Rashmit Pankhania rashmitpankhania

## gradient-descent.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                rashmitpankhania
                / gradient-descent.ipynb
            
            
              Created
              July 20, 2020 14:41
            
              
                Gradient Descent
              
          
        Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## Header.js
import AppBar from '@material-ui/core/AppBar';
import Toolbar from '@material-ui/core/Toolbar';
import useScrollTrigger from '@material-ui/core/useScrollTrigger';
import makeStyles from '@material-ui/styles/makeStyles';
import React from 'react';
import { Tab } from '@material-ui/core';
import Tabs from '@material-ui/core/Tabs';
import Button from '@material-ui/core/Button';
import { Link } from 'react-router-dom';
import Menu from '@material-ui/core/Menu';

## Simple_Arithmetics.py
def showoff(ans):
    shift = max(len(str(ans)), len(op[1]) + 1, len(op[0]))
    print(op[0].rjust(shift))
    print((f + op[1]).rjust(shift), end=" \n")
    print('-' *shift)
    print(str(ans).rjust(shift))


for _ in range(int(input())):
    f = ''

## 1.py
def high_precedence(a, b):
    if b == '^':
        return False
    else:
        if a in '/*' and b in '+-':
            return True
        else:
            return False


## index.py
import pandas as pd
from sklearn.cluster import k_means
from scipy.spatial import distance


def compute_s(i, x, labels, clusters):
	norm_c= len(clusters)
	s = 0
	for x in clusters:
		s += distance.euclidean(x, clusters[i])
	def showoff(ans):
	shift = max(len(str(ans)), len(op[1]) + 1, len(op[0]))
	print(op[0].rjust(shift))
	print((f + op[1]).rjust(shift), end=" \n")
	print('-' *shift)
	print(str(ans).rjust(shift))


	for _ in range(int(input())):
	f = ''
	def high_precedence(a, b):
	if b == '^':
	return False
	else:
	if a in '/*' and b in '+-':
	return True
	else:
	return False
	import pandas as pd
	from sklearn.cluster import k_means
	from scipy.spatial import distance


	def compute_s(i, x, labels, clusters):
	norm_c= len(clusters)
	s = 0
	for x in clusters:
	s += distance.euclidean(x, clusters[i])