Jonathan Greer j-greer

## AttentionWithContext.py
def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

## evaluation.ipynb

      
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                jbochi
                / evaluation.ipynb
            
            
              Last active
              October 18, 2023 07:52
            
              
                Recommending GitHub repositories with Google Big Query and implicit library: https://medium.com/@jbochi/recommending-github-repositories-with-google-bigquery-and-the-implicit-library-e6cce666c77
              
          
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## tune_als.py
from pyspark.ml.recommendation import ALS


def tune_ALS(train_data, validation_data, maxIter, regParams, ranks):
    """
    grid search function to select the best model based on RMSE of
    validation data

    Parameters
    ----------

## stubber.py
import io
import json
import botocore.session
from botocore.stub import Stubber
from botocore.response import StreamingBody


expected_message = {
    'message': 'readme'
}

## test_monkeypatch.py
from io import StringIO


def double():
    x = input("Enter an integer: ")
    return int(x) * 2


def adding():
    x = float(input('Enter the first number'))
	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	x (): input
	kernel (): weights
	Returns:
	"""
	if K.backend() == 'tensorflow':
	from pyspark.ml.recommendation import ALS


	def tune_ALS(train_data, validation_data, maxIter, regParams, ranks):
	"""
	grid search function to select the best model based on RMSE of
	validation data

	Parameters
	----------
	import io
	import json
	import botocore.session
	from botocore.stub import Stubber
	from botocore.response import StreamingBody


	expected_message = {
	'message': 'readme'
	}
	from io import StringIO


	def double():
	x = input("Enter an integer: ")
	return int(x) * 2


	def adding():
	x = float(input('Enter the first number'))