感知机更新规则 python

其中需要注意的一点事感知机的更新规则
的计算公式

# ----------## In this exercise, you will update the perceptron class so that it can update# its weights.## Finish writing the update() method so that it updates the weights according# to the perceptron update rule. Updates should be performed online, revising# the weights after each data point.## YOUR CODE WILL GO IN LINES 51 AND 59.# ----------import numpy as npclass Perceptron:    """    This class models an artificial neuron with step activation function.    """    def __init__(self, weights = np.array([1]), threshold = 0):        """        Initialize weights and threshold based on input arguments. Note that no        type-checking is being performed here for simplicity.        """        self.weights = weights.astype(float)         self.threshold = threshold    def activate(self, values):        """        Takes in @param values, a list of numbers equal to length of weights.        @return the output of a threshold perceptron with given inputs based on        perceptron weights and threshold.        """        # First calculate the strength with which the perceptron fires        strength = np.dot(values,self.weights)        # Then return 0 or 1 depending on strength compared to threshold          return int(strength > self.threshold)    def update(self, values, train, eta=.1):        """        Takes in a 2D array @param values consisting of a LIST of inputs and a        1D array @param train, consisting of a corresponding list of expected        outputs. Updates internal weights according to the perceptron training        rule using these values and an optional learning rate, @param eta.        """        # For each data point:        for data_point in xrange(len(values)):            # TODO: Obtain the neuron's prediction for the data_point --> values[data_point]            prediction = self.activate(values[data_point])            print values[0]            # Get the prediction accuracy calculated as (expected value - predicted value)            # expected value = train[data_point], predicted value = prediction            error = train[data_point] - prediction            # TODO: update self.weights based on the multiplication of:            # - prediction accuracy(error)            # - learning rate(eta)            # - input value(values[data_point])            weight_update = error * eta * values[data_point]            self.weights += weight_updatedef test():    """    A few tests to make sure that the perceptron class performs as expected.    Nothing should show up in the output if all the assertions pass.    """    def sum_almost_equal(array1, array2, tol = 1e-6):        return sum(abs(array1 - array2)) < tol    p1 = Perceptron(np.array([1,1,1]),0)    p1.update(np.array([[2,0,-3]]), np.array([1]))    assert sum_almost_equal(p1.weights, np.array([1.2, 1, 0.7]))    p2 = Perceptron(np.array([1,2,3]),0)    p2.update(np.array([[3,2,1],[4,0,-1]]),np.array([0,0]))    assert sum_almost_equal(p2.weights, np.array([0.7, 1.8, 2.9]))    p3 = Perceptron(np.array([3,0,2]),0)    p3.update(np.array([[2,-2,4],[-1,-3,2],[0,2,1]]),np.array([0,1,0]))    assert sum_almost_equal(p3.weights, np.array([2.7, -0.3, 1.7]))if __name__ == "__main__":    test()