def train(self,x,y,sigma,learningRate,maxIter):
K = rbf(x, sigma);
lenWeights = len(K[1,:]);
weights = np.random.rand(lenWeights);
bias = np.random.random();
t = 1;
converged = False;
# Kernel Perceptron Algorithm
while not converged and t < maxIter:
targets = [];
for i in range(len(x)):
# Calculate output of the network
# Kernel method here allows for non-linear classifier
output = np.dot(K[i,:],weights) + bias;
# Perceptron threshold decision:
# If w'x[i,:] + b > 0 then the output of x[i,:] is 1
# If w'x[i,:] + b < 0 then the output of x[i,:] is 0
if (output > 0):
target = 1;
else:
target = 0;
# Calculate error and update weights
# Shifts the decision boundary
error = y[i] - target;
weights = weights + ((K[i,:] * learningRate * error));
bias = bias + (learningRate * error);
targets.append(target);
t = t + 1;
if ( list(y) == list(targets) ) == True:
# As soon as a solution is found break out of the loop
converged = True;
model = {"weights":weights,"bias":bias,"sigma":sigma}
return model
def test(self,x, model): predictions = []; margins = []; sigma = model["sigma"]; weights = model["weights"]; bias = model["bias"]; K = rbf(x, sigma); for i in range(len(x)): # Calculate w'x + b output = np.dot(K[i,:],weights) + bias; margins.append(output); # Get decision from hardlim function if (output > 0): target = 1; else: target = 0; predictions.append(target); return predictions
