def draw_svm_db(kernel="linear", gamma=1000, C=1.0): #Train X_train, X_test, y_train, y_test = mytools.make_fake_data() clf = SVC(kernel=kernel, gamma=gamma, C=C) start = time.time() clf.fit(X_train, y_train) train_time = time.time() - start #Predict start = time.time() preds = clf.predict(X_test) test_time = time.time() - start #Score accuracy = accuracy_score(y_test, preds) print "" print "Kernel: ", kernel print "C: ", C, " , gamma: ", gamma print "Accuracy: ", accuracy print "Training time: ", round(train_time,3) print "Test time: ", round(test_time,3) #Visualization mytools.prettyPicture(clf, X_test, y_test) plt.show()
y = [round(bumpiness[i]*grade[i]+0.3+0.1) for i in xrange(0,n_points)] for i in xrange(0,n_points): if grade[i] > 0.8 and bumpiness[i]>0.8: y[i] = 1.0 #print y #split to test and train X = [[gg, bb] for gg,bb in zip(grade, bumpiness)] split = int(0.75*n_points) X_train = X[0:split] X_test = X[split:] y_train = y[0:split] y_test = y[split:] return X_train, X_test, y_train, y_test X_train, X_test, y_train, y_test = mytools.make_fake_data() #scatter plot xx=[] yy=[] colors=[] for idx, point in enumerate(X): xx.append(point[0]) yy.append(point[1]) if y[idx] == 1: colors.append('blue') #fase else: colors.append("yellow") plt.figure(0) plt.title("Training data") plt.xlabel('grade') plt.ylabel('bumpiness')