import numpy as np
import pandas as pd
from typing import Tuple, List

from queryexpander.semantic_similarity import CppSemanticSimilarity


class QueryExpander:
    def __init__(self, vocabulary_path: str, vocabulary_length: int, sums_cache_file: str, centroids_file_path: str):
        self._words: List[str] = pd.read_csv(
            vocabulary_path, sep=' ', quoting=3, header=None, usecols=(0,), na_filter=False).values.squeeze().tolist()
        self._vectors: np.ndarray = pd.read_csv(vocabulary_path, sep=' ', quoting=3, header=None,
                                                usecols=range(1, vocabulary_length + 1), na_filter=False,
                                                dtype=np.float32).values

        self._similarity = CppSemanticSimilarity(self._words, self._vectors, sums_cache_file)

    def generate_sums_cache(self):
        self._similarity.generate_sums_cache()

    def generate_local_centroids(self, centroids_neighbourhood_size: int):
        self._similarity.generate_local_centroids(centroids_neighbourhood_size)

    def find_most_similar_words(self, query: List[str], number_of_results: int) -> List[Tuple[str, float]]:
        return self._similarity.find_most_similar_words(query, number_of_results)

import numpy as np
import pandas as pd
from typing import Tuple, List

from queryexpander.semantic_similarity import CppSemanticSimilarity


class QueryExpander:
    def __init__(self, vocabulary_path: str, vocabulary_length: int, sums_cache_file: str, centroids_file_path: str):
        self._words: List[str] = pd.read_csv(
            vocabulary_path, sep=' ', quoting=3, header=None, usecols=(0,), na_filter=False).values.squeeze().tolist()
        self._vectors: np.ndarray = pd.read_csv(vocabulary_path, sep=' ', quoting=3, header=None,
                                                usecols=range(1, vocabulary_length + 1), na_filter=False,
                                                dtype=np.float32).values

        self._similarity = CppSemanticSimilarity(self._words, self._vectors, sums_cache_file, centroids_file_path)

    def generate_sums_cache(self):
        self._similarity.generate_sums_cache()

    def generate_local_centroids(self, centroids_neighbourhood_size: int):
        self._similarity.generate_local_centroids(centroids_neighbourhood_size)

    def find_most_similar_words(self, query: List[str], number_of_results: int) -> List[Tuple[str, float]]:
        return self._similarity.find_most_similar_words(query, number_of_results)

Fix C++ accelerator constructor invocation
Fix C++ accelerator constructor invocation

  import numpy as np
  import pandas as pd
  from typing import Tuple, List
  
  from queryexpander.semantic_similarity import CppSemanticSimilarity
  
  
  class QueryExpander:
      def __init__(self, vocabulary_path: str, vocabulary_length: int, sums_cache_file: str, centroids_file_path: str):
          self._words: List[str] = pd.read_csv(
              vocabulary_path, sep=' ', quoting=3, header=None, usecols=(0,), na_filter=False).values.squeeze().tolist()
          self._vectors: np.ndarray = pd.read_csv(vocabulary_path, sep=' ', quoting=3, header=None,
                                                  usecols=range(1, vocabulary_length + 1), na_filter=False,
                                                  dtype=np.float32).values
  
-         self._similarity = CppSemanticSimilarity(self._words, self._vectors, sums_cache_file)
+         self._similarity = CppSemanticSimilarity(self._words, self._vectors, sums_cache_file, centroids_file_path)
  
      def generate_sums_cache(self):
          self._similarity.generate_sums_cache()
  
      def generate_local_centroids(self, centroids_neighbourhood_size: int):
          self._similarity.generate_local_centroids(centroids_neighbourhood_size)
  
      def find_most_similar_words(self, query: List[str], number_of_results: int) -> List[Tuple[str, float]]:
          return self._similarity.find_most_similar_words(query, number_of_results)
  
Fix C++ accelerator constructor invocation
## Code Before:
import numpy as np
import pandas as pd
from typing import Tuple, List

from queryexpander.semantic_similarity import CppSemanticSimilarity


class QueryExpander:
    def __init__(self, vocabulary_path: str, vocabulary_length: int, sums_cache_file: str, centroids_file_path: str):
        self._words: List[str] = pd.read_csv(
            vocabulary_path, sep=' ', quoting=3, header=None, usecols=(0,), na_filter=False).values.squeeze().tolist()
        self._vectors: np.ndarray = pd.read_csv(vocabulary_path, sep=' ', quoting=3, header=None,
                                                usecols=range(1, vocabulary_length + 1), na_filter=False,
                                                dtype=np.float32).values

        self._similarity = CppSemanticSimilarity(self._words, self._vectors, sums_cache_file)

    def generate_sums_cache(self):
        self._similarity.generate_sums_cache()

    def generate_local_centroids(self, centroids_neighbourhood_size: int):
        self._similarity.generate_local_centroids(centroids_neighbourhood_size)

    def find_most_similar_words(self, query: List[str], number_of_results: int) -> List[Tuple[str, float]]:
        return self._similarity.find_most_similar_words(query, number_of_results)

## Instruction:
Fix C++ accelerator constructor invocation
## Code After:
import numpy as np
import pandas as pd
from typing import Tuple, List

from queryexpander.semantic_similarity import CppSemanticSimilarity


class QueryExpander:
    def __init__(self, vocabulary_path: str, vocabulary_length: int, sums_cache_file: str, centroids_file_path: str):
        self._words: List[str] = pd.read_csv(
            vocabulary_path, sep=' ', quoting=3, header=None, usecols=(0,), na_filter=False).values.squeeze().tolist()
        self._vectors: np.ndarray = pd.read_csv(vocabulary_path, sep=' ', quoting=3, header=None,
                                                usecols=range(1, vocabulary_length + 1), na_filter=False,
                                                dtype=np.float32).values

        self._similarity = CppSemanticSimilarity(self._words, self._vectors, sums_cache_file, centroids_file_path)

    def generate_sums_cache(self):
        self._similarity.generate_sums_cache()

    def generate_local_centroids(self, centroids_neighbourhood_size: int):
        self._similarity.generate_local_centroids(centroids_neighbourhood_size)

    def find_most_similar_words(self, query: List[str], number_of_results: int) -> List[Tuple[str, float]]:
        return self._similarity.find_most_similar_words(query, number_of_results)
