class_docstr: Holds necessary (minimal) information needed for inference
class_docstr_tok: ["Holds","necessary","(","minimal",")","information","needed","for","inference"]
docstr: Initialize parameters
  
  Args:
      method (str): The inference method. Currently 'dot'
          (Dot product) is supported.
      k (uint): `k` for 'topk' transformation.
      transformation (str): Transform the inferred values into a
          different scale. Currently 'mean' (Binarize the values
          using mean of inferred matrix as the threshold), 'topk'
          (Pick Top-K inferred values per row and assign them 1,
          setting rest of them to 0), '' (No transformation) are
          supported.
docstr_tok: ["Initialize","parameters","Args",":","method","(","str",")",":","The","inference","method",".","Currently","'dot'","(","Dot","product",")","is","supported",".","k","(","uint",")",":","`","k","`","for","'topk","'","transformation",".","transformation","(","str",")",":","Transform","the","inferred","values","into","a","different","scale",".","Currently","'mean","'","(","Binarize","the","values","using","mean","of","inferred","matrix","as","the","threshold",")",",","'topk'","(","Pick","Top-K","inferred","values","per","row","and","assign","them","1",",","setting","rest","of","them","to","0",")",",","``","(","No","transformation",")","are","supported","."]
code: def __init__(self, method="dot", k=10, transformation=""):
      """Initialize parameters
  
      Args:
          method (str): The inference method. Currently 'dot'
              (Dot product) is supported.
          k (uint): `k` for 'topk' transformation.
          transformation (str): Transform the inferred values into a
              different scale. Currently 'mean' (Binarize the values
              using mean of inferred matrix as the threshold), 'topk'
              (Pick Top-K inferred values per row and assign them 1,
              setting rest of them to 0), '' (No transformation) are
              supported.
      """
      self.method = self._get_method(method)
      self.k = k
      self.transformation = transformation
  
file_path: recommenders/recommenders/models/geoimc/geoimc_predict.py
imports: import numpy
  from scipy.linalg import sqrtm
  from recommenders.utils.python_utils import binarize
label_desc: Use image node_id 1 to create a new Inferer object  with example: obj = Inferer(method, k, transformation)