prerakmody

% Loading data points
data = hall;
scatterd(hall); % 14 clusers || 3 clusters
o2c  = interactclust(data,'a');

prerakmody

Check JAVA

• sudo apt update
• sudo ap install default-jre
• java -showversion (make sure its version > 1.8)

Download Genius

• Download genius
• java -jar <>.jar

Genius

prerakmody

import torch
import torchsummary
torch.set_default_tensor_type(torch.FloatTensor)

from pynvml import *
try: nvmlInit()
except:pass

class VoxelNetFeat(torch.nn.Module):

prerakmody

"""
Creating a spherical gaussian heatmap
 - in 2D
 - in 3D
"""
import pdb
import numpy as np

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

prerakmody

\usepackage[]{algorithm2e}

\RestyleAlgo{boxruled}
\LinesNumbered
\begin{algorithm}[H]
\caption{Lawler's pseudo-polynomial algorithm}
\SetAlgoLined
\KwData{S(i,j,k) with S sorted by due-date}
\KwResult{integer representing the minimum tardiness of a schedule S}

prerakmody

Fresh Install

• pip install jupyter

Check Installations

• pip freeze | grep jupyter
• pip freeze | grep ipy
• pip freeze | grep notebook

Notes

• To upgrade any specific package : pip install --upgrade

prerakmody

\newcommand*\xbar[1]{%
   \hbox{%
     \vbox{%
       \hrule height 0.5pt % The actual bar
       \kern0.5ex%         % Distance between bar and symbol
       \hbox{%
         \kern-0.1em%      % Shortening on the left side
         \ensuremath{#1}%
         \kern-0.1em%      % Shortening on the right side
       }%

prerakmody

import pdb; #pdb.set_trace()
import pandas as pd
from IPython.display import display

# 1. Kick Off
df      = pd.read_csv('myfile.csv')
print (df.head())
print (df.head(n=10))
print (df.columns)
col_Y   = ''

prerakmody

from sklearn import metrics
from sklearn import preprocessing
from sklearn.preprocessing import normalize
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold

from imblearn.over_sampling import SMOTE

from sklearn.svm import SVC

prerakmody

# memory footprint support libraries/code
# ! ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi
# ! pip install humanize
# ! pip install jupyter_contrib_nbextensions
# ! jupyter contrib nbextension install --user
# ! jupyter nbextension enable codefolding/main
# ! jupyter nbextension enable hinterland/hinterland


import psutil