linear svm

SVM-using-Python/Linear-Data-Without-Noise/svm-classify.py

@@ -7,26 +7,30 @@
 
 sys.path.append(os.path.abspath("../"))
 from utils import read_data, plot_data, plot_decision_function
-  
+
 # Read data
 x, labels = read_data("points_class_0.txt", "points_class_1.txt")
 
 # Split data to train and test on 80-20 ratio
 X_train, X_test, y_train, y_test = train_test_split(x, labels, test_size = 0.2, random_state=0)
 
+print("Displaying data. Close window to continue.")  
 # Plot traning and test data
 plot_data(X_train, y_train, X_test, y_test)
 
-
+
+print('Training Linear SVM')
 # Create a linear SVM classifier 
-clf = svm.SVC(C = 1.0, kernel='linear')
+clf = svm.SVC(kernel='linear')
 
 # Train classifier 
 clf.fit(X_train, y_train)
 
-# Make predictions on unseen test data
-clf_predictions = clf.predict(X_test)
-
+print("Displaying decision function. Close window to continue.")  
 # Plot decision function on training and test data
 plot_decision_function(X_train, y_train, X_test, y_test, clf)
 
+# Make predictions on unseen test data
+clf_predictions = clf.predict(X_test)
+print("Accuracy: {}%".format(clf.score(X_test, y_test) * 100 ))
+