如何根据注释完成代码 python

import numpy as np

def sigmoid(x):
# the sigmoid function
pass

class LogisticReg(object):
def init(self, indim=1):
# initialize the parameters with all zeros
# w: shape of [d+1, 1]
pass

def set_param(self, weights, bias):
    # helper function to set the parameters
    # NOTE: you need to implement this to pass the autograde.
    # weights: vector of shape [d, ]
    # bias: scaler
    pass

def get_param(self):
    # helper function to return the parameters
    # NOTE: you need to implement this to pass the autograde.
    # returns:
        # weights: vector of shape [d, ]
        # bias: scaler
    pass

def compute_loss(self, X, t):
    # compute the loss
    # X: feature matrix of shape [N, d]
    # t: input label of shape [N, ]
    # NOTE: return the average of the log-likelihood, NOT the sum.

    # extend the input matrix

    # compute the loss and return the loss
    pass


def compute_grad(self, X, t):
    # X: feature matrix of shape [N, d]
    # grad: shape of [d, 1]
    # NOTE: return the average gradient, NOT the sum.
    pass


def update(self, grad, lr=0.001):
    # update the weights
    # by the gradient descent rule
    pass


def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7):
    # implement the .fit() using the gradient descent method.
    # args:
    #   X: input feature matrix of shape [N, d]
    #   t: input label of shape [N, ]
    #   lr: learning rate
    #   max_iters: maximum number of iterations
    #   eps: tolerance of the loss difference 
    # TO NOTE: 
    #   extend the input features before fitting to it.
    #   return the weight matrix of shape [indim+1, 1]

    loss = 1e10
    for epoch in range(max_iters):
        # compute the loss 
        new_loss = self.compute_loss(X, t)

        # compute the gradient
        grad = self.compute_grad(X, t)

        # update the weight
        self.update(grad, lr=lr)

        # decide whether to break the loop
        if np.abs(new_loss - loss) < eps:
            return self.w


def predict_prob(self, X):
    # implement the .predict_prob() using the parameters learned by .fit()
    # X: input feature matrix of shape [N, d]
    #   NOTE: make sure you extend the feature matrix first,
    #   the same way as what you did in .fit() method.
    # returns the prediction (likelihood) of shape [N, ]

    pass

def predict(self, X, threshold=0.5):
    # implement the .predict() using the .predict_prob() method
    # X: input feature matrix of shape [N, d]
    # returns the prediction of shape [N, ], where each element is -1 or 1.
    # if the probability p>threshold, we determine t=1, otherwise t=-1
    pass

implement logistic regression model

你这个需求不是很明确

这个要根据你的实际需求来完善代码，框架搭好了，每个人的需求都不同的。

这题目除了框架代码以及注释以外，还有其他的要求实现的功能描述吗？有的话一起放上来