sandeepnmenon

big oh and time complecity : http://bigocheatsheet.com/
                           : http://www.studytonight.com/data-structures/time-complexity-of-algorithms
                           : http://web.mit.edu/16.070/www/lecture/big_o.pdf



Prime Check: http://ideone.com/Sp58lL

searcing algorithms : 
binary search : 

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def get_cnn_architecture(weights_path=None):
    
    input_img = Input(shape=(64,64,3))  # adapt this if using `channels_first` image data format
    x1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_img)
    
    gateFactor = Input(tensor = K.variable([0.3]))
    fractionG = Multiply()([x1,gateFactor])
    complement = Lambda(lambda x: x[0] - x[1])([x1,fractionG])
    
    x = MaxPooling2D((2, 2), padding='same')(fractionG)

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def get_gated_connections(gatePercentageFactor,inputLayer):
    gateFactor = Input(tensor = K.variable([gatePercentageFactor]))
    fractionG = Lambda(lambda x: x[0]*x[1])([inputLayer,gateFactor])
    complement = Lambda(lambda x: x[0] - x[1])([inputLayer,fractionG])
    
    return gateFactor,fractionG,complement

#x is conv layer
#y is de-conv layer
#gf is gating factor

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sandeepnmenon

def natural_number_generator(n):
    yield n
    yield from natural_number_generator(n+1)

def sieve_of_eratosthenes(number_list):
    next_prime = next(number_list)
    yield next_prime
    yield from sieve_of_eratosthenes(i for i in number_list if i%next_prime != 0)

if __name__ == '__main__':

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import numpy as np

# Generator for all possible sudoku grids
def sudoku_generator(sudoku):
    for y in range(9):
        for x in range(9):
            # If cell not filled
            if sudoku[y][x] == 0:
                # Check if any number in the range is valid in the empty cell
                for n in range(1,10):

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#include <iostream>
using namespace std;

int main()
{

    int count = 7;
    switch (
        count % 5)
    {

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from scipy.optimize import nnls
from scipy.optimize import curve_fit
import math

def optimus_fitting(df):
    """
    This function takes in a csv file and returns b0,b1 and b2 fitting the model
    l = 1/(b0*k + b1) + b2
    l is the training loss
    k is the number of iterations