class_docstr: Represents datasets we use for expriments
docstr: a1a dataset
docstr_tok: ["a1a","dataset"]
code: def a1a_dataset(self):
      """a1a dataset"""
      a1a_url = "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a1a.t"
      data_path = PATH_PREFIX + "/data/a1a"
      if not os.path.exists(data_path):
          _ = urllib.request.urlretrieve(a1a_url, data_path)
      data = sklearn.datasets.load_svmlight_file(data_path)
      inputs_vec, labels = data[0], data[1]
      inputs_vec = inputs_vec.toarray()
      scaler = preprocessing.StandardScaler().fit(inputs_vec)
      inputs_vec = scaler.transform(inputs_vec)
      labels = labels.astype(float)
      dataset = inputs_vec, labels
      w_opt = self.find_optimal_classifier(dataset)
      inputs_vec = np.hstack(
          (np.ones(shape=(np.shape(inputs_vec)[0], 1)), inputs_vec)
      )
      return inputs_vec, labels, w_opt
  
imports: import os
  import ssl
  import tarfile
  import urllib.request
  from my_logistic_regression import MyLogisticRegression
  import numpy
  import requests
  from sklearn import preprocessing
  import sklearn.datasets
  from sklearn.linear_model import LogisticRegression
  import torch
  from torchvision import datasets, transforms
label_desc: Use image node_id 4 for calling the Mydatasets obj's underlying member method code with example usage: obj.a1a_dataset() and returns: inputs_vec,  labels,  w_opt