AdityaSoni19031997

import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense

training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
target_data = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(32, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

AdityaSoni19031997

import Queue

class UndirectedGraph(object):
    """
    Undirected Graph, with graph represented as an adjacency matrix
    """

    def __init__(self, vertices):
        self.adjacency_matrix = []
        for i in range(vertices):

AdityaSoni19031997

import Queue

class DirectedGraph(object):
    """
    Directed Graph, with graph represented as an adjacency list
    """

    def __init__(self):
        self.adjacency_list = {}

AdityaSoni19031997

import numpy as np
X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ])
y = np.array([ [0, 1, 1, 0] ])
w1 = np.random.random((3, 4)) - 0.5
w2 = np.random.random((4, 1)) - 0.5
for i in range(45000):
  l1 = 1/(1+ np.exp(-(np.dot(X, w1))))
  l2 = 1/(1+ np.exp(-(np.dot(l1,w2))))
  delta_l2 = (y - l2) * (l2*(1-l2))
  delta_l1 = delta_l2.dot(w2.T) * (l1*(1-l1))

AdityaSoni19031997

#include <bits/stdc++.h>
 
using namespace std;
 
#ifndef ONLINE_JUDGE
	bool debug = false;
#else
	bool debug = true;
#endif
 

AdityaSoni19031997

## Sublime Text 3 Serial key build is 3176

The license key

----- BEGIN LICENSE -----
sgbteam
Single User License
EA7E-1153259
8891CBB9 F1513E4F 1A3405C1 A865D53F
115F202E 7B91AB2D 0D2A40ED 352B269B

AdityaSoni19031997

import seaborn as sns
from sklearn import preprocessing, ensemble
from scipy.stats import kendalltau
import pandas as pd
import random

#todo change module name
from tqdm import tqdm
import numpy as np
import pandas as pd

AdityaSoni19031997

AdityaSoni19031997

# This way we have randomness and are able to reproduce the behaviour within this cell.
np.random.seed(13)

def impact_coding(data, feature, target='y'):
    '''
    In this implementation we get the values and the dictionary as two different steps.
    This is just because initially we were ignoring the dictionary as a result variable.
    
    In this implementation the KFolds use shuffling. If you want reproducibility the cv 
    could be moved to a parameter.

AdityaSoni19031997

# Importing dataset
dataset <- read.csv('ASD.csv')

# Taking care of missing data
# Fixing ethnicity
dataset[dataset == "?"] <- NA
summary(dataset)

#install.packages('DescTools')
library(DescTools)