saisumit

kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.7.0/aio/deploy/recommended.yaml

apiVersion: v1
kind: ServiceAccount
metadata:
  name: admin-user
  namespace: kubernetes-dashboard
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding

saisumit

from __future__ import print_function
import wave
import numpy as np
import utils
import librosa
# from IPython import embed
import os
from sklearn import preprocessing

saisumit

# prediction=clf.predict(test_features)


import pandas as pd
import numpy as np 
import tensorflow as tf
from sklearn.cross_validation import train_test_split

saisumit

#include <bits/stdc++.h>
using namespace std ; 
#define REP(i, a, b) for (int i = a; i <= b; i++)
#define FOR(i, n) for (int i = 0; i < n; i++)
#define foreach(it, ar) for ( typeof(ar.begin()) it = ar.begin(); it != ar.end(); it++ )
#define fill(ar, val) memset(ar, val, sizeof(ar))
#define PI 3.1415926535897932385
#define uint64 unsigned long long
#define Int long long
#define int64 long long

saisumit

# import libraries
from __future__ import print_function
from __future__ import division

import numpy as np 
import pandas as pd
import os
import re

import tensorflow as tf

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%{
#include <stdio.h>
%}
op "+"|"-"|"*"|"/"
%%
[a-z] { yylval=*yytext;  return id; }
{op} { return (int) yytext[0]; }
\n { return(0); } 
. { return err; } 
%%

saisumit

# import libraries
from __future__ import print_function
from __future__ import division

import numpy as np 
import pandas as pd
import os
import re

import tensorflow as tf

saisumit

#include <bits/stdc++.h>
using namespace std; 

#define REP(i, a, b) for (int i = a; i <= b; i++)
#define FOR(i, n) for (int i = 0; i < n; i++)
#define foreach(it, ar) for ( typeof(ar.begin()) it = ar.begin(); it != ar.end(); it++ )
#define fill(ar, val) memset(ar, val, sizeof(ar))
#define PI 3.1415926535897932385
#define uint64 unsigned long long
#define Int long long

saisumit

w = [2,-3,-3] # assume some random weights and data
x = [-1, -2]

# forward pass
dot = w[0]*x[0] + w[1]*x[1] + w[2]
f = 1.0 / (1 + math.exp(-dot)) # sigmoid function

# backward pass through the neuron (backpropagation)
ddot = (1 - f) * f # gradient on dot variable, using the sigmoid gradient derivation
dx = [w[0] * ddot, w[1] * ddot] # backprop into x

saisumit

const int MAXN  = 3e5 + 50 ;
int  A[ MAXN ] ;
int  S[ MAXN ] ;
int  T[ 4*MAXN ] ;
int N ;
int LB = -1 ;
int UB = INT_MAX;

int  Case1( int idx )
 {