李航《统计学习方法》逻辑斯蒂回归

import numpy as np

def loadData(filename):
    dataset = []; labels = []
    with open(filename) as file:
        for line in file.readlines():
            lineArr = line.strip().split()
            dataset.append([1.0, float(lineArr[0]), float(lineArr[1])])
            labels.append(int(lineArr[-1]))
   
    return  dataset, labels

def sigomid(x):
    return 1.0/(1 + np.exp(-x))

def gradient_ascent(dataset, labels, alpha, max_iter):
    dataMat = np.matrix(dataset)
    labelMat = np.matrix(labels).reshape(-1, 1)
    m,n = dataMat.shape
    w = np.ones((n,1))
    for i in range(max_iter):
        error = labelMat - sigomid(dataMat*w)
        w += alpha * dataMat.T * error
    return w

def main():
    filename = "dataset.txt"
    dataset, labels = loadData(filename)
    weights = gradient_ascent(dataset,  labels,  alpha=0.001,  max_iter=500)
    print(weights)

if __name__ == '__main__':
    main()

[[ 4.12414349]
 [ 0.48007329]
 [-0.6168482 ]]