现有实体id、关系id、三元组数组利用TransH嵌入方法对实体和关系嵌入成向量python代码实现

好的，我可以为您提供一个示例代码。不过在此之前，请问您是否已经安装了相应的Python库，如`numpy`和`pandas`等？

以下是一个简单的示例代码：

import numpy as np
import pandas as pd

class TransH:
    def __init__(self, entity_num, relation_num, dim, margin, learning_rate):
        self.entity_num = entity_num
        self.relation_num = relation_num
        self.dim = dim
        self.margin = margin
        self.learning_rate = learning_rate

        self.entity_embedding = np.random.uniform(-6 / np.sqrt(dim), 6 / np.sqrt(dim), (entity_num, dim))
        self.relation_embedding = np.random.uniform(-6 / np.sqrt(dim), 6 / np.sqrt(dim), (relation_num, dim))
        self.relation_normal_vector = np.random.uniform(-6 / np.sqrt(dim), 6 / np.sqrt(dim), (relation_num, dim))

    def l2_norm(self, tensor):
        return np.sqrt(np.sum(np.square(tensor)))

    def transfer(self, e, norm):
        return e - np.dot(e, norm) / norm * norm / self.l2_norm(norm)

    def calc(self, head, relation, tail):
        head = self.entity_embedding[head]
        relation = self.relation_embedding[relation]
        tail = self.entity_embedding[tail]

        head = self.transfer(head, self.relation_normal_vector[relation])
        tail = self.transfer(tail, self.relation_normal_vector[relation])

        score = np.sum(np.abs(head + relation - tail), axis=-1)
        return score

    def train(self, triplets):
        for triplet in triplets:
            head, relation, tail = triplet
            score = self.calc(head, relation, tail)

            if score + self.margin > 0:
                positive_sample = 1
                negative_sample = 0
                head_gradient = self.relation_normal_vector[relation] / self.l2_norm(self.relation_normal_vector[relation])
                tail_gradient = - self.relation_normal_vector[relation] / self.l2_norm(self.relation_normal_vector[relation])
                relation_gradient = self.transfer(self.entity_embedding[head], self.relation_normal_vector[relation]) - self.transfer(self.entity_embedding[tail], self.relation_normal_vector[relation])
            else:
                positive_sample = 0
                negative_sample = 1
                head_gradient = - self.relation_normal_vector[relation] / self.l2_norm(self.relation_normal_vector[relation])
                tail_gradient = self.relation_normal_vector[relation] / self.l2_norm(self.relation_normal_vector[relation])
                relation_gradient = self.transfer(self.entity_embedding[tail], self.relation_normal_vector[relation]) - self.transfer(self.entity_embedding[head], self.relation_normal_vector[relation])

            self.entity_embedding[head] -= self.learning_rate * (positive_sample * head_gradient + negative_sample * head_gradient)
            self.entity_embedding[tail] -= self.learning_rate * (positive_sample * tail_gradient + negative_sample * tail_gradient)
            self.relation_embedding[relation] -= self.learning_rate * (positive_sample * relation_gradient + negative_sample * relation_gradient)

    def save_embedding(self, entity_file, relation_file):
        entity_df = pd.DataFrame(self.entity_embedding)
        relation_df = pd.DataFrame(self.relation_embedding)
        entity_df.to_csv(entity_file, index=False, header=False, sep='\t')
        relation_df.to_csv(relation_file, index=False, header=False, sep='\t')

这里的`TransH`类实现了TransH模型，包括了初始化、计算、训练和保存嵌入向量等方法。您可以根据需要进行修改和扩展。`train`方法需要输入三元组数组，其中每个三元组包括头实体id、关系id和尾实体id。`save_embedding`方法可以将学习到的实体和关系的嵌入向量保存到文件中。

希望这个示例代码对您有帮助！