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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'), |
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/* ----------------------------------- | |
* 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. | |
*/ |
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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 |
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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]]) |
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{ | |
"author": "Vigneash Sundararajan", | |
"name": "sample", | |
"version":"0.0.1" | |
} |
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def maxCount(arr): | |
prevMax = 0 | |
currMax = 0 | |
for ar in arr: | |
temp = currMax | |
currMax = max(prevMax+ar, currMax) | |
prevMax = temp | |
return currMax |
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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)) |
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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) |
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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): |
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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): |
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