var asyncRequestQueue = [ getBorrowCaseScoreData({ lsh: this.lsh }), getBorrowCaseDRGData({ lsh: this.lsh }), // 歧义数据 getBorrowCaseDRGDifferentData({ lsh: this.lsh }) // dip数据 getBorrowCaseDIPData({ lsh: this.lsh }) ] 使用js增加一个元素getTBJCDataFunction({ lsh: this.lsh })

你可以使用 `push()` 方法将 `getTBJCDataFunction({ lsh: this.lsh })` 添加到 `asyncRequestQueue` 数组的末尾：

asyncRequestQueue.push(getTBJCDataFunction({ lsh: this.lsh }));

修改后的代码如下所示：

var asyncRequestQueue = [
  getBorrowCaseScoreData({
    lsh: this.lsh
  }),
  getBorrowCaseDRGData({
    lsh: this.lsh
  }),
  // 歧义数据
  getBorrowCaseDRGDifferentData({
    lsh: this.lsh
  }),
  // dip数据
  getBorrowCaseDIPData({
    lsh: this.lsh
  })
];

asyncRequestQueue.push(getTBJCDataFunction({
  lsh: this.lsh
}));

相关问题

fname = open("C:\Users\LSH\Desktop\xiangsu.txt", 'w')

根据您提供的代码，fname = open("C:\Users\LSH\Desktop\xiangsu.txt", 'w') 的语法是错误的。在使用反斜杠(\)作为路径分隔符时，需要使用两个反斜杠(\\)来表示一个反斜杠。因此，正确的代码应该是 fname = open("C:\\Users\\LSH\\Desktop\\xiangsu.txt", 'w')。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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[ .reference_list ]

python实现LSH算法

LSH（局部敏感哈希）是一种用于高维数据的近似最近邻搜索的技术。以下是Python实现LSH算法的步骤：

1. 安装必要的库：numpy和bitarray。可以使用以下命令进行安装：

   pip install numpy bitarray

2. 导入库：

   import numpy as np
   from bitarray import bitarray

3. 定义哈希函数：

   def hash_vector(v, planes):
       return "".join(['1' if np.dot(v, plane) > 0 else '0' for plane in planes])

4. 定义LSH类：

   class LSH:
       def __init__(self, num_planes, input_dim):
           self.num_planes = num_planes
           self.input_dim = input_dim
           self.planes = np.random.normal(size=(num_planes, input_dim))
           self.hash_tables = {}
           for i in range(num_planes):
               self.hash_tables[i] = {}
   
       def add(self, v, label):
           hash_val = hash_vector(v, self.planes)
           for i in range(self.num_planes):
               if hash_val[i] not in self.hash_tables[i]:
                   self.hash_tables[i][hash_val[i]] = bitarray(self.input_dim)
                   self.hash_tables[i][hash_val[i]].setall(0)
               self.hash_tables[i][hash_val[i]][label] = 1
   
       def query(self, v):
           hash_val = hash_vector(v, self.planes)
           candidates = None
           for i in range(self.num_planes):
               if hash_val[i] not in self.hash_tables[i]:
                   return None
               if candidates is None:
                   candidates = self.hash_tables[i][hash_val[i]]
               else:
                   candidates = candidates & self.hash_tables[i][hash_val[i]]
           return candidates

5. 创建LSH对象并添加数据：

   lsh = LSH(num_planes=10, input_dim=100)
   for i in range(1000):
       v = np.random.normal(size=100)
       lsh.add(v, i)

6. 查询最近邻：

   query_v = np.random.normal(size=100)
   candidates = lsh.query(query_v)
   if candidates is not None:
       distances = np.linalg.norm(query_v - candidates, axis=1)
       nearest_neighbor = np.argmin(distances)