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()
plt.show()
##Initialize GaussianNB classifier
clf = GaussianNB()
clf.fit(X_train, y_train)
##Test and calculate %accuracy
#preds = clf.predict(X_test)
#n_test = len(X_test)
#accs = [preds[i] == y_test[i] for i in range(0,n_test)]
#print sum(accs) / float(len(accs))
#
##Alternatively, use built-in function
#print clf.score(X_test, y_test)
#Plot predictions: black if correctly labeled, red otherwise
#plt.figure(0)
#plt.xlabel("grade"); plt.ylabel("bumpiness")
#for i, point in enumerate(X_test):
# color = "black"
# if not accs[i]: #wrong classification
# color = "red"
# plt.scatter(point[0], point[1], marker = u'x', c=color)
#plt.show()
#viz
mytools.prettyPicture(clf, X_test, y_test)
### labels_train and labels_test are the corresponding item labels features_train, features_test, labels_train, labels_test = preprocess() kernel = "linear" clf = SVC(kernel=kernel) start = time() clf.fit(features_train, features_test) train_time = time() - start start = time() preds = clf.predict(features_test) test_time = time() - start acc = accuracy_score(labels_test, preds) print "train time: ", train_time print "test time: ", test_time print "accuracy: ", acc mytools.prettyPicture(clf, features_test, labels_test) ######################################################### ### your code goes here ### #########################################################
