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Colabs.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<html><head><title>Python: module Colabs</title>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
</head><body bgcolor="#f0f0f8">
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="heading">
<tr bgcolor="#7799ee">
<td valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"> <br><big><big><strong>Colabs</strong></big></big></font></td
><td align=right valign=bottom
><font color="#ffffff" face="helvetica, arial"><a href=".">index</a><br><a href="file:c%3A%5Cusers%5Caditya%20agarwal%5C.spyder-py3%5Ca3%5Ccolabs.py">c:\users\aditya agarwal\.spyder-py3\a3\colabs.py</a></font></td></tr></table>
<p><tt>Created on sat Nov 21 18:09:37 2020<br>
<br>
@author: Jalaj</tt></p>
<p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#aa55cc">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Modules</strong></big></font></td></tr>
<tr><td bgcolor="#aa55cc"><tt> </tt></td><td> </td>
<td width="100%"><table width="100%" summary="list"><tr><td width="25%" valign=top><a href="numpy.html">numpy</a><br>
</td><td width="25%" valign=top><a href="time.html">time</a><br>
</td><td width="25%" valign=top></td><td width="25%" valign=top></td></tr></table></td></tr></table><p>
<table width="100%" cellspacing=0 cellpadding=2 border=0 summary="section">
<tr bgcolor="#eeaa77">
<td colspan=3 valign=bottom> <br>
<font color="#ffffff" face="helvetica, arial"><big><strong>Functions</strong></big></font></td></tr>
<tr><td bgcolor="#eeaa77"><tt> </tt></td><td> </td>
<td width="100%"><dl><dt><a name="-basic_collaborative"><strong>basic_collaborative</strong></a>(data_train, testData, corrMatrix, K)</dt><dd><tt>Basic method is used to compute the reconstructed matrix<br>
<br>
Parameters<br>
----------<br>
data_train : <br>
data present in train.csv file<br>
testdata:<br>
data in test.csv file<br>
corrMatrix : <br>
correlation matrix<br>
K : <br>
amount of most similar k users<br>
<br>
Returns<br>
-------<br>
train_reconstructed_y :<br>
reconstructed matrix</tt></dd></dl>
<dl><dt><a name="-collaborative_baseline"><strong>collaborative_baseline</strong></a>(data_train, testData, corrMatrix, K)</dt><dd><tt>Baseline method is used to compute the reconstructed matrix<br>
<br>
Parameters<br>
----------<br>
data_train : <br>
data present in train.csv file<br>
<br>
testData :<br>
data in test.csv file<br>
<br>
corrMatrix :<br>
correlation matrix<br>
K : <br>
amount of most similar k users<br>
<br>
Returns<br>
-------<br>
train_reconstructed_y : <br>
reconstructed matrix</tt></dd></dl>
<dl><dt><a name="-for_corrMatrix_building"><strong>for_corrMatrix_building</strong></a>(data, name_of_file)</dt><dd><tt>For building correlation matrix<br>
<br>
parameters:<br>
data: <br>
training data<br>
<br>
name_of_file:<br>
name of the file to be passed<br>
<br>
Return:<br>
corrMatrix:<br>
correlation matrix</tt></dd></dl>
<dl><dt><a name="-main"><strong>main</strong></a>()</dt></dl>
</td></tr></table>
</body></html>