如何在python中implement/use人工免疫系统(AIS)?
How to implement/use Artificial immune system(AIS) in python?
我是机器学习算法和分类技术的新手。
我创建了一个数据集,并使用 sklearn
模块在 python 中训练了一个带有 SVM
的模型。
但现在我必须将我的方法从 SVM
更改为 artificial immune system
。因此,我的问题是,python 中是否有我可以使用的 AIS 模块?就像 Sklearn
提供 SVM
.
如果有 none,我在哪里可以找到示例或帮助实现一个?
下面是我在 SVM
中的代码,以备不时之需。
# In the name of GOD
# SeyyedMahdi Hassanpour
# SeyyedMahdihp@gmail.com
# SeyyedMahdihp @ github
import numpy as np
from sklearn import svm, model_selection
import pandas as pd
df = pd.read_csv('final_dataset123456.csv')
x = np.array(df.drop(['label'], 1))
y = np.array(df['label'])
x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.36, random_state=39)
clf = svm.SVC()
clf.fit(x_train, y_train)
accuracy = clf.score(x_test, y_test)
print(accuracy)
ar = [0,0,0,0,0,0,0,0,0,0,0,0,0.2,0.058824,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0.020833,0.2,0.090909,0,0.032258,0,0,0,0,0,0.0625,0,0,0,0.058333,0,0,0.1,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
br = [0.5,1,1,0.254902,0.853933,1,1,0.254902,1,0.27451,0.2,1,0.4,0.176471,1,1,1,1,0.625,1,0.125,1,0.393939,0.857143,0.052632,1,0.75,0.847826,1,1,0.583333,0.7,1,1,1,0.729167,0.6,0.818182,1,0.193548,0.333333,1,0.674419,1,1,1,0.8,1,1,0.2,0.37037,1,0.8,0.529412,0.375,1,1,0.23913,1,1,1,1,0.666667,1,1,1,1,0,1,0,1,0.23913,0.7,0.7,1,1,1,1,1,1,1,1,0.23913,1,1,1,1,1,1,1,1,1,0.666667,1,0.7,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1]
example_measures = np.array([ar,br])
example_measures = example_measures.reshape(len(example_measures), -1)
prediction = clf.predict(example_measures)
print(prediction)
在WEKA平台上实现了很多人工免疫系统应用。您可以从 sourceforge 下载并使用它。
这是 link:
https://sourceforge.net/directory/?q=artificial+immune+system
我是机器学习算法和分类技术的新手。
我创建了一个数据集,并使用 sklearn
模块在 python 中训练了一个带有 SVM
的模型。
但现在我必须将我的方法从 SVM
更改为 artificial immune system
。因此,我的问题是,python 中是否有我可以使用的 AIS 模块?就像 Sklearn
提供 SVM
.
如果有 none,我在哪里可以找到示例或帮助实现一个?
下面是我在 SVM
中的代码,以备不时之需。
# In the name of GOD
# SeyyedMahdi Hassanpour
# SeyyedMahdihp@gmail.com
# SeyyedMahdihp @ github
import numpy as np
from sklearn import svm, model_selection
import pandas as pd
df = pd.read_csv('final_dataset123456.csv')
x = np.array(df.drop(['label'], 1))
y = np.array(df['label'])
x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.36, random_state=39)
clf = svm.SVC()
clf.fit(x_train, y_train)
accuracy = clf.score(x_test, y_test)
print(accuracy)
ar = [0,0,0,0,0,0,0,0,0,0,0,0,0.2,0.058824,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0.020833,0.2,0.090909,0,0.032258,0,0,0,0,0,0.0625,0,0,0,0.058333,0,0,0.1,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
br = [0.5,1,1,0.254902,0.853933,1,1,0.254902,1,0.27451,0.2,1,0.4,0.176471,1,1,1,1,0.625,1,0.125,1,0.393939,0.857143,0.052632,1,0.75,0.847826,1,1,0.583333,0.7,1,1,1,0.729167,0.6,0.818182,1,0.193548,0.333333,1,0.674419,1,1,1,0.8,1,1,0.2,0.37037,1,0.8,0.529412,0.375,1,1,0.23913,1,1,1,1,0.666667,1,1,1,1,0,1,0,1,0.23913,0.7,0.7,1,1,1,1,1,1,1,1,0.23913,1,1,1,1,1,1,1,1,1,0.666667,1,0.7,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1]
example_measures = np.array([ar,br])
example_measures = example_measures.reshape(len(example_measures), -1)
prediction = clf.predict(example_measures)
print(prediction)
在WEKA平台上实现了很多人工免疫系统应用。您可以从 sourceforge 下载并使用它。 这是 link: https://sourceforge.net/directory/?q=artificial+immune+system