def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = array(pickle.load(f))
class_2 = array(pickle.load(f))
labels = pickle.load(f)
model = bayes.BayesClassifier()
model.train([class_1, class_2], [1, -1])
# Load test data.
with open(test_file) as f:
class_1 = array(pickle.load(f))
class_2 = array(pickle.load(f))
labels = pickle.load(f)
if check:
n = class_1.shape[0]
n_correct = 0
n_correct += sum(model.classify(class_1)[0] == labels[:n])
n_correct += sum(model.classify(class_2)[0] == labels[n:])
print 'percent correct:', 100 * n_correct / float(2 * n)
if draw:
def classify(x, y, model=model):
points = vstack((x, y))
return model.classify(points.T)[0]
imtools.plot_2d_boundary([-6, 6, -6, 6], [class_1, class_2], classify,
[1, -1])
show()
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
model = knn.KnnClassifier(labels, vstack((class_1, class_2)))
# Load test data.
with open(test_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
if check:
n = class_1.shape[0]
n_correct = 0
for i in range(n):
if model.classify(class_1[i]) == labels[i]: n_correct += 1
if model.classify(class_2[i]) == labels[n + i]: n_correct += 1
print 'percent correct:', 100 * n_correct / float(2 * n)
if draw:
def classify(x, y, model=model):
return array([model.classify([xx, yy]) for (xx, yy) in zip(x, y)])
imtools.plot_2d_boundary([-6, 6, -6, 6], [class_1, class_2], classify,
[1, -1])
show()
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
model = knn.KnnClassifier(labels, vstack((class_1, class_2)))
# Load test data.
with open(test_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
if check:
n = class_1.shape[0]
n_correct = 0
for i in range(n):
if model.classify(class_1[i]) == labels[i]: n_correct += 1
if model.classify(class_2[i]) == labels[n + i]: n_correct += 1
print 'percent correct:', 100 * n_correct / float(2 * n)
if draw:
def classify(x, y, model=model):
return array([model.classify([xx, yy]) for (xx, yy) in zip(x, y)])
imtools.plot_2d_boundary(
[-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
show()
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = array(pickle.load(f))
class_2 = array(pickle.load(f))
labels = pickle.load(f)
model = bayes.BayesClassifier()
model.train([class_1, class_2], [1, -1])
# Load test data.
with open(test_file) as f:
class_1 = array(pickle.load(f))
class_2 = array(pickle.load(f))
labels = pickle.load(f)
if check:
n = class_1.shape[0]
n_correct = 0
n_correct += sum(model.classify(class_1)[0] == labels[:n])
n_correct += sum(model.classify(class_2)[0] == labels[n:])
print "percent correct:", 100 * n_correct / float(2 * n)
if draw:
def classify(x, y, model=model):
points = vstack((x, y))
return model.classify(points.T)[0]
imtools.plot_2d_boundary([-6, 6, -6, 6], [class_1, class_2], classify, [1, -1])
show()
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# Convert data to lists for libsvm.
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
samples = class_1 + class_2
problem = svmutil.svm_problem(labels, samples)
# Don't print to stdout, use radial basis functions.
param = svmutil.svm_parameter('-q -t 2')
model = svmutil.svm_train(problem, param)
# Load test data.
with open(test_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
if check:
# Sadly, this prints to stdout too :-/
svmutil.svm_predict(labels, class_1 + class_2,
model) # Prints accuracy.
if draw:
def classify(x, y, model=model):
return array(
svmutil.svm_predict([0] * len(x), map(list, zip(x, y)),
model)[0])
imtools.plot_2d_boundary(
[-6, 6, -6, 6], [array(class_1), array(class_2)], classify,
[1, -1])
show()
def process(training_file, test_file, check, draw):
# Load training data.
with open(training_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
# Convert data to lists for libsvm.
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
samples = class_1 + class_2
problem = svmutil.svm_problem(labels, samples)
# Don't print to stdout, use radial basis functions.
param = svmutil.svm_parameter('-q -t 2')
model = svmutil.svm_train(problem, param)
# Load test data.
with open(test_file) as f:
class_1 = pickle.load(f)
class_2 = pickle.load(f)
labels = pickle.load(f)
class_1 = map(list, class_1)
class_2 = map(list, class_2)
labels = list(labels)
if check:
# Sadly, this prints to stdout too :-/
svmutil.svm_predict(labels, class_1 + class_2, model) # Prints accuracy.
if draw:
def classify(x, y, model=model):
return array(svmutil.svm_predict([0] * len(x), map(list, zip(x, y)),
model)[0])
imtools.plot_2d_boundary(
[-6, 6, -6, 6], [array(class_1), array(class_2)], classify, [1, -1])
show()
