class_docstr: Model of a Boolean classifier of positive/negative cross-product of input features and weights
class_docstr_tok: ["Model","of","a","Boolean","classifier","of","positive\/negative","cross-product","of","input","features","and","weights"]
docstr: generates data as SyntheticData = SampleDataPoint[num_points] where
  SampleDataPoint<x,y> = <float[__num_features],bool>,  and y= sum(x*w) >0;
  weigths ~ N(0,1)*10, and SampleDataPoint.x ~ N(0,1)*5.
  Returns tuple of train and test data, split at @train_test_split_pct %
docstr_tok: ["generates","data","as","SyntheticData","=","SampleDataPoint","[","num_points","]","where","SampleDataPoint","<","x",",","y",">","=","<","float","[","__num_features","]",",","bool",">",",","and","y=","sum","(","x","*","w",")",">","0",";","weigths","~","N","(","0,1",")","*","10",",","and","SampleDataPoint.x","~","N","(","0,1",")","*","5",".","Returns","tuple","of","train","and","test","data",",","split","at","@","train_test_split_pct","%"]
code: def __get_data(
      self,
      num_points: int,
      num_features: int,
      random_seed: Optional[int] = None,
      train_test_split_pct=0.9,
  ) -> Tuple[List[SampleDatapoint], List[SampleDatapoint]]:
      """
      generates data as SyntheticData = SampleDataPoint[num_points] where
      SampleDataPoint<x,y> = <float[__num_features],bool>,  and y= sum(x*w) >0;
      weigths ~ N(0,1)*10, and SampleDataPoint.x ~ N(0,1)*5.
      Returns tuple of train and test data, split at @train_test_split_pct %
      """
      data = SyntheticData(num_features, random_seed=random_seed)
      all_data = list(data.generate(num_points))
      train_test_split = int(num_points * train_test_split_pct)
      training_data, validation_data = (
          all_data[:train_test_split],
          all_data[train_test_split:],
      )
      return training_data, validation_data
  
file_path: ptgnn/ptgnn/tests/simplemodel/test_model.py
imports: import tempfile
  import unittest
  from pathlib import Path
  from typing import List, Optional, Tuple
  from ptgnn.baseneuralmodel import ModelTrainer
  from ptgnn.tests.simplemodel.data import SampleDatapoint, SyntheticData
  from ptgnn.tests.simplemodel.model import SimpleRegressionModel
label_desc: Use image node_id 1 for calling the TestPytorchModel obj's underlying member method code with example usage: obj.__get_data(num_points, num_features, random_seed, train_test_split_pct) and returns: training_data,  validation_data