Programming questions

class Perceptron(object): # Create a new Perceptron # # Params: bias - arbitrarily chosen value that affects the overall output # regardless of the inputs # # synaptic_weights - list of initial synaptic weights for this Perceptron def __init__(self, bias, synaptic_weights): self.bias = bias self.synaptic_weights = synaptic_weights # Activation function # Quantizes the induced local field # # Params: z - the value of the indiced local field # # Returns: an integer that corresponds to one of the two possible output values (usually 0 or 1) def activation_function(self, z): # Compute and return the weighted sum of all inputs (not including bias) # # Params: inputs - a single input vector (which may contain multiple individual inputs) # # Returns: a float value equal to the sum of each input multiplied by its # corresponding synaptic weight def weighted_sum_inputs(self, inputs): # Compute the induced local field (the weighted sum of the inputs + the bias) # # Params: inputs - a single input vector (which may contain multiple individual inputs) # # Returns: the sum of the weighted inputs adjusted by the bias def induced_local_field(self, inputs): # Predict the output for the specified input vector # # Params: input_vector - a vector or row containing a collection of individual inputs # # Returns: an integer value representing the final output, which must be one of the two # possible output values (usually 0 or 1) def predict(self, input_vector): # Train this Perceptron # # Params: training_set - a collection of input vectors that represents a subset of the entire dataset # learning_rate_parameter - the amount by which to adjust the synaptic weights following an # incorrect prediction # number_of_epochs - the number of times the entire training set is processed by the perceptron # # Returns: no return value def train(self, training_set, learning_rate_parameter, number_of_epochs): # Test this Perceptron # Params: test_set - the set of input vectors to be used to test the perceptron after it has been trained # # Returns: a collection or list containing the actual output (i.e., prediction) for each input vector def test(self, test_set):