什么是深度极限学习机？请写出深度极限学习机的python代码，详细解释并用IRIS数据集举例

深度极限学习机（Deep Extreme Learning Machine，DELM）是一种无需预训练的深度学习模型。相比于传统的深度学习模型，DELM的训练速度更快，且具有更好的鲁棒性和泛化能力。

下面是DELM的Python代码实现，以IRIS数据集为例：

import numpy as np
from sklearn.datasets import load_iris
from scipy.special import expit

class DELM:
    def __init__(self, input_size, hidden_size, output_size):
        self.num_hidden_layers = len(hidden_size)
        self.weights = []
        self.biases = []
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size = hidden_size
        for i in range(self.num_hidden_layers):
            if i == 0:
                self.weights.append(np.random.randn(input_size, hidden_size[i]))
            else:
                self.weights.append(np.random.randn(hidden_size[i-1], hidden_size[i]))
            self.biases.append(np.random.randn(hidden_size[i]))
        self.weights.append(np.random.randn(hidden_size[-1], output_size))
        self.biases.append(np.random.randn(output_size))

    def train(self, X, y, num_epochs, learning_rate):
        for i in range(num_epochs):
            a = X
            for j in range(self.num_hidden_layers):
                z = np.dot(a, self.weights[j]) + self.biases[j]
                a = expit(z)
            y_hat = np.dot(a, self.weights[-1]) + self.biases[-1]
            error = y - y_hat
            delta = error
            for j in range(self.num_hidden_layers-1, -1, -1):
                self.weights[j] += learning_rate * np.dot(a.T, delta)
                delta = np.dot(delta, self.weights[j].T) * a * (1 - a)
                a = expit(np.dot(a, self.weights[j]) + self.biases[j])
            self.weights[-1] += learning_rate * np.dot(a.T, delta)
            self.biases[-1] += np.sum(delta, axis=0)

    def predict(self, X):
        a = X
        for i in range(self.num_hidden_layers):
            z = np.dot(a, self.weights[i]) + self.biases[i]
            a = expit(z)
        y_hat = np.dot(a, self.weights[-1]) + self.biases[-1]
        return y_hat

# 加载IRIS数据集
iris = load_iris()
X = iris.data
y = iris.target

# 将输出变量转换为独热编码
y_onehot = np.zeros((len(y), 3))
for i in range(len(y)):
    y_onehot[i, y[i]] = 1

# 随机划分训练集和测试集
indices = np.random.permutation(len(X))
train_indices = indices[:int(0.8*len(X))]
test_indices = indices[int(0.8*len(X)):]

X_train = X[train_indices]
y_train = y_onehot[train_indices]
X_test = X[test_indices]
y_test = y_onehot[test_indices]

# 训练DELM模型
input_size = X_train.shape[1]
hidden_size = [10, 10]
output_size = y_train.shape[1]
de = DELM(input_size, hidden_size, output_size)
de.train(X_train, y_train, 1000, 0.01)

# 测试DELM模型
y_pred = de.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
accuracy = np.mean(y_pred == np.argmax(y_test, axis=1))
print("Accuracy:", accuracy)

上述代码中，DELM类的`__init__`方法用于初始化模型，`train`方法用于训练模型，`predict`方法用于对新样本进行预测。IRIS数据集被加载到`X`和`y`中，其中`X`是输入变量，`y`是输出变量。在训练之前，将输出变量`y`转换为独热编码形式`y_onehot`。随机划分训练集和测试集之后，使用训练集训练DELM模型，并在测试集上进行测试，输出模型的准确率。

需要注意的是，DELM模型的超参数（如隐藏层大小、学习率等）需要根据具体应用进行调整，上述代码中的超参数仅供参考。