Perceptron answers the questions

dengjiaqing2012 注册会员
2023-02-25 22:06

the reference answer GPT ᴼ ᴾ ᴱ ᴺ ᴬ ᴵ < br / > here are the modified Perceptron class code, the class to receive four input signals:

class Perceptron:
    def __init__(self):
        self.weights = [0.0, 0.0, 0.0, 0.0]
        self.bias = 0.0
    def predict(self, inputs):
        activation = self.bias
        for i in range(len(inputs)):
            activation += inputs[i] * self.weights[i]
        if activation >= 0.0:
            return 1.0
            return -1.0
    def train(self, training_inputs, labels, learning_rate, num_epochs):
        for epoch in range(num_epochs):
            for i in range(len(training_inputs)):
                prediction = self.predict(training_inputs[i])
                error = labels[i] - prediction
                self.bias += learning_rate * error
                for j in range(len(self.weights)):
                    self.weights[j] += learning_rate * error * training_inputs[i][j]

In the init function, we need to change the length of the weight array from 2 to 4 and initialize all its values to 0.0. The bias variable is still initialized to 0.0.

In the predict function we need to change the cycle from 2 to 4 and sum the first 4 elements of the inputs array by multiplying them against the weighted array to get the inputs active. If the activation value is greater than or equal to 0.0, then 1.0 is returned, otherwise -1.0 is returned.

In the train function, we need to adjust the weight and input length accordingly. Specifically, the length of the weight should be 4, and the input vector of each training sample should contain 4 elements. In the inner loop, we need to multiply the first 4 elements of the input vector with the weight array, and then update the weights and deviations in the same way as in the original version.

edream_zhao 注册会员
2023-02-25 22:06

This answer part references GPT, GPT_Pro for better problem solving
If you want to change Perceptron to accept 4 inputs, The first thing you need to do is define four inputs in your init, X1, X2, X3, X4. Then, the corresponding weight variables W1, W2, W3 and W4 are defined for the four input variables respectively to represent the influence degree of each input. Finally, an offset b is defined to adjust the total input. The above are Perceptron input and weight variable definitions, which are learnable parameters.

With the above definition, the Perceptron van init function can be written as follows:
def __init__(self):
    # 定义4个输入变量X1,X2,X3,X4
    self.X1 = None
    self.X2 = None
    self.X3 = None
    self.X4 = None
    # 定义4个权重变量W1,W2,W3,W4
    self.W1 = 0.0
    self.W2 = 0.0
    self.W3 = 0.0
    self.W4 = 0.0
    # 定义一个偏置b
    self.b = 0.0

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