score(np1, np2)
for k in ['rbf']:
clf = svm.SVR(kernel=k, C=100, gamma=0.0001)
clf.fit(np1, np2)
confidence = clf.score(np1, np2)
print(k,confidence)
a = clf.predict(np1)
print ('\npredicted values')
print (a)
print ('\nreal values')
print (np2)

PSO 代码：

def fitness_function(a,x,np2,np1,C,gamma,):    
C = x[0]
gamma = x[1]
clf = svm.SVR(kernel=k, C=10, gamma=0.0001,swarmsize=50)
clf.fit(np1, np2)
confidence = clf.score(np1, np2)
print(k,confidence)
mse = sqrt(mean_squared_error(np2, a))
return mse
lb = [10, 0.0001]
ub = [1000,0.1]
xopt, fopt = pso(fitness_function, lb, ub)

我想使用 RMSE 作为适应度函数，但我认为适应度函数必须包含要优化的参数。但是，在 SVR 中，目标函数太复杂，无法用 python 编写。

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