def BuildModel(self, data, responses):
# Create and train the classifier.
model = Perceptron(RealFeatures(data.T), MulticlassLabels(responses))
if self.iterations:
model.set_max_iter(self.iterations)
model.train()
return model
def classifier_perceptron_modular(n=100,
dim=2,
distance=5,
learn_rate=1.,
max_iter=1000,
num_threads=1,
seed=1):
from modshogun import RealFeatures, BinaryLabels
from modshogun import Perceptron
random.seed(seed)
# produce some (probably) linearly separable training data by hand
# Two Gaussians at a far enough distance
X = array(random.randn(dim, n)) + distance
Y = array(random.randn(dim, n)) - distance
X_test = array(random.randn(dim, n)) + distance
Y_test = array(random.randn(dim, n)) - distance
label_train_twoclass = hstack((ones(n), -ones(n)))
#plot(X[0,:], X[1,:], 'x', Y[0,:], Y[1,:], 'o')
fm_train_real = hstack((X, Y))
fm_test_real = hstack((X_test, Y_test))
feats_train = RealFeatures(fm_train_real)
feats_test = RealFeatures(fm_test_real)
labels = BinaryLabels(label_train_twoclass)
perceptron = Perceptron(feats_train, labels)
perceptron.set_learn_rate(learn_rate)
perceptron.set_max_iter(max_iter)
# only guaranteed to converge for separable data
perceptron.train()
perceptron.set_features(feats_test)
out_labels = perceptron.apply().get_labels()
return perceptron, out_labels
def classifier_perceptron_graphical(n=100,
distance=5,
learn_rate=1.,
max_iter=1000,
num_threads=1,
seed=None,
nperceptrons=5):
from modshogun import RealFeatures, BinaryLabels
from modshogun import Perceptron
from modshogun import MSG_INFO
# 2D data
_DIM = 2
# To get the nice message that the perceptron has converged
dummy = BinaryLabels()
dummy.io.set_loglevel(MSG_INFO)
np.random.seed(seed)
# Produce some (probably) linearly separable training data by hand
# Two Gaussians at a far enough distance
X = np.array(np.random.randn(_DIM, n)) + distance
Y = np.array(np.random.randn(_DIM, n))
label_train_twoclass = np.hstack((np.ones(n), -np.ones(n)))
fm_train_real = np.hstack((X, Y))
feats_train = RealFeatures(fm_train_real)
labels = BinaryLabels(label_train_twoclass)
perceptron = Perceptron(feats_train, labels)
perceptron.set_learn_rate(learn_rate)
perceptron.set_max_iter(max_iter)
perceptron.set_initialize_hyperplane(False)
# Find limits for visualization
x_min = min(np.min(X[0, :]), np.min(Y[0, :]))
x_max = max(np.max(X[0, :]), np.max(Y[0, :]))
y_min = min(np.min(X[1, :]), np.min(Y[1, :]))
y_max = max(np.max(X[1, :]), np.max(Y[1, :]))
for i in xrange(nperceptrons):
# Initialize randomly weight vector and bias
perceptron.set_w(np.random.random(2))
perceptron.set_bias(np.random.random())
# Run the perceptron algorithm
perceptron.train()
# Construct the hyperplane for visualization
# Equation of the decision boundary is w^T x + b = 0
b = perceptron.get_bias()
w = perceptron.get_w()
hx = np.linspace(x_min - 1, x_max + 1)
hy = -w[1] / w[0] * hx
plt.plot(hx, -1 / w[1] * (w[0] * hx + b))
# Plot the two-class data
plt.scatter(X[0, :],
X[1, :],
s=40,
marker='o',
facecolors='none',
edgecolors='b')
plt.scatter(Y[0, :],
Y[1, :],
s=40,
marker='s',
facecolors='none',
edgecolors='r')
# Customize the plot
plt.axis([x_min - 1, x_max + 1, y_min - 1, y_max + 1])
plt.title('Rosenblatt\'s Perceptron Algorithm')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
return perceptron
