shubham0704

# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

shubham0704

<?xml version="1.0"?>
<robot name="simple_two_link_arm">

  <!-- Base Link -->
  <link name="base_link">
    <visual>
      <geometry>
        <cylinder length="0.6" radius="0.1"/>
      </geometry>
      <material name="orange"/>

shubham0704

import numpy as np
import matplotlib.pyplot as plt

from matplotlib import animation, cm
from mpl_toolkits.mplot3d import Axes3D


# ------------------------------------------
#  GPs for regression utils
# ------------------------------------------

shubham0704

backports.shutil-get-terminal-size==1.0.0
bokeh==0.11.1
chest==0.2.3
cloudpickle==0.2.1
cycler==0.10.0
Cython==0.24
dask==0.10.0
decorator==4.0.10
HeapDict==1.0.0

shubham0704

Online Non Negative Matrix Factorization

Name and Contact Information

Name : Shubham Bhardwaj
University : VIT University, India
Email : [email protected]

Github/ Gitter : shubham0704

Technical Details

shubham0704

Entity Embeddings

Usage

Entity embeddings are used to map categorial variables into eucledian space. Works particularly well with features with high cardinality. i.e the categorial variable would be having a lot of discrete values.

How it works

INPUT- TABULAR DATA -> ||| ALGORITHM ||| -> OUTPUT - ENTITY EMBEDDINGS

Algorithm -

shubham0704

from sklearn.cluster import k_means
from sklearn.datasets.samples_generator import make_blobs
import matplotlib.pyplot as plt
import numpy as np

# create a sample dataSet
dataSet, clusterAssgn = make_blobs(n_samples=100, centers=3,
                                   n_features=2, random_state=0)

# kmeans = k_means(x,n_clusters=3,random_state = 0)

shubham0704

import time
import tornado.httpserver
import tornado.websocket
import tornado.ioloop
from tornado.ioloop import PeriodicCallback
import tornado.web

class WSHandler(tornado.websocket.WebSocketHandler):
    def open(self):
        self.callback = PeriodicCallback(self.send_temp, 120)