implementation of sorted vector machines

machine_learning/sorted_vector_machines.py

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+from sklearn.datasets import load_iris
+from sklearn import svm
+from sklearn.model_selection import train_test_split
+import doctest
+
+# 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()
+    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=-1, random_state=None)
+    # various parameters like "kernal","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__":
+    doctest.testmod()