vikene

var observable = Rx.Observable.create(function(observer){
  observer.next("Hello")
  observer.next("world")
  observer.complete()
})

observable.subscribe({
  next: data => console.log(`${data}`),
  complete: () => console.log('done'),
  error: err => console.log('error'),

vikene

/* -----------------------------------
 *  WARNING:
 * -----------------------------------
 *  Your code may fail to compile
 *  because it contains public class
 *  declarations.
 *  To fix this, please remove the
 *  "public" keyword from your class
 *  declarations.
 */

vikene

import torch  #import torch

X = torch.tensor(1.0)
Y = torch.tensor([1.0,2.0])
#Creates two tensor objects 
#Alternatively we can also create a tensor from data like this 
Z = torch.tensor([[1.0,2.0,3.0],
                  [2.0,3.0,4.0],
                  [3.0,4.0,5.0]])
#We can also look at the shape of each tensor as follows 

vikene

import torch  
#Creates two tensor objects 
#Where X is a simple 1 Dimentional Tensor 
#Y is a vector
X = torch.tensor(1.0)
Y = torch.tensor([1.0,2.0])
#Alternatively we can also create a tensor from data like this 
Z = torch.tensor([[1.0,2.0,3.0],
                  [2.0,3.0,4.0],
                  [3.0,4.0,5.0]])

vikene

{
  "author": "Vigneash Sundararajan",
  "name": "sample",
  "version":"0.0.1"
}

vikene

def maxCount(arr):
    prevMax = 0
    currMax = 0
    for ar in arr:
        temp = currMax
        currMax = max(prevMax+ar, currMax)
        prevMax = temp
    return currMax

vikene

def lcs(X,Y, m, n):
    if m < 0 or n < 0:
        return 0
    elif X[m] == Y[n]:
        return 1 + lcs(X,Y, m-1,n-1)
    else:
        return max(lcs(X,Y,m-1,n),lcs(X,Y,m,n-1))

vikene

def lis_dp(X):
    if not X:
        return 0
    memo = [1] * len(X)
    for i in range(1,len(X)):
        for j in range(i):
            if X[j] < X[i]:
                memo[i] = max(memo[i],memo[j]+1)
    return max(memo)

vikene

import numpy as np

input1 = [0,0,1,1]
input2 = [1,0,1,0]
truth = [0,0,1,0]

weight1 = 1
weight2 = 1
bias = -2
def perceptron(input1, input2):

vikene

import numpy as np

input1 = [0,0,1,1]
input2 = [1,0,1,0]
truth = [0,1,0,1]

weight1 = 0
weight2 = -1
bias = 0
def perceptron(input1, input2):