心电图式梯度下降算法代码

以下是心电图式梯度下降算法的伪代码：

1. 初始化参数w和b
2. while (not converged):
3. 计算损失函数J(w,b)
4. 计算梯度dw和db
5. 更新参数w和b: w = w - learning_rate * dw, b = b - learning_rate * db
6. if (损失函数J(w,b)已经收敛):
7. converged = True

以下是使用Python实现的心电图式梯度下降算法代码：

import numpy as np

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

def initialize_params(dim):
    w = np.zeros((dim, 1))
    b = 0
    return w, b

def propagate(w, b, X, Y):
    m = X.shape[1]
    A = sigmoid(np.dot(w.T, X) + b)
    cost = (-1/m) * np.sum(Y * np.log(A) + (1-Y) * np.log(1-A))
    dw = (1/m) * np.dot(X, (A-Y).T)
    db = (1/m) * np.sum(A-Y)
    grads = {"dw": dw, "db": db}
    return grads, cost

def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost=False):
    costs = []
    for i in range(num_iterations):
        grads, cost = propagate(w, b, X, Y)
        dw = grads["dw"]
        db = grads["db"]
        w = w - learning_rate * dw
        b = b - learning_rate * db
        if i % 100 == 0:
            costs.append(cost)
        if print_cost and i % 100 == 0:
            print("迭代次数 %i: 损失函数值为 %f" % (i, cost))
    params = {"w": w, "b": b}
    grads = {"dw": dw, "db": db}
    return params, grads, costs

def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1, m))
    w = w.reshape(X.shape[0], 1)
    A = sigmoid(np.dot(w.T, X) + b)
    for i in range(A.shape[1]):
        if A[0,i] > 0.5:
            Y_prediction[0,i] = 1
        else:
            Y_prediction[0,i] = 0
    return Y_prediction

def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
    w, b = initialize_params(X_train.shape[0])
    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    w = parameters["w"]
    b = parameters["b"]
    Y_prediction_train = predict(w, b, X_train)
    Y_prediction_test = predict(w, b, X_test)
    train_accuracy = 100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100
    test_accuracy = 100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100
    print("训练集准确率: " + str(train_accuracy) + "%")
    print("测试集准确率: " + str(test_accuracy) + "%")
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test,
         "Y_prediction_train" : Y_prediction_train,
         "w" : w,
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}
    return d

这是一个二分类的逻辑回归模型，可以用于处理心电图数据。其中，sigmoid函数用于将输入值转换为概率值，在propagate函数中计算损失函数和梯度，在optimize函数中使用梯度下降算法更新参数，在predict函数中预测输出值，在model函数中调用前面的函数并输出准确率和其他信息。