#博客地址:https://blog.csdn.net/kelsel
#代码
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
import numpy as np
import argparse
# 正则化函数
def sigmoid_activation(x):
return 1.0 / (1 + np.exp(-x))
def predict(X, W):
preds = sigmoid_activation(X.dot(W))
preds[preds <= 0.5] = 0
preds[preds > 0] = 1
return preds
ap = argparse.ArgumentParser()
ap.add_argument("-e", "--epochs", type = float, default = 100,
help="# of epochs")
ap.add_argument("-a", "--alpha", type = float, default = 0.01,
help = "learning rate")
args = vars(ap.parse_args())
# 造数据
(X, y) = make_blobs(n_samples = 1000, n_features = 2, centers = 2,
cluster_std = 1.5, random_state = 1)
# y.shape (1000,) -> (1000, 1)
y = y.reshape((y.shape[0], 1))
# 按行连接
# In [1]: from numpy import c_
# In [2]: a = ones(4)
# In [3]: b = zeros((4,10))
# In [4]: c_[a,b]
# Out[4]:
# array([[ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
# [ 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
X = np.c_[X, np.ones((X.shape[0]))]
(trainX, testX, trainY, testY) = train_test_split(X, y,
test_size = 0.5, random_state = 42)
# 随机初始化W和loss
print("[INFO] training...")
W = np.random.randn(X.shape[1], 1)
losses = []
import types
for epoch in np.arange(0, args["epochs"]):
preds = sigmoid_activation(trainX.dot(W))
# 计算方差
error = preds - trainY
loss = np.sum(error ** 2)
losses.append(loss)
gradient = trainX.T.dot(error)
# 右边的计算结果是(3, 1)的矩阵
W += -args["alpha"] * gradient
if epoch == 0 or (epoch + 1) % 5 == 0:
print("[INFO] epoch = {}, loss = {:.7f}".format(int(epoch + 1), loss))
print("[INFO] evaluating...")
preds = predict(testX, W)
print(classification_report(testY, preds))