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from sklearn import svm

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

# different functions implementing different types of SVM's

def NuSVC(train_x, train_y):

svc_NuSVC = svm.NuSVC()

svc_NuSVC.fit(train_x, train_y)

return svc_NuSVC

def Linearsvc(train_x, train_y):

svc_linear = svm.LinearSVC(tol=10e-2)

svc_linear.fit(train_x, train_y)

return svc_linear

def SVC(train_x, train_y):

# svm.SVC(C=1.0, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True,

# probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False,

# max_iter=1000, random_state=None)

# various parameters like "kernel","gamma","C" can effectively tuned for a given

# machine learning model.

SVC = svm.SVC(gamma="auto")

SVC.fit(train_x, train_y)

return SVC

def test(X_new):

"""

3 test cases to be passed

an array containing the sepal length (cm), sepal width (cm), petal length (cm),

petal width (cm) based on which the target name will be predicted

>>> test([1,2,1,4])

'virginica'

>>> test([5, 2, 4, 1])

'versicolor'

>>> test([6,3,4,1])

'versicolor'

"""

iris = load_iris()

# splitting the dataset to test and train

train_x, test_x, train_y, test_y = train_test_split(

iris["data"], iris["target"], random_state=4

)

# any of the 3 types of SVM can be used

# current_model=SVC(train_x, train_y)

# current_model=NuSVC(train_x, train_y)

current_model = Linearsvc(train_x, train_y)

prediction = current_model.predict([X_new])

return iris["target_names"][prediction][0]

if __name__ == "__main__":

import doctest

doctest.testmod()

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