请用python写一段代码，要求利用Fisher准则实现对三维模式样本进行分类

好的，以下是代码实现：

import numpy as np

# 三维样本数据，每行数据表示一个三维样本
samples = np.array([[3.2, 2.5, 1.2], [2.5, 1.8, 0.8], [4.0, 3.5, 1.5], [1.2, 2.5, 4.0], [0.8, 1.8, 3.5]])

# 每个样本所属的类别，0表示一类，1表示另一类
labels = np.array([0, 0, 0, 1, 1])

# 计算每个类别的均值向量
mean_vecs = []
for label in np.unique(labels):
    mean_vecs.append(np.mean(samples[labels == label], axis=0))

# 计算类内散度矩阵
within_scatter_matrix = np.zeros((3, 3))
for label, mean_vec in zip(np.unique(labels), mean_vecs):
    class_sc_mat = np.zeros((3, 3))
    for row in samples[labels == label]:
        row, mean_vec = row.reshape(3, 1), mean_vec.reshape(3, 1)
        class_sc_mat += (row - mean_vec).dot((row - mean_vec).T)
    within_scatter_matrix += class_sc_mat

# 计算类间散度矩阵
all_mean = np.mean(samples, axis=0)
between_scatter_matrix = np.zeros((3, 3))
for i, mean_vec in enumerate(mean_vecs):
    n = samples[labels == i, :].shape[0]
    mean_vec = mean_vec.reshape(3, 1)
    all_mean = all_mean.reshape(3, 1)
    between_scatter_matrix += n * (mean_vec - all_mean).dot((mean_vec - all_mean).T)

# 计算 Fisher 准则下的投影矩阵
eigen_vals, eigen_vecs = np.linalg.eig(np.linalg.inv(within_scatter_matrix).dot(between_scatter_matrix))
# 取最大的两个特征值对应的特征向量构成投影矩阵
projection_matrix = np.column_stack((eigen_vecs[:, np.argmax(eigen_vals)], eigen_vecs[:, np.argsort(eigen_vals)[-2:][::-1]]))

# 将样本投影到 Fisher 准则下的新空间中
new_samples = samples.dot(projection_matrix)

# 输出分类结果
for i in range(len(new_samples)):
    if new_samples[i, 0] > np.mean(new_samples[labels == 0, 0]):
        print("第", i+1, "个样本被分类为第一类")
    else:
        print("第", i+1, "个样本被分类为第二类")

注意：这段代码只是 Fisher 准则分类的一个简单示例，实际应用中需要根据数据集的特点做出相应的修改。