python不用第三方库，使用皮尔逊系数计算多组多维数据的距离，最后进行dbscan聚类，给出聚类分组

好的，我可以帮你实现这个任务。以下是一个Python示例代码，用于实现你所需的功能：

import math

# 计算两个向量之间的皮尔逊系数
def pearson(x, y):
    n = len(x)
    sum_x = sum(x)
    sum_y = sum(y)
    sum_xy = sum([x[i] * y[i] for i in range(n)])
    sum_x2 = sum([x[i] ** 2 for i in range(n)])
    sum_y2 = sum([y[i] ** 2 for i in range(n)])
    numerator = sum_xy - (sum_x * sum_y / n)
    denominator = math.sqrt((sum_x2 - sum_x ** 2 / n) * (sum_y2 - sum_y ** 2 / n))
    if denominator == 0:
        return 0
    else:
        return numerator / denominator

# 计算距离矩阵
def distance_matrix(data):
    n = len(data)
    distances = [[0] * n for i in range(n)]
    for i in range(n):
        for j in range(i+1, n):
            distances[i][j] = pearson(data[i], data[j])
            distances[j][i] = distances[i][j]
    return distances

# 使用DBSCAN算法进行聚类
def dbscan(data, eps, min_pts):
    labels = [None] * len(data)
    cluster = 0
    for i in range(len(data)):
        if labels[i] is not None:
            continue
        neighbors = get_neighbors(i, data, eps)
        if len(neighbors) < min_pts:
            labels[i] = -1
        else:
            cluster += 1
            labels[i] = cluster
            expand_cluster(i, neighbors, cluster, eps, min_pts, data, labels)
    return labels

# 获取邻居点
def get_neighbors(point, data, eps):
    neighbors = []
    for i in range(len(data)):
        if pearson(data[point], data[i]) >= eps:
            neighbors.append(i)
    return neighbors

# 扩展聚类
def expand_cluster(point, neighbors, cluster, eps, min_pts, data, labels):
    for neighbor in neighbors:
        if labels[neighbor] is None:
            labels[neighbor] = cluster
            new_neighbors = get_neighbors(neighbor, data, eps)
            if len(new_neighbors) >= min_pts:
                neighbors += new_neighbors

# 示例数据
data = [
    [1, 2, 3, 4],
    [2, 3, 4, 5],
    [3, 4, 5, 6],
    [4, 5, 6, 7],
    [5, 6, 7, 8],
    [6, 7, 8, 9],
    [2, 4, 6, 8],
    [1, 3, 5, 7],
    [2, 4, 6, 8],
    [1, 2, 3, 4],
    [3, 5, 7, 9],
    [4, 6, 8, 10]
]

# 计算距离矩阵
distances = distance_matrix(data)

# 聚类
labels = dbscan(distances, 0.5, 3)

# 输出聚类结果
for i in range(len(labels)):
    print("第%d个样本的聚类标签为：%d" % (i+1, labels[i]))

在上面的示例代码中，我们使用了自定义的`pearson`函数计算两个向量之间的皮尔逊系数，然后使用`distance_matrix`函数计算距离矩阵。接着，我们使用DBSCAN算法进行聚类，并将聚类结果打印输出。

如果您需要使用自己的数据进行聚类，只需将示例数据替换为您自己的数据即可。此外，您还可以调整`dbscan`函数中的`eps`和`min_pts`参数以改变聚类效果。