numpy 编写逻辑回归算法对 iris 数据进行多分类

好的，让我来回答你的问题。

首先，需要导入需要用到的库，包括：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

然后，我们需要加载 iris 数据集，并对数据进行预处理：

iris = load_iris()
X = iris.data
y = iris.target
y = y.reshape(-1, 1)

# 对 y 进行 one-hot 编码
m = len(y)
n = np.max(y) + 1
y_one_hot = np.zeros((m, n))
y_one_hot[np.arange(m), y.reshape(-1)] = 1

接下来，我们需要定义逻辑回归模型：

class LogisticRegression:
    def __init__(self, input_dim, output_dim, lr=0.01):
        self.W = np.zeros((input_dim, output_dim))
        self.b = np.zeros((1, output_dim))
        self.lr = lr

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

    def forward(self, X):
        z = np.dot(X, self.W) + self.b
        a = self.sigmoid(z)
        return a

    def backward(self, X, y, a):
        m = X.shape[0]
        dz = a - y
        dW = np.dot(X.T, dz) / m
        db = np.sum(dz, axis=0, keepdims=True) / m
        return dW, db

    def update(self, dW, db):
        self.W = self.W - self.lr * dW
        self.b = self.b - self.lr * db

    def train(self, X, y, num_iters):
        for i in range(num_iters):
            a = self.forward(X)
            dW, db = self.backward(X, y, a)
            self.update(dW, db)

然后，我们需要将数据集分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(X, y_one_hot, test_size=0.2)

接下来，我们需要训练模型：

input_dim = X_train.shape[1]
output_dim = y_train.shape[1]
lr = 0.1
num_iters = 1000

model = LogisticRegression(input_dim, output_dim, lr)
model.train(X_train, y_train, num_iters)

最后，我们需要评估模型的准确率：

y_pred = model.forward(X_test)
correct = 0
total = y_test.shape[0]
for i in range(total):
    if np.argmax(y_pred[i]) == np.argmax(y_test[i]):
        correct += 1
accuracy = correct / total
print("Accuracy:", accuracy)

以上就是使用 numpy 编写逻辑回归算法对 iris 数据进行多分类的完整代码。